[Medical human resources planning in Europe: A literature review of the forecasting models].

Science.gov (United States)

Benahmed, N; Deliège, D; De Wever, A; Pirson, M

2018-02-01

Healthcare is a labor-intensive sector in which half of the expenses are dedicated to human resources. Therefore, policy makers, at national and internal levels, attend to the number of practicing professionals and the skill mix. This paper aims to analyze the European forecasting model for supply and demand of physicians. To describe the forecasting tools used for physician planning in Europe, a grey literature search was done in the OECD, WHO, and European Union libraries. Electronic databases such as Pubmed, Medine, Embase and Econlit were also searched. Quantitative methods for forecasting medical supply rely mainly on stock-and-flow simulations and less often on systemic dynamics. Parameters included in forecasting models exhibit wide variability for data availability and quality. The forecasting of physician needs is limited to healthcare consumption and rarely considers overall needs and service targets. Besides quantitative methods, horizon scanning enables an evaluation of the changes in supply and demand in an uncertain future based on qualitative techniques such as semi-structured interviews, Delphi Panels, or focus groups. Finally, supply and demand forecasting models should be regularly updated. Moreover, post-hoc analyze is also needed but too rarely implemented. Medical human resource planning in Europe is inconsistent. Political implementation of the results of forecasting projections is essential to insure efficient planning. However, crucial elements such as mobility data between Member States are poorly understood, impairing medical supply regulation policies. These policies are commonly limited to training regulations, while horizontal and vertical substitution is less frequently taken into consideration. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Planning and forecasting demand for aircraft engines airline fleet

Directory of Open Access Journals (Sweden)

А.Г. Кучер

2007-03-01

Full Text Available  The questions of air-engines supply system processes analysis on the basis of order planning and air-engine demand forecasting of airline’s air fleet with the use of imitating simulation methods are considered.

Arctic Forecasts Available from Polar Bear Exhibit as an Example of Formal/Informal Collaboration

Science.gov (United States)

Landis, C. E.; Cervenec, J.

2012-12-01

A subset of the general population enjoys and frequents informal education venues, offering an opportunity for lifelong learning that also enhances and supports formal education efforts. The Byrd Polar Research Center (BPRC) at The Ohio State University collaborated with the Columbus Zoo & Aquarium (CZA) in the development of their Polar Frontier exhibit, from its initial planning to the Grand Opening of the exhibit, through the present. Of course, the addition to the Zoo of polar bears and Arctic fox in the Polar Frontier has been very popular, with almost a 7% increase in visitors in 2010 when the exhibit opened. The CZA and BPRC are now investigating ways to increase the climate literacy impact of the exhibit, and to increase engagement with the topics through follow-on activities. For example, individuals or classes anywhere in the world can check forecasts from the Polar Weather and Research Forecasting model and compare them to observed conditions-- allowing deep investigation into changes in the Arctic. In addition, opportunities exist to adapt the Zoo School experience (affecting several Central Ohio school districts) and/or to enable regular participation through social media such as Facebook, Twitter, and other forms of digital communication. BPRC's sustained engagement with the CZA is an example of a trusted and meaningful partnership where open dialogue exists about providing the best learning experience for visitors. This presentation will share some of the lessons learned from this unique partnership, and strategies that are adopted to move it forward.

Forecasting value added of agricultural sub-sectors during fourth five-year development plan in iran

International Nuclear Information System (INIS)

Nassabian, S.

2009-01-01

This article focuses on forecasting the values added of agricultural sub-sectors, including agronomy, fishing, forestry, animal husbandry and agricultural services, using the Artificial Neural Networks (ANN) model. It compares the resulting figures with the target estimates throughout the plan within the years 1384-1388 (2005-2009). It turns out that the forecasted values added in the sub-sectors of agronomy and agricultural services are higher and slower than the estimated values added required due to the plan, respectively. Also the high conformity of the estimated and forecasted value added on the horizon of the fourth five-year plan, while the other sub-sectors both the values are close to each other. The results indicate that the capability of ANN method for forecasting variables is more suitable than the other methods. (author)

Collaborative planning for city development. A perspective from a city planner

Directory of Open Access Journals (Sweden)

Kamalia Purbani

2017-04-01

Full Text Available A number of definitions related to collaborative governance have been developed since early 2000. The common characteristics of collaborative governance are, among others, policy consensus, community visioning, consensus rule-making, and collaborative network structures. Collaborative planning is a new paradigm of planning for a complex contemporary society through which it encourages people to be engaged in a dialogue in a situation of equal empowerment and shared information to learn new ideas through mutual understanding, to create innovative outcomes and to build institutional capacity. This indicates that collaborative planning can provide policy makers with more effective community participation. Collaborative process is the key of collaborative planning which also emphasizes the significant role of collaborative leadership. The process includes a participatory activity of dialogue oriented to the joint decision and summarized in a collaborative process. The collaborative leadership is crucial for setting and maintaining clear ground rules, building trust, facilitating dialogue, and exploring mutual gains. Along with the shift of planning paradigm, the role of city planner will also change since the city planning deals with the political process. In the political process, city planners must be able to perform as technocrats, bureaucrats, lawyers and politicians who always uphold their ethics because they are responsible to the society, the assignor for their integrity and professionalism.

Energy operations and planning decision support for systems using weather forecast information

International Nuclear Information System (INIS)

Altalo, M.G.

2004-01-01

Hydroelectric utilities deal with uncertainties on a regular basis. These include uncertainties in weather, policy and markets. This presentation outlined regional studies to define uncertainty, sources of uncertainty and their affect on power managers, power marketers, power insurers and end users. Solutions to minimize uncertainties include better forecasting and better business processes to mobilize action. The main causes of uncertainty in energy operations and planning include uncaptured wind, precipitation and wind events. Load model errors also contribute to uncertainty in energy operations. This presentation presented the results of a 2002-2003 study conducted by the National Oceanic and Atmospheric Administration (NOAA) on the impact uncertainties in northeast energy weather forecasts. The study demonstrated the cost of seabreeze error on transmission and distribution. The impact of climate scale events were also presented along with energy demand implications. It was suggested that energy planners should incorporate climate change parameters into planning, and that models should include probability distribution forecasts and ensemble forecasting methods that incorporate microclimate estimates. It was also suggested that seabreeze, lake effect, fog, afternoon thunderstorms and frontal passage should be incorporated into forecasts. tabs., figs

Energy and electricity demand forecasting for nuclear power planning in developing countries

International Nuclear Information System (INIS)

1988-07-01

This Guidebook is designed to be a reference document to forecast energy and electricity demand. It presents concepts and methodologies that have been developed to make an analytical approach to energy/electricity demand forecasting as part of the planning process. The Guidebook is divided into 6 main chapters: (Energy demand and development, energy demand analysis, electric load curve analysis, energy and electricity demand forecasting, energy and electricity demand forecasting tools used in various organizations, IAEA methodologies for energy and electricity demand forecasting) and 3 appendices (experience with case studies carried out by the IAEA, reference technical data, reference economic data). A bibliography and a glossary complete the Guidebook. Refs, figs and tabs

Developing the Capacity of Farmers to Understand and Apply Seasonal Climate Forecasts through Collaborative Learning Processes

Science.gov (United States)

Cliffe, Neil; Stone, Roger; Coutts, Jeff; Reardon-Smith, Kathryn; Mushtaq, Shahbaz

2016-01-01

Purpose: This paper documents and evaluates collaborative learning processes aimed at developing farmer's knowledge, skills and aspirations to use seasonal climate forecasting (SCF). Methodology: Thirteen workshops conducted in 2012 engaged over 200 stakeholders across Australian sugar production regions. Workshop design promoted participant…

Do Director Networks Help Manager Plan and Forecast Better?

NARCIS (Netherlands)

Schabus, M.

I examine whether directors' superior access to information and resources through their board network improves the quality of firms' planning and forecasting. Managers may benefit from well-connected directors as, even though managers have firm specific knowledge, they may have only limited insight

Collaborative Planning: Cooking up an Inclusive Service-Learning Project

Science.gov (United States)

Bonati, Michelle L.

2018-01-01

Collaborative planning between special education teachers and general education teachers that focuses on curriculum, instruction, and assessment can improve learning outcomes for students with and without disabilities. Service-learning is a teaching practice that can provide a flexible approach for teachers to collaboratively plan to meet the…

Forecast Mekong 2012: Building scientific capacity

Science.gov (United States)

Stefanov, James E.

2012-01-01

In 2009, U.S. Secretary of State Hillary R. Clinton joined the Foreign Ministers of Cambodia, Laos, Thailand, and Vietnam in launching the Lower Mekong Initiative to enhance U.S. engagement with the countries of the Lower Mekong River Basin in the areas of environment, health, education, and infrastructure. The U.S. Geological Survey Forecast Mekong supports the Lower Mekong Initiative through a variety of activities. The principal objectives of Forecast Mekong include the following: * Build scientific capacity in the Lower Mekong Basin and promote cooperation and collaboration among scientists working in the region. * Provide data, information, and scientific models to help resource managers there make informed decisions. * Produce forecasting and visualization tools to support basin planning, including climate change adaptation. The focus of this product is Forecast Mekong accomplishments and current activities related to the development of scientific capacity at organizations and institutions in the region. Building on accomplishments in 2010 and 2011, Forecast Mekong continues to enhance scientific capacity in the Lower Mekong Basin with a suite of activities in 2012.

Integrated production-distribution planning optimization models: A review in collaborative networks context

Directory of Open Access Journals (Sweden)

Beatriz Andres

2017-01-01

Full Text Available Researchers in the area of collaborative networks are more and more aware of proposing collaborative approaches to address planning processes, due to the advantages associated when enterprises perform integrated planning models. Collaborative production-distribution planning, among the supply network actors, is considered a proper mechanism to support enterprises on dealing with uncertainties and dynamicity associated to the current markets. Enterprises, and especially SMEs, should be able to overcome the continuous changes of the market by increasing their agility. Carrying out collaborative planning allows enterprises to enhance their readiness and agility for facing the market turbulences. However, SMEs have limited access when incorporating optimization tools to deal with collaborative planning, reducing their ability to respond to the competition. The problem to solve is to provide SMEs affordable solutions to support collaborative planning. In this regard, new optimisation algorithms are required in order to improve the collaboration within the supply network partners. As part of the H2020 Cloud Collaborative Manufacturing Networks (C2NET research project, this paper presents a study on integrated production and distribution plans. The main objective of the research is to identify gaps in current optimization models, proposed to address integrated planning, taking into account the requirements and needs of the industry. Thus, the needs of the companies belonging to the industrial pilots, defined in the C2NET project, are identified; analysing how these needs are covered by the optimization models proposed in the literature, to deal with the integrated production-distribution planning.

Effective Heuristics for Capacitated Production Planning with Multiperiod Production and Demand with Forecast Band Refinement

OpenAIRE

Philip Kaminsky; Jayashankar M. Swaminathan

2004-01-01

In this paper we extend forecast band evolution and capacitated production modelling to the multiperiod demand case. In this model, forecasts of discrete demand for any period are modelled as bands and defined by lower and upper bounds on demand, such that future forecasts lie within the current band. We develop heuristics that utilize knowledge of demand forecast evolution to make production decisions in capacitated production planning environments. In our computational study we explore the ...

VSP - Discussion: The Common Interest on Planning Hulls and Plan for Collaboration Studies between NSWC and KRISO - Washington DC

Science.gov (United States)

2016-03-07

planning hulls and plan for N62909-15-1-2052 collaboration studies between NSWC and KRISO - Washington DC Sb. GRANT NUMBER N62909-15-1-2052 Sc. PROGRAM...be carried out in MASK’s facilities. We discussed common interests on planing hulls, and made plans for collaboration studies between NSWC and KRISO ...Presentation of USV project in KRISO 8/19/2015 (Wed) • Discussion of common interests about planing hu lls • Planning for collaboration studies

Survey on collaborative planning in the supply chain: Deterministic and uncertain environment

Directory of Open Access Journals (Sweden)

Imma Ribas Vila

2007-08-01

Full Text Available The current globalization forces to the companies and the current technology allow them to have warehouse and distribution centers, own or subcontracted that can be dispersed geographically. The synchronization of the diverse partners involved in the Supply Chain (SC requires a Collaborative Planning with the purpose of work coordinated to be able to satisfy the demands of a more and more competitive market. In this work papers with respect to the collaborative planning in the supply chain are revised analyzing strategic aspects, like they can be the types, forms, relationships or benefits of the collaboration and tactical such aspects as the planning, material requirement and scheduling. The different models proposed to formalize the collaborative planning among the different partners in the SC are analyzed.

Collaborative planning of operations in industrial symbiosis

DEFF Research Database (Denmark)

Herczeg, Gabor; Akkerman, Renzo

2014-01-01

Industrial symbiosis (IS) is cooperation between companies to achieve collective benefits by supplying and reusing industrial waste to substitute virgin resources in production. In this paper, we investigate the IS phenomenon from a supply chain management perspective. We propose a collaborative...... planning model to coordinate master planning of operations of waste suppliers and buyers. Furthermore, we analyze planning decisions related to IS when waste exchange is combined with virgin resource procurement. We demonstrate that conditions of virgin resource procurement affect the economic feasibility...

The Death and Life of Collaborative Planning Theory

OpenAIRE

Robert Goodspeed

2016-01-01

It has been over 20 years since Judith Innes proclaimed communicative action to be the “emerging paradigm” for planning theory, a theoretical perspective which has been developed into what is known as collaborative planning theory (CPT). With planning theory shifting to a new generation of scholars, this commentary considers the fate of this intellectual movement within planning. CPT never achieved the paradigmatic status its advocates desired because of its internal diversity and limited sco...

Load forecasting

International Nuclear Information System (INIS)

Mak, H.

1995-01-01

Slides used in a presentation at The Power of Change Conference in Vancouver, BC in April 1995 about the changing needs for load forecasting were presented. Technological innovations and population increase were said to be the prime driving forces behind the changing needs in load forecasting. Structural changes, market place changes, electricity supply planning changes, and changes in planning objectives were other factors discussed. It was concluded that load forecasting was a form of information gathering, that provided important market intelligence

Looking Back to Move Forward: Collaborative Planning to Revise the Green Mountain and Finger Lakes National Forests Land and Resource Management Plans

Directory of Open Access Journals (Sweden)

Michael J Dockry

2015-07-01

Full Text Available The United States Department of Agriculture Forest Service (Forest Service manages 154 national forests and 20 grasslands in 44 states and Puerto Rico. National Forest Land and Resource Management Plans (forest plans form the basis for land and resource management of national forests in the United States. For more than a decade the Forest Service has been attempting to incorporate innovative, collaborative public involvement strategies into the process for revising forest plans. In 2012 and 2015 the Forest Service codified new regulations for developing, revising, and amending forest plans. Collaboration and public involvement are explicit goals of the new regulations. This paper briefly reviews the literature on collaborative planning on national forests and explores a successful collaborative planning process used by the Green Mountain and Finger Lakes National Forests, located in Vermont and New York respectively, to develop their 2006 forest plans. This paper shows how the Green Mountain and Finger Lakes National Forests developed parallel public and internal collaborative processes to build trust, relationships, and partnership, and discusses the implications for process design, capacity building, and facilitating agreements. By looking back at this successful case of collaborative forest planning, key lessons can provide ideas for developing collaborative processes for future planning efforts.

Developing health and social care planning in collaboration.

Science.gov (United States)

Rämgård, Margareta; Blomqvist, Kerstin; Petersson, Pia

2015-01-01

Collaboration between different professions in community care for older people is often both difficult and complex. In this project, a participatory action research (PAR) was conducted in order to support the professions involved in the care for older people to develop individualized health and social care plans. Cases from daily work were discussed in different professional groups over a period of one year. A key finding was that lack of knowledge regarding the other professions' field of expertise and their underlying professional culture and values was a barrier in their collaboration. However, as the continuous reflective dialogue process progressed, the participants began to reflect more about the importance of collaboration as a prerequisite to achieve the best possible care for the recipient. This process of reflection led to the often complex needs of the care recipients being given a more central position and thus care plans being better tailored to each person's needs.

Visual analysis of uncertainties in ocean forecasts for planning and operation of off-shore structures

KAUST Repository

Hollt, Thomas; Magdy, Ahmed; Chen, Guoning; Gopalakrishnan, Ganesh; Hoteit, Ibrahim; Hansen, Charles D.; Hadwiger, Markus

2013-01-01

We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations used in ocean forecasting, i.e, simulations of sea surface elevation. Our system enables the interactive planning of both the placement and operation of off-shore structures. We illustrate this using a real-world simulation of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by strong loop currents. The oil and gas industry therefore relies on accurate ocean forecasting systems for planning their operations. Nowadays, these forecasts are based on multiple spatio-temporal simulations resulting in multidimensional, multivariate and multivalued data, so-called ensemble data. Changes in sea surface elevation are a good indicator for the movement of loop current eddies, and our visualization approach enables their interactive exploration and analysis. We enable analysis of the spatial domain, for planning the placement of structures, as well as detailed exploration of the temporal evolution at any chosen position, for the prediction of critical ocean states that require the shutdown of rig operations. © 2013 IEEE.

Visual analysis of uncertainties in ocean forecasts for planning and operation of off-shore structures

KAUST Repository

Hollt, Thomas

2013-02-01

We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations used in ocean forecasting, i.e, simulations of sea surface elevation. Our system enables the interactive planning of both the placement and operation of off-shore structures. We illustrate this using a real-world simulation of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by strong loop currents. The oil and gas industry therefore relies on accurate ocean forecasting systems for planning their operations. Nowadays, these forecasts are based on multiple spatio-temporal simulations resulting in multidimensional, multivariate and multivalued data, so-called ensemble data. Changes in sea surface elevation are a good indicator for the movement of loop current eddies, and our visualization approach enables their interactive exploration and analysis. We enable analysis of the spatial domain, for planning the placement of structures, as well as detailed exploration of the temporal evolution at any chosen position, for the prediction of critical ocean states that require the shutdown of rig operations. © 2013 IEEE.

Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

Energy Technology Data Exchange (ETDEWEB)

Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat

2015-08-05

Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.

A Case Study of the Degree of Collaboration Between Various Levels in the Reparable Chain in the United States Air Force

National Research Council Canada - National Science Library

Lee, Robert A., Jr

2005-01-01

Collaborative Planning, Forecasting, and Replenishment and other logistics processes were developed in the commercial sector to reduce total system costs of production while simultaneously providing...

Towards a framework for geospatial tangible user interfaces in collaborative urban planning

Science.gov (United States)

Maquil, Valérie; Leopold, Ulrich; De Sousa, Luís Moreira; Schwartz, Lou; Tobias, Eric

2018-03-01

The increasing complexity of urban planning projects today requires new approaches to better integrate stakeholders with different professional backgrounds throughout a city. Traditional tools used in urban planning are designed for experts and offer little opportunity for participation and collaborative design. This paper introduces the concept of geospatial tangible user interfaces (GTUI) and reports on the design and implementation as well as the usability of such a GTUI to support stakeholder participation in collaborative urban planning. The proposed system uses physical objects to interact with large digital maps and geospatial data projected onto a tabletop. It is implemented using a PostGIS database, a web map server providing OGC web services, the computer vision framework reacTIVision, a Java-based TUIO client, and GeoTools. We describe how a GTUI has be instantiated and evaluated within the scope of two case studies related to real world collaborative urban planning scenarios. Our results confirm the feasibility of our proposed GTUI solutions to (a) instantiate different urban planning scenarios, (b) support collaboration, and (c) ensure an acceptable usability.

Towards a framework for geospatial tangible user interfaces in collaborative urban planning

Science.gov (United States)

Maquil, Valérie; Leopold, Ulrich; De Sousa, Luís Moreira; Schwartz, Lou; Tobias, Eric

2018-04-01

The increasing complexity of urban planning projects today requires new approaches to better integrate stakeholders with different professional backgrounds throughout a city. Traditional tools used in urban planning are designed for experts and offer little opportunity for participation and collaborative design. This paper introduces the concept of geospatial tangible user interfaces (GTUI) and reports on the design and implementation as well as the usability of such a GTUI to support stakeholder participation in collaborative urban planning. The proposed system uses physical objects to interact with large digital maps and geospatial data projected onto a tabletop. It is implemented using a PostGIS database, a web map server providing OGC web services, the computer vision framework reacTIVision, a Java-based TUIO client, and GeoTools. We describe how a GTUI has be instantiated and evaluated within the scope of two case studies related to real world collaborative urban planning scenarios. Our results confirm the feasibility of our proposed GTUI solutions to (a) instantiate different urban planning scenarios, (b) support collaboration, and (c) ensure an acceptable usability.

Electricity demand forecasting techniques

International Nuclear Information System (INIS)

Gnanalingam, K.

1994-01-01

Electricity demand forecasting plays an important role in power generation. The two areas of data that have to be forecasted in a power system are peak demand which determines the capacity (MW) of the plant required and annual energy demand (GWH). Methods used in electricity demand forecasting include time trend analysis and econometric methods. In forecasting, identification of manpower demand, identification of key planning factors, decision on planning horizon, differentiation between prediction and projection (i.e. development of different scenarios) and choosing from different forecasting techniques are important

Future Plans for the ACORNE Collaboration

International Nuclear Information System (INIS)

Thompson, Lee F

2007-01-01

A summary of the future plans for ACORNE collaboration are presented. Of particular note is the intended development of an acoustic calibrator to be deployed in the deep sea above the Rona hydrophone array. Crucial to this goal is work recently completed on the understanding of hydrophone response and the generation of bipolar acoustic signals; this work is presented in detail

Role of hybrid forecasting techniques for transportation planning of broiler meat under uncertain demand in thailand

Directory of Open Access Journals (Sweden)

Thoranin Sujjaviriyasup

2014-12-01

Full Text Available One of numerous problems experiencing in supply chain management is the demand. Most demands are appeared in terms of uncertainty. The broiler meat industry is inevitably encountering the same problem. In this research, hybrid forecasting model of ARIMA and Support Vector Machine (SVMs are developed to forecast broiler meat export. In addition, ARIMA, SVMs, and Moving Average (MA are chosen for comparing the forecasting efficiency. All the forecasting models are tested and validated using the data of Brazil’s export, Canada’s export, and Thailand’s export. The hybrid model provides accuracy of the forecasted values that are 98.71%, 97.50%, and 93.01%, respectively. In addition, the hybrid model presents the least error of all MAE, RMSE, and MAPE comparing with other forecasting models. As forecasted data are applied to transportation planning, the mean absolute percentage error (MAPE of optimal value of forecasted value and actual value is 14.53%. The hybrid forecasting model shows an ability to reduce risk of total cost of transportation when broiler meat export is forecasted by using MA(2, MA(3, ARIMA, and SVM are 50.59%, 60.18%, 68.01%, and 46.55%, respectively. The results indicate that the developed forecasting model is recommended to broiler meat industries’ supply chain decision.

Early Transition and Use of VIIRS and GOES-R Products by NWS Forecast Offices

Science.gov (United States)

Fuell, Kevin K.; Smith, Mathew; Jedlovec, Gary

2012-01-01

The Visible Infrared Imaging Radiometer Suite (VIIRS) on the NPOESS Preparatory Project (NPP) satellite, part of the Joint Polar Satellite System (JPSS), and the ABI and GLM sensors scheduled for the GOES-R geostationary satellite will bring advanced observing capabilities to the operational weather community. The NASA Short-term Prediction Research and Transition (SPoRT) project at Marshall Space Flight Center has been facilitating the use of real-time experimental and research satellite data by NWS Weather Forecast Offices (WFOs) for a number of years to demonstrate the planned capabilities of future sensors to address particular forecast challenges through improve situational awareness and short-term weather forecasts. For the NOAA GOES-R Proving Ground (PG) activity, SPoRT is developing and disseminating selected GOES-R proxy products to collaborating WFOs and National Centers. SPoRT developed the a pseudo-Geostationary Lightning Mapper product and helped in the transition of the Algorithm Working Group (AWG) Convective Initiation (CI) proxy product for the Hazardous Weather Testbed (HWT) Spring Experiment,. Along with its partner WFOs, SPoRT is evaluating MODIS/GOES Hybrid products, which brings ABI-like data sets from existing NASA instrumentation in front of the forecaster for everyday use. The Hybrid uses near real-time MODIS imagery to demonstrate future ABI capabilities, while utilizing standard GOES imagery to provide the temporal frequency of geostationary imagery expected by operational forecasters. In addition, SPoRT is collaborating with the GOES-R hydrology AWG to transition a baseline proxy product for rainfall rate / quantitative precipitation estimate (QPE) to the OCONUS regions. For VIIRS, SPoRT is demonstrating multispectral observing capabilities and the utility of low-light channels not previously available on operational weather satellites to address a variety of weather forecast challenges. This presentation will discuss the results of

A Lesson Plan Incorporating Collaborative Strategic Reading

Institute of Scientific and Technical Information of China (English)

陈江萍

2017-01-01

This essay is going to have an in-depth analysis of the Collaborative Strategic Reading, a four-step reading comprehen-sion strategy popular in the Western classrooms. It will start with some brief introduction about this instructional approach in company with its theoretical rationale and research evidence for its effectiveness of improving learners 'reading competence. Fo-cused on the previewing skill, the first step of the reading instruction, a modified lesson plan is designed in the Chinese high school setting, followed by justification of the major elements of the plan, and some practical implications.

A Lesson Plan Incorporating Collaborative Strategic Reading

Institute of Scientific and Technical Information of China (English)

陈江萍

2017-01-01

This essay is going to have an in-depth analysis of the Collaborative Strategic Reading, a four-step reading comprehen?sion strategy popular in the Western classrooms. It will start with some brief introduction about this instructional approach in company with its theoretical rationale and research evidence for its effectiveness of improving learners 'reading competence. Fo?cused on the previewing skill, the first step of the reading instruction, a modified lesson plan is designed in the Chinese high school setting, followed by justification of the major elements of the plan, and some practical implications.

Improved Collaborative Transport Planning at Dutch Logistics Service Provider Fritom

NARCIS (Netherlands)

Buijs, Paul; Lopez Alvarez, Jose Alejandro; Veenstra, Marjolein; Roodbergen, Kees Jan

2016-01-01

We study the collaborative transport planning for two autonomous business units of Fritom, a Dutch logistics service provider. This difficult planning problem does not fit any existing type of vehicle routing problem proposed in the academic literature; therefore, we define a new problem class, the

A Collaborative, Ongoing University Strategic Planning Framework: Process, Landmines, and Lessons

Science.gov (United States)

Hill, Susan E. Kogler; Thomas, Edward G.; Keller, Lawrence F.

2009-01-01

This article examines the strategic planning process at Cleveland State University, a large metropolitan state university in Ohio. A faculty-administrative team used a communicative planning approach to develop a collaborative, ongoing, bottom-up, transparent strategic planning process. This team then spearheaded the process through plan…

Impact of forecast errors on expansion planning of power systems with a renewables target

DEFF Research Database (Denmark)

Pineda, Salvador; Morales González, Juan Miguel; Boomsma, Trine Krogh

2015-01-01

This paper analyzes the impact of production forecast errors on the expansion planning of a power system and investigates the influence of market design to facilitate the integration of renewable generation. For this purpose, we propose a programming modeling framework to determine the generation...... and transmission expansion plan that minimizes system-wide investment and operating costs, while ensuring a given share of renewable generation in the electricity supply. Unlike existing ones, this framework includes both a day-ahead and a balancing market so as to capture the impact of both production forecasts...... and the associated prediction errors. Within this framework, we consider two paradigmatic market designs that essentially differ in whether the day-ahead generation schedule and the subsequent balancing re-dispatch are co-optimized or not. The main features and results of the model set-ups are discussed using...

Verification of Space Weather Forecasts using Terrestrial Weather Approaches

Science.gov (United States)

Henley, E.; Murray, S.; Pope, E.; Stephenson, D.; Sharpe, M.; Bingham, S.; Jackson, D.

2015-12-01

The Met Office Space Weather Operations Centre (MOSWOC) provides a range of 24/7 operational space weather forecasts, alerts, and warnings, which provide valuable information on space weather that can degrade electricity grids, radio communications, and satellite electronics. Forecasts issued include arrival times of coronal mass ejections (CMEs), and probabilistic forecasts for flares, geomagnetic storm indices, and energetic particle fluxes and fluences. These forecasts are produced twice daily using a combination of output from models such as Enlil, near-real-time observations, and forecaster experience. Verification of forecasts is crucial for users, researchers, and forecasters to understand the strengths and limitations of forecasters, and to assess forecaster added value. To this end, the Met Office (in collaboration with Exeter University) has been adapting verification techniques from terrestrial weather, and has been working closely with the International Space Environment Service (ISES) to standardise verification procedures. We will present the results of part of this work, analysing forecast and observed CME arrival times, assessing skill using 2x2 contingency tables. These MOSWOC forecasts can be objectively compared to those produced by the NASA Community Coordinated Modelling Center - a useful benchmark. This approach cannot be taken for the other forecasts, as they are probabilistic and categorical (e.g., geomagnetic storm forecasts give probabilities of exceeding levels from minor to extreme). We will present appropriate verification techniques being developed to address these forecasts, such as rank probability skill score, and comparing forecasts against climatology and persistence benchmarks. As part of this, we will outline the use of discrete time Markov chains to assess and improve the performance of our geomagnetic storm forecasts. We will also discuss work to adapt a terrestrial verification visualisation system to space weather, to help

Collaborative Lesson Planning as Professional Development for Beginning Primary Teachers

Science.gov (United States)

Bauml, Michelle

2014-01-01

This qualitative case study describes how one beginning primary grade teacher benefited from collaborative lesson-planning meetings with her grade-level colleagues. The teacher accumulated knowledge of curriculum, pedagogy, and professional contexts as she participated in planning meetings each week during her first year of teaching. Furthermore,…

Improved ant Colony Optimization for Virtual Teams Building in Collaborative Process Planning

Directory of Open Access Journals (Sweden)

Yingying Su

2014-02-01

Full Text Available Virtual teams have been adopted by organizations to gain competitive advantages in this global economy. Virtual teams are a ubiquitous part of getting work done in almost every organization. For the purpose of building virtual teams in collaborative process planning, the method based on improved ant colony algorithm (IMACO was proposed. The concept of virtual team was illustrated and the necessity of building virtual teams in collaborative process planning was analyzed. The sub tasks with certain timing relationship were described and the model of building virtual teams in collaborative process planning was established, which was solved by improved ant colony algorithm. In this paper applications of the IMACO and ACO are compared and demonstrate that the use of the IMACO algorithm performs better. An example was studied to illustrate the effectiveness of the strategy.

Integrated resource planning-concepts and principles

Energy Technology Data Exchange (ETDEWEB)

Atkinson, S.

1994-12-31

The concepts and principles of integrated resource planning (IRP) are outlined. The following topics are discussed: utility opportunities and methodologies, application considerations, ambitious energy-efficient programs, the future of IRP, three methods to study resource alternatives, the load adjustment method, simultaneous optimization, static analysis, utility profile data, load forecasts and shapes, load data, conversion, variable costs, external analysis, internal analysis, DSM objectives, supply-side prescreening, DSM screening analysis, DSM evaluation, the IRP process, risk analysis, collaborative planning process, and load shape objectives.

Delineating Partnerships from other Forms of Collaboration in Regional Development Planning

DEFF Research Database (Denmark)

Larsen, Peter Wilgaard

2017-01-01

The idea of an all-encompassing partnership has vindicated all sorts of collaboration models to be articulated as partnerships although not occurring as ones, leaving practitioners and politicians of regional development planning with suboptimal ways of collaborating. Reviewing the partnership...... of regional planning and development, this paper defines a partnership to be a promise of a promise denoting other collaborative models such as network, cluster and governance-partnership as a promise only. Based on qualitative interviews, discursive analyses and strategic documents, the comparative case...... analysis of two Danish regional development agencies shows how one remained as a governance-partnership while the other turned into a partnership by continuously creating possibilities for the groups of actors involved. This transformation from a promise only into a promise of a promise displays how...

Strengths of the Business Plan and Industrial Collaboration ...

African Journals Online (AJOL)

The study was conducted to identify the strengths of business plan and industrial collaboration strategies in the teaching of entrepreneurship in tertiary institutions. The study was a survey and was conducted using 63 business educators in tertiary institutions in Anambra State, Nigeria. Two research questions and two null ...

Evaluation of a collaborative model: a case study analysis of watershed planning in the intermountain west

Science.gov (United States)

Gary Bentrup

2001-01-01

Collaborative planning processes have become increasingly popular for addressing environmental planning issues, resulting in a number of conceptual models for collaboration. A model proposed by Selin and Chavez suggests that collaboration emerges from a series of antecedents and then proceeds sequentially through problem-setting, direction-setting, implementation, and...

Proposal for a strategic planning for the replacement of products in stores based on sales forecast

Directory of Open Access Journals (Sweden)

Cassius Tadeu Scarpin

2011-08-01

Full Text Available This paper presents a proposal for strategic planning for the replacement of products in stores of a supermarket network. A quantitative method for forecasting time series is used for this, the Artificial Radial Basis Neural Networks (RBFs, and also a qualitative method to interpret the forecasting results and establish limits for each product stock for each store in the network. The purpose with this strategic planning is to reduce the levels of out-of-stock products (lack of products on the shelves, as well as not to produce overstocking, in addition to increase the level of logistics service to customers. The results were highly satisfactory reducing the Distribution Center (DC to shop out-of-stock levels, in average, from 12% to about 0.7% in hypermarkets and from 15% to about 1.7% in supermarkets, thereby generating numerous competitive advantages for the company. The use of RBFs for forecasting proved to be efficient when used in conjunction with the replacement strategy proposed in this work, making effective the operational processes.

‘Strategic navigation’ in collaborative innovation planning processes

DEFF Research Database (Denmark)

Hansen, Jesper Rohr

2013-01-01

Today’s planning dilemmas for the urban fringe in western cities can be perceived of as a struggle between self-organization and control. This is pertinent in times of austerity. Public planning authorities are dependent on cross-sector collaboration in order to enhance the urban development...... leads to the formulation of an analytical model that can be used in future studies of leadership emergence in the urban fringe....... of city areas in the urban fringes, such as brown field and suburbs. Urban planning needs to develop practices of ‘strategic navigation’ (Jean Hillier) in order to come up with new type of responses for this type of dilemma. These practices of navigation emerge in uncertain environments, in which several...

The Death and Life of Collaborative Planning Theory

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Robert Goodspeed

2016-11-01

Full Text Available It has been over 20 years since Judith Innes proclaimed communicative action to be the “emerging paradigm” for planning theory, a theoretical perspective which has been developed into what is known as collaborative planning theory (CPT. With planning theory shifting to a new generation of scholars, this commentary considers the fate of this intellectual movement within planning. CPT never achieved the paradigmatic status its advocates desired because of its internal diversity and limited scope. However, its useful combination of analytical and normative insights is attracting the interest of a new generation of researchers, who are subjecting it to rigorous empirical testing and addressing longstanding theoretical weaknesses. Like Jane Jacob’s classic book the Death and Life of Great American Cities, CPT has made an enduring impact on planning theory, even as it has failed to achieve a total revolution in thinking.

Spatial electric load forecasting

CERN Document Server

Willis, H Lee

2002-01-01

Containing 12 new chapters, this second edition contains offers increased-coverage of weather correction and normalization of forecasts, anticipation of redevelopment, determining the validity of announced developments, and minimizing risk from over- or under-planning. It provides specific examples and detailed explanations of key points to consider for both standard and unusual utility forecasting situations, information on new algorithms and concepts in forecasting, a review of forecasting pitfalls and mistakes, case studies depicting challenging forecast environments, and load models illustrating various types of demand.

Wind power forecasting: IEA Wind Task 36 & future research issues

DEFF Research Database (Denmark)

Giebel, Gregor; Cline, J.; Frank, Helmut Paul

2016-01-01

the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind Energy tries to organise international collaboration, among national meteorological centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD...

Empirical testing of forecast update procedure forseasonal products

DEFF Research Database (Denmark)

Wong, Chee Yew; Johansen, John

2008-01-01

Updating of forecasts is essential for successful collaborative forecasting, especially for seasonal products. This paper discusses the results of a theoretical simulation and an empirical test of a proposed time-series forecast updating procedure. It involves a two-stage longitudinal case study...... of a toy supply chain. The theoretical simulation involves historical weekly consumer demand data for 122 toy products. The empirical test is then carried out in real-time with 291 toy products. The results show that the proposed forecast updating procedure: 1) reduced forecast errors of the annual...... provided less forecast accuracy improvement and it needed a longer time to achieve relatively acceptable forecast uncertainty....

Selection of a Planning Horizon for a Hybrid Microgrid Using Simulated Wind Forecasts

Science.gov (United States)

2014-12-01

microgrid robustness and efficiency and may provide operators with real-time guidance and control policies for microgrid operation. ACKNOWLEDGMENTS The...A PLANNING HORIZON FOR A HYBRID MICROGRID USING SIMULATED WIND FORECASTS Mumtaz Karatas Turkish Naval Academy Tuzla, Istanbul, 34942, TURKEY Emily M...Craparo Dashi I. Singham Naval Postgraduate School 1411 Cunningham Road Monterey, CA, 93943 USA ABSTRACT Hybrid microgrids containing renewable energy

Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy

Energy Technology Data Exchange (ETDEWEB)

Parks, K.; Wan, Y. H.; Wiener, G.; Liu, Y.

2011-10-01

The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'), or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability

A Collaborative Approach to Transportation Planning: Federal and State Perspectives on Section 180(c) Program Development

International Nuclear Information System (INIS)

Macaluso, C.; Strong, T.; Janairo, L.; Helvey, E.

2006-01-01

The Department of Energy's Office of Civilian Radioactive Waste Management (OCRWM) committed in its November 2003 Strategic Plan for the Safe Transportation of Spent Nuclear Fuel and High-Level Radioactive Waste to Yucca Mountain: A Guide to Stakeholder Interactions to develop the transportation system collaboratively with stakeholders. The Strategic Plan further stated that four state regional groups (SRGs) would be the 'anchors' for OCRWM's collaboration with the states. The first major transportation planning activity that OCRWM initiated after publication of the Strategic Plan was the development of the Section 180(c) grant program. This document describes that collaboration and its outcomes from the perspective of the OCRWM participants and one of the SRGs, the Council of State Governments - Midwestern Office (CSG Midwest). (authors)

Forecasting telecommunication new service demand by analogy method and combined forecast

Directory of Open Access Journals (Sweden)

Lin Feng-Jenq

2005-01-01

Full Text Available In the modeling forecast field, we are usually faced with the more difficult problems of forecasting market demand for a new service or product. A new service or product is defined as that there is absence of historical data in this new market. We hardly use models to execute the forecasting work directly. In the Taiwan telecommunication industry, after liberalization in 1996, there are many new services opened continually. For optimal investment, it is necessary that the operators, who have been granted the concessions and licenses, forecast this new service within their planning process. Though there are some methods to solve or avoid this predicament, in this paper, we will propose one forecasting procedure that integrates the concept of analogy method and the idea of combined forecast to generate new service forecast. In view of the above, the first half of this paper describes the procedure of analogy method and the approach of combined forecast, and the second half provides the case of forecasting low-tier phone demand in Taiwan to illustrate this procedure's feasibility.

Operational forecasting based on a modified Weather Research and Forecasting model

Energy Technology Data Exchange (ETDEWEB)

Lundquist, J; Glascoe, L; Obrecht, J

2010-03-18

Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.

Interprofessional collaboration regarding patients' care plans in primary care: a focus group study into influential factors.

Science.gov (United States)

van Dongen, Jerôme Jean Jacques; Lenzen, Stephanie Anna; van Bokhoven, Marloes Amantia; Daniëls, Ramon; van der Weijden, Trudy; Beurskens, Anna

2016-05-28

The number of people with multiple chronic conditions demanding primary care services is increasing. To deal with the complex health care demands of these people, professionals from different disciplines collaborate. This study aims to explore influential factors regarding interprofessional collaboration related to care plan development in primary care. A qualitative study, including four semi-structured focus group interviews (n = 4). In total, a heterogeneous group of experts (n = 16) and health care professionals (n = 15) participated. Participants discussed viewpoints, barriers, and facilitators regarding interprofessional collaboration related to care plan development. The data were analysed by means of inductive content analysis. The findings show a variety of factors influencing the interprofessional collaboration in developing a care plan. Factors can be divided into 5 key categories: (1) patient-related factors: active role, self-management, goals and wishes, membership of the team; (2) professional-related factors: individual competences, domain thinking, motivation; (3) interpersonal factors: language differences, knowing each other, trust and respect, and motivation; (4) organisational factors: structure, composition, time, shared vision, leadership and administrative support; and (5) external factors: education, culture, hierarchy, domain thinking, law and regulations, finance, technology and ICT. Improving interprofessional collaboration regarding care plan development calls for an integral approach including patient- and professional related factors, interpersonal, organisational, and external factors. Further, the leader of the team seems to play a key role in watching the patient perspective, organising and coordinating interprofessional collaborations, and guiding the team through developments. The results of this study can be used as input for developing tools and interventions targeted at executing and improving interprofessional

Ecological forecasts: An emerging imperative

Science.gov (United States)

James S. Clark; Steven R. Carpenter; Mary Barber; Scott Collins; Andy Dobson; Jonathan A. Foley; David M. Lodge; Mercedes Pascual; Roger Pielke; William Pizer; Cathy Pringle; Walter V. Reid; Kenneth A. Rose; Osvaldo Sala; William H. Schlesinger; Diana H. Wall; David Wear

2001-01-01

Planning and decision-making can be improved by access to reliable forecasts of ecosystem state, ecosystem services, and natural capital. Availability of new data sets, together with progress in computation and statistics, will increase our ability to forecast ecosystem change. An agenda that would lead toward a capacity to produce, evaluate, and communicate forecasts...

Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang.

Science.gov (United States)

Liu, Bing-Chun; Binaykia, Arihant; Chang, Pei-Chann; Tiwari, Manoj Kumar; Tsao, Cheng-Chin

2017-01-01

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction.

Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes

Directory of Open Access Journals (Sweden)

Adeshina Y. Alani

2017-10-01

Full Text Available Energy consumption in the form of fuel or electricity is ubiquitous globally. Among energy types, electricity is crucial to human life in terms of cooking, warming and cooling of shelters, powering of electronic devices as well as commercial and industrial operations. Users of electronic devices sometimes consume fluctuating amounts of electricity generated from smart-grid infrastructure owned by the government or private investors. However, frequent imbalance is noticed between the demand and supply of electricity, hence effective planning is required to facilitate its distribution among consumers. Such effective planning is stimulated by the need to predict future consumption within a short period. Although several interesting classical techniques have been used for such predictions, they still require improvement for the purpose of reducing significant predictive errors when used for short-term load forecasting. This research develops a near-zero cooperative probabilistic scenario analysis and decision tree (PSA-DT model to address the lacuna of enormous predictive error faced by the state-of-the-art models. The PSA-DT is based on a probabilistic technique in view of the uncertain nature of electricity consumption, complemented by a DT to reinforce the collaboration of the two techniques. Based on detailed experimental analytics on residential, commercial and industrial data loads, the PSA-DT model outperforms the state-of-the-art models in terms of accuracy to a near-zero error rate. This implies that its deployment for electricity demand planning will be of great benefit to various smart-grid operators and homes.

A Web-based Tool for Transparent, Collaborative Urban Water System Planning for Monterrey, Mexico

Science.gov (United States)

Rheinheimer, D. E.; Medellin-Azuara, J.; Garza Díaz, L. E.; Ramírez, A. I.

2017-12-01

Recent rapid advances in web technologies and cloud computing show great promise for facilitating collaboration and transparency in water planning efforts. Water resources planning is increasingly in the context of a rapidly urbanizing world, particularly in developing countries. In such countries with democratic traditions, the degree of transparency and collaboration in water planning can mean the difference between success and failure of water planning efforts. This is exemplified in the city of Monterrey, Mexico, where an effort to build a new long-distance aqueduct to increase water supply to the city dramatically failed due to lack of transparency and top-down planning. To help address, we used a new, web-based water system modeling platform, called OpenAgua, to develop a prototype decision support system for water planning in Monterrey. OpenAgua is designed to promote transparency and collaboration, as well as provide strong, cloud-based, water system modeling capabilities. We developed and assessed five water management options intended to increase water supply yield and/or reliability, a dominant water management concern in Latin America generally: 1) a new long-distance source (the previously-rejected project), 2) a new nearby reservoir, 3) expansion/re-operation of an existing major canal, 4) desalination, and 5) industrial water reuse. Using the integrated modeling and analytic capabilities of OpenAgua, and some customization, we assessed the performance of these options for water supply yield and reliability to help identify the most promising ones. In presenting this assessment, we demonstrate the viability of using online, cloud-based modeling systems for improving transparency and collaboration in decision making, reducing the gap between citizens, policy makers and water managers, and future directions.

Identifying needs for streamflow forecasting in the Incomati basin, Southern Africa

Science.gov (United States)

Sunday, Robert; Werner, Micha; Masih, Ilyas; van der Zaag, Pieter

2013-04-01

little detail and clarity. In some cases this was attributed to the short time and space allotted in media such as television and newspapers respectively. Major uses of the weather forecast were made in personal planning i.e., travelling (29%) and dressing (23%). The usefulness in water sector was reported for water allocation (23%), farming (11%) and flood monitoring (9%), but was considered as a factor having minor influence on the actual decision making in operational water management mainly due to uncertainty of the weather forecast, difference in the time scale and institutional arrangements. In the incidences where streamflow forecasts were received (5% of the cases), its application in decision making was not carried out due to high uncertainty. Moreover, dam operators indicated weekly streamflow forecast as very important in releasing water for agriculture but this was not the format in which forecasts were available to them. Generally, users affirmed the accuracy and benefits of weather forecasts and had no major concerns on the impacts of wrong forecasts. However, respondents indicated the need to improve the accuracy and accessibility of the forecast. Likewise, water managers expressed the need for both rainfall and flow forecasts but indicated that they face hindrances due to financial and human resource constraints. This shows that there is a need to strengthen water related forecasts and the consequent uses in the basin. This can be done through collaboration among forecasting and water organisations such as the SAWS, Research Institutions and users like ICMA, KOBWA and farmers. Collaboration between the meteorology and water resources sectors is important to establish consistent forecast information. The forecasts themselves should be detailed and user specific to ensure these are indeed used and can answer to the needs of the users.

LOAD FORECASTING FOR POWER SYSTEM PLANNING AND OPERATION USING ARTIFICIAL NEURAL NETWORK AT AL BATINAH REGION OMAN

Directory of Open Access Journals (Sweden)

HUSSEIN A. ABDULQADER

2012-08-01

Full Text Available Load forecasting is essential part for the power system planning and operation. In this paper the modeling and design of artificial neural network for load forecasting is carried out in a particular region of Oman. Neural network approach helps to reduce the problem associated with conventional method and has the advantage of learning directly from the historical data. The neural network here uses data such as past load; weather information like humidity and temperatures. Once the neural network is trained for the past set of data it can give a prediction of future load. This reduces the capital investment reducing the equipments to be installed. The actual data are taken from the Mazoon Electrical Company, Oman. The data of load for the year 2007, 2008 and 2009 are collected for a particular region called Al Batinah in Oman and trained using neural networks to forecast the future. The main objective is to forecast the amount of electricity needed for better load distribution in the areas of this region in Oman. The load forecasting is done for the year 2010 and is validated for the accuracy.

Market-based demand forecasting promotes informed strategic financial planning.

Science.gov (United States)

Beech, A J

2001-11-01

Market-based demand forecasting is a method of estimating future demand for a healthcare organization's services by using a broad range of data that describe the nature of demand within the organization's service area. Such data include the primary and secondary service areas, the service-area populations by various demographic groupings, discharge utilization rates, market size, and market share by service line and organizationwide. Based on observable market dynamics, strategic planners can make a variety of explicit assumptions about future trends regarding these data to develop scenarios describing potential future demand. Financial planners then can evaluate each scenario to determine its potential effect on selected financial and operational measures, such as operating margin, days cash on hand, and debt-service coverage, and develop a strategic financial plan that covers a range of contingencies.

Mobile, Collaborative Situated Knowledge Creation for Urban Planning

Directory of Open Access Journals (Sweden)

Nelson Baloian

2012-05-01

Full Text Available Geo-collaboration is an emerging research area in computer sciences studying the way spatial, geographically referenced information and communication technologies can support collaborative activities. Scenarios in which information associated to its physical location are of paramount importance are often referred as Situated Knowledge Creation scenarios. To date there are few computer systems supporting knowledge creation that explicitly incorporate physical context as part of the knowledge being managed in mobile face-to-face scenarios. This work presents a collaborative software application supporting visually-geo-referenced knowledge creation in mobile working scenarios while the users are interacting face-to-face. The system allows to manage data information associated to specific physical locations for knowledge creation processes in the field, such as urban planning, identifying specific physical locations, territorial management, etc.; using Tablet-PCs and GPS in order to geo-reference data and information. It presents a model for developing mobile applications supporting situated knowledge creation in the field, introducing the requirements for such an application and the functionalities it should have in order to fulfill them. The paper also presents the results of utility and usability evaluations.

Institutional Framework for Collaborative Urban Planning in Afghanistan in view of the Transferring Process of International Urban Planning Systems

Directory of Open Access Journals (Sweden)

Habib Ahmad Javid

2015-09-01

Full Text Available This article provides an overview of Afghanistan’s urban planning institutional change in certain historical periods, particular dilemmas within the current urban planning system and its gradual shift from totalitarian urban planning approaches practiced during 1960s - 1980s to a different form of planning being practiced by the current government. In addition, it will seek to analyze the ease and tension caused by the three recent phenomena that have emerged after the establishment of a new democratic government in Afghanistan since 2001, such as private sector-led urban development, international funding community’s and NGOs’ role in planning and the delegation of certain roles given to different tires of the government. Another purpose of this work is to analyze the collaboration among urban planning institutions, private sector, international funding community, NGOs and civil society within the current urban planning arena of Afghanistan and to identify the roles, responsibilities and functions of urban planning institutions in different levels of urban governance. Finally find out what possible and necessary institutional changes and framework are needed in order to foster grassroots based inter-institutional collaboration and partnership among various tires of government. The methodological approach to the research is based on qualitative data analysis. For the analysis purpose, government urban planning data and in-depth, semi-structured, face-to-face interviews with Afghanistan’s urban planning officials were thematically used, which provided in-depth information about involved actors in urban planning and their roles and relationships.

Developing a national dissemination plan for collaborative care for depression: QUERI Series

Directory of Open Access Journals (Sweden)

Rubenstein Lisa V

2008-12-01

Full Text Available Abstract Background Little is known about effective strategies for disseminating and implementing complex clinical innovations across large healthcare systems. This paper describes processes undertaken and tools developed by the U.S. Department of Veterans Affairs (VA Mental Health Quality Enhancement Research Initiative (MH-QUERI to guide its efforts to partner with clinical leaders to prepare for national dissemination and implementation of collaborative care for depression. Methods An evidence-based quality improvement (EBQI process was used to develop an initial set of goals to prepare the VA for national dissemination and implementation of collaborative care. The resulting product of the EBQI process is referred to herein as a "National Dissemination Plan" (NDP. EBQI participants included: a researchers with expertise on the collaborative care model for depression, clinical quality improvement, and implementation science, and b VA clinical and administrative leaders with experience and expertise on how to adapt research evidence to organizational needs, resources and capacity. Based on EBQI participant feedback, drafts of the NDP were revised and refined over multiple iterations before a final version was approved by MH-QUERI leadership. 'Action Teams' were created to address each goal. A formative evaluation framework and related tools were developed to document processes, monitor progress, and identify and act upon barriers and facilitators in addressing NDP goals. Results The National Dissemination Plan suggests that effectively disseminating collaborative care for depression in the VA will likely require attention to: Guidelines and Quality Indicators (4 goals, Training in Clinical Processes and Evidence-based Quality Improvement (6 goals, Marketing (7 goals, and Informatics Support (1 goal. Action Teams are using the NDP as a blueprint for developing infrastructure to support system-wide adoption and sustained implementation of

Remote Laboratory Collaboration Plan in Communications Engineering

Directory of Open Access Journals (Sweden)

Akram Ahmad Abu-aisheh

2012-11-01

Full Text Available Communications laboratories for electrical engineering undergraduates typically require that students perform practical experiments and document findings as part of their knowledge and skills development. Laboratory experiments are usally designed to support and reinforce theories presented in the classroom and foster independent thinking; however, the capital cost of equipment needed to sustain a viable laboratory environment is large and ongoing maintenance is an annual expense. Consequently, there is a need to identify and validate more economic solutions for engineering laboratories. This paper presents a remote laboratory collaboration plan for use in an elctrical engineering communications course.

Ebola Preparedness Planning and Collaboration by Two Health Systems in Wisconsin, September to December 2014.

Science.gov (United States)

Leonhardt, Kathryn Kraft; Keuler, Megan; Safdar, Nasia; Hunter, Paul

2016-08-01

We describe the collaborative approach used by 2 health systems in Wisconsin to plan and prepare for the threat of Ebola virus disease. This was a descriptive study of the preparedness planning, infection prevention, and collaboration with public health agencies undertaken by 2 health systems in Wisconsin between September and December 2014. The preparedness approach used by the 2 health systems relied successfully on their robust infrastructure for planning and infection prevention. In the setting of rapidly evolving guidance and unprecedented fear regarding Ebola, the 2 health systems enhanced their response through collaboration and coordination with each other and government public health agencies. Key lessons learned included the importance of a rigorous planning process, robust infection prevention practices, and coalitions between public and private health sectors. The potential threat of Ebola virus disease stimulated emergency preparedness in which acute care facilities played a leading role in the public health response. Leveraging the existing expertise of health systems is essential when faced with emerging infectious diseases. (Disaster Med Public Health Preparedness. 2016;10:691-697).

Manifestations of metacognitive activity during the collaborative planning of chemistry practical investigations

Science.gov (United States)

Mathabathe, Kgadi Clarrie; Potgieter, Marietjie

2017-07-01

This paper elaborates a process followed to characterise manifestations of cognitive regulation during the collaborative planning of chemistry practical investigations. Metacognitive activity was defined as the demonstration of planning, monitoring, control and evaluation of cognitive activities by students while carrying out the chemistry task. Inherent in collaborative learning is the social aspect of metacognition, which in this study was evidenced in social cognitive regulation (notably of intra- and interpersonal metacognitive regulations) as groups of students went about planning their practical investigations. Discussions of two of the learning groups (n = 4; n = 3) as they planned the extended practical investigation were recorded, transcribed and analysed for indicators of any inherent metacognitive activity. The process of characterising the manifestations of metacognition resulted in the development of a coding system which specifies not only the regulatory strategies at play but the type of regulation (self or other), the area of regulation (cognition, task performance or behaviour) as well as the depth of regulatory contributions (high or low). The fine-grained coding system allowed for a finer theoretical elucidation of the social nature of metacognition. The implications of this study for metacognition and chemistry education research are highlighted.

Economic impact analysis of load forecasting

International Nuclear Information System (INIS)

Ranaweera, D.K.; Karady, G.G.; Farmer, R.G.

1997-01-01

Short term load forecasting is an essential function in electric power system operations and planning. Forecasts are needed for a variety of utility activities such as generation scheduling, scheduling of fuel purchases, maintenance scheduling and security analysis. Depending on power system characteristics, significant forecasting errors can lead to either excessively conservative scheduling or very marginal scheduling. Either can induce heavy economic penalties. This paper examines the economic impact of inaccurate load forecasts. Monte Carlo simulations were used to study the effect of different load forecasting accuracy. Investigations into the effect of improving the daily peak load forecasts, effect of different seasons of the year and effect of utilization factors are presented

Integrated Wind Power Planning Tool

DEFF Research Database (Denmark)

Rosgaard, M. H.; Giebel, Gregor; Nielsen, T. S.

2012-01-01

model to be developed in collaboration with ENFOR A/S; a danish company that specialises in forecasting and optimisation for the energy sector. This integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting......This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the working title "Integrated Wind Power Planning Tool". The project commenced October 1, 2011, and the goal is to integrate a numerical weather prediction (NWP) model with purely...

Closing the Communication Gap: "Web 2.0 Tools for Enhanced Planning and Collaboration"

Science.gov (United States)

Charles, Kelly J.; Dickens, Virginia

2012-01-01

Web 2.0 is expanding the way general and special educators collaborate, especially in co-teaching situations. This article draws attention to several free web-based tools and a co-teaching lesson plan supplement that can be used to incorporate Web 2.0 technologies during the co-planning, co-teaching and shared reflection processes between the…

THE STUDY OF THE FORECASTING PROCESS INFRASTRUCTURAL SUPPORT BUSINESS

Directory of Open Access Journals (Sweden)

E. V. Sibirskaia

2014-01-01

Full Text Available Summary. When forecasting the necessary infrastructural support entrepreneurship predict rational distribution of the potential and expected results based on capacity development component of infrastructural maintenance, efficient use of resources, expertise and development of regional economies, the rationalization of administrative decisions, etc. According to the authors, the process of predicting business infrastructure software includes the following steps: analysis of the existing infrastructure support business to the top of the forecast period, the structure of resources, identifying disparities, their causes, identifying positive trends in the analysis and the results of research; research component of infrastructural support entrepreneurship, assesses complex system of social relations, institutions, structures and objects made findings and conclusions of the study; identification of areas of strategic change and the possibility of eliminating weaknesses and imbalances, identifying prospects for the development of entrepreneurship; identifying a set of factors and conditions affecting each component of infrastructure software, calculated the degree of influence of each of them and the total effect of all factors; adjustment indicators infrastructure forecasts. Research of views of category says a method of strategic planning and forecasting that methods of strategic planning are considered separately from forecasting methods. In a combination methods of strategic planning and forecasting, in relation to infrastructure ensuring business activity aren't given in literature. Nevertheless, authors consider that this category should be defined for the characteristic of the intrinsic and substantial nature of strategic planning and forecasting of infrastructure ensuring business activity.processing.

Collaborative Cyber-infrastructures for the Management of the UNESCO-IGCP Research Project "Forecast of tephra fallout"

Science.gov (United States)

Folch, A.; Costa, A.; Cordoba, G.

2009-04-01

Tephra fallout following explosive volcanic eruptions produces several hazardous effects on inhabitants, infrastructure, and property and represents a serious threat for communities located around active volcanoes. In order to mitigate the effects on the surrounding areas, scientists and civil decision-making authorities need reliable short-term forecasts during episodes of eruptive crisis and long-term probabilistic maps to plan territorial policies and land use. Modelling, together with field studies and volcano monitoring, constitutes an indispensable tool to achieve these objectives. The UNESCO-IGCP research project proposal "Forecast of tephra fallout" has the aim to produce a series of tools capable to elaborate both short-term forecasts and long-term hazard assessments using the cutting-edge models for tephra transport and sedimentation. A special project website will be designed to supply a set of models, procedures and expertise to several Latino-American Institutes based in countries seriously threatened by this geo-hazard (Argentina, Chile, Colombia, Ecuador, Mexico, and Nicaragua). This will proportionate to the final users a tool to elaborate short-term forecasts of tephra deposition on the ground, and determine airborne ash concentrations (a quantity of special relevance for aerial navigation safety) during eruptions and emergencies. The project web-site will have a public section and a password-protected area to exchange information and data among participants and, eventually, to allow remote execution of high-resolution mesoscale meteorological forecasts at the BSC facilities. The public website section will be updated periodically and will include sections describing the project objectives and achievements as well as the hazard maps for the investigated volcanoes, and will be linked to other relevant websites such as IAVCEI, IGCP, IUGS and UNESCO homepages. A part of the public section of the website will be devoted to disseminate achieved

Collaborating with Industry to Enhance Financial Planning and Accounting Education

Directory of Open Access Journals (Sweden)

Mark Brimble

2012-11-01

Full Text Available Higher education is integral to the professionalisation of financial planning in Australia. However, thetraditional separation between ‘content’ and ‘practice’ in tertiary curriculum does not necessarily equipstudents with the skills required to apply the content in a professional context. Contextualisation of thefinancial planning curriculum requires collaboration between higher education institutions and the professionto develop authentic learning environments, such as work-integrated learning (WIL. This paper describes thecollaboration by one Australian university to develop a professionally integrated Financial Planning andAccounting degree and provides evidence of its impact from an industry perspective. The results reveal a highlevel of industry satisfaction with the degree, substantial professional integration, as well as the developmentof students’ professional skills. Students also developed an improved impression of the university, its studentsand its staff.

A Study on planning of the international collaboration foundation for the Advanced Nuclear Technology Development Project

Energy Technology Data Exchange (ETDEWEB)

Chang, Moon Hee; Kim, H. R.; Kim, H. J. and others

2005-03-15

Korea has participated in the international collaboration programs for the development of future nuclear energy systems driven by the countries holding advanced nuclear technology and Korea and U.S. have cooperated in the INERI. This study aimed mainly at developing the plan for participation in the collaborative development of the Gen IV, searching the participation strategy for INERI and the INPRO, and the international cooperation in these programs. Contents and scope of the study for successful achievement are as follows; Investigation and analysis of international and domestic trends related to advanced nuclear technologies, Development of the plan for collaborative development of the Gen IV and conducting the international cooperation activities, Support for the activities related to I-NERI between Korea and U.S. and conducting the international cooperation, International cooperation activities for the INPRO. This study can be useful for planning the research plan and setting up of the strategy of integrating the results of the international collaboration and the domestic R and D results by combining the Gen IV and the domestic R and D in the field of future nuclear technology. Futhermore, this study can contribute to establishing the effective foundation and broadening the cooperation activities not only with the advanced countries for acquisition of the advanced technologies but also with the developing countries for the export of the domestic nuclear energy systems.

A Study on planning of promotion for international collaborative development of Generation IV Nuclear Energy Systems

International Nuclear Information System (INIS)

Hee, Chang Moon; Yang, M. S.; Ha, J. J.

2006-06-01

Korea has participated in the international collaboration programs for the development of future nuclear energy systems driven by the countries holding advanced nuclear technology and Korea and U. S. have cooperated in the INERI. This study is mainly at developing the plan for participation in the collaborative development of the Gen IV, searching the participation strategy for INERI and the INPRO, and the international cooperation in these programs. Contents and scope of the study for successful achievement are as follows; - Investigation and analysis of international and domestic trends related to advanced nuclear technologies - Development of the plan for collaborative development of the Gen IV and conducting the international cooperation activities - Support for the activities related to I-NERI between Korea and U. S. and conducting the international cooperation - International cooperation activities for the INPRO This study can be useful for planning the research plan and setting up of the strategy of integrating the results of the international collaboration and the domestic R and D results by combining the Gen IV and the domestic R and D in the field of future nuclear technology. Furthermore, this study can contribute to establishing the effective foundation and broadening the cooperation activities not only with the advanced countries for acquisition of the advanced technologies but also with the developing countries for the export of the domestic nuclear energy systems

A Study on planning of the international collaboration foundation for the Advanced Nuclear Technology Development Project

International Nuclear Information System (INIS)

Chang, Moon Hee; Kim, H. R.; Kim, H. J. and others

2005-03-01

Korea has participated in the international collaboration programs for the development of future nuclear energy systems driven by the countries holding advanced nuclear technology and Korea and U.S. have cooperated in the INERI. This study aimed mainly at developing the plan for participation in the collaborative development of the Gen IV, searching the participation strategy for INERI and the INPRO, and the international cooperation in these programs. Contents and scope of the study for successful achievement are as follows; Investigation and analysis of international and domestic trends related to advanced nuclear technologies, Development of the plan for collaborative development of the Gen IV and conducting the international cooperation activities, Support for the activities related to I-NERI between Korea and U.S. and conducting the international cooperation, International cooperation activities for the INPRO. This study can be useful for planning the research plan and setting up of the strategy of integrating the results of the international collaboration and the domestic R and D results by combining the Gen IV and the domestic R and D in the field of future nuclear technology. Futhermore, this study can contribute to establishing the effective foundation and broadening the cooperation activities not only with the advanced countries for acquisition of the advanced technologies but also with the developing countries for the export of the domestic nuclear energy systems

Gearbox Reliability Collaborative Phase 3 Gearbox 3 Test Plan

Energy Technology Data Exchange (ETDEWEB)

Keller, Jonathan [National Renewable Energy Lab. (NREL), Golden, CO (United States); Wallen, Robb [National Renewable Energy Lab. (NREL), Golden, CO (United States)

2016-10-01

This document describes the Phase 3 test plan for the Gearbox Reliability Collaborative Gearbox #3. The primary test objective is to measure the planetary load sharing characteristics in the same conditions as the original gearbox design. If the measured load-sharing characteristics are close to the design model, the projected three-times improvement in planetary section predicted fatigue life and the efficacy of preloaded tapered roller bearings in mitigating the planetary bearing fatigue failure mode will have been demonstrated.

A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting

Directory of Open Access Journals (Sweden)

Chengshi Tian

2018-03-01

Full Text Available Short-term load forecasting plays an indispensable role in electric power systems, which is not only an extremely challenging task but also a concerning issue for all society due to complex nonlinearity characteristics. However, most previous combined forecasting models were based on optimizing weight coefficients to develop a linear combined forecasting model, while ignoring that the linear combined model only considers the contribution of the linear terms to improving the model’s performance, which will lead to poor forecasting results because of the significance of the neglected and potential nonlinear terms. In this paper, a novel nonlinear combined forecasting system, which consists of three modules (improved data pre-processing module, forecasting module and the evaluation module is developed for short-term load forecasting. Different from the simple data pre-processing of most previous studies, the improved data pre-processing module based on longitudinal data selection is successfully developed in this system, which further improves the effectiveness of data pre-processing and then enhances the final forecasting performance. Furthermore, the modified support vector machine is developed to integrate all the individual predictors and obtain the final prediction, which successfully overcomes the upper drawbacks of the linear combined model. Moreover, the evaluation module is incorporated to perform a scientific evaluation for the developed system. The half-hourly electrical load data from New South Wales are employed to verify the effectiveness of the developed forecasting system, and the results reveal that the developed nonlinear forecasting system can be employed in the dispatching and planning for smart grids.

EXPENSES FORECASTING MODEL IN UNIVERSITY PROJECTS PLANNING

Directory of Open Access Journals (Sweden)

Sergei A. Arustamov

2016-11-01

Full Text Available The paper deals with mathematical model presentation of cash flows in project funding. We describe different types of expenses linked to university project activities. Problems of project budgeting that contribute most uncertainty have been revealed. As an example of the model implementation we consider calculation of vacation allowance expenses for project participants. We define problems of forecast for funds reservation: calculation based on methodology established by the Ministry of Education and Science calculation according to the vacation schedule and prediction of the most probable amount. A stochastic model for vacation allowance expenses has been developed. We have proposed methods and solution of the problems that increase the accuracy of forecasting for funds reservation based on 2015 data.

The attitudinal and cognitive effects of interdisciplinary collaboration on elementary pre-service teachers development of biological science related lesson plans

Science.gov (United States)

Mills, Jada Jamerson

There is a need for STEM (science, technology, engineering, and mathematics) education to be taught effectively in elementary schools. In order to achieve this, teacher preparation programs should graduate confident, content strong teachers to convey knowledge to elementary students. This study used interdisciplinary collaboration between the School of Education and the College of Liberal Arts through a Learning-by-Teaching method (LdL): Lernen durch Lernen in German. Pre-service teacher (PST) achievement levels of understanding science concepts based on pretest and posttest data, quality of lesson plans developed, and enjoyment of the class based on the collaboration with science students. The PSTs enrolled in two treatment sections of EDEL 404: Science in the Elementary Classroom collaborated with science students enrolled in BISC 327: Introductory Neuroscience to enhance their science skills and create case-based lesson plans on neurothology topics: echolocation, electrosensory reception, steroid hormones, and vocal learning. The PSTs enrolled in the single control section of EDEL 404 collaborated with fellow elementary education majors to develop lesson plans also based on the same selected topics. Qualitative interviews of education faculty, science faculty, and PSTs provided depth to the quantitative findings. Upon lesson plan completion, in-service teachers also graded the two best and two worst plans for the treatment and control sections and a science reviewer graded the plans for scientific accuracy. Statistical analyses were conducted for hypotheses, and one significant hypothesis found that PSTs who collaborated with science students had more positive science lesson plan writing attitudes than those who did not. Despite overall insignificant statistical analyses, all PSTs responded as more confident after collaboration. Additionally, interviews provided meaning and understanding to the insignificant statistical results as well as scientific accuracy of

Burning through organizational boundaries? Examining inter-organizational communication networks in policy-mandated collaborative bushfire planning groups

Science.gov (United States)

Rachel F. Brummel; Kristen C. Nelson; Pamela J. Jakes

2012-01-01

Collaboration can enhance cooperation across geographic and organizational scales, effectively "burning through" those boundaries. Using structured social network analysis (SNA) and qualitative in-depth interviews, this study examined three collaborative bushfire planning groups in New South Wales, Australia and asked: How does participation in policy-...

Using a Software Tool in Forecasting: a Case Study of Sales Forecasting Taking into Account Data Uncertainty

Science.gov (United States)

Fabianová, Jana; Kačmáry, Peter; Molnár, Vieroslav; Michalik, Peter

2016-10-01

Forecasting is one of the logistics activities and a sales forecast is the starting point for the elaboration of business plans. Forecast accuracy affects the business outcomes and ultimately may significantly affect the economic stability of the company. The accuracy of the prediction depends on the suitability of the use of forecasting methods, experience, quality of input data, time period and other factors. The input data are usually not deterministic but they are often of random nature. They are affected by uncertainties of the market environment, and many other factors. Taking into account the input data uncertainty, the forecast error can by reduced. This article deals with the use of the software tool for incorporating data uncertainty into forecasting. Proposals are presented of a forecasting approach and simulation of the impact of uncertain input parameters to the target forecasted value by this case study model. The statistical analysis and risk analysis of the forecast results is carried out including sensitivity analysis and variables impact analysis.

Weather-centric rangeland revegetation planning

Science.gov (United States)

Hardegree, Stuart P.; Abatzoglou, John T.; Brunson, Mark W.; Germino, Matthew; Hegewisch, Katherine C.; Moffet, Corey A.; Pilliod, David S.; Roundy, Bruce A.; Boehm, Alex R.; Meredith, Gwendwr R.

2018-01-01

Invasive annual weeds negatively impact ecosystem services and pose a major conservation threat on semiarid rangelands throughout the western United States. Rehabilitation of these rangelands is challenging due to interannual climate and subseasonal weather variability that impacts seed germination, seedling survival and establishment, annual weed dynamics, wildfire frequency, and soil stability. Rehabilitation and restoration outcomes could be improved by adopting a weather-centric approach that uses the full spectrum of available site-specific weather information from historical observations, seasonal climate forecasts, and climate-change projections. Climate data can be used retrospectively to interpret success or failure of past seedings by describing seasonal and longer-term patterns of environmental variability subsequent to planting. A more detailed evaluation of weather impacts on site conditions may yield more flexible adaptive-management strategies for rangeland restoration and rehabilitation, as well as provide estimates of transition probabilities between desirable and undesirable vegetation states. Skillful seasonal climate forecasts could greatly improve the cost efficiency of management treatments by limiting revegetation activities to time periods where forecasts suggest higher probabilities of successful seedling establishment. Climate-change projections are key to the application of current environmental models for development of mitigation and adaptation strategies and for management practices that require a multidecadal planning horizon. Adoption of new weather technology will require collaboration between land managers and revegetation specialists and modifications to the way we currently plan and conduct rangeland rehabilitation and restoration in the Intermountain West.

Socioeconomic and travel demand forecasts for Virginia and potential policy responses : a report for VTrans2035 : Virginia's statewide multimodal transportation plan.

Science.gov (United States)

2009-01-01

VTrans2035, Virginia's statewide multimodal transportation plan, requires 25-year forecasts of socioeconomic and travel activity. Between 2010 and 2035, daily vehicle miles traveled (DVMT) will increase between 35% and 45%, accompanied by increases i...

Collaborative planning in natural resource management – the case of regulation of nitrogen in the agri-environment

DEFF Research Database (Denmark)

Vejre, Henrik; Andersen, Erling; Andersen, Peter Stubkjær

approach is that local knowledge on, e.g. farming practices, soil, water and climate, can feed into the regulation process, making general rules less important, maybe even obsolete. Methods: The planning approach adapted for the study was inspired by concepts of collaborative planning in urban areas...... approaches are gaining momentum. The aim of this paper is to test a collaborative planning approach in the regulation of nitrogen in the farming sector. The overarching question is whether this regulation can be organised locally rather than by general, national rules. The benefits by adopting a local...... and various other concepts of participatory environmental planning. The approach was tested simultaneously in six case areas of rural Denmark, each comprising small watersheds (20-76km2). The strategic aim was to reduce the loss of nitrogen from farms to the aquatic environment. The planning process consisted...

Networking: a study in planning and developing cross-cultural collaboration

Directory of Open Access Journals (Sweden)

Sanjeev Singh

2002-12-01

Full Text Available This paper reports on a collaboration between the authors at the University of Brighton (UK and the University of Delhi, South Campus. The collaboration came about as a result of the EU-India Cross-Cultural Innovation Network collaboration programme, a project involving several universities and organizations across Europe and India. The authors of this paper both lecture in the area of computer networking. Following meetings in Delhi, they agreed to work together to produce a Web-based networking resource to be generated by the students of both institutions. The first phase of development involved the mounting of Web-based tutorials and documents produced by the students. The second phase will centre on the development of a knowledge base generated by the interaction of the students within an asynchronous forum. Running alongside these phases will be a collaborative bookmarking system, a database in which the students will post URLs of Web-based resources that they find useful in their studies. This system incorporates a form of collaborative filtering, an evolutionary mechanism which seeks to embody the qualities that students value in resources to provide a dynamic set of ratings to assist in the selection of those of most use. The planning of such a system raises some unusual issues, not least in the process of collaboration itself, with concerns as diverse as technical compatibility, institutional and cultural differences, timezones and the reliability of email. Limited bandwidth between our institutions causes special problems with the interactive elements of the resource. We present the methods we are investigating to reduce the impact of this. The fact that the students share an intellectual discipline but are otherwise separated by a cultural and geographical divide is expected to lead to fruitful diversity in thinking and approaches to problem-solving.

Wind power forecast

Energy Technology Data Exchange (ETDEWEB)

Pestana, Rui [Rede Electrica Nacional (REN), S.A., Lisboa (Portugal). Dept. Systems and Development System Operator; Trancoso, Ana Rosa; Delgado Domingos, Jose [Univ. Tecnica de Lisboa (Portugal). Seccao de Ambiente e Energia

2012-07-01

Accurate wind power forecast are needed to reduce integration costs in the electric grid caused by wind inherent variability. Currently, Portugal has a significant wind power penetration level and consequently the need to have reliable wind power forecasts at different temporal scales, including localized events such as ramps. This paper provides an overview of the methodologies used by REN to forecast wind power at national level, based on statistical and probabilistic combinations of NWP and measured data with the aim of improving accuracy of pure NWP. Results show that significant improvement can be achieved with statistical combination with persistence in the short-term and with probabilistic combination in the medium-term. NWP are also able to detect ramp events with 3 day notice to the operational planning. (orig.)

Social learning in a policy-mandated collaboration: Community wildfire protection planning in the eastern United States

Science.gov (United States)

Rachel F. Brummel; Kristen C. Nelson; Pamela J. Jakes; Daniel R. Williams

2010-01-01

Policies such as the US Healthy Forests Restoration Act (HFRA) mandate collaboration in planning to create benefits such as social learning and shared understanding among partners. However, some question the ability of top-down policy to foster successful local collaboration. Through in-depth interviews and document analysis, this paper investigates social learning and...

1994 Solid waste forecast container volume summary

International Nuclear Information System (INIS)

Templeton, K.J.; Clary, J.L.

1994-09-01

This report describes a 30-year forecast of the solid waste volumes by container type. The volumes described are low-level mixed waste (LLMW) and transuranic/transuranic mixed (TRU/TRUM) waste. These volumes and their associated container types will be generated or received at the US Department of Energy Hanford Site for storage, treatment, and disposal at Westinghouse Hanford Company's Solid Waste Operations Complex (SWOC) during a 30-year period from FY 1994 through FY 2023. The forecast data for the 30-year period indicates that approximately 307,150 m 3 of LLMW and TRU/TRUM waste will be managed by the SWOC. The main container type for this waste is 55-gallon drums, which will be used to ship 36% of the LLMW and TRU/TRUM waste. The main waste generator forecasting the use of 55-gallon drums is Past Practice Remediation. This waste will be generated by the Environmental Restoration Program during remediation of Hanford's past practice sites. Although Past Practice Remediation is the primary generator of 55-gallon drums, most waste generators are planning to ship some percentage of their waste in 55-gallon drums. Long-length equipment containers (LECs) are forecasted to contain 32% of the LLMW and TRU/TRUM waste. The main waste generator forecasting the use of LECs is the Long-Length Equipment waste generator, which is responsible for retrieving contaminated long-length equipment from the tank farms. Boxes are forecasted to contain 21% of the waste. These containers are primarily forecasted for use by the Environmental Restoration Operations--D ampersand D of Surplus Facilities waste generator. This waste generator is responsible for the solid waste generated during decontamination and decommissioning (D ampersand D) of the facilities currently on the Surplus Facilities Program Plan. The remaining LLMW and TRU/TRUM waste volume is planned to be shipped in casks and other miscellaneous containers

Developing and Implementing a Citywide Asthma Action Plan: A Community Collaborative Partnership.

Science.gov (United States)

Staudt, Amanda Marie; Alamgir, Hasanat; Long, Debra Lynn; Inscore, Stephen Curtis; Wood, Pamela Runge

2015-12-01

Asthma affects 1 in 10 children in the United States, with higher prevalence among children living in poverty. Organizations in San Antonio, Texas, partnered to design and implement a uniform, citywide asthma action plan to improve asthma management capacity in schools. The asthma action plan template was modified from that of the Global Initiative for Asthma. School personnel were trained in symptom recognition, actions to take, and use of equipment before the asthma action plan implementation. The annual Asthma Action Plan Summit was organized as a forum for school nurses, healthcare providers, and members of the community to exchange ideas and strategies on implementation, as well as to revise the plan. The asthma action plan was implemented in all 16 local school districts. Feedback received from school nurses suggests that the citywide asthma action plan resulted in improved asthma management and student health at schools. The evidence in this study suggests that community organizations can successfully collaborate to implement a citywide health initiative similar to the asthma action plan.

Short-term forecasting of emergency inpatient flow.

Science.gov (United States)

Abraham, Gad; Byrnes, Graham B; Bain, Christopher A

2009-05-01

Hospital managers have to manage resources effectively, while maintaining a high quality of care. For hospitals where admissions from the emergency department to the wards represent a large proportion of admissions, the ability to forecast these admissions and the resultant ward occupancy is especially useful for resource planning purposes. Since emergency admissions often compete with planned elective admissions, modeling emergency demand may result in improved elective planning as well. We compare several models for forecasting daily emergency inpatient admissions and occupancy. The models are applied to three years of daily data. By measuring their mean square error in a cross-validation framework, we find that emergency admissions are largely random, and hence, unpredictable, whereas emergency occupancy can be forecasted using a model combining regression and autoregressive integrated moving average (ARIMA) model, or a seasonal ARIMA model, for up to one week ahead. Faced with variable admissions and occupancy, hospitals must prepare a reserve capacity of beds and staff. Our approach allows estimation of the required reserve capacity.

Forecasting residential electricity demand in provincial China.

Science.gov (United States)

Liao, Hua; Liu, Yanan; Gao, Yixuan; Hao, Yu; Ma, Xiao-Wei; Wang, Kan

2017-03-01

In China, more than 80% electricity comes from coal which dominates the CO2 emissions. Residential electricity demand forecasting plays a significant role in electricity infrastructure planning and energy policy designing, but it is challenging to make an accurate forecast for developing countries. This paper forecasts the provincial residential electricity consumption of China in the 13th Five-Year-Plan (2016-2020) period using panel data. To overcome the limitations of widely used predication models with unreliably prior knowledge on function forms, a robust piecewise linear model in reduced form is utilized to capture the non-deterministic relationship between income and residential electricity consumption. The forecast results suggest that the growth rates of developed provinces will slow down, while the less developed will be still in fast growing. The national residential electricity demand will increase at 6.6% annually during 2016-2020, and populous provinces such as Guangdong will be the main contributors to the increments.

Collaborative Optimization of Stop Schedule Plan and Ticket Allotment for the Intercity Train

Directory of Open Access Journals (Sweden)

Xichun Chen

2016-01-01

Full Text Available As regards the ticket allotment issue of the intercity passenger corridor designed for different train grades, the matching relationship between the ticket allotment and the passenger flow demand is studied. The passenger flow conversion equation which is based on the collaborative optimization of the intercity train stop schedule plan and ticket allotment is established. Then the mathematical model aiming at the maximum revenue of intercity train system and the highest satisfaction from the passengers is established. The particle swarm harmony search algorithm is designed to solve the model. The example verifies the effectiveness of the model and algorithm, which indicates that, through the collaborative optimization of the stop schedule plan and ticket allotment for different grades intercity trains, the sectional utilization rate of the train can be improved; meanwhile, the optimum matching between the intercity train revenue and the passenger satisfaction can be realized.

Computer aided planning of distribution systems and connection with medium term load forecast

International Nuclear Information System (INIS)

di Salvatore, F.; Grattieri, W.; Insinga, F.; Malafarina, L.; Mazzoni, M.; Nicola, G.

1990-01-01

In order to perform planning studies on HV (40-l50 kV), MV and LV networks, ENEL (Italian Electricity Board) has developed a computation system composed of a set of integrated programs which utilize the information stored in several data bases, with the aim of: providing energy consumption forecasts for each area of the country; transferring consumption for each area to the distribution network nodes and to evaluating the electric demand by using a statistical power/energy correlation model; analyzing several network development alternatives and selecting the optimum development plan by comparing the overall costs (investments, operation, risk). In order to make its utilization by planners easier, the computation system will be operated with interactive and graphic procedures made available by the use of graphic work stations. This report describes the main objectives and basic hypotheses assumed in the preparation of the computation system, as well as, the system's general architecture

Computer aided planning of distribution systems and connection with medium term load forecast

Energy Technology Data Exchange (ETDEWEB)

di Salvatore, F; Grattieri, W; Insinga, F; Malafarina, L; Mazzoni, M; Nicola, G

1991-12-31

In order to perform planning studies on HV (40-l50 kV), MV and LV networks, ENEL (Italian Electricity Board) has developed a computation system composed of a set of integrated programs which utilize the information stored in several data bases, with the aim of: providing energy consumption forecasts for each area of the country; transfering consumption for each area to the distribution network nodes and to evaluating the electric demand by using a statistical power/energy correlation model; analyzing several network development alternatives and selecting the optimum development plan by comparing the overall costs (investments, operation, risk). In order to make its utilization by planners easier, the computation system will be operated with interactive and graphic procedures made available by the use of graphic work stations. This report describes the main objectives and basic hypotheses assumed in the preparation of the computation system, as well as, the system`s general architecture.

Wind Resource Assessment and Forecast Planning with Neural Networks

Directory of Open Access Journals (Sweden)

Nicolus K. Rotich

2014-06-01

Full Text Available In this paper we built three types of artificial neural networks, namely: Feed forward networks, Elman networks and Cascade forward networks, for forecasting wind speeds and directions. A similar network topology was used for all the forecast horizons, regardless of the model type. All the models were then trained with real data of collected wind speeds and directions over a period of two years in the municipal of Puumala, Finland. Up to 70th percentile of the data was used for training, validation and testing, while 71–85th percentile was presented to the trained models for validation. The model outputs were then compared to the last 15% of the original data, by measuring the statistical errors between them. The feed forward networks returned the lowest errors for wind speeds. Cascade forward networks gave the lowest errors for wind directions; Elman networks returned the lowest errors when used for short term forecasting.

Frost Monitoring and Forecasting Using MODIS Land Surface Temperature Data and a Numerical Weather Prediction Model Forecasts for Eastern Africa

Science.gov (United States)

Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh

2014-01-01

Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.

Travail, transparency and trust : a case study of computer-supported collaborative supply chain planning in high-tech electronics

NARCIS (Netherlands)

Akkermans, H.A.; Bogerd, P.; van Doremalen, J.B.M.

2004-01-01

Describes a case study of supply chain collaboration facilitated by a decision support environment in a high-tech electronics supply chain with multiple independent companies. In a business process called collaborative planning, representatives from these companies jointly take decisions regarding

Improving Collaborative Planning and Reflection Practices at International Baccalaureate Diploma Schools in Amman

Science.gov (United States)

Saa'd AlDin, Kawther

2014-01-01

In 2010, the International Baccalaureate (IB) Organization mandated that all its schools, including Diploma (DP) schools, adhere to the collaborative planning and reflection requirements, which emphasized the importance of integrating its theory of knowledge (TOK) core component into all disciplines. Many schools officials and educations in Amman…

Hanford's self-assessment of the solid waste forecast process

International Nuclear Information System (INIS)

Hauth, J.; Skumanich, M.; Morgan, J.

1996-01-01

In fiscal year (FY) 1995 the forecast process used at Hanford to project future solid waste volumes was evaluated. Data on current and future solid waste generation are used by Hanford site planners to determine near-term and long-term planning needs. Generators who plan to ship their waste to Hanford's Solid Waste Program for treatment, storage, and disposal provide volume information on the types of waste that could be potentially generated, waste characteristics, and container types. Generators also provide limited radionuclide data and supporting assumptions. A self-assessment of the forecast process identified many effective working elements, including a well-established and systematic process for data collection, analysis and reporting; sufficient resources to obtain the necessary information; and dedicated support and analytic staff. Several areas for improvement were identified, including the need to improve confidence in the forecast data, integrate forecast data with other site-level and national data calls, enhance the electronic data collection system, and streamline the forecast process

42 CFR 417.572 - Budget and enrollment forecast and interim reports.

Science.gov (United States)

2010-10-01

... 42 Public Health 3 2010-10-01 2010-10-01 false Budget and enrollment forecast and interim reports... PLANS, AND HEALTH CARE PREPAYMENT PLANS Medicare Payment: Cost Basis § 417.572 Budget and enrollment forecast and interim reports. (a) Annual submittal. The HMO or CMP must submit an annual operating budget...

Demand forecast: a case study at a meat agribusiness in west Santa Catarina

Directory of Open Access Journals (Sweden)

Cleunice Zanella

2016-03-01

Full Text Available Based on demand forecasts, companies plan production, financial and personnel scenarios, both in the long and short term. The forecasts are essential, especially for companies working with a push production system, for which there is no sale of collateral. They should therefore plan their production and financial systems with the aim of meeting the demand forecast of their products or services. Thus, this study was conducted at a meat agribusiness located in Chapecó, in the state of Santa Catarina, in order to analyze the demand forecasting methods used by the company. It is a case study with a qualitative approach. Data collection was conducted through semi-structured interviews with the operations manager, commercial manager and analysts who respond to the demand forecasts made. The main results highlight the use of both quantitative and qualitative methods, as well as indicating the importance of demand forecasts for the planning of the company

Distributed Cognition and Embodiment in Text Planning: A Situated Study of Collaborative Writing in the Workplace

Science.gov (United States)

Clayson, Ashley

2018-01-01

Through a study of collaborative writing at a student advocacy nonprofit, this article explores how writers distribute their text planning across tools, artifacts, and gestures, with a particular focus on how embodied representations of texts are present in text planning. Findings indicate that these and other representations generated by the…

Results from the second year of a collaborative effort to forecast influenza seasons in the United States.

Science.gov (United States)

Biggerstaff, Matthew; Johansson, Michael; Alper, David; Brooks, Logan C; Chakraborty, Prithwish; Farrow, David C; Hyun, Sangwon; Kandula, Sasikiran; McGowan, Craig; Ramakrishnan, Naren; Rosenfeld, Roni; Shaman, Jeffrey; Tibshirani, Rob; Tibshirani, Ryan J; Vespignani, Alessandro; Yang, Wan; Zhang, Qian; Reed, Carrie

2018-02-24

Accurate forecasts could enable more informed public health decisions. Since 2013, CDC has worked with external researchers to improve influenza forecasts by coordinating seasonal challenges for the United States and the 10 Health and Human Service Regions. Forecasted targets for the 2014-15 challenge were the onset week, peak week, and peak intensity of the season and the weekly percent of outpatient visits due to influenza-like illness (ILI) 1-4 weeks in advance. We used a logarithmic scoring rule to score the weekly forecasts, averaged the scores over an evaluation period, and then exponentiated the resulting logarithmic score. Poor forecasts had a score near 0, and perfect forecasts a score of 1. Five teams submitted forecasts from seven different models. At the national level, the team scores for onset week ranged from <0.01 to 0.41, peak week ranged from 0.08 to 0.49, and peak intensity ranged from <0.01 to 0.17. The scores for predictions of ILI 1-4 weeks in advance ranged from 0.02-0.38 and was highest 1 week ahead. Forecast skill varied by HHS region. Forecasts can predict epidemic characteristics that inform public health actions. CDC, state and local health officials, and researchers are working together to improve forecasts. Published by Elsevier B.V.

Preventing Elder Abuse: The Texas Plan for a Coordinated Service Delivery System. Collaborative Elder Abuse Prevention Project.

Science.gov (United States)

McDaniel, Garry L.

The Texas Department of Human Services, in collaboration with 13 other public and private organizations, co-sponsored a statewide Collaborative Elder Abuse Prevention project. The goal of this project is to develop a comprehensive, long-range plan for the prevention of elder abuse, a method for achieving a coordinated service delivery system for…

Development of a sales forecasting model for canopy windows

OpenAIRE

2014-01-01

M.Com. (Business Management) Forecasting is an important function used in a wide range of business planning or decision-making situations. The purpose ofthis study was to build a sales forecasting model that would be practical and cost effective, from the various forecasting methods and techniques available. Various forecast models, methods and techniques are outlined in the initial part of this study by the author. The author has outlined some of the fundamentals and limitations that unde...

Uncertainty in dispersion forecasts using meteorological ensembles

International Nuclear Information System (INIS)

Chin, H N; Leach, M J

1999-01-01

The usefulness of dispersion forecasts depends on proper interpretation of results. Understanding the uncertainty in model predictions and the range of possible outcomes is critical for determining the optimal course of action in response to terrorist attacks. One of the objectives for the Modeling and Prediction initiative is creating tools for emergency planning for special events such as the upcoming the Olympics. Meteorological forecasts hours to days in advance are used to estimate the dispersion at the time of the event. However, there is uncertainty in any meteorological forecast, arising from both errors in the data (both initial conditions and boundary conditions) and from errors in the model. We use ensemble forecasts to estimate the uncertainty in the forecasts and the range of possible outcomes

Exploring Collaborative and Community Based Planning in Tourism Case Study Sitia-Cavo Sidero Project

OpenAIRE

Katsouli, Penelope

2007-01-01

The present paper has explored the policy planning and development in emerging tourism settings in Sitia. Comprehensively, this study, in the name of sustainable development, focused on the extent of collaborative and community-based planning. For that reason exploratory research has been used; the context and the structure of this paper aimed to uncover the socially constructed reality of Sitia's stakeholders, within the dynamic environment, and respond to and questions. Therein significant ...

Network Bandwidth Utilization Forecast Model on High Bandwidth Network

Energy Technology Data Exchange (ETDEWEB)

Yoo, Wucherl; Sim, Alex

2014-07-07

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

Network bandwidth utilization forecast model on high bandwidth networks

Energy Technology Data Exchange (ETDEWEB)

Yoo, Wuchert (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2015-03-30

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

Simulation of regional day-ahead PV power forecast scenarios

DEFF Research Database (Denmark)

Nuno, Edgar; Koivisto, Matti Juhani; Cutululis, Nicolaos Antonio

2017-01-01

Uncertainty associated with Photovoltaic (PV) generation can have a significant impact on real-time planning and operation of power systems. This obstacle is commonly handled using multiple forecast realizations, obtained using for example forecast ensembles and/or probabilistic forecasts, often...... at the expense of a high computational burden. Alternatively, some power system applications may require realistic forecasts rather than actual estimates; able to capture the uncertainty of weatherdriven generation. To this end, we propose a novel methodology to generate day-ahead forecast scenarios of regional...... PV production matching the spatio-temporal characteristics while preserving the statistical properties of actual records....

Advances in electric power and energy systems load and price forecasting

CERN Document Server

2017-01-01

A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally. Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial ar nas. Short-run forecasting of electricity prices has become nece...

A New Strategy for Short-Term Load Forecasting

Directory of Open Access Journals (Sweden)

Yi Yang

2013-01-01

Full Text Available Electricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamental in many applications. Considering that an individual forecasting model usually cannot work very well for STLF, a hybrid model based on the seasonal ARIMA model and BP neural network is presented in this paper to improve the forecasting accuracy. Firstly the seasonal ARIMA model is adopted to forecast the electric load demand day ahead; then, by using the residual load demand series obtained in this forecasting process as the original series, the follow-up residual series is forecasted by BP neural network; finally, by summing up the forecasted residual series and the forecasted load demand series got by seasonal ARIMA model, the final load demand forecasting series is obtained. Case studies show that the new strategy is quite useful to improve the accuracy of STLF.

A methodology for Electric Power Load Forecasting

Directory of Open Access Journals (Sweden)

Eisa Almeshaiei

2011-06-01

Full Text Available Electricity demand forecasting is a central and integral process for planning periodical operations and facility expansion in the electricity sector. Demand pattern is almost very complex due to the deregulation of energy markets. Therefore, finding an appropriate forecasting model for a specific electricity network is not an easy task. Although many forecasting methods were developed, none can be generalized for all demand patterns. Therefore, this paper presents a pragmatic methodology that can be used as a guide to construct Electric Power Load Forecasting models. This methodology is mainly based on decomposition and segmentation of the load time series. Several statistical analyses are involved to study the load features and forecasting precision such as moving average and probability plots of load noise. Real daily load data from Kuwaiti electric network are used as a case study. Some results are reported to guide forecasting future needs of this network.

Modeling and forecasting of electrical power demands for capacity planning

Energy Technology Data Exchange (ETDEWEB)

Al-Shobaki, Salman [Department of Industrial Engineering, Hashemite University, Zarka 13115 (Jordan); Mohsen, Mousa [Department of Mechanical Engineering, Hashemite University, Zarka 13115 (Jordan)

2008-11-15

This paper describes the development of forecasting models to predict future generation and electrical power consumption in Jordan. This is critical to production cost since power is generated by burning expensive imported oil. Currently, the National Electric Power Company (NEPCO) is using regression models that only accounts for trend dynamics in their planning of loads and demand levels. The models are simplistic and are based on generated energy historical levels. They produce results on yearly bases and do not account for monthly variability in demand levels. The paper presents two models, one based on the generated energy data and the other is based on the consumed energy data. The models account for trend, monthly seasonality, and cycle dynamics. Both models are compared to NEPCO's model and indicate that NEPCO is producing energy at levels higher than needed (5.25%) thus increasing the loss in generated energy. The developed models also show a 13% difference between the generated energy and the consumed energy that is lost due to transmission line and in-house consumption. (author)

Modeling and forecasting of electrical power demands for capacity planning

International Nuclear Information System (INIS)

Al-Shobaki, Salman; Mohsen, Mousa

2008-01-01

This paper describes the development of forecasting models to predict future generation and electrical power consumption in Jordan. This is critical to production cost since power is generated by burning expensive imported oil. Currently, the National Electric Power Company (NEPCO) is using regression models that only accounts for trend dynamics in their planning of loads and demand levels. The models are simplistic and are based on generated energy historical levels. They produce results on yearly bases and do not account for monthly variability in demand levels. The paper presents two models, one based on the generated energy data and the other is based on the consumed energy data. The models account for trend, monthly seasonality, and cycle dynamics. Both models are compared to NEPCO's model and indicate that NEPCO is producing energy at levels higher than needed (5.25%) thus increasing the loss in generated energy. The developed models also show a 13% difference between the generated energy and the consumed energy that is lost due to transmission line and in-house consumption

A national framework for flood forecasting model assessment for use in operations and investment planning over England and Wales

Science.gov (United States)

Moore, Robert J.; Wells, Steven C.; Cole, Steven J.

2016-04-01

It has been common for flood forecasting systems to be commissioned at a catchment or regional level in response to local priorities and hydrological conditions, leading to variety in system design and model choice. As systems mature and efficiencies of national management are sought, there can be a drive towards system rationalisation, gaining an overview of model performance and consideration of simplification through model-type convergence. Flood forecasting model assessments, whilst overseen at a national level, may be commissioned and managed at a catchment and regional level, take a variety of forms and be large in number. This presents a challenge when an integrated national assessment is required to guide operational use of flood forecasts and plan future investment in flood forecasting models and supporting hydrometric monitoring. This contribution reports on how a nationally consistent framework for flood forecasting model performance has been developed to embrace many past, ongoing and future assessments for local river systems by engineering consultants across England & Wales. The outcome is a Performance Summary for every site model assessed which, on a single page, contains relevant catchment information for context, a selection of overlain forecast and observed hydrographs and a set of performance statistics with associated displays of novel condensed form. One display provides performance comparison with other models that may exist for the site. The performance statistics include skill scores for forecasting events (flow/level threshold crossings) of differing severity/rarity, indicating their probability and likely timing, which have real value in an operational setting. The local models assessed can be of any type and span rainfall-runoff (conceptual and transfer function) and flow routing (hydrological and hydrodynamic) forms. Also accommodated by the framework is the national G2G (Grid-to-Grid) distributed hydrological model, providing area

1991 Pacific Northwest loads and resources study, Pacific Northwest economic and electricity use forecast

International Nuclear Information System (INIS)

1992-01-01

This publication provides detailed documentation of the load forecast scenarios and assumptions used in preparing BPA's 1991 Pacific Northwest Loads and Resources Study (the Study). This is one of two technical appendices to the Study; the other appendix details the utility-specific loads and resources used in the Study. The load forecasts and assumption were developed jointly by Bonneville Power Administration (BPA) and Northwest Power Planning Council (Council) staff. This forecast is also used in the Council's 1991 Northwest Conservation and Electric Power Plan (1991 Plan)

Characteristics of Effective Collaboration in Response to Diversified Transportation Planning Authority

Directory of Open Access Journals (Sweden)

John S. Miller

2011-01-01

Full Text Available Advantages of decentralized transportation planning responsibilities may include reduced project delivery cost, the integration of comprehensive long-range planning with regional land use, and greater accountability regarding local concerns. Yet disadvantages may include a tendency for operations to dominate longer-term planning, inconsistent operational standards, and difficulty in achieving network benefits, since some transportation links may provide benefits to the entire system but not to the immediate area where the link is located. To surmount these challenges in a decentralized environment, agencies need to cooperate effectively. Characteristics of effective collaboration include (1 experienced personnel, (2 credibility of the deciding entity, (3 transparency of decision-making processes, (4 an authority with sufficient power and/or funding to encourage implementation of key initiatives, (5 clear incentives for cooperation, and, of special importance given limited resources, and (6 an ability to evaluate tradeoffs. An access management case study demonstrates how agencies may use these characteristics to advance challenging projects in a decentralized environment.

Environmental data processing by clustering methods for energy forecast and planning

Energy Technology Data Exchange (ETDEWEB)

Di Piazza, Annalisa [Dipartimento di Ingegneria Idraulica e Applicazioni Ambientali (DIIAA), viale delle Scienze, Universita degli Studi di Palermo, 90128 Palermo (Italy); Di Piazza, Maria Carmela; Ragusa, Antonella; Vitale, Gianpaolo [Consiglio Nazionale delle Ricerche Istituto di Studi sui Sistemi Intelligenti per l' Automazione (ISSIA - CNR), sezione di Palermo, Via Dante, 12, 90141 Palermo (Italy)

2011-03-15

This paper presents a statistical approach based on the k-means clustering technique to manage environmental sampled data to evaluate and to forecast of the energy deliverable by different renewable sources in a given site. In particular, wind speed and solar irradiance sampled data are studied in association to the energy capability of a wind generator and a photovoltaic (PV) plant, respectively. The proposed method allows the sub-sets of useful data, describing the energy capability of a site, to be extracted from a set of experimental observations belonging the considered site. The data collection is performed in Sicily, in the south of Italy, as case study. As far as the wind generation is concerned, a suitable generator, matching the wind profile of the studied sites, has been selected for the evaluation of the producible energy. With respect to the photovoltaic generation, the irradiance data have been taken from the acquisition system of an actual installation. It is demonstrated, in both cases, that the use of the k-means clustering method allows data that do not contribute to the produced energy to be grouped into a cluster, moreover it simplifies the problem of the energy assessment since it permits to obtain the desired information on energy capability by managing a reduced amount of experimental samples. In the studied cases, the proposed method permitted a reduction of the 50% of the data with a maximum discrepancy of 10% in energy estimation compared to the classical statistical approach. Therefore, the adopted k-means clustering technique represents an useful tool for an appropriate and less demanding energy forecast and planning in distributed generation systems. (author)

Secure environment for real-time tele-collaboration on virtual simulation of radiation treatment planning.

Science.gov (United States)

Ntasis, Efthymios; Maniatis, Theofanis A; Nikita, Konstantina S

2003-01-01

A secure framework is described for real-time tele-collaboration on Virtual Simulation procedure of Radiation Treatment Planning. An integrated approach is followed clustering the security issues faced by the system into organizational issues, security issues over the LAN and security issues over the LAN-to-LAN connection. The design and the implementation of the security services are performed according to the identified security requirements, along with the need for real time communication between the collaborating health care professionals. A detailed description of the implementation is given, presenting a solution, which can directly be tailored to other tele-collaboration services in the field of health care. The pilot study of the proposed security components proves the feasibility of the secure environment, and the consistency with the high performance demands of the application.

A Hybrid Approach on Tourism Demand Forecasting

Science.gov (United States)

Nor, M. E.; Nurul, A. I. M.; Rusiman, M. S.

2018-04-01

Tourism has become one of the important industries that contributes to the country’s economy. Tourism demand forecasting gives valuable information to policy makers, decision makers and organizations related to tourism industry in order to make crucial decision and planning. However, it is challenging to produce an accurate forecast since economic data such as the tourism data is affected by social, economic and environmental factors. In this study, an equally-weighted hybrid method, which is a combination of Box-Jenkins and Artificial Neural Networks, was applied to forecast Malaysia’s tourism demand. The forecasting performance was assessed by taking the each individual method as a benchmark. The results showed that this hybrid approach outperformed the other two models

Methods and tools to support real time risk-based flood forecasting - a UK pilot application

Directory of Open Access Journals (Sweden)

Brown Emma

2016-01-01

Full Text Available Flood managers have traditionally used probabilistic models to assess potential flood risk for strategic planning and non-operational applications. Computational restrictions on data volumes and simulation times have meant that information on the risk of flooding has not been available for operational flood forecasting purposes. In practice, however, the operational flood manager has probabilistic questions to answer, which are not completely supported by the outputs of traditional, deterministic flood forecasting systems. In a collaborative approach, HR Wallingford and Deltares have developed methods, tools and techniques to extend existing flood forecasting systems with elements of strategic flood risk analysis, including probabilistic failure analysis, two dimensional flood spreading simulation and the analysis of flood impacts and consequences. This paper presents the results of the application of these new operational flood risk management tools to a pilot catchment in the UK. It discusses the problems of performing probabilistic flood risk assessment in real time and how these have been addressed in this study. It also describes the challenges of the communication of risk to operational flood managers and to the general public, and how these new methods and tools can provide risk-based supporting evidence to assist with this process.

Spatial load forecasting

Energy Technology Data Exchange (ETDEWEB)

Willis, H.L.; Engel, M.V.; Buri, M.J.

1995-04-01

The reliability, efficiency, and economy of a power delivery system depend mainly on how well its substations, transmission lines, and distribution feeders are located within the utility service area, and how well their capacities match power needs in their respective localities. Often, utility planners are forced to commit to sites, rights of way, and equipment capacities year in advance. A necessary element of effective expansion planning is a forecast of where and how much demand must be served by the future T and D system. This article reports that a three-stage method forecasts with accuracy and detail, allowing meaningful determination of sties and sizes for future substation, transmission, and distribution facilities.

USA Nutrient managment forecasting via the "Fertilizer Forecaster": linking surface runnof, nutrient application and ecohydrology.

Science.gov (United States)

Drohan, Patrick; Buda, Anthony; Kleinman, Peter; Miller, Douglas; Lin, Henry; Beegle, Douglas; Knight, Paul

2017-04-01

USA and state nutrient management planning offers strategic guidance that strives to educate farmers and those involved in nutrient management to make wise management decisions. A goal of such programs is to manage hotspots of water quality degradation that threaten human and ecosystem health, water and food security. The guidance provided by nutrient management plans does not provide the day-to-day support necessary to make operational decisions, particularly when and where to apply nutrients over the short term. These short-term decisions on when and where to apply nutrients often make the difference between whether the nutrients impact water quality or are efficiently utilized by crops. Infiltrating rainfall events occurring shortly after broadcast nutrient applications are beneficial, given they will wash soluble nutrients into the soil where they are used by crops. Rainfall events that generate runoff shortly after nutrients are broadcast may wash off applied nutrients, and produce substantial nutrient losses from that site. We are developing a model and data based support tool for nutrient management, the Fertilizer Forecaster, which identifies the relative probability of runoff or infiltrating events in Pennsylvania (PA) landscapes in order to improve water quality. This tool will support field specific decisions by farmers and land managers on when and where to apply fertilizers and manures over 24, 48 and 72 hour periods. Our objectives are to: (1) monitor agricultural hillslopes in watersheds representing four of the five Physiographic Provinces of the Chesapeake Bay basin; (2) validate a high resolution mapping model that identifies soils prone to runoff; (3) develop an empirically based approach to relate state-of-the-art weather forecast variables to site-specific rainfall infiltration or runoff occurrence; (4) test the empirical forecasting model against alternative approaches to forecasting runoff occurrence; and (5) recruit farmers from the four

Aviation Turbulence: Dynamics, Forecasting, and Response to Climate Change

Science.gov (United States)

Storer, Luke N.; Williams, Paul D.; Gill, Philip G.

2018-03-01

Atmospheric turbulence is a major hazard in the aviation industry and can cause injuries to passengers and crew. Understanding the physical and dynamical generation mechanisms of turbulence aids with the development of new forecasting algorithms and, therefore, reduces the impact that it has on the aviation industry. The scope of this paper is to review the dynamics of aviation turbulence, its response to climate change, and current forecasting methods at the cruising altitude of aircraft. Aviation-affecting turbulence comes from three main sources: vertical wind shear instabilities, convection, and mountain waves. Understanding these features helps researchers to develop better turbulence diagnostics. Recent research suggests that turbulence will increase in frequency and strength with climate change, and therefore, turbulence forecasting may become more important in the future. The current methods of forecasting are unable to predict every turbulence event, and research is ongoing to find the best solution to this problem by combining turbulence predictors and using ensemble forecasts to increase skill. The skill of operational turbulence forecasts has increased steadily over recent decades, mirroring improvements in our understanding. However, more work is needed—ideally in collaboration with the aviation industry—to improve observations and increase forecast skill, to help maintain and enhance aviation safety standards in the future.

Educating collaborative planners: the learning potential of multi-actor regional learning environments for planning education

NARCIS (Netherlands)

Oonk, C.; Gulikers, J.T.M.; Mulder, M.

2013-01-01

Recent changes in planning context, object, subject and approaches characterised by the key words wickedness, collaborative processes and boundary crossing, require a reconsideration of competencies needed for professional planners and evidence for the effectiveness of learning environments in which

Price forecasting of day-ahead electricity markets using a hybrid forecast method

International Nuclear Information System (INIS)

Shafie-khah, M.; Moghaddam, M. Parsa; Sheikh-El-Eslami, M.K.

2011-01-01

Research highlights: → A hybrid method is proposed to forecast the day-ahead prices in electricity market. → The method combines Wavelet-ARIMA and RBFN network models. → PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. → One of the merits of the proposed method is lower need to the input data. → The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.

Price forecasting of day-ahead electricity markets using a hybrid forecast method

Energy Technology Data Exchange (ETDEWEB)

Shafie-khah, M., E-mail: miadreza@gmail.co [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Moghaddam, M. Parsa, E-mail: parsa@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Sheikh-El-Eslami, M.K., E-mail: aleslam@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of)

2011-05-15

Research highlights: {yields} A hybrid method is proposed to forecast the day-ahead prices in electricity market. {yields} The method combines Wavelet-ARIMA and RBFN network models. {yields} PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. {yields} One of the merits of the proposed method is lower need to the input data. {yields} The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.

Using Seasonal Forecasting Data for Vessel Routing

Science.gov (United States)

Bell, Ray; Kirtman, Ben

2017-04-01

We present an assessment of seasonal forecasting of surface wind speed, significant wave height and ocean surface current speed in the North Pacific for potential use of vessel routing from Singapore to San Diego. WaveWatchIII is forced with surface winds and ocean surface currents from the Community Climate System Model 4 (CCSM4) retrospective forecasts for the period of 1982-2015. Several lead time forecasts are used from zero months to six months resulting in 2,720 model years, ensuring the findings from this study are robust. July surface wind speed and significant wave height can be skillfully forecast with a one month lead time, with the western North Pacific being the most predictable region. Beyond May initial conditions (lead time of two months) the El Niño Southern Oscillation (ENSO) Spring predictability barrier limits skill of significant wave height but there is skill for surface wind speed with January initial conditions (lead time of six months). In a separate study of vessel routing between Norfolk, Virginia and Gibraltar we demonstrate the benefit of a multimodel approach using the North American Multimodel Ensemble (NMME). In collaboration with Charles River Analytics an all-encompassing forecast is presented by using machine learning on the various ensembles which can be using used for industry applications.

A municipal guide to least cost utility planning

International Nuclear Information System (INIS)

1992-03-01

The recent track record of ''traditional'' electricity planning, which entails selection of supply side resources to meet forecasted demand, has not been good. There are numerous examples of utilities incorrectly forecasting demand and over-building generating capacity while others underestimated growth and have had to cut demand and find alternate power sources to avoid outages. A potential solution to this problem is the continuing development of Least Cost Utility Plannning (LCUP). Regulatory commissions, consumer advocates and utilities are increasingly relying an LCUP as the most responsible way to avoid construction of new capacity and alleviate anticipated shortages caused by cancellation of construction projects, load growth, or natural replacement of aging capacity. The purpose of this report is to provide municipalities a starting point for evaluating their servicing utilities or states' least cost plan. This was accomplished by: Identifying key issues in LCUP; reviewing examples of the collaborative and classic approaches to LCUP in Illinois, California, New York State and Michigan; cataloging municipal authorities and strategies which can influence or support LCUP activities. Results of the project indicate that through a basic understanding of LCUP processes and issues, municipalities will be in a better position to influence plans or, if necessary, intervene in regulatory proceedings where plans are adopted. Constraints to municipal involvement in LCUP include statutory limitations, resource constraints, and a lack of knowledge of indirect authorities that support the LCUP process

A municipal guide to least cost utility planning

Energy Technology Data Exchange (ETDEWEB)

1992-03-01

The recent track record of ``traditional`` electricity planning, which entails selection of supply side resources to meet forecasted demand, has not been good. There are numerous examples of utilities incorrectly forecasting demand and over-building generating capacity while others underestimated growth and have had to cut demand and find alternate power sources to avoid outages. A potential solution to this problem is the continuing development of Least Cost Utility Plannning (LCUP). Regulatory commissions, consumer advocates and utilities are increasingly relying an LCUP as the most responsible way to avoid construction of new capacity and alleviate anticipated shortages caused by cancellation of construction projects, load growth, or natural replacement of aging capacity. The purpose of this report is to provide municipalities a starting point for evaluating their servicing utilities or states` least cost plan. This was accomplished by: Identifying key issues in LCUP; reviewing examples of the collaborative and classic approaches to LCUP in Illinois, California, New York State and Michigan; cataloging municipal authorities and strategies which can influence or support LCUP activities. Results of the project indicate that through a basic understanding of LCUP processes and issues, municipalities will be in a better position to influence plans or, if necessary, intervene in regulatory proceedings where plans are adopted. Constraints to municipal involvement in LCUP include statutory limitations, resource constraints, and a lack of knowledge of indirect authorities that support the LCUP process.

A municipal guide to least cost utility planning

Energy Technology Data Exchange (ETDEWEB)

1992-03-01

The recent track record of traditional'' electricity planning, which entails selection of supply side resources to meet forecasted demand, has not been good. There are numerous examples of utilities incorrectly forecasting demand and over-building generating capacity while others underestimated growth and have had to cut demand and find alternate power sources to avoid outages. A potential solution to this problem is the continuing development of Least Cost Utility Plannning (LCUP). Regulatory commissions, consumer advocates and utilities are increasingly relying an LCUP as the most responsible way to avoid construction of new capacity and alleviate anticipated shortages caused by cancellation of construction projects, load growth, or natural replacement of aging capacity. The purpose of this report is to provide municipalities a starting point for evaluating their servicing utilities or states' least cost plan. This was accomplished by: Identifying key issues in LCUP; reviewing examples of the collaborative and classic approaches to LCUP in Illinois, California, New York State and Michigan; cataloging municipal authorities and strategies which can influence or support LCUP activities. Results of the project indicate that through a basic understanding of LCUP processes and issues, municipalities will be in a better position to influence plans or, if necessary, intervene in regulatory proceedings where plans are adopted. Constraints to municipal involvement in LCUP include statutory limitations, resource constraints, and a lack of knowledge of indirect authorities that support the LCUP process.

Computer technology forecasting at the National Laboratories

International Nuclear Information System (INIS)

Peskin, A.M.

1980-01-01

The DOE Office of ADP Management organized a group of scientists and computer professionals, mostly from their own national laboratories, to prepare an annually updated technology forecast to accompany the Department's five-year ADP Plan. The activities of the task force were originally reported in an informal presentation made at the ACM Conference in 1978. This presentation represents an update of that report. It also deals with the process of applying the results obtained at a particular computing center, Brookhaven National Laboratory. Computer technology forecasting is a difficult and hazardous endeavor, but it can reap considerable advantage. The forecast performed on an industry-wide basis can be applied to the particular needs of a given installation, and thus give installation managers considerable guidance in planning. A beneficial side effect of this process is that it forces installation managers, who might otherwise tend to preoccupy themselves with immediate problems, to focus on longer term goals and means to their ends

3-D visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

Science.gov (United States)

Rautenhaus, M.; Grams, C. M.; Schäfler, A.; Westermann, R.

2015-02-01

We present the application of interactive 3-D visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the ECMWF ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and forecast wind field resolution. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (three to seven days before take-off).

A hybrid spatiotemporal drought forecasting model for operational use

Science.gov (United States)

Vasiliades, L.; Loukas, A.

2010-09-01

Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. This study develops a hybrid spatiotemporal scheme for integrated spatial and temporal forecasting. Temporal forecasting is achieved using feed-forward neural networks and the temporal forecasts are extended to the spatial dimension using a spatial recurrent neural network model. The methodology is demonstrated for an operational meteorological drought index the Standardized Precipitation Index (SPI) calculated at multiple timescales. 48 precipitation stations and 18 independent precipitation stations, located at Pinios river basin in Thessaly region, Greece, were used for the development and spatiotemporal validation of the hybrid spatiotemporal scheme. Several quantitative temporal and spatial statistical indices were considered for the performance evaluation of the models. Furthermore, qualitative statistical criteria based on contingency tables between observed and forecasted drought episodes were calculated. The results show that the lead time of forecasting for operational use depends on the SPI timescale. The hybrid spatiotemporal drought forecasting model could be operationally used for forecasting up to three months ahead for SPI short timescales (e.g. 3-6 months) up to six months ahead for large SPI timescales (e.g. 24 months). The above findings could be useful in developing a drought preparedness plan in the region.

Combining Activity DSM with Temporal Logic for Collaborative Planning and Scheduling

Directory of Open Access Journals (Sweden)

Mathieu Baudin

2013-09-01

Full Text Available This work presents an approach that implements enhanced scheduling algorithms to plan and schedule interventions in scientific facilities emitting various types of ionizing radiation such as the ones present at CERN in Geneva, (Switzerland or at GSI in Darmstadt (Germany. To deal with the collaborative process of activity creation and submission used in those organizations, we propose a framework to set the appropriate sequence or skeleton for the activities. This framework combines Allen's Interval Algebra and DSM (Design Structure Matrix. It enables the sequence of activities to be automatically computed while gathering and taking into account the needs of users and testing their compatibility. It also deals with technical-type or resource-type constraints between activities as well as incompatible submissions. The work described in this paper includes details of the collaborative submission process and introduces suggestions for compromising as well as temporal calculations with the introduction of parameterized DSMs rather than binary ones.

Improving family planning services delivery and uptake: experiences from the "Reversing the Stall in Fertility Decline in Western Kenya Project".

Science.gov (United States)

Amo-Adjei, Joshua; Mutua, Michael; Athero, Sherine; Izugbara, Chimaraoke; Ezeh, Alex

2017-10-10

In this paper, we reflect on our experiences of implementing a multipronged intervention to improve sexual and reproductive health outcomes. The project used family planning as its entry point and was implemented in two high fertility counties-Busia and Siaya in Kenya. The intervention, implemented by a seven-member consortium, involved: family planning services delivery; regular training of service providers to deliver high quality services; monitoring and evaluation; strengthening of commodity chain delivery and forecasting; school-based and out-of-school based sexuality education; and advocacy and stakeholder engagements at the community, county and national levels. Over a 5-year period, the project contributed to raising demand for family planning considerably, evidenced in fertility decline. It also improved the capacity of family planning services providers, increased commitment and awareness of county government and other community stakeholders on the importance of investments in family planning. Our collaborations with organisations interested in sexual and reproductive health issues substantially enhanced the consortium's ability to increase demand for, and supply of family planning commodities. These collaborations are proving useful in the continuity and sustainability of project achievements.

Flood Risk Assessment and Forecasting for the Ganges-Brahmaputra-Meghna River Basins

Science.gov (United States)

Hopson, T. M.; Priya, S.; Young, W.; Avasthi, A.; Clayton, T. D.; Brakenridge, G. R.; Birkett, C. M.; Riddle, E. E.; Broman, D.; Boehnert, J.; Sampson, K. M.; Kettner, A.; Singh, D.

2017-12-01

During the 2017 South Asia monsoon, torrential rains and catastrophic floods affected more than 45 million people, including 16 million children, across the Ganges-Brahmaputra-Meghna (GBM) basins. The basin is recognized as one of the world's most disaster-prone regions, with severe floods occurring almost annually causing extreme loss of life and property. In light of this vulnerability, the World Bank and collaborators have contributed toward reducing future flood impacts through recent developments to improve operational preparedness for such events, as well as efforts in more general preparedness and resilience building through planning based on detailed risk assessments. With respect to improved event-specific flood preparedness through operational warnings, we discuss a new forecasting system that provides probability-based flood forecasts developed for more than 85 GBM locations. Forecasts are available online, along with near-real-time data maps of rainfall (predicted and actual) and river levels. The new system uses multiple data sets and multiple models to enhance forecasting skill, and provides improved forecasts up to 16 days in advance of the arrival of high waters. These longer lead times provide the opportunity to save both lives and livelihoods. With sufficient advance notice, for example, farmers can harvest a threatened rice crop or move vulnerable livestock to higher ground. Importantly, the forecasts not only predict future water levels but indicate the level of confidence in each forecast. Knowing whether the probability of a danger-level flood is 10 percent or 90 percent helps people to decide what, if any, action to take. With respect to efforts in general preparedness and resilience building, we also present a recent flood risk assessment, and how it provides, for the first time, a numbers-based view of the impacts of different size floods across the Ganges basin. The findings help identify priority areas for tackling flood risks (for

Forecasting electricity market pricing using artificial neural networks

International Nuclear Information System (INIS)

Pao, Hsiao-Tien

2007-01-01

Electricity price forecasting is extremely important for all market players, in particular for generating companies: in the short term, they must set up bids for the spot market; in the medium term, they have to define contract policies; and in the long term, they must define their expansion plans. For forecasting long-term electricity market pricing, in order to avoid excessive round-off and prediction errors, this paper proposes a new artificial neural network (ANN) with single output node structure by using direct forecasting approach. The potentials of ANNs are investigated by employing a rolling cross validation scheme. Out of sample performance evaluated with three criteria across five forecasting horizons shows that the proposed ANNs are a more robust multi-step ahead forecasting method than autoregressive error models. Moreover, ANN predictions are quite accurate even when the length of the forecast horizon is relatively short or long

Three-dimensional visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

Science.gov (United States)

Rautenhaus, M.; Grams, C. M.; Schäfler, A.; Westermann, R.

2015-07-01

We present the application of interactive three-dimensional (3-D) visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX - North Atlantic Waveguide and Downstream Impact Experiment) campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs) has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF) ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off).

Effects of a Patient-Provider, Collaborative, Medication-Planning Tool: A Randomized, Controlled Trial

Directory of Open Access Journals (Sweden)

James F. Graumlich

2016-01-01

Full Text Available Among patients with various levels of health literacy, the effects of collaborative, patient-provider, medication-planning tools on outcomes relevant to self-management are uncertain. Objective. Among adult patients with type II diabetes mellitus, we tested the effectiveness of a medication-planning tool (Medtable™ implemented via an electronic medical record to improve patients’ medication knowledge, adherence, and glycemic control compared to usual care. Design. A multicenter, randomized controlled trial in outpatient primary care clinics. 674 patients received either the Medtable tool or usual care and were followed up for up to 12 months. Results. Patients who received Medtable had greater knowledge about indications for medications in their regimens and were more satisfied with the information about their medications. Patients’ knowledge of drug indication improved with Medtable regardless of their literacy status. However, Medtable did not improve patients’ demonstrated medication use, regimen adherence, or glycemic control (HbA1c. Conclusion. The Medtable tool supported provider/patient collaboration related to medication use, as reflected in patient satisfaction with communication, but had limited impact on patient medication knowledge, adherence, and HbA1c outcomes. This trial is registered with ClinicalTrials.gov NCT01296633.

A DICOM based radiotherapy plan database for research collaboration and reporting

International Nuclear Information System (INIS)

Westberg, J; Krogh, S; Brink, C; Vogelius, I R

2014-01-01

Purpose: To create a central radiotherapy (RT) plan database for dose analysis and reporting, capable of calculating and presenting statistics on user defined patient groups. The goal is to facilitate multi-center research studies with easy and secure access to RT plans and statistics on protocol compliance. Methods: RT institutions are able to send data to the central database using DICOM communications on a secure computer network. The central system is composed of a number of DICOM servers, an SQL database and in-house developed software services to process the incoming data. A web site within the secure network allows the user to manage their submitted data. Results: The RT plan database has been developed in Microsoft .NET and users are able to send DICOM data between RT centers in Denmark. Dose-volume histogram (DVH) calculations performed by the system are comparable to those of conventional RT software. A permission system was implemented to ensure access control and easy, yet secure, data sharing across centers. The reports contain DVH statistics for structures in user defined patient groups. The system currently contains over 2200 patients in 14 collaborations. Conclusions: A central RT plan repository for use in multi-center trials and quality assurance was created. The system provides an attractive alternative to dummy runs by enabling continuous monitoring of protocol conformity and plan metrics in a trial.

A DICOM based radiotherapy plan database for research collaboration and reporting

Science.gov (United States)

Westberg, J.; Krogh, S.; Brink, C.; Vogelius, I. R.

2014-03-01

Purpose: To create a central radiotherapy (RT) plan database for dose analysis and reporting, capable of calculating and presenting statistics on user defined patient groups. The goal is to facilitate multi-center research studies with easy and secure access to RT plans and statistics on protocol compliance. Methods: RT institutions are able to send data to the central database using DICOM communications on a secure computer network. The central system is composed of a number of DICOM servers, an SQL database and in-house developed software services to process the incoming data. A web site within the secure network allows the user to manage their submitted data. Results: The RT plan database has been developed in Microsoft .NET and users are able to send DICOM data between RT centers in Denmark. Dose-volume histogram (DVH) calculations performed by the system are comparable to those of conventional RT software. A permission system was implemented to ensure access control and easy, yet secure, data sharing across centers. The reports contain DVH statistics for structures in user defined patient groups. The system currently contains over 2200 patients in 14 collaborations. Conclusions: A central RT plan repository for use in multi-center trials and quality assurance was created. The system provides an attractive alternative to dummy runs by enabling continuous monitoring of protocol conformity and plan metrics in a trial.

Forecasting metal prices: Do forecasters herd?

DEFF Research Database (Denmark)

Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.

2013-01-01

We analyze more than 20,000 forecasts of nine metal prices at four different forecast horizons. We document that forecasts are heterogeneous and report that anti-herding appears to be a source of this heterogeneity. Forecaster anti-herding reflects strategic interactions among forecasters...

Energy production forecasting, experiences from Lillgrund. Lillgrund Pilot Project

Energy Technology Data Exchange (ETDEWEB)

Johansson, Lasse; Schelander, Peter; Haakansson, Maans; Hansson, Johan (Vattenfall Vindkraft AB, Stockholm (Sweden))

2010-01-15

assumptions. Production forecasts are not used as a tool in the planning of the operation and maintenance within the wind farm. Maintenance planning is done with a much longer time horizon than the current production forecasts offer. For example, Vattenfall has to inform and coordinate with the owner of the local power network typically three weeks in advance before any maintenance that can cause significant loss of production. It's an inevitable fact that wind turbines must be stopped for maintenance with regular intervals in order to maintain secure and stable operations. However, it is in the power producers' interest to minimize these stops for maintenance work. And of even higher importance, minimize the loss of production by making sure the stops coincide with periods of expected low production. At the end it's always the current weather that determines if the maintenance can be carried out as planned. Maintenance involving boats is dependent on calm weather and thus is correlated with periods of low wind speed and low energy production. Due to the seasonal variations in the wind climate, the summer season offers the best opportunities for planned maintenance work in the wind park. A production stop of larger dignity where sometimes the complete park is set out of operations is always done during the summer season. An analysis of production data from the first year of operation shows that the loss of production due to scheduled stops was almost negligible. However, a six week, unplanned stop of a few turbines caused a loss in production of almost 1.5 GWh

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Directory of Open Access Journals (Sweden)

Yuqi Dong

2016-12-01

Full Text Available Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people’s livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

Managing collaborative innovation networks

DEFF Research Database (Denmark)

Stevens, Vidar; Agger, Annika

2017-01-01

Collaborative innovation networks are increasingly used as vehicles for fostering innovative policy solutions. However, scholars have noted that the extent to which collaborative networks can actually contribute to the development of innovative policy solutions depends on how they are managed...... a Flemish administrative network to develop a radical new Spatial Planning Policy Plan. This study shows that the best way to manage collaborative innovation networks is not to press directly for results, but take the time to invest in relationship-building and together agree on a planning and clear process...... steps. Such a management approach allows actors to get to know each other and from thereon expand, with more background and appreciation for the others’ goals, behaviors, and intentions, their group activities concerning the formulation of a radical and innovative policy plan....

Evaluating the strategic capacity of collaborative spatial planning initiatives by the performance of its process, output and outcomes : The case of the southern Randstad Holland

NARCIS (Netherlands)

Harteveld, E.; Waterhout, B.; Broekhans, B.; Zonneveld, W.A.M.

2015-01-01

Spatial planning practices are constantly evolving to be more effective in a dynamic context. In the face of the latest developments, the practice of collaborative spatial planning through the formation of regional collaborations has emerged as the contemporary solution. The practice of working with

The development rainfall forecasting using kalman filter

Science.gov (United States)

Zulfi, Mohammad; Hasan, Moh.; Dwidja Purnomo, Kosala

2018-04-01

Rainfall forecasting is very interesting for agricultural planing. Rainfall information is useful to make decisions about the plan planting certain commodities. In this studies, the rainfall forecasting by ARIMA and Kalman Filter method. Kalman Filter method is used to declare a time series model of which is shown in the form of linear state space to determine the future forecast. This method used a recursive solution to minimize error. The rainfall data in this research clustered by K-means clustering. Implementation of Kalman Filter method is for modelling and forecasting rainfall in each cluster. We used ARIMA (p,d,q) to construct a state space for KalmanFilter model. So, we have four group of the data and one model in each group. In conclusions, Kalman Filter method is better than ARIMA model for rainfall forecasting in each group. It can be showed from error of Kalman Filter method that smaller than error of ARIMA model.

Accelerated Lane-Changing Trajectory Planning of Automated Vehicles with Vehicle-to-Vehicle Collaboration

Directory of Open Access Journals (Sweden)

Haijian Bai

2017-01-01

Full Text Available Considering the complexity of lane changing using automated vehicles and the frequency of turning lanes in city settings, this paper aims to generate an accelerated lane-changing trajectory using vehicle-to-vehicle collaboration (V2VC. Based on the characteristics of accelerated lane changing, we used a polynomial method and cooperative strategies for trajectory planning to establish a lane-changing model under different degrees of collaboration with the following vehicle in the target lane by considering vehicle kinematics and comfort requirements. Furthermore, considering the shortcomings of the traditional elliptical vehicle and round vehicle models, we established a rectangular vehicle model with collision boundary conditions by analysing the relationships between the possible collision points and the outline of the vehicle. Then, we established a simulation model for the accelerated lane-changing process in different environments under different degrees of collaboration. The results show that, by using V2VC, we can achieve safe accelerated lane-changing trajectories and simultaneously satisfy the requirements of vehicle kinematics and comfort control.

Worldwide satellite market demand forecast

Science.gov (United States)

Bowyer, J. M.; Frankfort, M.; Steinnagel, K. M.

1981-01-01

The forecast is for the years 1981 - 2000 with benchmark years at 1985, 1990 and 2000. Two typs of markets are considered for this study: Hardware (worldwide total) - satellites, earth stations and control facilities (includes replacements and spares); and non-hardware (addressable by U.S. industry) - planning, launch, turnkey systems and operations. These markets were examined for the INTELSAT System (international systems and domestic and regional systems using leased transponders) and domestic and regional systems. Forecasts were determined for six worldwide regions encompassing 185 countries using actual costs for existing equipment and engineering estimates of costs for advanced systems. Most likely (conservative growth rate estimates) and optimistic (mid range growth rate estimates) scenarios were employed for arriving at the forecasts which are presented in constant 1980 U.S. dollars. The worldwide satellite market demand forecast predicts that the market between 181 and 2000 will range from $35 to $50 billion. Approximately one-half of the world market, $16 to $20 billion, will be generated in the United States.

In Brief: Forecasting meningitis threats

Science.gov (United States)

Showstack, Randy

2008-12-01

The University Corporation for Atmospheric Research (UCAR), in conjunction with a team of health and weather organizations, has launched a project to provide weather forecasts to medical officials in Africa to help reduce outbreaks of meningitis. The forecasts will enable local health care providers to target vaccination programs more effectively. In 2009, meteorologists with the National Center for Atmospheric Research, which is managed by UCAR, will begin issuing 14-day forecasts of atmospheric conditions in Ghana. Later, UCAR plans to work closely with health experts from several African countries to design and test a decision support system to provide health officials with useful meteorological information. ``By targeting forecasts in regions where meningitis is a threat, we may be able to help vulnerable populations. Ultimately, we hope to build on this project and provide information to public health programs battling weather-related diseases in other parts of the world,'' said Rajul Pandya, director of UCAR's Community Building Program. Funding for the project comes from a $900,000 grant from Google.org, the philanthropic arm of the Internet search company.

An Efficient and Simplified Model for Forecasting using SRM

Directory of Open Access Journals (Sweden)

Hafiz Muhammad Shahzad Asif

2014-01-01

Full Text Available Learning form continuous financial systems play a vital role in enterprise operations. One of the most sophisticated non-parametric supervised learning classifiers, SVM (Support Vector Machines, provides robust and accurate results, however it may require intense computation and other resources. The heart of SLT (Statistical Learning Theory, SRM (Structural Risk Minimization Principle can also be used for model selection. In this paper, we focus on comparing the performance of model estimation using SRM with SVR (Support Vector Regression for forecasting the retail sales of consumer products. The potential benefits of an accurate sales forecasting technique in businesses are immense. Retail sales forecasting is an integral part of strategic business planning in areas such as sales planning, marketing research, pricing, production planning and scheduling. Performance comparison of support vector regression with model selection using SRM shows comparable results to SVR but in a computationally efficient manner. This research targeted the real life data to conclude the results after investigating the computer generated datasets for different types of model building

An efficient and simplified model for forecasting using SRM

International Nuclear Information System (INIS)

Asif, H.M.; Hyat, M.F.; Ahmad, T.

2014-01-01

Learning form continuous financial systems play a vital role in enterprise operations. One of the most sophisticated non-parametric supervised learning classifiers, SVM (Support Vector Machines), provides robust and accurate results, however it may require intense computation and other resources. The heart of SLT (Statistical Learning Theory), SRM (Structural Risk Minimization )Principle can also be used for model selection. In this paper, we focus on comparing the performance of model estimation using SRM with SVR (Support Vector Regression) for forecasting the retail sales of consumer products. The potential benefits of an accurate sales forecasting technique in businesses are immense. Retail sales forecasting is an integral part of strategic business planning in areas such as sales planning, marketing research, pricing, production planning and scheduling. Performance comparison of support vector regression with model selection using SRM shows comparable results to SVR but in a computationally efficient manner. This research targeted the real life data to conclude the results after investigating the computer generated datasets for different types of model building. (author)

Development of a web-based pharmaceutical care plan to facilitate collaboration between healthcare providers and patients

NARCIS (Netherlands)

Geurts, Marlies M E; Ivens, Martijn; van Gelder, Egbert; de Gier, Johan J

2013-01-01

BACKGROUND: In medication therapy management there is a need for a tool to document medication reviews and pharmaceutical care plans (PCPs) as well as facilitate collaboration and sharing of patient data between different healthcare providers. Currently, pharmacists and general practitioners (GPs)

[Demography perspectives and forecasts of the demand for electricity].

Science.gov (United States)

Roy, L; Guimond, E

1995-01-01

"Demographic perspectives form an integral part in the development of electric load forecasts. These forecasts in turn are used to justify the addition and repair of generating facilities that will supply power in the coming decades. The goal of this article is to present how demographic perspectives are incorporated into the electric load forecasting in Quebec. The first part presents the methods, hypotheses and results of population and household projections used by Hydro-Quebec in updating its latest development plan. The second section demonstrates applications of such demographic projections for forecasting the electric load, with a focus on the residential sector." (SUMMARY IN ENG AND SPA) excerpt

Forecasting reliability of transformer populations

NARCIS (Netherlands)

Schijndel, van A.; Wetzer, J.; Wouters, P.A.A.F.

2007-01-01

The expected replacement wave in the current power grid faces asset managers with challenging questions. Setting up a replacement strategy and planning calls for a forecast of the long term component reliability. For transformers the future failure probability can be predicted based on the ongoing

Missing Kettles and Too Few Toasters: The Forecasting Methodology at Morphy Richards

OpenAIRE

Lane, D; Hughes, D

2002-01-01

Faced with problems in forecasting at Morphy Richards, this research represents an investigation into their forecasting methodology following the hypothesis that the current forecasting system was no longer sufficient to ensure guaranteed supply to customers, or to enable forward planning.\\ud \\ud The purpose of the research was to identify any requirements for change within the forecasting system and to identify the ‘best practice’ within the industry. Primary research was carried out using a...

Hybrid ellipsoidal fuzzy systems in forecasting regional electricity loads

Energy Technology Data Exchange (ETDEWEB)

Pai, Ping-Feng [Department of Information Management, National Chi Nan University, 1 University Road, Puli, Nantou 545, Taiwan (China)

2006-09-15

Because of the privatization of electricity in many countries, load forecasting has become one of the most crucial issues in the planning and operations of electric utilities. In addition, accurate regional load forecasting can provide the transmission and distribution operators with more information. The hybrid ellipsoidal fuzzy system was originally designed to solve control and pattern recognition problems. The main objective of this investigation is to develop a hybrid ellipsoidal fuzzy system for time series forecasting (HEFST) and apply the proposed model to forecast regional electricity loads in Taiwan. Additionally, a scaled conjugate gradient learning method is employed in the supervised learning phase of the HEFST model. Subsequently, numerical data taken from the existing literature is used to demonstrate the forecasting performance of the HEFST model. Simulation results reveal that the proposed model has better forecasting performance than the artificial neural network model and the regression model. Thus, the HEFST model is a valid and promising alternative for forecasting regional electricity loads. (author)

Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Ames Code I Private Cloud Computing Environment

Science.gov (United States)

Molthan, Andrew; Case, Jonathan; Venner, Jason; Moreno-Madrinan, Max J.; Delgado, Francisco

2012-01-01

Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.

Short-term Forecasting Tools for Agricultural Nutrient Management.

Science.gov (United States)

Easton, Zachary M; Kleinman, Peter J A; Buda, Anthony R; Goering, Dustin; Emberston, Nichole; Reed, Seann; Drohan, Patrick J; Walter, M Todd; Guinan, Pat; Lory, John A; Sommerlot, Andrew R; Sharpley, Andrew

2017-11-01

The advent of real-time, short-term farm management tools is motivated by the need to protect water quality above and beyond the general guidance offered by existing nutrient management plans. Advances in high-performance computing and hydrologic or climate modeling have enabled rapid dissemination of real-time information that can assist landowners and conservation personnel with short-term management planning. This paper reviews short-term decision support tools for agriculture that are under various stages of development and implementation in the United States: (i) Wisconsin's Runoff Risk Advisory Forecast (RRAF) System, (ii) New York's Hydrologically Sensitive Area Prediction Tool, (iii) Virginia's Saturated Area Forecast Model, (iv) Pennsylvania's Fertilizer Forecaster, (v) Washington's Application Risk Management (ARM) System, and (vi) Missouri's Design Storm Notification System. Although these decision support tools differ in their underlying model structure, the resolution at which they are applied, and the hydroclimates to which they are relevant, all provide forecasts (range 24-120 h) of runoff risk or soil moisture saturation derived from National Weather Service Forecast models. Although this review highlights the need for further development of robust and well-supported short-term nutrient management tools, their potential for adoption and ultimate utility requires an understanding of the appropriate context of application, the strategic and operational needs of managers, access to weather forecasts, scales of application (e.g., regional vs. field level), data requirements, and outreach communication structure. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Canadian crude oil production and supply forecast 2006-2020

International Nuclear Information System (INIS)

2006-05-01

In order to enable members to plan for pipeline capacity requirements for transporting Canadian crude oil to markets, the Canadian Association of Petroleum Producers (CAPP) 2006-2020 crude oil production and supply forecast provides a long-range outlook of Canadian crude oil production. It provides a forecast of supply and demand for both western and eastern Canada. Because offshore eastern oil production does not rely on pipeline access to reach markets, the analysis primarily focuses on western Canadian production and supply. Over the next fifteen years, Alberta's oil sands provides the main source of growth in the western Canadian production forecast. A survey of CAPP members encompassing all oil sands projects was conducted. Survey responses reflect both planned and envisioned projects over a fifteen year period, although some of the envisioned projects have been risk adjusted by modifying the potential completion schedules for projects which are deemed more uncertain. Detailed tables are provided on forecast data. Three sets of tables are included to show production, two supply scenarios and a high level assessment of the need for incremental pipeline capacity. The report also discusses delays and risk factors that could slow the pace of oil sands development and the corresponding increase in production being forecast in the base case. 16 tabs

COST ES0602: towards a European network on chemical weather forecasting and information systems

Directory of Open Access Journals (Sweden)

J. Kukkonen

2009-04-01

Full Text Available The COST ES0602 action provides a forum for benchmarking approaches and practices in data exchange and multi-model capabilities for chemical weather forecasting and near real-time information services in Europe. The action includes approximately 30 participants from 19 countries, and its duration is from 2007 to 2011 (http://www.chemicalweather.eu/. Major efforts have been dedicated in other actions and projects to the development of infrastructures for data flow. We have therefore aimed for collaboration with ongoing actions towards developing near real-time exchange of input data for air quality forecasting. We have collected information on the operational air quality forecasting models on a regional and continental scale in a structured form, and inter-compared and evaluated the physical and chemical structure of these models. We have also constructed a European chemical weather forecasting portal that includes links to most of the available chemical weather forecasting systems in Europe. The collaboration also includes the examination of the case studies that have been organized within COST-728, in order to inter-compare and evaluate the models against experimental data. We have also constructed an operational model forecasting ensemble. Data from a representative set of regional background stations have been selected, and the operational forecasts for this set of sites will be inter-compared and evaluated. The Action has investigated, analysed and reviewed existing chemical weather information systems and services, and will provide recommendations on best practices concerning the presentation and dissemination of chemical weather information towards the public and decision makers.

Medium-term load forecasting and wholesale transaction profitability

International Nuclear Information System (INIS)

Selker, F.K.; Wroblewski, W.R.

1996-01-01

The volume of wholesale transactions quoted at firm prices is increasing. The cost, and thus profitability, of serving these contracts strongly depends upon native load during the time of delivery. However, transactions extend beyond load forecasts based on weather information, and long-term resource planning forecasts of load peaks and energy provide inadequate detail. To address this need, Decision Focus Inc. (DFI) and Commonwealth Edison (ComEd) developed a probabilistic, medium-term load forecasting capability. In this paper the authors use a hypothetical utility to explore the impact of uncertain medium-term loads on transaction profitability

Ex-post evaluations of demand forecast accuracy

DEFF Research Database (Denmark)

Nicolaisen, Morten Skou; Driscoll, Patrick Arthur

2014-01-01

Travel demand forecasts play a crucial role in the preparation of decision support to policy makers in the field of transport planning. The results feed directly into impact appraisals such as cost benefit analyses and environmental impact assessments, which are mandatory for large public works...... projects in many countries. Over the last couple of decades there has been an increasing attention to the lack of demand forecast accuracy, but since data availability for comprehensive ex- post appraisals is problematic, such studies are still relatively rare. The present paper presents a review...... of the largest ex-post studies of demand forecast accuracy for transport infrastructure projects. The focus is twofold; to provide an overview of observed levels of demand forecast inaccuracy and to explore the primary explanations offered for the observed inaccuracy. Inaccuracy in the form of both bias...

Comparison of two new short-term wind-power forecasting systems

Energy Technology Data Exchange (ETDEWEB)

Ramirez-Rosado, Ignacio J. [Department of Electrical Engineering, University of Zaragoza, Zaragoza (Spain); Fernandez-Jimenez, L. Alfredo [Department of Electrical Engineering, University of La Rioja, Logrono (Spain); Monteiro, Claudio; Sousa, Joao; Bessa, Ricardo [FEUP, Fac. Engenharia Univ. Porto (Portugal)]|[INESC - Instituto de Engenharia de Sistemas e Computadores do Porto, Porto (Portugal)

2009-07-15

This paper presents a comparison of two new advanced statistical short-term wind-power forecasting systems developed by two independent research teams. The input variables used in both systems were the same: forecasted meteorological variable values obtained from a numerical weather prediction model; and electric power-generation registers from the SCADA system of the wind farm. Both systems are described in detail and the forecasting results compared, revealing great similarities, although the proposed structures of the two systems are different. The forecast horizon for both systems is 72 h, allowing the use of the forecasted values in electric market operations, as diary and intra-diary power generation bid offers, and in wind-farm maintenance planning. (author)

Leveraging Literacies through Collaborative, Source-Based Planning and Teaching in Social Studies and Language Arts

Science.gov (United States)

Patterson, Nancy; Weaver, Joanna; Fletcher, Jamie; Connor, Bryce; Thomas, Angela; Ross, Cindy

2018-01-01

The value of preparing students for college, careers, and civic life is a shared outcome of social studies and language arts teachers. This study explores how developing content and civic literacy to these ends can be fortified through language arts and social studies teacher collaboration in source-based planning and teaching. Although numerous…

Demand Forecasting in the Fashion Industry: A Review

Directory of Open Access Journals (Sweden)

Maria Elena Nenni

2013-08-01

Full Text Available Forecasting demand is a crucial issue for driving efficient operations management plans. This is especially the case in the fashion industry, where demand uncertainty, lack of historical data and seasonal trends usually coexist. Many approaches to this issue have been proposed in the literature over the past few decades. In this paper, forecasting methods are compared with the aim of linking approaches to the market features.

Business Planning in the Light of Neuro-fuzzy and Predictive Forecasting

Science.gov (United States)

Chakrabarti, Prasun; Basu, Jayanta Kumar; Kim, Tai-Hoon

In this paper we have pointed out gain sensing on forecast based techniques.We have cited an idea of neural based gain forecasting. Testing of sequence of gain pattern is also verifies using statsistical analysis of fuzzy value assignment. The paper also suggests realization of stable gain condition using K-Means clustering of data mining. A new concept of 3D based gain sensing has been pointed out. The paper also reveals what type of trend analysis can be observed for probabilistic gain prediction.

Three-dimensional visualization of ensemble weather forecasts – Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

Directory of Open Access Journals (Sweden)

M. Rautenhaus

2015-07-01

Full Text Available We present the application of interactive three-dimensional (3-D visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off.

Evaluating the strategic capacity of collaborative spatial planning initiatives by the performance of its process, output and outcomes: The case of the southern Randstad Holland

OpenAIRE

Harteveld, E.; Waterhout, B.; Broekhans, B.; Zonneveld, W.A.M.

2015-01-01

Spatial planning practices are constantly evolving to be more effective in a dynamic context. In the face of the latest developments, the practice of collaborative spatial planning through the formation of regional collaborations has emerged as the contemporary solution. The practice of working with a multitude of public actors that cooperate to formulate spatial strategies for issues that transcend their own planning capacity is relatively new and the ideal structure, organization and scope ...

TRIANA. A control strategy for Smart Grids. Forecasting, planning and real-time control

International Nuclear Information System (INIS)

Bakker, V.

2012-01-01

Increasing demand, extra fluctuation and a large share of distributed electricity generation will put more stress on the electricity supply chain. Therefore, changes are required in the supply chain to maintain a properly functioning, stable and affordable grid. Currently the supply chain is completely driven by demand by constantly adapting the production to the demand. The exploitation of flexibility on the demand side of the supply chain allows for a more efficient and sustainable electricity production. Techniques like controllable distributed generation, distributed storage, and smart appliances can introduce this required flexibility. To exploit the new flexibility, the grid has to become more intelligent, i.e. become a Smart Grid. In this thesis TRIANA, a three step control strategy for the Smart Grid is presented. In the first step of TRIANA, the forecasting step, the scheduling freedom (flexibility) of a device is determined for each individual device by a local controller. In the second step, a (central) planner tries to exploit the freedom of the devices determined in the first step for his objective. The last step of TRIANA is the real-time control step performed by the local controller achieving the planning in the best possible way. In order to analyze the impact of control methodologies for Smart Grid, a simulator based on an energy model has been built. The basic elements of the model are individual devices and between devices energy streams are defined. The energy streams are connected via so called pools, which represent the physical connections between the devices. To study the effectiveness of the control methodology and study the most economic use of the flexibility of devices, multiple scenarios have been simulated. Simulations show that TRIANA can optimize the energy flows and can control the operation of the domestic devices in an economic manner without discomfort for the residents. TRIANA is a methodology capable of adjusting the energy

Using the Theory of Planned Behaviour to examine health professional students' behavioural intentions in relation to medication safety and collaborative practice.

Science.gov (United States)

Lapkin, Samuel; Levett-Jones, Tracy; Gilligan, Conor

2015-08-01

Safe medication practices depend upon, not only on individual responsibilities, but also effective communication and collaboration between members of the medication team. However, measurement of these skills is fraught with conceptual and practical difficulties. The aims of this study were to explore the utility of a Theory of Planned Behaviour-based questionnaire to predict health professional students' behavioural intentions in relation to medication safety and collaborative practice; and to determine the contribution of attitudes, subjective norms, and perceived control to behavioural intentions. A descriptive cross-sectional survey based upon the Theory of Planned Behaviour was designed and tested. A convenience sample of 65 undergraduate pharmacy, nursing and medicine students from one semi-metropolitan Australian university were recruited for the study. Participants' behavioural intentions, attitudes, subjective norms, and perceived control to behavioural intentions in relation to medication safety were measured using an online version of the Theory of Planned Behaviour Medication Safety Questionnaire. The Questionnaire had good internal consistency with a Cronbach's alpha of 0.844. The three predictor variables of attitudes, subjective norms, and perceived control accounted for between 30 and 46% of the variance in behavioural intention; this is a strong prediction in comparison to previous studies using the Theory of Planned Behaviour. Data analysis also indicated that attitude was the most significant predictor of participants' intention to collaborate with other team members to improve medication safety. The results from this study provide preliminary support for the Theory of Planned Behaviour-Medication Safety Questionnaire as a valid instrument for examining health professional students' behavioural intentions in relation to medication safety and collaborative practice. Copyright © 2015 Elsevier Ltd. All rights reserved.

Analyzing the effects of human-aware motion planning on close-proximity human-robot collaboration.

Science.gov (United States)

Lasota, Przemyslaw A; Shah, Julie A

2015-02-01

The objective of this work was to examine human response to motion-level robot adaptation to determine its effect on team fluency, human satisfaction, and perceived safety and comfort. The evaluation of human response to adaptive robotic assistants has been limited, particularly in the realm of motion-level adaptation. The lack of true human-in-the-loop evaluation has made it impossible to determine whether such adaptation would lead to efficient and satisfying human-robot interaction. We conducted an experiment in which participants worked with a robot to perform a collaborative task. Participants worked with an adaptive robot incorporating human-aware motion planning and with a baseline robot using shortest-path motions. Team fluency was evaluated through a set of quantitative metrics, and human satisfaction and perceived safety and comfort were evaluated through questionnaires. When working with the adaptive robot, participants completed the task 5.57% faster, with 19.9% more concurrent motion, 2.96% less human idle time, 17.3% less robot idle time, and a 15.1% greater separation distance. Questionnaire responses indicated that participants felt safer and more comfortable when working with an adaptive robot and were more satisfied with it as a teammate than with the standard robot. People respond well to motion-level robot adaptation, and significant benefits can be achieved from its use in terms of both human-robot team fluency and human worker satisfaction. Our conclusion supports the development of technologies that could be used to implement human-aware motion planning in collaborative robots and the use of this technique for close-proximity human-robot collaboration.

MACROECONOMIC FORECASTING USING BAYESIAN VECTOR AUTOREGRESSIVE APPROACH

Directory of Open Access Journals (Sweden)

D. Tutberidze

2017-04-01

Full Text Available There are many arguments that can be advanced to support the forecasting activities of business entities. The underlying argument in favor of forecasting is that managerial decisions are significantly dependent on proper evaluation of future trends as market conditions are constantly changing and require a detailed analysis of future dynamics. The article discusses the importance of using reasonable macro-econometric tool by suggesting the idea of conditional forecasting through a Vector Autoregressive (VAR modeling framework. Under this framework, a macroeconomic model for Georgian economy is constructed with the few variables believed to be shaping business environment. Based on the model, forecasts of macroeconomic variables are produced, and three types of scenarios are analyzed - a baseline and two alternative ones. The results of the study provide confirmatory evidence that suggested methodology is adequately addressing the research phenomenon and can be used widely by business entities in responding their strategic and operational planning challenges. Given this set-up, it is shown empirically that Bayesian Vector Autoregressive approach provides reasonable forecasts for the variables of interest.

Collaborative Supply Chain Planning and Coordination

DEFF Research Database (Denmark)

Wong, Chee Yew

products, differentiating retailers, accurate response (with forecast adjustment and premature replenishment), quick response (order-penetration-point relocation and lead-time reduction). These analyses extend the Fisher Model of responsiveness and refine six propositions or principles of responsiveness...... processes, particularly the behaviour of risk-taking/avoidance, conflict resolutions, and self-interest. All these lead to conclusion of five propositions or principles of supply chain coordination, and the theory of coordination process and behaviour. Combining the propositions of responsiveness...

Artificial Neural Network forecasting of storm surge water levels at major estuarine ports to supplement national tide-surge models and improve port resilience planning

Science.gov (United States)

French, Jon; Mawdsley, Robert; Fujiyama, Taku; Achuthan, Kamal

2017-04-01

Effective prediction of tidal storm surge is of considerable importance for operators of major ports, since much of their infrastructure is necessarily located close to sea level. Storm surge inundation can damage critical elements of this infrastructure and significantly disrupt port operations and downstream supply chains. The risk of surge inundation is typically approached using extreme value analysis, while short-term forecasting generally relies on coastal shelf-scale tide and surge models. However, extreme value analysis does not provide information on the duration of a surge event and can be sensitive to the assumptions made and the historic data available. Also, whilst regional tide and surge models perform well along open coasts, their fairly coarse spatial resolution means that they do not always provide accurate predictions for estuarine ports. As part of a NERC Environmental Risks to Infrastructure Innovation Programme project, we have developed a tool that is specifically designed to forecast the North Sea storm surges on major ports along the east coast of the UK. Of particular interest is the Port of Immingham, Humber estuary, which handles the largest volume of bulk cargo in the UK including major flows of coal and biomass for power generation. A tidal surge in December 2013, with an estimated return period of 760 years, partly flooded the port, damaged infrastructure and disrupted operations for several weeks. This and other recent surge events highlight the need for additional tools to supplement the national UK Storm Tide Warning Service. Port operators are also keen to have access to less computationally expensive forecasting tools for scenario planning and to improve their resilience to actual events. In this paper, we demonstrate the potential of machine learning methods based on Artificial Neural Networks (ANNs) to generate accurate short-term forecasts of extreme water levels at estuarine North Sea ports such as Immingham. An ANN is

Genetic Algorithms vs. Artificial Neural Networks in Economic Forecasting Process

Directory of Open Access Journals (Sweden)

Nicolae Morariu

2008-01-01

Full Text Available This paper aims to describe the implementa-tion of a neural network and a genetic algorithm system in order to forecast certain economic indicators of a free market economy. In a free market economy forecasting process precedes the economic planning (a management function, providing important information for the result of the last process. Forecasting represents a starting point in setting of target for a firm, an organization or even a branch of the economy. Thus, the forecasting method used can influence in a significant mode the evolution of an entity. In the following we will describe the forecasting of an economic indicator using two intelligent systems. The difference between the results obtained by this two systems are described in chapter IV.

A Review of Demand Forecast for Charging Facilities of Electric Vehicles

Science.gov (United States)

Jiming, Han; Lingyu, Kong; Yaqi, Shen; Ying, Li; Wenting, Xiong; Hao, Wang

2017-05-01

The demand forecasting of charging facilities is the basis of its planning and locating, which has important role in promoting the development of electric vehicles and alleviating the energy crisis. Firstly, this paper analyzes the influence of the charging mode, the electric vehicle population and the user’s charging habits on the demand of charging facilities; Secondly, considering these factors, the recent analysis on charging and switching equipment demand forecast is divided into two methods—forecast based on electric vehicle population and user traveling behavior. Then, the article analyzes the two methods and puts forward the advantages and disadvantages. Finally, in view of the defects of current research, combined with the current situation of the development of the city and comprehensive consideration of economic, political, environmental and other factors, this paper proposes an improved demand forecasting method which has great practicability and pertinence and lays the foundation for the plan of city electric facilities.

Forecasting accidental marine pollution drift: the French operational plan

International Nuclear Information System (INIS)

Daniel, P.; Poitevin, J.; Tiercelin, C.; Marchand, M.

1998-01-01

In case of accidental marine pollution, Cedre and Meteo-France, within the framework of their own public service missions, provide assistance to the French authorities in charge of pollution response. Meteo-France has developed a numerical marine oil pollution transport model, named MOTHY, designed to simulate the transport of oil in three dimensions. A hydrodynamic ocean model is linked to an oil spill model including current shear, vertical movements and fate of the oil. The use of a global atmospheric model for atmospheric forcing enables world-wide application of the model. This oil spill response system has been operational since February 1994. In case of marine pollution, Meteo-France send meteorological forecasts and oil spill drift forecasts to Cedre. In return, by its experimentations and interventions on actual pollution, Cedre is contributing to the improvement and validation of the model. New developments, exercises and training are conducted jointly. This paper summarizes the key features of MOTHY and presents some examples of model applications. (author)

1994 Electricity plan: An introduction to BC Hydro's integrated resource plan

International Nuclear Information System (INIS)

1994-01-01

A summary is provided of British Columbia Hydro's 1994 electricity plan. The plan provides up-to-date information on the options available to meet the 20-year forecast of domestic electricity requirements as well as an evaluation of alternative resource acquisitions to assess how to best minimize social cost, fulfill corporate objectives, and meet economic development initiatives of the corporation and the provincial government. The resource evaluations also incorporate the most recent information available on energy capabilities, construction, operation, and fuel supply costs, and environmental and socio-economic impacts. The 1994 plan includes updated estimates of the costs and expected contributions from currently committed resource acquisitions, including the Power Smart and Resource Smart programs and the Burrard Upgrade Project. The continuing uncertainty concerning the return of the Canadian Entitlement to the Columbia River Treaty downstream benefits, the purchase of power from the Kemano Completion Project, and the impact of customer self-generation are also considered in the plan. Considering the forecast net energy load and the existing and committed supply, a deficit in supply is forecast by 2003/04 if the treaty entitlement is not returned and by 2008/09 if it is returned. A dependable capacity balance is forecast to be in a negative position only by 2013/14 with a full sale of the treaty entitlement. A glossary is included. 9 figs., 8 tabs

Shared Decision-Making in Youth Mental Health Care: Using the Evidence to Plan Treatments Collaboratively.

Science.gov (United States)

Langer, David A; Jensen-Doss, Amanda

2016-12-02

The shared decision-making (SDM) model is one in which providers and consumers of health care come together as collaborators in determining the course of care. The model is especially relevant to youth mental health care, when planning a treatment frequently entails coordinating both youth and parent perspectives, preferences, and goals. The present article first provides the historical context of the SDM model and the rationale for increasing our field's use of SDM when planning psychosocial treatments for youth and families. Having established the potential utility of SDM, the article then discusses how to apply the SDM model to treatment planning for youth psychotherapy, proposing a set of steps consistent with the model and considerations when conducting SDM with youth and families.

Optics Supply Planning System

International Nuclear Information System (INIS)

Gaylord, J.

2009-01-01

The purpose of this study is to specify the design for an initial optics supply planning system for NIF, and to present quality assurance and test plans for the construction of the system as specified. The National Ignition Facility (NIF) is a large laser facility that is just starting operations. Thousands of specialized optics are required to operate the laser, and must be exchanged over time based on the laser shot plan and predictions of damage. Careful planning and tracking of optic exchanges is necessary because of the tight inventory of spare optics, and the long lead times for optics procurements and production changes. Automated inventory forecasting and production planning tools are required to replace existing manual processes. The optics groups members who are expected to use the supply planning system are the stakeholders for this project, and are divided into three groups. Each of these groups participated in a requirements specification that was used to develop this design. (1) Optics Management--These are the top level stakeholdersk, and the final decision makers. This group is the interface to shot operations, is ultimately responsible for optics supply, and decides which exchanges will be made. (2) Work Center Managers--This group manages the on site optics processing work centers. They schedule the daily work center operations, and are responsible for developing long term processing, equipment, and staffing plans. (3) Component Engineers--This group manages the vendor contracts for the manufacture of new optics and the off site rework of existing optics. They are responsible for sourcing vendors, negotiating contracts, and managing vendor processes. The scope of this analysis is to describe the structure and design details of a system that will meet all requirements that were described by stakeholders and documented in the analysis model for this project. The design specifies the architecture, components, interfaces, and data stores of the system

Forecasting market developments

International Nuclear Information System (INIS)

Weller, T.

1997-01-01

Traditional planning in essence consists of linear extrapolation of established facts and experience. This approach was good enough until recently, when progress would be relatively foreseeable within a stable system. The situation has been changing with developments and modifications in the global economic sector proceeding at accelerated pace, so that conventional planning methods become hopelessly inadequate. The past is of low significance to emerging markets; planners today have to keep abreast with and take into account the possible and emerging influencing factors. Experience is a factor to be replaced by intelligent analysis and conclusion within the framework of system networks. Modern scenario modelling methods are based on this approach: They are able to simulate and forecast a whole range of ''possible futures'', derived from perceivable trends. The article illustrates the novel planning methodology by assessing the future of the renewable energy sources, applying a computerized planning method (vision design) which is based on intelligent comparative analysis of all relevant trends. (Orig./RHM) [de

Neural network based photovoltaic electrical forecasting in south Algeria

International Nuclear Information System (INIS)

Hamid Oudjana, S.; Hellal, A.; Hadj Mahammed, I

2014-01-01

Photovoltaic electrical forecasting is significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants, and it is important task in renewable energy electrical system planning and operating. This paper explores the application of neural networks (NN) to study the design of photovoltaic electrical forecasting systems for one week ahead using weather databases include the global irradiance, and temperature of Ghardaia city (south of Algeria) for one year of 2013 using a data acquisition system. Simulations were run and the results are discussed showing that neural networks Technique is capable to decrease the photovoltaic electrical forecasting error. (author)

Satellite based Ocean Forecasting, the SOFT project

Science.gov (United States)

Stemmann, L.; Tintoré, J.; Moneris, S.

2003-04-01

The knowledge of future oceanic conditions would have enormous impact on human marine related areas. For such reasons, a number of international efforts are being carried out to obtain reliable and manageable ocean forecasting systems. Among the possible techniques that can be used to estimate the near future states of the ocean, an ocean forecasting system based on satellite imagery is developped through the Satelitte based Ocean ForecasTing project (SOFT). SOFT, established by the European Commission, considers the development of a forecasting system of the ocean space-time variability based on satellite data by using Artificial Intelligence techniques. This system will be merged with numerical simulation approaches, via assimilation techniques, to get a hybrid SOFT-numerical forecasting system of improved performance. The results of the project will provide efficient forecasting of sea-surface temperature structures, currents, dynamic height, and biological activity associated to chlorophyll fields. All these quantities could give valuable information on the planning and management of human activities in marine environments such as navigation, fisheries, pollution control, or coastal management. A detailed identification of present or new needs and potential end-users concerned by such an operational tool is being performed. The project would study solutions adapted to these specific needs.

High-Resolution Hydrological Sub-Seasonal Forecasting for Water Resources Management Over Europe

Science.gov (United States)

Wood, E. F.; Wanders, N.; Pan, M.; Sheffield, J.; Samaniego, L. E.; Thober, S.; Kumar, R.; Prudhomme, C.; Houghton-Carr, H.

2017-12-01

For decision-making at the sub-seasonal and seasonal time scale, hydrological forecasts with a high temporal and spatial resolution are required by water managers. So far such forecasts have been unavailable due to 1) lack of availability of meteorological seasonal forecasts, 2) coarse temporal resolution of meteorological seasonal forecasts, requiring temporal downscaling, 3) lack of consistency between observations and seasonal forecasts, requiring bias-correction. The EDgE (End-to-end Demonstrator for improved decision making in the water sector in Europe) project commissioned by the ECMWF (C3S) created a unique dataset of hydrological seasonal forecasts derived from four global climate models (CanCM4, FLOR-B01, ECMF, LFPW) in combination with four global hydrological models (PCR-GLOBWB, VIC, mHM, Noah-MP), resulting in 208 forecasts for any given day. The forecasts provide a daily temporal and 5-km spatial resolution, and are bias corrected against E-OBS meteorological observations. The forecasts are communicated to stakeholders via Sectoral Climate Impact Indicators (SCIIs), created in collaboration with the end-user community of the EDgE project (e.g. the percentage of ensemble realizations above the 10th percentile of monthly river flow, or below the 90th). Results show skillful forecasts for discharge from 3 months to 6 months (latter for N Europe due to snow); for soil moisture up to three months due precipitation forecast skill and short initial condition memory; and for groundwater greater than 6 months (lowest skill in western Europe.) The SCIIs are effective in communicating both forecast skill and uncertainty. Overall the new system provides an unprecedented ensemble for seasonal forecasts with significant skill over Europe to support water management. The consistency in both the GCM forecasts and the LSM parameterization ensures a stable and reliable forecast framework and methodology, even if additional GCMs or LSMs are added in the future.

Short-term Probabilistic Load Forecasting with the Consideration of Human Body Amenity

Directory of Open Access Journals (Sweden)

Ning Lu

2013-02-01

Full Text Available Load forecasting is the basis of power system planning and design. It is important for the economic operation and reliability assurance of power system. However, the results of load forecasting given by most existing methods are deterministic. This study aims at probabilistic load forecasting. First, the support vector machine regression is used to acquire the deterministic results of load forecasting with the consideration of human body amenity. Then the probabilistic load forecasting at a certain confidence level is given after the analysis of error distribution law corresponding to certain heat index interval. The final simulation shows that this probabilistic forecasting method is easy to implement and can provide more information than the deterministic forecasting results, and thus is helpful for decision-makers to make reasonable decisions.

Scenarios for energy forecasting: papers of the symposium

International Nuclear Information System (INIS)

1987-01-01

Energy planning is important for every developed country and therefore also for South Africa. However, during 1984 it was felt by interested parties that the work in this field should be coordinated through mutual discussion. With this in mind a 'Task Team for Energy Forecasting' was formed with the task to generate acceptable forecasts of the energy set-up in South Africa. Knowledge of the relationship between energy and variables such as the economy and the population is necessary to the Task Team. However, the Task Team also needs some insight into the future paths of such variables if it has to generate energy forecasts. It is the purpose of this symposium to improve this insight through having experts in all relevant fields to set out and develop their possible future scenarios independently of energy forecasting

Development of S-ARIMA Model for Forecasting Demand in a Beverage Supply Chain

Science.gov (United States)

Mircetic, Dejan; Nikolicic, Svetlana; Maslaric, Marinko; Ralevic, Nebojsa; Debelic, Borna

2016-11-01

Demand forecasting is one of the key activities in planning the freight flows in supply chains, and accordingly it is essential for planning and scheduling of logistic activities within observed supply chain. Accurate demand forecasting models directly influence the decrease of logistics costs, since they provide an assessment of customer demand. Customer demand is a key component for planning all logistic processes in supply chain, and therefore determining levels of customer demand is of great interest for supply chain managers. In this paper we deal with exactly this kind of problem, and we develop the seasonal Autoregressive IntegratedMoving Average (SARIMA) model for forecasting demand patterns of a major product of an observed beverage company. The model is easy to understand, flexible to use and appropriate for assisting the expert in decision making process about consumer demand in particular periods.

Cassini Information Management System in Distributed Operations Collaboration and Cassini Science Planning

Science.gov (United States)

Equils, Douglas J.

2008-01-01

Launched on October 15, 1997, the Cassini-Huygens spacecraft began its ambitious journey to the Saturnian system with a complex suite of 12 scientific instruments, and another 6 instruments aboard the European Space Agencies Huygens Probe. Over the next 6 1/2 years, Cassini would continue its relatively simplistic cruise phase operations, flying past Venus, Earth, and Jupiter. However, following Saturn Orbit Insertion (SOI), Cassini would become involved in a complex series of tasks that required detailed resource management, distributed operations collaboration, and a data base for capturing science objectives. Collectively, these needs were met through a web-based software tool designed to help with the Cassini uplink process and ultimately used to generate more robust sequences for spacecraft operations. In 2001, in conjunction with the Southwest Research Institute (SwRI) and later Venustar Software and Engineering Inc., the Cassini Information Management System (CIMS) was released which enabled the Cassini spacecraft and science planning teams to perform complex information management and team collaboration between scientists and engineers in 17 countries. Originally tailored to help manage the science planning uplink process, CIMS has been actively evolving since its inception to meet the changing and growing needs of the Cassini uplink team and effectively reduce mission risk through a series of resource management validation algorithms. These algorithms have been implemented in the web-based software tool to identify potential sequence conflicts early in the science planning process. CIMS mitigates these sequence conflicts through identification of timing incongruities, pointing inconsistencies, flight rule violations, data volume issues, and by assisting in Deep Space Network (DSN) coverage analysis. In preparation for extended mission operations, CIMS has also evolved further to assist in the planning and coordination of the dual playback redundancy of

Analyzing the Effects of Human-Aware Motion Planning on Close-Proximity Human–Robot Collaboration

Science.gov (United States)

Shah, Julie A.

2015-01-01

Objective: The objective of this work was to examine human response to motion-level robot adaptation to determine its effect on team fluency, human satisfaction, and perceived safety and comfort. Background: The evaluation of human response to adaptive robotic assistants has been limited, particularly in the realm of motion-level adaptation. The lack of true human-in-the-loop evaluation has made it impossible to determine whether such adaptation would lead to efficient and satisfying human–robot interaction. Method: We conducted an experiment in which participants worked with a robot to perform a collaborative task. Participants worked with an adaptive robot incorporating human-aware motion planning and with a baseline robot using shortest-path motions. Team fluency was evaluated through a set of quantitative metrics, and human satisfaction and perceived safety and comfort were evaluated through questionnaires. Results: When working with the adaptive robot, participants completed the task 5.57% faster, with 19.9% more concurrent motion, 2.96% less human idle time, 17.3% less robot idle time, and a 15.1% greater separation distance. Questionnaire responses indicated that participants felt safer and more comfortable when working with an adaptive robot and were more satisfied with it as a teammate than with the standard robot. Conclusion: People respond well to motion-level robot adaptation, and significant benefits can be achieved from its use in terms of both human–robot team fluency and human worker satisfaction. Application: Our conclusion supports the development of technologies that could be used to implement human-aware motion planning in collaborative robots and the use of this technique for close-proximity human–robot collaboration. PMID:25790568

A Simplified Short Term Load Forecasting Method Based on Sequential Patterns

DEFF Research Database (Denmark)

Kouzelis, Konstantinos; Bak-Jensen, Birgitte; Mahat, Pukar

2014-01-01

Load forecasting is an essential part of a power system both for planning and daily operation purposes. As far as the latter is concerned, short term load forecasting has been broadly used at the transmission level. However, recent technological advancements and legislation have facilitated the i...... in comparison with an ARIMA model....

Net load forecasting for high renewable energy penetration grids

International Nuclear Information System (INIS)

Kaur, Amanpreet; Nonnenmacher, Lukas; Coimbra, Carlos F.M.

2016-01-01

We discuss methods for net load forecasting and their significance for operation and management of power grids with high renewable energy penetration. Net load forecasting is an enabling technology for the integration of microgrid fleets with the macrogrid. Net load represents the load that is traded between the grids (microgrid and utility grid). It is important for resource allocation and electricity market participation at the point of common coupling between the interconnected grids. We compare two inherently different approaches: additive and integrated net load forecast models. The proposed methodologies are validated on a microgrid with 33% annual renewable energy (solar) penetration. A heuristics based solar forecasting technique is proposed, achieving skill of 24.20%. The integrated solar and load forecasting model outperforms the additive model by 10.69% and the uncertainty range for the additive model is larger than the integrated model by 2.2%. Thus, for grid applications an integrated forecast model is recommended. We find that the net load forecast errors and the solar forecasting errors are cointegrated with a common stochastic drift. This is useful for future planning and modeling because the solar energy time-series allows to infer important features of the net load time-series, such as expected variability and uncertainty. - Highlights: • Net load forecasting methods for grids with renewable energy generation are discussed. • Integrated solar and load forecasting outperforms the additive model by 10.69%. • Net load forecasting reduces the uncertainty between the interconnected grids.

Establishment of the international collaboration and licensing preparation planning for the specific design of a prototype SFR

International Nuclear Information System (INIS)

Kim, Y. G.; Joo, H. K.; Cho, C. H.; Yoo, J. W.; Lee, D. U.; Ahn, K. S.; Hwang, Y. S.

2013-05-01

The conceptual design of prototype of Gen IV SFR (PGSFR) will be early determined through the review of the international experts. After this, the technology demonstration plan and validation of fuel design will be determined in more detail. The project will be accomplished efficiently by introducing the proven technology already validated from the international collaboration. The conceptual design and its requirements of PGSFR will be reviewed by ANL, who has a lot of design experiences in the metal fueled SFR development. The collaboration with ANL has been done through Work For Others (WFO) contract, and the MOU was signed between SFRA and Terra Power(USA), and SFRA and IGCAR. The licensing issues raised during PFBR and FBTR licensing in India will be discussed and reflected into the PGSFR design by inviting the high level expert from India, for example Dr. Chetal in IGCAR. The specific design, technology validation plan and fuel development plan will be established in more detail through the annual International Technical Review Meeting (ITRM) and experimental facilities available from the international institute and companies, which will be the basis for shortening the project period and to reduce the development cost

Financial forecasts accuracy in Brazil's social security system.

Directory of Open Access Journals (Sweden)

Carlos Patrick Alves da Silva

Full Text Available Long-term social security statistical forecasts produced and disseminated by the Brazilian government aim to provide accurate results that would serve as background information for optimal policy decisions. These forecasts are being used as support for the government's proposed pension reform that plans to radically change the Brazilian Constitution insofar as Social Security is concerned. However, the reliability of official results is uncertain since no systematic evaluation of these forecasts has ever been published by the Brazilian government or anyone else. This paper aims to present a study of the accuracy and methodology of the instruments used by the Brazilian government to carry out long-term actuarial forecasts. We base our research on an empirical and probabilistic analysis of the official models. Our empirical analysis shows that the long-term Social Security forecasts are systematically biased in the short term and have significant errors that render them meaningless in the long run. Moreover, the low level of transparency in the methods impaired the replication of results published by the Brazilian Government and the use of outdated data compromises forecast results. In the theoretical analysis, based on a mathematical modeling approach, we discuss the complexity and limitations of the macroeconomic forecast through the computation of confidence intervals. We demonstrate the problems related to error measurement inherent to any forecasting process. We then extend this exercise to the computation of confidence intervals for Social Security forecasts. This mathematical exercise raises questions about the degree of reliability of the Social Security forecasts.

Financial forecasts accuracy in Brazil's social security system.

Science.gov (United States)

Silva, Carlos Patrick Alves da; Puty, Claudio Alberto Castelo Branco; Silva, Marcelino Silva da; Carvalho, Solon Venâncio de; Francês, Carlos Renato Lisboa

2017-01-01

Long-term social security statistical forecasts produced and disseminated by the Brazilian government aim to provide accurate results that would serve as background information for optimal policy decisions. These forecasts are being used as support for the government's proposed pension reform that plans to radically change the Brazilian Constitution insofar as Social Security is concerned. However, the reliability of official results is uncertain since no systematic evaluation of these forecasts has ever been published by the Brazilian government or anyone else. This paper aims to present a study of the accuracy and methodology of the instruments used by the Brazilian government to carry out long-term actuarial forecasts. We base our research on an empirical and probabilistic analysis of the official models. Our empirical analysis shows that the long-term Social Security forecasts are systematically biased in the short term and have significant errors that render them meaningless in the long run. Moreover, the low level of transparency in the methods impaired the replication of results published by the Brazilian Government and the use of outdated data compromises forecast results. In the theoretical analysis, based on a mathematical modeling approach, we discuss the complexity and limitations of the macroeconomic forecast through the computation of confidence intervals. We demonstrate the problems related to error measurement inherent to any forecasting process. We then extend this exercise to the computation of confidence intervals for Social Security forecasts. This mathematical exercise raises questions about the degree of reliability of the Social Security forecasts.

Facilitating State-Wide Collaboration around Family Planning Care in the Context of Zika.

Science.gov (United States)

Dehlendorf, Christine; Gavin, Loretta; Witt, Jacki; Moskosky, Susan

Family planning providers have an important role to play in the response to the public health challenge posed by Zika. In the United States, there are high rates of unintended pregnancy, especially in states most at risk for mosquito-borne transmission of the Zika virus. This paper describes efforts by eight of these states (Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and Texas) to build capacity for quality family planning care in the context of Zika. Drawing on resources developed by the Office of Population Affairs, including a toolkit for family planning care in the context of Zika, agencies and stakeholders involved in the family planning delivery system in Southern states at risk for mosquito-borne transmission met over several months in the summer of 2016 to coordinate efforts to respond to the risk of Zika in their jurisdictions. Through proactive communication and collaboration, states took steps to integrate Zika-related family planning care, including screening for Zika risk and providing appropriate, client-centered counseling. Challenges faced by the states included not having family planning included as a component of their state's Zika response effort, limited funding for family planning activities, and the need for robust communication networks between multiple state and federal agencies. The efforts described in this paper can help other states to integrate family planning into their Zika response. This is relevant to all states; even when mosquito-borne transmission is not occurring or expected, all states experience travel-related and sexually transmitted Zika infections. Copyright © 2017 Jacobs Institute of Women's Health. All rights reserved.

The Impact of Forecasting on Strategic Planning and Decision Making

African Journals Online (AJOL)

Nekky Umera

strategies for the investment manager in relations to Nigeria stock market ... Forecasting generally involves using information from the past to make decision ..... that both number of share in unit and number of deal in share cannot account.

Medium Range Ensembles Flood Forecasts for Community Level Applications

Science.gov (United States)

Fakhruddin, S.; Kawasaki, A.; Babel, M. S.; AIT

2013-05-01

Early warning is a key element for disaster risk reduction. In recent decades, there has been a major advancement in medium range and seasonal forecasting. These could provide a great opportunity to improve early warning systems and advisories for early action for strategic and long term planning. This could result in increasing emphasis on proactive rather than reactive management of adverse consequences of flood events. This can be also very helpful for the agricultural sector by providing a diversity of options to farmers (e.g. changing cropping pattern, planting timing, etc.). An experimental medium range (1-10 days) flood forecasting model has been developed for Bangladesh which provides 51 set of discharge ensembles forecasts of one to ten days with significant persistence and high certainty. This could help communities (i.e. farmer) for gain/lost estimation as well as crop savings. This paper describe the application of ensembles probabilistic flood forecast at the community level for differential decision making focused on agriculture. The framework allows users to interactively specify the objectives and criteria that are germane to a particular situation, and obtain the management options that are possible, and the exogenous influences that should be taken into account before planning and decision making. risk and vulnerability assessment was conducted through community consultation. The forecast lead time requirement, users' needs, impact and management options for crops, livestock and fisheries sectors were identified through focus group discussions, informal interviews and questionnaire survey.

Modeling and forecasting of electrical power demands for capacity planning

International Nuclear Information System (INIS)

Al-Shobaki, S.; Mohsen, M.

2007-01-01

Jordan imports oil from neighboring countries for use in power production. As such, the cost of electricity production is high compared to oil producing countries. It is anticipated that Jordan will face major challenges in trying to meet the growing energy and electricity demands while also developing the energy sector in a way that reduces any adverse impacts on the economy, the environment and social life. This paper described the development of forecasting models to predict future generation and sales loads of electrical power in Jordan. Two models that could be used for the prediction of electrical energy demand in Amman, Jordan were developed and validated. An analysis of the data was also presented. The first model was based on the levels of energy generated by the National Electric Power Company (NEPCO) and the other was based on the levels of energy sold by the company in the same area. The models were compared and the percent error was presented. Energy demand was also forecasted across the next 60 months for both models. Results were then compared with the output of the in-house forecast model used by NEPCO to predict the levels of generated energy needed across the 60 months time period. It was concluded that the NEPCO model predicted energy demand higher than the validated generated data model by an average of 5.25 per cent. 8 refs., 5 tabs., 15 figs

Modeling and forecasting of electrical power demands for capacity planning

Energy Technology Data Exchange (ETDEWEB)

Al-Shobaki, S. [Hashemite Univ., Zarka (Jordan). Dept. of Industrial Engineering; Mohsen, M. [Hashemite Univ., Zarka (Jordan). Dept. of Mechanical Engineering

2007-07-01

Jordan imports oil from neighboring countries for use in power production. As such, the cost of electricity production is high compared to oil producing countries. It is anticipated that Jordan will face major challenges in trying to meet the growing energy and electricity demands while also developing the energy sector in a way that reduces any adverse impacts on the economy, the environment and social life. This paper described the development of forecasting models to predict future generation and sales loads of electrical power in Jordan. Two models that could be used for the prediction of electrical energy demand in Amman, Jordan were developed and validated. An analysis of the data was also presented. The first model was based on the levels of energy generated by the National Electric Power Company (NEPCO) and the other was based on the levels of energy sold by the company in the same area. The models were compared and the percent error was presented. Energy demand was also forecasted across the next 60 months for both models. Results were then compared with the output of the in-house forecast model used by NEPCO to predict the levels of generated energy needed across the 60 months time period. It was concluded that the NEPCO model predicted energy demand higher than the validated generated data model by an average of 5.25 per cent. 8 refs., 5 tabs., 15 figs.

Application of seasonal rainfall forecasts and satellite rainfall observations to crop yield forecasting for Africa

Science.gov (United States)

Greatrex, H. L.; Grimes, D. I. F.; Wheeler, T. R.

2009-04-01

Rain-fed agriculture is of utmost importance in sub-Saharan Africa; the FAO estimates that over 90% of food consumed in the region is grown in rain-fed farming systems. As the climate in sub-Saharan Africa has a high interannual variability, this dependence on rainfall can leave communities extremely vulnerable to food shortages, especially when coupled with a lack of crop management options. The ability to make a regional forecast of crop yield on a timescale of months would be of enormous benefit; it would enable both governmental and non-governmental organisations to be alerted in advance to crop failure and could facilitate national and regional economic planning. Such a system would also enable individual communities to make more informed crop management decisions, increasing their resilience to climate variability and change. It should be noted that the majority of crops in the region are rainfall limited, therefore the ability to create a seasonal crop forecast depends on the ability to forecast rainfall at a monthly or seasonal timescale and to temporally downscale this to a daily time-series of rainfall. The aim of this project is to develop a regional-scale seasonal forecast for sub-Saharan crops, utilising the General Large Area Model for annual crops (GLAM). GLAM would initially be driven using both dynamical and statistical seasonal rainfall forecasts to provide an initial estimate of crop yield. The system would then be continuously updated throughout the season by replacing the seasonal rainfall forecast with daily weather observations. TAMSAT satellite rainfall estimates are used rather than rain-gauge data due to the scarcity of ground based observations. An important feature of the system is the use of the geo-statistical method of sequential simulation to create an ensemble of daily weather inputs from both the statistical seasonal rainfall forecasts and the satellite rainfall estimates. This allows a range of possible yield outputs to be

A Study of Scenic Spot Living Facility Recommendation Based on Collaborative Filtering

Directory of Open Access Journals (Sweden)

Luo Wenbiao

2015-01-01

Full Text Available For the collection of massive complex information, the collaborative filtering system can work as a highly efficient information screening tool. It can recommend reasonable information reserve with multi angles according to the living service facility information of the scenic spots. The collaborative filtering system can collect information and forecast rating results based on users’ preference. According to different recommendation goals, the collaborative filtering system can recommend results for user feedback and give feedback of the recommendation results in various forms.

Probabilistic eruption forecasting at short and long time scales

Science.gov (United States)

Marzocchi, Warner; Bebbington, Mark S.

2012-10-01

Any effective volcanic risk mitigation strategy requires a scientific assessment of the future evolution of a volcanic system and its eruptive behavior. Some consider the onus should be on volcanologists to provide simple but emphatic deterministic forecasts. This traditional way of thinking, however, does not deal with the implications of inherent uncertainties, both aleatoric and epistemic, that are inevitably present in observations, monitoring data, and interpretation of any natural system. In contrast to deterministic predictions, probabilistic eruption forecasting attempts to quantify these inherent uncertainties utilizing all available information to the extent that it can be relied upon and is informative. As with many other natural hazards, probabilistic eruption forecasting is becoming established as the primary scientific basis for planning rational risk mitigation actions: at short-term (hours to weeks or months), it allows decision-makers to prioritize actions in a crisis; and at long-term (years to decades), it is the basic component for land use and emergency planning. Probabilistic eruption forecasting consists of estimating the probability of an eruption event and where it sits in a complex multidimensional time-space-magnitude framework. In this review, we discuss the key developments and features of models that have been used to address the problem.

House Price Forecasts, Forecaster Herding, and the Recent Crisis

DEFF Research Database (Denmark)

Stadtmann, Georg; Pierdzioch; Ruelke

2013-01-01

We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-)herding of forecasters. Forecasts are consistent with herding (anti-herding) of forecasters if forecasts are biased towards (away from) t......) the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time....

Supplier Short Term Load Forecasting Using Support Vector Regression and Exogenous Input

Science.gov (United States)

Matijaš, Marin; Vukićcević, Milan; Krajcar, Slavko

2011-09-01

In power systems, task of load forecasting is important for keeping equilibrium between production and consumption. With liberalization of electricity markets, task of load forecasting changed because each market participant has to forecast their own load. Consumption of end-consumers is stochastic in nature. Due to competition, suppliers are not in a position to transfer their costs to end-consumers; therefore it is essential to keep forecasting error as low as possible. Numerous papers are investigating load forecasting from the perspective of the grid or production planning. We research forecasting models from the perspective of a supplier. In this paper, we investigate different combinations of exogenous input on the simulated supplier loads and show that using points of delivery as a feature for Support Vector Regression leads to lower forecasting error, while adding customer number in different datasets does the opposite.

Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

Science.gov (United States)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In part I of this study, the operational flood forecasting system in Bavaria and an approach to identify and quantify forecast uncertainty was introduced. The approach is split into the calculation of an empirical 'overall error' from archived forecasts and the calculation of an empirical 'model error' based on hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The 'model error' can especially in upstream catchments where forecast uncertainty is strongly dependent on the current predictability of the atrmosphere be superimposed on the spread of a hydrometeorological ensemble forecast. In Bavaria, two meteorological ensemble prediction systems are currently tested for operational use: the 16-member COSMO-LEPS forecast and a poor man's ensemble composed of DWD GME, DWD Cosmo-EU, NCEP GFS, Aladin-Austria, MeteoSwiss Cosmo-7. The determination of the overall forecast uncertainty is dependent on the catchment characteristics: 1. Upstream catchment with high influence of weather forecast a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. b) Corresponding to the characteristics of the meteorological ensemble forecast, each resulting forecast hydrograph can be regarded as equally likely. c) The 'model error' distribution, with parameters dependent on hydrological case and lead time, is added to each forecast timestep of each ensemble member d) For each forecast timestep, the overall (i.e. over all 'model error' distribution of each ensemble member) error distribution is calculated e) From this distribution, the uncertainty range on a desired level (here: the 10% and 90% percentile) is extracted and drawn as forecast envelope. f) As the mean or median of an ensemble forecast does not necessarily exhibit meteorologically sound temporal evolution, a single hydrological forecast termed 'lead forecast' is chosen and shown in addition to the uncertainty bounds. This can be

Linking Science of Flood Forecasts to Humanitarian Actions for Improved Preparedness and Effective Response

Science.gov (United States)

Uprety, M.; Dugar, S.; Gautam, D.; Kanel, D.; Kshetri, M.; Kharbuja, R. G.; Acharya, S. H.

2017-12-01

Advances in flood forecasting have provided opportunities for humanitarian responders to employ a range of preparedness activities at different forecast time horizons. Yet, the science of prediction is less understood and realized across the humanitarian landscape, and often preparedness plans are based upon average level of flood risk. Working under the remit of Forecast Based Financing (FbF), we present a pilot from Nepal on how available flood and weather forecast products are informing specific pre-emptive actions in the local preparedness and response plans, thereby supporting government stakeholders and humanitarian agencies to take early actions before an impending flood event. In Nepal, forecasting capabilities are limited but in a state of positive flux. Whilst local flood forecasts based upon rainfall-runoff models are yet to be operationalized, streamflow predictions from Global Flood Awareness System (GLoFAS) can be utilized to plan and implement preparedness activities several days in advance. Likewise, 3-day rainfall forecasts from Nepal Department of Hydrology and Meteorology (DHM) can further inform specific set of early actions for potential flash floods due to heavy precipitation. Existing community based early warning systems in the major river basins of Nepal are utilizing real time monitoring of water levels and rainfall together with localised probabilistic flood forecasts which has increased warning lead time from 2-3 hours to 7-8 hours. Based on these available forecast products, thresholds and trigger levels have been determined for different flood scenarios. Matching these trigger levels and assigning responsibilities to relevant actors for early actions, a set of standard operating procedures (SOPs) are being developed, broadly covering general preparedness activities and science informed anticipatory actions for different forecast lead times followed by the immediate response activities. These SOPs are currently being rolled out and

House Price Forecasts, Forecaster Herding, and the Recent Crisis

Directory of Open Access Journals (Sweden)

Christian Pierdzioch

2012-11-01

Full Text Available We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-herding of forecasters. Forecasts are consistent with herding (anti-herding of forecasters if forecasts are biased towards (away from the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time.

Optimal operation and forecasting policy for pump storage plants in day-ahead markets

International Nuclear Information System (INIS)

Muche, Thomas

2014-01-01

Highlights: • We investigate unit commitment deploying stochastic and deterministic approaches. • We consider day-ahead markets, its forecast and weekly price based unit commitment. • Stochastic and deterministic unit commitment are identical for the first planning day. • Unit commitment and bidding policy can be based on the deterministic approach. • Robust forecasting models should be estimated based on the whole planning horizon. - Abstract: Pump storage plants are an important electricity storage technology at present. Investments in this technology are expected to increase. The necessary investment valuation often includes expected cash flows from future price-based unit commitment policies. A price-based unit commitment policy has to consider market price uncertainty and the information revealing nature of electricity markets. For this environment stochastic programming models are suggested to derive the optimal unit commitment policy. For the considered day-ahead price electricity market stochastic and deterministic unit commitment policies are comparable suggesting an application of easier implementable deterministic models. In order to identify suitable unit commitment and forecasting policies, deterministic unit commitment models are applied to actual day-ahead electricity prices of a whole year. As a result, a robust forecasting model should consider the unit commitment planning period. This robust forecasting models result in expected cash flows similar to realized ones allowing a reliable investment valuation

Demand forecasting for automotive sector in Malaysia by system dynamics approach

International Nuclear Information System (INIS)

Zulkepli, Jafri; Abidin, Norhaslinda Zainal; Fong, Chan Hwa

2015-01-01

In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars

Demand forecasting for automotive sector in Malaysia by system dynamics approach

Energy Technology Data Exchange (ETDEWEB)

Zulkepli, Jafri, E-mail: zhjafri@uum.edu.my; Abidin, Norhaslinda Zainal, E-mail: nhaslinda@uum.edu.my [School of Quantitative Sciences, Universiti Utara Malaysia, Sintok, Kedah (Malaysia); Fong, Chan Hwa, E-mail: hfchan7623@yahoo.com [SWM Environment Sdn. Bhd.Level 17, Menara LGB, Taman Tun Dr. Ismail Kuala Lumpur (Malaysia)

2015-12-11

In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.

Collaborative survey of perinatal loss in planned and unplanned home births. Northern Region Perinatal Mortality Survey Coordinating Group.

OpenAIRE

1996-01-01

OBJECTIVE: To document the outcome of planned and unplanned births outside hospital. DESIGN: Confidential review of every pregnancy ending in stillbirth or neonatal death in which plans had been made for home delivery, irrespective of where delivery eventually occurred. The review was part of a sustained collaborative survey of all perinatal deaths. SETTING: Northern Regional Health Authority area. SUBJECTS: All 558,691 registered births to women normally resident in the former Northern Regio...

Collaboration on DIII-D Five Year Plan

International Nuclear Information System (INIS)

Allen, S

2003-01-01

This document summarizes Lawrence Livermore National Laboratory's (LLNL) plan for fusion research on the DIII-D Tokamak, located at General Atomics (GA) in San Diego, California, in the time period FY04-FY08. This document is a companion document to the DIII-D Five-Year Program Plan; which hereafter will be referred to as the ''D3DPP''. The LLNL Collaboration on DIII-D is a task-driven program in which we bring to bear the full range of expertise needed to complete specific goals of plasma science research on the DIII-D facility. This document specifies our plasma performance and physics understanding goals and gives detailed plans to achieve those goals in terms of experimental leadership, code development and analysis, and diagnostic development. Our program is designed to be consistent with the long-term mission of the DIII-D program as documented in the D3DPP. The overall DIII-D Program mission is ''to establish the scientific basis for the optimization of the tokamak approach to fusion energy production''. LLNL Magnetic Fusion Energy (MFE) supports this mission, and we contribute to two areas of the DIII-D program: divertor physics and advanced tokamak (AT) physics. We lead or contribute to the whole cycle of research: experimental planning, diagnostic development, execution of experiments, and detailed analysis. We plan to continue this style in the next five years. DIII-D has identified three major research themes: AT physics, confinement physics, and mass transport. The LLNL program is part of the AT theme: measurement of the plasma current profile, and the mass transport theme: measurement and modeling of plasma flow. In the AT area, we have focused on the measurement and modeling of the current profile in Advanced Tokamak plasmas. The current profile, and it's effect on MHD stability of the high-β ''AT'' plasma are at the heart of the DIII-D program. LLNL has played a key role in the development of the Motional Stark Effect (MSE) diagnostic. Starting

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

OpenAIRE

Syed Aziz Ur Rehman; Yanpeng Cai; Rizwan Fazal; Gordhan Das Walasai; Nayyar Hussain Mirjat

2017-01-01

Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fo...

Next-generation probabilistic seismicity forecasting

Energy Technology Data Exchange (ETDEWEB)

Hiemer, S.

2014-07-01

The development of probabilistic seismicity forecasts is one of the most important tasks of seismologists at present time. Such forecasts form the basis of probabilistic seismic hazard assessment, a widely used approach to generate ground motion exceedance maps. These hazard maps guide the development of building codes, and in the absence of the ability to deterministically predict earthquakes, good building and infrastructure planning is key to prevent catastrophes. Probabilistic seismicity forecasts are models that specify the occurrence rate of earthquakes as a function of space, time and magnitude. The models presented in this thesis are time-invariant mainshock occurrence models. Accordingly, the reliable estimation of the spatial and size distribution of seismicity are of crucial importance when constructing such probabilistic forecasts. Thereby we focus on data-driven approaches to infer these distributions, circumventing the need for arbitrarily chosen external parameters and subjective expert decisions. Kernel estimation has been shown to appropriately transform discrete earthquake locations into spatially continuous probability distributions. However, we show that neglecting the information from fault networks constitutes a considerable shortcoming and thus limits the skill of these current seismicity models. We present a novel earthquake rate forecast that applies the kernel-smoothing method to both past earthquake locations and slip rates on mapped crustal faults applied to Californian and European data. Our model is independent from biases caused by commonly used non-objective seismic zonations, which impose artificial borders of activity that are not expected in nature. Studying the spatial variability of the seismicity size distribution is of great importance. The b-value of the well-established empirical Gutenberg-Richter model forecasts the rates of hazard-relevant large earthquakes based on the observed rates of abundant small events. We propose a

Next-generation probabilistic seismicity forecasting

International Nuclear Information System (INIS)

Hiemer, S.

2014-01-01

The development of probabilistic seismicity forecasts is one of the most important tasks of seismologists at present time. Such forecasts form the basis of probabilistic seismic hazard assessment, a widely used approach to generate ground motion exceedance maps. These hazard maps guide the development of building codes, and in the absence of the ability to deterministically predict earthquakes, good building and infrastructure planning is key to prevent catastrophes. Probabilistic seismicity forecasts are models that specify the occurrence rate of earthquakes as a function of space, time and magnitude. The models presented in this thesis are time-invariant mainshock occurrence models. Accordingly, the reliable estimation of the spatial and size distribution of seismicity are of crucial importance when constructing such probabilistic forecasts. Thereby we focus on data-driven approaches to infer these distributions, circumventing the need for arbitrarily chosen external parameters and subjective expert decisions. Kernel estimation has been shown to appropriately transform discrete earthquake locations into spatially continuous probability distributions. However, we show that neglecting the information from fault networks constitutes a considerable shortcoming and thus limits the skill of these current seismicity models. We present a novel earthquake rate forecast that applies the kernel-smoothing method to both past earthquake locations and slip rates on mapped crustal faults applied to Californian and European data. Our model is independent from biases caused by commonly used non-objective seismic zonations, which impose artificial borders of activity that are not expected in nature. Studying the spatial variability of the seismicity size distribution is of great importance. The b-value of the well-established empirical Gutenberg-Richter model forecasts the rates of hazard-relevant large earthquakes based on the observed rates of abundant small events. We propose a

Nowcasting of precipitation – Advective statistical forecast model (SAM) for the Czech Republic

Czech Academy of Sciences Publication Activity Database

Sokol, Zbyněk; Pešice, Petr

2012-01-01

Roč. 103, - (2012), s. 70-79 ISSN 0169-8095 R&D Projects: GA MŠk ME09033; GA ČR GA205/07/0905 Institutional research plan: CEZ:AV0Z30420517 Keywords : Precipitation forecast * Statistical models * Regression * Quantitative precipitation forecast * Extrapolation forecast Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.200, year: 2012 http://dx.doi.org/10.1016/j. atm osres.2011.07.013

Students' online collaborative intention for group projects: Evidence from an extended version of the theory of planned behaviour.

Science.gov (United States)

Cheng, Eddie W L; Chu, Samuel K W

2016-08-01

Given the increasing use of web technology for teaching and learning, this study developed and examined an extended version of the theory of planned behaviour (TPB) model, which explained students' intention to collaborate online for their group projects. Results indicated that past experience predicted the three antecedents of intention, while past behaviour was predictive of subjective norm and perceived behavioural control. Moreover, the three antecedents (attitude towards e-collaboration, subjective norm and perceived behavioural control) were found to significantly predict e-collaborative intention. This study explored the use of the "remember" type of awareness (i.e. past experience) and evaluated the value of the "know" type of awareness (i.e. past behaviour) in the TPB model. © 2015 International Union of Psychological Science.

The Influences of Social Collaboration on Web 2.0 Self-Efficacy for Higher Education

Science.gov (United States)

Turky, Mohamed Abdullah

2016-01-01

The present study tries to research the relationship between Social Collaboration Activity and Web 2.0 Self-Efficacy for Higher Education student. It additionally looks to decide how Social Collaboration adds to the forecast of their sense Web 2.0 Self-Efficacy. The study reported in this paper was led to inspect the relationship Social…

Medium Range Flood Forecasting for Agriculture Damage Reduction

Science.gov (United States)

Fakhruddin, S. H. M.

2014-12-01

Early warning is a key element for disaster risk reduction. In recent decades, major advancements have been made in medium range and seasonal flood forecasting. This progress provides a great opportunity to reduce agriculture damage and improve advisories for early action and planning for flood hazards. This approach can facilitate proactive rather than reactive management of the adverse consequences of floods. In the agricultural sector, for instance, farmers can take a diversity of options such as changing cropping patterns, applying fertilizer, irrigating and changing planting timing. An experimental medium range (1-10 day) flood forecasting model has been developed for Bangladesh and Thailand. It provides 51 sets of discharge ensemble forecasts of 1-10 days with significant persistence and high certainty. This type of forecast could assist farmers and other stakeholders for differential preparedness activities. These ensembles probabilistic flood forecasts have been customized based on user-needs for community-level application focused on agriculture system. The vulnerabilities of agriculture system were calculated based on exposure, sensitivity and adaptive capacity. Indicators for risk and vulnerability assessment were conducted through community consultations. The forecast lead time requirement, user-needs, impacts and management options for crops were identified through focus group discussions, informal interviews and community surveys. This paper illustrates potential applications of such ensembles for probabilistic medium range flood forecasts in a way that is not commonly practiced globally today.

International and interlaboratory collaboration on Neutron Science Project

Energy Technology Data Exchange (ETDEWEB)

Oyama, Yukio [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

1997-11-01

For effectiveness of facility development for Neutron Science Projects at JAERI, international and interlaboratory collaborations have been extensively planned and promoted, especially in the areas of accelerator and target technology. Here status of two collaborations relevant to a spallation neutron target development is highlighted from those collaborations. The two collaborations are experiments on BNL-AGS spallation target simulation and PSI materials irradiation. Both are planned to start in spring of 1997. (author)

Salmon and Sagebrush: The Shoshone-Bannock Tribes Collaborative Approach to Adaptation Planning

Science.gov (United States)

Petersen, A.; Nasser, E.; Stone, D.; Krosby, M.; Whitley-Binder, L.; Morgan, H.; Rupp, D. E.; Dello, K.; Dalton, M. M.; Fox, M.; Rodgers, K.

2017-12-01

The Shoshone-Bannock Tribes reside in the Upper Snake River Watershed in southeast Idaho. Their lives and culture are intertwined with the lands where they live; lands which continue to sustain the Tribes cultural, spiritual, dietary and economic needs. Climate change presents a new threat to the region requiring innovative approaches to prepare for changes as well as to protect the natural resources within the region. As a critical first step in building climate resilience, the Tribes worked with Adaptation International, the University of Washington's Climate Impacts Group (CIG) and the Oregon Climate Change Research Institute (OCCRI) to complete a collaborative climate change vulnerability assessment and adaptation planning process. This presentation provides an overview of collaborative process, shares the results of the project, and includes a 3-minute video presentation. The project started with the identification of 34 plant and animal species to focus the vulnerability assessment. OCCRI analyzed detailed downscaled climate projections for two key climate scenarios (RCP 4.5 and RCP 8.5) and timescales (2050s and 2080s). CIG then used NatureServe's Climate Change Vulnerability Index (CCVI) to develop initial relative vulnerability results for these species. A core team of Tribal staff members from various departments refined these results, drawing upon and integrating rich local and traditional knowledges of the natural environmental and cultural resources. The adaptation planning phase of the project continued in a similar collaborative manner with the project team identifying promising adaptation actions and working directly with Tribal staff to refine and customize these strategies. Tailoring the actions to the local context provides a framework for action that the Tribes can continue to build on in the future. By engaging in these efforts to identify vulnerable species and adaptation strategies and actions to minimize the negative effects of climate

Decadal-Scale Forecasting of Climate Drivers for Marine Applications.

Science.gov (United States)

Salinger, J; Hobday, A J; Matear, R J; O'Kane, T J; Risbey, J S; Dunstan, P; Eveson, J P; Fulton, E A; Feng, M; Plagányi, É E; Poloczanska, E S; Marshall, A G; Thompson, P A

Climate influences marine ecosystems on a range of time scales, from weather-scale (days) through to climate-scale (hundreds of years). Understanding of interannual to decadal climate variability and impacts on marine industries has received less attention. Predictability up to 10 years ahead may come from large-scale climate modes in the ocean that can persist over these time scales. In Australia the key drivers of climate variability affecting the marine environment are the Southern Annular Mode, the Indian Ocean Dipole, the El Niño/Southern Oscillation, and the Interdecadal Pacific Oscillation, each has phases that are associated with different ocean circulation patterns and regional environmental variables. The roles of these drivers are illustrated with three case studies of extreme events-a marine heatwave in Western Australia, a coral bleaching of the Great Barrier Reef, and flooding in Queensland. Statistical and dynamical approaches are described to generate forecasts of climate drivers that can subsequently be translated to useful information for marine end users making decisions at these time scales. Considerable investment is still needed to support decadal forecasting including improvement of ocean-atmosphere models, enhancement of observing systems on all scales to support initiation of forecasting models, collection of important biological data, and integration of forecasts into decision support tools. Collaboration between forecast developers and marine resource sectors-fisheries, aquaculture, tourism, biodiversity management, infrastructure-is needed to support forecast-based tactical and strategic decisions that reduce environmental risk over annual to decadal time scales. © 2016 Elsevier Ltd. All rights reserved.

Planning documents: a business planning strategy.

Science.gov (United States)

Kaehrle, P A

2000-06-01

Strategic planning and business plan development are essential nursing management skills in today's competitive, fast paced, continually changing health care environment. Even in times of great uncertainty, nurse managers need to plan and forecast for the future. A well-written business plan allows nurse managers to communicate their expertise and proactively contribute to the programmatic decisions and changes occurring within their patient population or service area. This article presents the use of planning documents as a practical, strategic business planning strategy. Although the model addresses orthopedic services specifically, nurse managers can gain an understanding and working knowledge of planning concepts that can be applied to all patient populations.

Nevada nuclear waste storage investigations: FY 1980 Project Plan and FY 1981 forecast

International Nuclear Information System (INIS)

1980-02-01

The DOE is responsible for developing or improving the technology for safely and permanently isolating radioactive wastes from the biosphere. The National Waste Terminal Storage Program, which is a part of the US Nuclear Waste Management Program, is concerned with disposing of the high-level wastes associated with DOE and commercial nuclear reactor fuel cycles. The DOE/NV has been delegated the responsibility to evaluate the geohydrologic setting and underground rock masses of the Nevada Test Site (NTS) area to determine whether a suitable site exists for constructing a repository for isolating highly radioactive solid wastes. Accordingly, the Nevada Nuclear Waste Storage Investigations (NNWSI) were established by NV to conduct these evaluations. The NNWSI are managed by the DOE/NV, but the field and laboratory investigations are being performed by scientific investigators from several organizations. The four primary organizations involved are: Los Alamos Scientific Laboratory (LASL), Lawrence Livermore Laboratory (LLL), Sandia Laboratories (SL), and the US Geological Survey (USGS). DOE/NV is responsible for coordinating these investigations. This document presents the Project Plan for the NNWSI for FY 1980 and forecasts activities for FY 1981. Each task is divided into subtasks and described. This Plan is subject ot periodic review and revision by the DOE/NV. Changes will be addressed as they occur in NNWSI Quarterly Reports. This document also presents information on the Project's technical approach as well as its history, organization, and management

Electronic Collaboration Logbook

International Nuclear Information System (INIS)

Gysin, Suzanne; Mandrichenko, Igor; Podstavkov, Vladimir; Vittone, Margherita

2012-01-01

In HEP, scientific research is performed by large collaborations of organizations and individuals. The logbook of a scientific collaboration is an important part of the collaboration record. Often it contains experimental data. At Fermi National Accelerator Laboratory (FNAL), we developed an Electronic Collaboration Logbook (ECL) application, which is used by about 20 different collaborations, experiments and groups at FNAL. The ECL is the latest iteration of the project formerly known as the Control Room Logbook (CRL). We have been working on mobile (IOS and Android) clients for the ECL. We will present the history, current status and future plans of the project, as well as design, implementation and support solutions made by the project.

A mathematical model to forecast uranium production

International Nuclear Information System (INIS)

Camisani-Calzolari, F.A.G.M.

1987-01-01

The uranium production forecasting program described in this paper projects production from reasonably assured, estimated additional and speculative resources in the cost categories of less than $130/kg U. Originally designed to handle South African production, it has been expanded and redimensioned using available published information to forecast production for countries of the Western World. The program forecasts production from up to 400 plants over a period of fifty years and has built-in production models derived from documented historical data of the more important uranium provinces. It is particularly suitable to assess production capabilities on a national and global scale where variations in outputs for the individual plants tend to even out. The program is aimed at putting the uranium potential of any one country into a realistic perspective, and it could thus be useful for planning purposes and marketing strategies

Forecasting urban water demand: A meta-regression analysis.

Science.gov (United States)

Sebri, Maamar

2016-12-01

Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike. Copyright © 2016. Published by Elsevier Ltd.

Flood forecasting and uncertainty of precipitation forecasts

International Nuclear Information System (INIS)

Kobold, Mira; Suselj, Kay

2004-01-01

The timely and accurate flood forecasting is essential for the reliable flood warning. The effectiveness of flood warning is dependent on the forecast accuracy of certain physical parameters, such as the peak magnitude of the flood, its timing, location and duration. The conceptual rainfall - runoff models enable the estimation of these parameters and lead to useful operational forecasts. The accurate rainfall is the most important input into hydrological models. The input for the rainfall can be real time rain-gauges data, or weather radar data, or meteorological forecasted precipitation. The torrential nature of streams and fast runoff are characteristic for the most of the Slovenian rivers. Extensive damage is caused almost every year- by rainstorms affecting different regions of Slovenia' The lag time between rainfall and runoff is very short for Slovenian territory and on-line data are used only for now casting. Forecasted precipitations are necessary for hydrological forecast for some days ahead. ECMWF (European Centre for Medium-Range Weather Forecasts) gives general forecast for several days ahead while more detailed precipitation data with limited area ALADIN/Sl model are available for two days ahead. There is a certain degree of uncertainty using such precipitation forecasts based on meteorological models. The variability of precipitation is very high in Slovenia and the uncertainty of ECMWF predicted precipitation is very large for Slovenian territory. ECMWF model can predict precipitation events correctly, but underestimates amount of precipitation in general The average underestimation is about 60% for Slovenian region. The predictions of limited area ALADIN/Si model up to; 48 hours ahead show greater applicability in hydrological forecasting. The hydrological models are sensitive to precipitation input. The deviation of runoff is much bigger than the rainfall deviation. Runoff to rainfall error fraction is about 1.6. If spatial and time distribution

Forecasting monthly peak demand of electricity in India—A critique

International Nuclear Information System (INIS)

Rallapalli, Srinivasa Rao; Ghosh, Sajal

2012-01-01

The nature of electricity differs from that of other commodities since electricity is a non-storable good and there have been significant seasonal and diurnal variations of demand. Under such condition, precise forecasting of demand for electricity should be an integral part of the planning process as this enables the policy makers to provide directions on cost-effective investment and on scheduling the operation of the existing and new power plants so that the supply of electricity can be made adequate enough to meet the future demand and its variations. Official load forecasting in India done by Central Electricity Authority (CEA) is often criticized for being overestimated due to inferior techniques used for forecasting. This paper tries to evaluate monthly peak demand forecasting performance predicted by CEA using trend method and compare it with those predicted by Multiplicative Seasonal Autoregressive Integrated Moving Average (MSARIMA) model. It has been found that MSARIMA model outperforms CEA forecasts both in-sample static and out-of-sample dynamic forecast horizons in all five regional grids in India. For better load management and grid discipline, this study suggests employing sophisticated techniques like MSARIMA for peak load forecasting in India. - Highlights: ► This paper evaluates monthly peak demand forecasting performance by CEA. ► Compares CEA forecasts it with those predicted by MSARIMA model. ► MSARIMA model outperforms CEA forecasts in all five regional grids in India. ► Opportunity exists to improve the performance of CEA forecasts.

Forecast of the energy final consumption for Minas Gerais State

International Nuclear Information System (INIS)

Almeida, P.E.F. de; Bechtlufft, P.C.T.; Araujo, M.E.A.; Vasconcelos, E.C.; Las Casas, H.B. de; Monteiro, M.A.G.

1990-01-01

This paper is included among the activities of the Energy Planning of Minas Gerais State and presents a forecast of the energy final consumption for the State up to year 2010. Two Scenarios are presented involving brazilian economy's evolution, the State's demography and its sectors: residential, services, transportation, agriculture and cattle-breeding and industry. Finally, it shows two forecast on energy final consumption for Minas Gerais State. (author)

A fuzzy inference model for short-term load forecasting

International Nuclear Information System (INIS)

Mamlook, Rustum; Badran, Omar; Abdulhadi, Emad

2009-01-01

This paper is concerned with the short-term load forecasting (STLF) in power system operations. It provides load prediction for generation scheduling and unit commitment decisions, and therefore precise load forecasting plays an important role in reducing the generation cost and the spinning reserve capacity. Short-term electricity demand forecasting (i.e., the prediction of hourly loads (demand)) is one of the most important tools by which an electric utility/company plans, dispatches the loading of generating units in order to meet system demand. The accuracy of the dispatching system, which is derived from the accuracy of the forecasting algorithm used, will determine the economics of the operation of the power system. The inaccuracy or large error in the forecast simply means that load matching is not optimized and consequently the generation and transmission systems are not being operated in an efficient manner. In the present study, a proposed methodology has been introduced to decrease the forecasted error and the processing time by using fuzzy logic controller on an hourly base. Therefore, it predicts the effect of different conditional parameters (i.e., weather, time, historical data, and random disturbances) on load forecasting in terms of fuzzy sets during the generation process. These parameters are chosen with respect to their priority and importance. The forecasted values obtained by fuzzy method were compared with the conventionally forecasted ones. The results showed that the STLF of the fuzzy implementation have more accuracy and better outcomes

Robust Forecasting for Energy Efficiency of Wireless Multimedia Sensor Networks.

Science.gov (United States)

Wang, Xue; Ma, Jun-Jie; Ding, Liang; Bi, Dao-Wei

2007-11-15

An important criterion of wireless sensor network is the energy efficiency inspecified applications. In this wireless multimedia sensor network, the observations arederived from acoustic sensors. Focused on the energy problem of target tracking, this paperproposes a robust forecasting method to enhance the energy efficiency of wirelessmultimedia sensor networks. Target motion information is acquired by acoustic sensornodes while a distributed network with honeycomb configuration is constructed. Thereby,target localization is performed by multiple sensor nodes collaboratively through acousticsignal processing. A novel method, combining autoregressive moving average (ARMA)model and radial basis function networks (RBFNs), is exploited to perform robust targetposition forecasting during target tracking. Then sensor nodes around the target areawakened according to the forecasted target position. With committee decision of sensornodes, target localization is performed in a distributed manner and the uncertainty ofdetection is reduced. Moreover, a sensor-to-observer routing approach of the honeycombmesh network is investigated to solve the data reporting considering the residual energy ofsensor nodes. Target localization and forecasting are implemented in experiments.Meanwhile, sensor node awakening and dynamic routing are evaluated. Experimentalresults verify that energy efficiency of wireless multimedia sensor network is enhanced bythe proposed target tracking method.

Financial forecasts accuracy in Brazil’s social security system

Science.gov (United States)

2017-01-01

Long-term social security statistical forecasts produced and disseminated by the Brazilian government aim to provide accurate results that would serve as background information for optimal policy decisions. These forecasts are being used as support for the government’s proposed pension reform that plans to radically change the Brazilian Constitution insofar as Social Security is concerned. However, the reliability of official results is uncertain since no systematic evaluation of these forecasts has ever been published by the Brazilian government or anyone else. This paper aims to present a study of the accuracy and methodology of the instruments used by the Brazilian government to carry out long-term actuarial forecasts. We base our research on an empirical and probabilistic analysis of the official models. Our empirical analysis shows that the long-term Social Security forecasts are systematically biased in the short term and have significant errors that render them meaningless in the long run. Moreover, the low level of transparency in the methods impaired the replication of results published by the Brazilian Government and the use of outdated data compromises forecast results. In the theoretical analysis, based on a mathematical modeling approach, we discuss the complexity and limitations of the macroeconomic forecast through the computation of confidence intervals. We demonstrate the problems related to error measurement inherent to any forecasting process. We then extend this exercise to the computation of confidence intervals for Social Security forecasts. This mathematical exercise raises questions about the degree of reliability of the Social Security forecasts. PMID:28859172

An EMD–SARIMA-Based Modeling Approach for Air Traffic Forecasting

Directory of Open Access Journals (Sweden)

Wei Nai

2017-12-01

Full Text Available The ever-increasing air traffic demand in China has brought huge pressure on the planning and management of, and investment in, air terminals as well as airline companies. In this context, accurate and adequate short-term air traffic forecasting is essential for the operations of those entities. In consideration of such a problem, a hybrid air traffic forecasting model based on empirical mode decomposition (EMD and seasonal auto regressive integrated moving average (SARIMA has been proposed in this paper. The model proposed decomposes the original time series into components at first, and models each component with the SARIMA forecasting model, then integrates all the models together to form the final combined forecast result. By using the monthly air cargo and passenger flow data from the years 2006 to 2014 available at the official website of the Civil Aviation Administration of China (CAAC, the effectiveness in forecasting of the model proposed has been demonstrated, and by a horizontal performance comparison between several other widely used forecasting models, the advantage of the proposed model has also been proved.

Too Many, Too Few, or Just Right? Making Sense Of Conflicting RN Supply and Demand Forecasts.

Science.gov (United States)

Spetz, Joanne

2015-01-01

Forecasts of future supply and demand of health professionals are tools to guide policy, not a final statement about how the world will be in the future. Recent forecasts of RN supply and demand vary widely and are incredibly confusing for nurse leaders, nurse educators, and policymakers. To effectively incorporate forecasts into policy and planning, one must understand the structure of the forecasts and underlying assumptions. One should treat all forecasts cautiously, and use them as guides to policy rather than definitive future outcomes.

The Promise of Collaboration

Science.gov (United States)

Bauml, Michelle

2016-01-01

Whether a teacher loves it or dreads it, lesson planning is a crucial step in the teaching process. Done effectively, collaborative lesson planning--in which teachers work together to design lessons--leads to increased professional learning, higher job satisfaction for teachers, and better lesson plans. The process poses challenges for both…

Spatiotemporal drought forecasting using nonlinear models

Science.gov (United States)

Vasiliades, Lampros; Loukas, Athanasios

2010-05-01

Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. In order to achieve spatiotemporal forecasting, some mature analysis tools, e.g., time series and spatial statistics are extended to the spatial dimension and the temporal dimension, respectively. Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Despite the widespread application of nonlinear mathematical models, comparative studies on spatiotemporal drought forecasting using different models are still a huge task for modellers. This study uses a promising approach, the Gamma Test (GT), to select the input variables and the training data length, so that the trial and error workload could be greatly reduced. The GT enables to quickly evaluate and estimate the best mean squared error that can be achieved by a smooth model on any unseen data for a given selection of inputs, prior to model construction. The GT is applied to forecast droughts using monthly Standardized Precipitation Index (SPI) timeseries at multiple timescales in several precipitation stations at Pinios river basin in Thessaly region, Greece. Several nonlinear models have been developed efficiently, with the aid of the GT, for 1-month up to 12-month ahead forecasting. Several temporal and spatial statistical indices were considered for the performance evaluation of the models. The predicted results show reasonably good agreement with the actual data for short lead times, whereas the forecasting accuracy decreases with

Should we use seasonnal meteorological ensemble forecasts for hydrological forecasting? A case study for nordic watersheds in Canada.

Science.gov (United States)

Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert; Guay, Catherine

2017-04-01

Hydro-electricity is a major source of energy for many countries throughout the world, including Canada. Long lead-time streamflow forecasts are all the more valuable as they help decision making and dam management. Different techniques exist for long-term hydrological forecasting. Perhaps the most well-known is 'Extended Streamflow Prediction' (ESP), which considers past meteorological scenarios as possible, often equiprobable, future scenarios. In the ESP framework, those past-observed meteorological scenarios (climatology) are used in turn as the inputs of a chosen hydrological model to produce ensemble forecasts (one member corresponding to each year in the available database). Many hydropower companies, including Hydro-Québec (province of Quebec, Canada) use variants of the above described ESP system operationally for long-term operation planning. The ESP system accounts for the hydrological initial conditions and for the natural variability of the meteorological variables. However, it cannot consider the current initial state of the atmosphere. Climate models can help remedy this drawback. In the context of a changing climate, dynamical forecasts issued from climate models seem to be an interesting avenue to improve upon the ESP method and could help hydropower companies to adapt their management practices to an evolving climate. Long-range forecasts from climate models can also be helpful for water management at locations where records of past meteorological conditions are short or nonexistent. In this study, we compare 7-month hydrological forecasts obtained from climate model outputs to an ESP system. The ESP system mimics the one used operationally at Hydro-Québec. The dynamical climate forecasts are produced by the European Center for Medium range Weather Forecasts (ECMWF) System4. Forecasts quality is assessed using numerical scores such as the Continuous Ranked Probability Score (CRPS) and the Ignorance score and also graphical tools such as the

Forecast Combinations

OpenAIRE

Timmermann, Allan G

2005-01-01

Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the d...

Forecaster Behaviour and Bias in Macroeconomic Forecasts

OpenAIRE

Roy Batchelor

2007-01-01

This paper documents the presence of systematic bias in the real GDP and inflation forecasts of private sector forecasters in the G7 economies in the years 1990–2005. The data come from the monthly Consensus Economics forecasting service, and bias is measured and tested for significance using parametric fixed effect panel regressions and nonparametric tests on accuracy ranks. We examine patterns across countries and forecasters to establish whether the bias reflects the inefficient use of i...

Forecasting daily patient volumes in the emergency department.

Science.gov (United States)

Jones, Spencer S; Thomas, Alun; Evans, R Scott; Welch, Shari J; Haug, Peter J; Snow, Gregory L

2008-02-01

Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. This study confirms the widely held belief that daily demand for ED services is characterized by

FORMASY : forecasting and recruitment in manpower systems

NARCIS (Netherlands)

Wessels, J.; van Nunen, J.A.E.E.

1975-01-01

In this paper the tools are developed for forecasting and recruitment planning in a graded manpower system. Basic features of the presented approach are: - the system contains several grades or job categories in which the employees stay for a certain time before being promoted or leaving the system,

Long Range Financial Forecasting for School Districts.

Science.gov (United States)

Baker, Michael E.

Public school systems infrequently project their financial outlook beyond the coming year. Yet, financial projections over a multiyear period are necessary if the financial "crises" that frequently occur in public organizations are to be avoided. This paper discusses the importance of financial forecasting and planning, the development of…

FORMASY : forecasting and recruitment in manpower systems

NARCIS (Netherlands)

Wessels, J.; van Nunen, J.A.E.E.

1976-01-01

In this paper the tools are developed for forecasting and recruitment planning in a eraded manpower system. Basic features of the presented approach arc: - the system contains several &fades or job catea:ories in which the employees slay for a certain time before being promoted or leaving the

Science-society collaboration for robust adaptation planning in water management - The Maipo River Basin in Chile

Science.gov (United States)

Ocampo Melgar, Anahí; Vicuña, Sebastián; Gironás, Jorge

2015-04-01

The Metropolitan Region (M.R.) in Chile is populated by over 6 million people and supplied by the Maipo River and its large number of irrigation channels. Potential environmental alterations caused by global change will extremely affect managers and users of water resources in this semi-arid basin. These hydro-climatological impacts combined with demographic and economic changes will be particularly complex in the city of Santiago, due to the diverse, counterpoised and equally important existing activities and demands. These challenges and complexities request the implementation of flexible plans and actions to adapt policies, institutions, infrastructure and behaviors to a new future with climate change. Due to the inherent uncertainties in the future, a recent research project entitled MAPA (Maipo Adaptation Plan for its initials in Spanish) has formed a collaborative science-society platform to generate insights into the vulnerabilities, challenges and possible mitigation measures that would be necessary to deal with the potential changes in the M.R. This large stakeholder platform conformed by around 30 public, private and civil society organizations, both at the local and regional level and guided by a Robust Decision Making Framework (RDMF) has identified vulnerabilities, future scenarios, performance indicators and mitigation measures for the Maipo River basin. The RDMF used in this project is the XLRM framework (Lempert et al. 2006) that incorporates policy levers (L), exogenous uncertainties (X), measures of performance standards (M) and relationships (R) in an interlinked process. Both stakeholders' expertise and computational capabilities have been used to create hydrological models for the urban, rural and highland sectors supported also by the Water Evaluation and Planning system software (WEAP). The identification of uncertainties and land use transition trends was used to develop future development scenarios to explore possible water management

Peak Wind Tool for General Forecasting

Science.gov (United States)

Barrett, Joe H., III

2010-01-01

The expected peak wind speed of the day is an important forecast element in the 45th Weather Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in the cool season months of October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the forecast of peak wind speed, 5-minute average wind speed at the time of the peak wind, timing of the peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. For the current task (Phase II ), the 45 WS requested additional observations be used for the creation of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk Richardson Number, mixing depth, vertical wind shear, temperature inversion strength and depth and wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). The AMU first expanded the POR by two years by adding tower observations, surface observations and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was expanded

Forecast combinations

OpenAIRE

Aiolfi, Marco; Capistrán, Carlos; Timmermann, Allan

2010-01-01

We consider combinations of subjective survey forecasts and model-based forecasts from linear and non-linear univariate specifications as well as multivariate factor-augmented models. Empirical results suggest that a simple equal-weighted average of survey forecasts outperform the best model-based forecasts for a majority of macroeconomic variables and forecast horizons. Additional improvements can in some cases be gained by using a simple equal-weighted average of survey and model-based fore...

Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station (CCAFS)

Science.gov (United States)

Barrett, Joe H., III; Roeder, William P.

2010-01-01

Peak wind speed is important element in 24-Hour and Weekly Planning Forecasts issued by 45th Weather Squadron (45 WS). Forecasts issued for planning operations at KSC/CCAFS. 45 WS wind advisories issued for wind gusts greater than or equal to 25 kt. 35 kt and 50 kt from surface to 300 ft. AMU developed cool-season (Oct - Apr) tool to help 45 WS forecast: daily peak wind speed, 5-minute average speed at time of peak wind, and probability peak speed greater than or equal to 25 kt, 35 kt, 50 kt. AMU tool also forecasts daily average wind speed from 30 ft to 60 ft. Phase I and II tools delivered as a Microsoft Excel graphical user interface (GUI). Phase II tool also delivered as Meteorological Interactive Data Display System (MIDDS) GUI. Phase I and II forecast methods were compared to climatology, 45 WS wind advisories and North American Mesoscale model (MesoNAM) forecasts in a verification data set.

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

Directory of Open Access Journals (Sweden)

Syed Aziz Ur Rehman

2017-11-01

Full Text Available Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fossil fuel resources. In this study, Pakistan’s energy demand forecast for electricity, natural gas, oil, coal and LPG across all the sectors of the economy have been undertaken. Three different energy demand forecasting methodologies, i.e., Autoregressive Integrated Moving Average (ARIMA, Holt-Winter and Long-range Energy Alternate Planning (LEAP model were used. The demand forecast estimates of each of these methods were compared using annual energy demand data. The results of this study suggest that ARIMA is more appropriate for energy demand forecasting for Pakistan compared to Holt-Winter model and LEAP model. It is estimated that industrial sector’s demand shall be highest in the year 2035 followed by transport and domestic sectors. The results further suggest that energy fuel mix will change considerably, such that oil will be the most highly consumed energy form (38.16% followed by natural gas (36.57%, electricity (16.22%, coal (7.52% and LPG (1.52% in 2035. In view of higher demand forecast of fossil fuels consumption, this study recommends that government should take the initiative for harnessing renewable energy resources for meeting future energy demand to not only avert huge import bill but also achieving energy security and sustainability in the long run.

A model for marketing planning for new products

DEFF Research Database (Denmark)

Martensen, Anne

1993-01-01

awareness model. The model can be used to generate improved marketing plans. In this connection, it is important that the model includes the marketing variables, for it is only in this way that the model can be used for marketing plan optimization. It is predicted that a future direction of development...... and use of sales forecasting models for new products will be within optimization of the marketing plan for launching a new product, and not only as a tool for forecasting sales for just one marketing plan....

Review of Variable Generation Forecasting in the West: July 2013 - March 2014

Energy Technology Data Exchange (ETDEWEB)

Widiss, R. [Exeter Associates Inc., Columbia, MD (United States); Porter, K. [Exeter Associates Inc., Columbia, MD (United States)

2014-03-01

This report interviews 13 operating entities (OEs) in the Western Interconnection about their implementation of wind and solar forecasting. The report updates and expands upon one issued by NREL in 2012. As in the 2012 report, the OEs interviewed vary in size and character; the group includes independent system operators, balancing authorities, utilities, and other entities. Respondents' advice for other utilities includes starting sooner rather than later as it can take time to plan, prepare, and train a forecast; setting realistic expectations; using multiple forecasts; and incorporating several performance metrics.

Ten-year operational dust forecasting - Recent model development and future plans

International Nuclear Information System (INIS)

Kallos, G; Spyrou, C; Astitha, M; Mitsakou, C; Solomos, S; Kushta, J; Pytharoulis, I; Katsafados, P; Mavromatidis, E; Papantoniou, N; Vlastou, G

2009-01-01

The Sahara desert is one of the major sources of mineral dust on Earth, producing up to 2x10 8 t yr- 1 . A combined effort has been devoted during the last ten years at the University of Athens (UOA) from the Atmospheric Modeling and Weather Forecasting Group (AM and WFG) to the development of an analysis and forecasting tool that will provide early warning of Saharan dust outbreaks. The developed tool is the SKIRON limited-area forecasting system, based on the Eta limited area modeling system with embedded algorithms describing the dust cycle. A new version of the model is currently available, with extra features like eight-size particle bins, radiative transfer corrections, new dust source identification and utilization of rocky soil characterization and incorporation of more accurate deposition schemes. The new version of SKIRON modeling system is coupled with the photochemical model CAMx in order to study processes like the shading effect of dust particles on photochemical processes and the production of second and third generation of aerosols. Moreover, another new development in the AM and WFG is based on the RAMS model, with the incorporation of processes like dust and sea-salt production, gas and aqueous phase chemistry and particle formation. In this study, the major characteristics of the developed (and under development) modeling systems are presented, as well as the spatiotemporal distribution of the transported dust amounts, the interaction with anthropogenically-produced particles and the potential implications on radiative transfer.

Ten-year operational dust forecasting - Recent model development and future plans

Energy Technology Data Exchange (ETDEWEB)

Kallos, G; Spyrou, C; Astitha, M; Mitsakou, C; Solomos, S; Kushta, J; Pytharoulis, I; Katsafados, P; Mavromatidis, E; Papantoniou, N; Vlastou, G [University of Athens, School of Physics, Atmospheric Modeling and Weather Forecasting Group - UOA/AM and WFG, University Campus, Bldg. PHYS-V, Athens 15784 (Greece)], E-mail: kallos@mg.uoa.gr

2009-03-01

The Sahara desert is one of the major sources of mineral dust on Earth, producing up to 2x10{sup 8} t yr-{sup 1}. A combined effort has been devoted during the last ten years at the University of Athens (UOA) from the Atmospheric Modeling and Weather Forecasting Group (AM and WFG) to the development of an analysis and forecasting tool that will provide early warning of Saharan dust outbreaks. The developed tool is the SKIRON limited-area forecasting system, based on the Eta limited area modeling system with embedded algorithms describing the dust cycle. A new version of the model is currently available, with extra features like eight-size particle bins, radiative transfer corrections, new dust source identification and utilization of rocky soil characterization and incorporation of more accurate deposition schemes. The new version of SKIRON modeling system is coupled with the photochemical model CAMx in order to study processes like the shading effect of dust particles on photochemical processes and the production of second and third generation of aerosols. Moreover, another new development in the AM and WFG is based on the RAMS model, with the incorporation of processes like dust and sea-salt production, gas and aqueous phase chemistry and particle formation. In this study, the major characteristics of the developed (and under development) modeling systems are presented, as well as the spatiotemporal distribution of the transported dust amounts, the interaction with anthropogenically-produced particles and the potential implications on radiative transfer.

Plans for a Collaboratively Developed Distributed Control System for the Spallation Neutron Source

International Nuclear Information System (INIS)

DeVan, W.R.; Gurd, D.P.; Hammonds, J.; Lewis, S.A.; Smith, J.D.

1999-01-01

The Spallation Neutron Source (SNS) is an accelerator-based pulsed neutron source to be built in Oak Ridge, Tennessee. The facility has five major sections - a ''front end'' consisting of a 65 keV H - ion source followed by a 2.5 MeV RFQ; a 1 GeV linac; a storage ring; a 1MW spallation neutron target (upgradeable to 2 MW); the conventional facilities to support these machines and a suite of neutron scattering instruments to exploit them. These components will be designed and implemented by five collaborating institutions: Lawrence Berkeley National Laboratory (Front End), Los Alamos National Laboratory (Linac); Brookhaven National Laboratory (Storage Ring); Argonne National Laboratory (Instruments); and Oak Ridge National Laboratory (Neutron Source and Conventional Facilities). It is proposed to implement a fully integrated control system for all aspects of this complex. The system will be developed collaboratively, with some degree of local autonomy for distributed systems, but centralized accountability. Technical integration will be based upon the widely-used EPICS control system toolkit, and a complete set of hardware and software standards. The scope of the integrated control system includes site-wide timing and synchronization, networking and machine protection. This paper discusses the technical and organizational issues of planning a large control system to be developed collaboratively at five different institutions, the approaches being taken to address those issues, as well as some of the particular technical challenges for the SNS control system

Short-Term Forecasts Using NU-WRF for the Winter Olympics 2018

Science.gov (United States)

Srikishen, Jayanthi; Case, Jonathan L.; Petersen, Walter A.; Iguchi, Takamichi; Tao, Wei-Kuo; Zavodsky, Bradley T.; Molthan, Andrew

2017-01-01

The NASA Unified-Weather Research and Forecasting model (NU-WRF) will be included for testing and evaluation in the forecast demonstration project (FDP) of the International Collaborative Experiment -PyeongChang 2018 Olympic and Paralympic (ICE-POP) Winter Games. An international array of radar and supporting ground based observations together with various forecast and now-cast models will be operational during ICE-POP. In conjunction with personnel from NASA's Goddard Space Flight Center, the NASA Short-term Prediction Research and Transition (SPoRT) Center is developing benchmark simulations for a real-time NU-WRF configuration to run during the FDP. ICE-POP observational datasets will be used to validate model simulations and investigate improved model physics and performance for prediction of snow events during the research phase (RDP) of the project The NU-WRF model simulations will also support NASA Global Precipitation Measurement (GPM) Mission ground-validation physical and direct validation activities in relation to verifying, testing and improving satellite-based snowfall retrieval algorithms over complex terrain.

Collaborating To Teach Prosocial Skills.

Science.gov (United States)

Allsopp, David H.; Santos, Karen E.; Linn, Reid

2000-01-01

This article describes a collaborative prosocial skills program. Steps of the intervention include forming teams of educators, targeting necessary prosocial skills, developing an instructional plan, determining the setting and collaborative roles, delivery instruction, and providing opportunities for student practice, reinforcement, and…

The Implementation of NEMS GFS Aerosol Component (NGAC) Version 1.0 for Global Dust Forecasting at NOAA NCEP

Science.gov (United States)

Lu, Cheng-Hsuan; Da Silva, Arlindo M.; Wang, Jun; Moorthi, Shrinivas; Chin, Mian; Colarco, Peter; Tang, Youhua; Bhattacharjee, Partha S.; Chen, Shen-Po; Chuang, Hui-Ya;

The impact of wind forecast errors on the efficiency of the Ontario electricity market

International Nuclear Information System (INIS)

Ng, H.

2008-01-01

Ontario's Independent System Operator (IESO) is currently involved in a number of wind projects in the province, and has developed both a resource commitment and dispatch timeline in relation to increased wind power penetration in the Ontario electricity grid. This presentation discussed the impacts of wind forecast errors on the province's electricity market. Day-ahead planning is used to commit fossil fuels and gas resources, while 3-hours ahead planning is used to commit generation in real time. Inter-ties are committed 1 hour ahead of dispatch. Over-forecasts for wind can cause market prices to increase in real-time, or cause markets to miss opportunities to schedule cheaper imports. The inefficient scheduling caused by overforecasts can also lead to exports not being purchases at high enough prices. Under-forecasts can cause market prices to decrease, and may cause imports to be scheduled that would not have been economic at lower prices. The scheduling difficulties related to under-forecasting can cause markets to miss opportunities to schedule efficient exports. Wind facility forecast errors typically improve closer to real-time. One-hour ahead wind forecast errors can reach approximately 12 per cent. The annual costs of overforecasting are under $200,000. Underforecasting costs are usually less than $30,000. The costs of the wind forecasting inefficiencies are relatively small in the $10 billion electricity market. It was concluded that system operators will continue to track forecast errors and inefficiencies as wind power capacity in the electric power industry increases. tabs., figs

Daily rainfall forecasting for one year in a single run using Singular Spectrum Analysis

Science.gov (United States)

Unnikrishnan, Poornima; Jothiprakash, V.

2018-06-01

Effective modelling and prediction of smaller time step rainfall is reported to be very difficult owing to its highly erratic nature. Accurate forecast of daily rainfall for longer duration (multi time step) may be exceptionally helpful in the efficient planning and management of water resources systems. Identification of inherent patterns in a rainfall time series is also important for an effective water resources planning and management system. In the present study, Singular Spectrum Analysis (SSA) is utilized to forecast the daily rainfall time series pertaining to Koyna watershed in Maharashtra, India, for 365 days after extracting various components of the rainfall time series such as trend, periodic component, noise and cyclic component. In order to forecast the time series for longer time step (365 days-one window length), the signal and noise components of the time series are forecasted separately and then added together. The results of the study show that the method of SSA could extract the various components of the time series effectively and could also forecast the daily rainfall time series for longer duration such as one year in a single run with reasonable accuracy.

36 CFR 219.12 - Collaboration and cooperatively developed landscape goals.

Science.gov (United States)

2010-07-01

... cooperatively developed landscape goals. 219.12 Section 219.12 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE PLANNING National Forest System Land and Resource Management Planning Collaborative Planning for Sustainability § 219.12 Collaboration and cooperatively developed landscape goals. (a...

Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs

Directory of Open Access Journals (Sweden)

Jaime Buitrago

2017-01-01

Full Text Available Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN with exogenous multi-variable input (NARX. The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. The New England electrical load data are used to train and validate the forecast prediction.

Operational hydrological forecasting in Bavaria. Part I: Forecast uncertainty

Science.gov (United States)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In Bavaria, operational flood forecasting has been established since the disastrous flood of 1999. Nowadays, forecasts based on rainfall information from about 700 raingauges and 600 rivergauges are calculated and issued for nearly 100 rivergauges. With the added experience of the 2002 and 2005 floods, awareness grew that the standard deterministic forecast, neglecting the uncertainty associated with each forecast is misleading, creating a false feeling of unambiguousness. As a consequence, a system to identify, quantify and communicate the sources and magnitude of forecast uncertainty has been developed, which will be presented in part I of this study. In this system, the use of ensemble meteorological forecasts plays a key role which will be presented in part II. Developing the system, several constraints stemming from the range of hydrological regimes and operational requirements had to be met: Firstly, operational time constraints obviate the variation of all components of the modeling chain as would be done in a full Monte Carlo simulation. Therefore, an approach was chosen where only the most relevant sources of uncertainty were dynamically considered while the others were jointly accounted for by static error distributions from offline analysis. Secondly, the dominant sources of uncertainty vary over the wide range of forecasted catchments: In alpine headwater catchments, typically of a few hundred square kilometers in size, rainfall forecast uncertainty is the key factor for forecast uncertainty, with a magnitude dynamically changing with the prevailing predictability of the atmosphere. In lowland catchments encompassing several thousands of square kilometers, forecast uncertainty in the desired range (usually up to two days) is mainly dependent on upstream gauge observation quality, routing and unpredictable human impact such as reservoir operation. The determination of forecast uncertainty comprised the following steps: a) From comparison of gauge

COLLABORATIVE MULTI-LEVEL PLAN MONITORING

Directory of Open Access Journals (Sweden)

Mohamad K. ALLOUCHE

2011-01-01

Full Text Available The recent worldwide connectivity and the net-centricity of military operations (coalition-based operations are witnessing an increasing need for the monitoring of plan execution for enhanced resource management and decision making. Monitoring of ongoing operations is the process of continuous observation recording and reporting. In this process the plan becomes a resource that needs to be managed effi ciently. The centralized approach to plan monitoring soon reaches its limits when plan execution is distributed across different organizations/countries. We propose a new framework that would allow different monitoring nodes distributed across the network. An effi cient propagation mechanism that allows information exchange between the different nodes would also be needed. The main purpose of this mechanism is to present the right information, to the right person, at the right time. To cope with a rapid increase of information fl ow through the network, an effi cient alarm management mechanism allows the presentation of the information with an appropriate level of details.

Development and Implementation of Dynamic Scripts to Support Local Model Verification at National Weather Service Weather Forecast Offices

Science.gov (United States)

Zavodsky, Bradley; Case, Jonathan L.; Gotway, John H.; White, Kristopher; Medlin, Jeffrey; Wood, Lance; Radell, Dave

2014-01-01

Local modeling with a customized configuration is conducted at National Weather Service (NWS) Weather Forecast Offices (WFOs) to produce high-resolution numerical forecasts that can better simulate local weather phenomena and complement larger scale global and regional models. The advent of the Environmental Modeling System (EMS), which provides a pre-compiled version of the Weather Research and Forecasting (WRF) model and wrapper Perl scripts, has enabled forecasters to easily configure and execute the WRF model on local workstations. NWS WFOs often use EMS output to help in forecasting highly localized, mesoscale features such as convective initiation, the timing and inland extent of lake effect snow bands, lake and sea breezes, and topographically-modified winds. However, quantitatively evaluating model performance to determine errors and biases still proves to be one of the challenges in running a local model. Developed at the National Center for Atmospheric Research (NCAR), the Model Evaluation Tools (MET) verification software makes performing these types of quantitative analyses easier, but operational forecasters do not generally have time to familiarize themselves with navigating the sometimes complex configurations associated with the MET tools. To assist forecasters in running a subset of MET programs and capabilities, the Short-term Prediction Research and Transition (SPoRT) Center has developed and transitioned a set of dynamic, easily configurable Perl scripts to collaborating NWS WFOs. The objective of these scripts is to provide SPoRT collaborating partners in the NWS with the ability to evaluate the skill of their local EMS model runs in near real time with little prior knowledge of the MET package. The ultimate goal is to make these verification scripts available to the broader NWS community in a future version of the EMS software. This paper provides an overview of the SPoRT MET scripts, instructions for how the scripts are run, and example use

Forecast electricity demand in Quebec: Development plan 1993

International Nuclear Information System (INIS)

1992-01-01

Demographic, economic, and energy prospects are the determining factors in estimating demand for electricity in Quebec. In average scenarios developed for 1992-2010, the Quebec population will grow 0.5%/y and the gross domestic product will increase 2.6%/y. Firm electricity sales by Hydro-Quebec will grow to 197.9 TWh by 2010, or 2.2%/y. Sales in the residential and farm sectors should grow 1.3%/y and sales in the general and institutional sectors should rise by 2.2%/y. Electricity demand in the industrial sector, rising at an estimated 2.9%/y in 1992-2010, is chiefly responsible for the anticipated growth in Hydro-Quebec's overall sales. The nonferrous smelting, refining, chemicals, and paper industries will account for ca 60% of this growth. In the municipal services and public transportation sectors, demand should grow 3.3%/y, and over half the growth forecast in this sector can be attributed to the impact that new uses of electricity are expected to have after 2005. High- and low-growth scenarios offer alternative visions of demand growth based on different but equally valid assumptions about demographic and economic growth. In terms of firm electricity sales, the high- and low-growth scenarios differ by 50 TWh in 2010. Hydro-Quebec has retained two strategic orientations that will influence growth in electricity sales: the development of industrial markets and extension of the energy-savings objective of 9.3 TWh forecast to the year 2000. Taking these two orientations into account, the growth rate for electricity sales in the average scenario would be 1.8%/y rather than 2.2%/y. 25 figs., 81 tabs

Application and verification of ECMWF seasonal forecast for wind energy

Science.gov (United States)

Žagar, Mark; Marić, Tomislav; Qvist, Martin; Gulstad, Line

2015-04-01

A good understanding of long-term annual energy production (AEP) is crucial when assessing the business case of investing in green energy like wind power. The art of wind-resource assessment has emerged into a scientific discipline on its own, which has advanced at high pace over the last decade. This has resulted in continuous improvement of the AEP accuracy and, therefore, increase in business case certainty. Harvesting the full potential output of a wind farm or a portfolio of wind farms depends heavily on optimizing operation and management strategy. The necessary information for short-term planning (up to 14 days) is provided by standard weather and power forecasting services, and the long-term plans are based on climatology. However, the wind-power industry is lacking quality information on intermediate scales of the expected variability in seasonal and intra-annual variations and their geographical distribution. The seasonal power forecast presented here is designed to bridge this gap. The seasonal power production forecast is based on the ECMWF seasonal weather forecast and the Vestas' high-resolution, mesoscale weather library. The seasonal weather forecast is enriched through a layer of statistical post-processing added to relate large-scale wind speed anomalies to mesoscale climatology. The resulting predicted energy production anomalies, thus, include mesoscale effects not captured by the global forecasting systems. The turbine power output is non-linearly related to the wind speed, which has important implications for the wind power forecast. In theory, the wind power is proportional to the cube of wind speed. However, due to the nature of turbine design, this exponent is close to 3 only at low wind speeds, becomes smaller as the wind speed increases, and above 11-13 m/s the power output remains constant, called the rated power. The non-linear relationship between wind speed and the power output generally increases sensitivity of the forecasted power

Verification of Ensemble Forecasts for the New York City Operations Support Tool

Science.gov (United States)

Day, G.; Schaake, J. C.; Thiemann, M.; Draijer, S.; Wang, L.

2012-12-01

The New York City water supply system operated by the Department of Environmental Protection (DEP) serves nine million people. It covers 2,000 square miles of portions of the Catskill, Delaware, and Croton watersheds, and it includes nineteen reservoirs and three controlled lakes. DEP is developing an Operations Support Tool (OST) to support its water supply operations and planning activities. OST includes historical and real-time data, a model of the water supply system complete with operating rules, and lake water quality models developed to evaluate alternatives for managing turbidity in the New York City Catskill reservoirs. OST will enable DEP to manage turbidity in its unfiltered system while satisfying its primary objective of meeting the City's water supply needs, in addition to considering secondary objectives of maintaining ecological flows, supporting fishery and recreation releases, and mitigating downstream flood peaks. The current version of OST relies on statistical forecasts of flows in the system based on recent observed flows. To improve short-term decision making, plans are being made to transition to National Weather Service (NWS) ensemble forecasts based on hydrologic models that account for short-term weather forecast skill, longer-term climate information, as well as the hydrologic state of the watersheds and recent observed flows. To ensure that the ensemble forecasts are unbiased and that the ensemble spread reflects the actual uncertainty of the forecasts, a statistical model has been developed to post-process the NWS ensemble forecasts to account for hydrologic model error as well as any inherent bias and uncertainty in initial model states, meteorological data and forecasts. The post-processor is designed to produce adjusted ensemble forecasts that are consistent with the DEP historical flow sequences that were used to develop the system operating rules. A set of historical hindcasts that is representative of the real-time ensemble

Short-term wind power combined forecasting based on error forecast correction

International Nuclear Information System (INIS)

Liang, Zhengtang; Liang, Jun; Wang, Chengfu; Dong, Xiaoming; Miao, Xiaofeng

2016-01-01

Highlights: • The correlation relationships of short-term wind power forecast errors are studied. • The correlation analysis method of the multi-step forecast errors is proposed. • A strategy selecting the input variables for the error forecast models is proposed. • Several novel combined models based on error forecast correction are proposed. • The combined models have improved the short-term wind power forecasting accuracy. - Abstract: With the increasing contribution of wind power to electric power grids, accurate forecasting of short-term wind power has become particularly valuable for wind farm operators, utility operators and customers. The aim of this study is to investigate the interdependence structure of errors in short-term wind power forecasting that is crucial for building error forecast models with regression learning algorithms to correct predictions and improve final forecasting accuracy. In this paper, several novel short-term wind power combined forecasting models based on error forecast correction are proposed in the one-step ahead, continuous and discontinuous multi-step ahead forecasting modes. First, the correlation relationships of forecast errors of the autoregressive model, the persistence method and the support vector machine model in various forecasting modes have been investigated to determine whether the error forecast models can be established by regression learning algorithms. Second, according to the results of the correlation analysis, the range of input variables is defined and an efficient strategy for selecting the input variables for the error forecast models is proposed. Finally, several combined forecasting models are proposed, in which the error forecast models are based on support vector machine/extreme learning machine, and correct the short-term wind power forecast values. The data collected from a wind farm in Hebei Province, China, are selected as a case study to demonstrate the effectiveness of the proposed

The influence of interdisciplinary collaboration on decision making: a framework to analyse stakeholder coalitions, evolution and learning in strategic delta planning

Science.gov (United States)

Vermoolen, Myrthe; Hermans, Leon

2015-04-01

The sustained development of urbanizing deltas requires that conflicting interests are reconciled, in an environment characterized by technical complexity and knowledge limitations. However, integrating ideas and establishing cooperation between actors with different backgrounds and roles still proves a challenge. Agreeing on strategic choices is difficult and implementation of agreed plans may lead to unanticipated and unintended outcomes. How can individual disciplinary perspectives come together and establish a broadly-supported and well-informed plan, the implementation of which contributes to sustainable delta development? The growing recognition of this need to bring together different stakeholders and different disciplinary perspectives runs parallel to a paradigm shift from 'hard' hydrological engineering to multi-functional and more 'soft' hydrological engineering in water management. As a result, there is now more attention for interdisciplinary collaboration that not only takes the physical characteristics of water systems into account, but also the interaction between physical and societal components of these systems. Thus, it is important to study interdisciplinary collaboration and how this influences decision-making. Our research looks into this connection, using a case in delta planning in the Netherlands, where there have been several (attempts for) integration of spatial planning and flood risk/ water management, e.g. in the case of the Dutch Delta Programme. This means that spatial designers and their designs play an important role in the strategic delta planning process as well, next to civil engineers, etc. This study explores the roles of stakeholders, experts and policy makers in interdisciplinary decision-making in dynamic delta planning processes, using theories and methods that focus on coalitions, learning and changes over time in policy and planning processes. This requires an expansion of the existing frameworks to study

Streamflow Forecasting Using Nuero-Fuzzy Inference System

Science.gov (United States)

Nanduri, U. V.; Swain, P. C.

2005-12-01

The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A

Fuel cycle forecasting - there are forecasts and there are forecasts

International Nuclear Information System (INIS)

Puechl, K.H.

1975-01-01

The FORECAST-NUCLEAR computer program described recognizes that forecasts are made to answer a variety of questions and, therefore, that no single forecast is universally appropriate. Also, it recognizes that no two individuals will completely agree as to the input data that are appropriate for obtaining an answer to even a single simple question. Accordingly, the program was written from a utilitarian standpoint: it allows working with multiple projections; data inputting is simple to allow game-playing; computation time is short to minimize the cost of 'what if' assessements; and detail is internally carried to allow meaningful analysis. (author)

Fuel cycle forecasting - there are forecasts and there are forecasts

Energy Technology Data Exchange (ETDEWEB)

Puechl, K H

1975-12-01

The FORECAST-NUCLEAR computer program described recognizes that forecasts are made to answer a variety of questions and, therefore, that no single forecast is universally appropriate. Also, it recognizes that no two individuals will completely agree as to the input data that are appropriate for obtaining an answer to even a single simple question. Accordingly, the program was written from a utilitarian standpoint: it allows working with multiple projections; data inputting is simple to allow game-playing; computation time is short to minimize the cost of 'what if' assessements; and detail is internally carried to allow meaningful analysis.

Time series modelling and forecasting of emergency department overcrowding.

Science.gov (United States)

Kadri, Farid; Harrou, Fouzi; Chaabane, Sondès; Tahon, Christian

2014-09-01

Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand.

International Collaboration in the field of GNSS-Meteorology and Climate Monitoring

Science.gov (United States)

Jones, J.; Guerova, G.; Dousa, J.; Bock, O.; Elgered, G.; Vedel, H.; Pottiaux, E.; de Haan, S.; Pacione, R.; Dick, G.; Wang, J.; Gutman, S. I.; Wickert, J.; Rannat, K.; Liu, G.; Braun, J. J.; Shoji, Y.

2012-12-01

International collaboration in the field of GNSS-meteorology and climate monitoring is essential, as severe weather and climate change have no respect for national boundaries. The use of Global Navigation Satellite Systems (GNSS) for meteorological purposes is an established atmospheric observing technique, which can accurately sense water vapour, the most abundant greenhouse gas, accounting for 60-70% of atmospheric warming. Severe weather forecasting is challenging, in part due to the high temporal and spatial variation of atmospheric water vapour. Water vapour is currently under-sampled and obtaining and exploiting more high-quality humidity observations is essential to severe weather forecasting and climate monitoring. A proposed EU COST Action (http://www.cost.eu) will address new and improved capabilities from concurrent developments in both GNSS and atmospheric communities to improve (short-range) weather forecasts and climate projections. For the first time, the synergy of the three GNSS systems, GPS, GLONASS and Galileo, will be used to develop new, advanced tropospheric products, stimulating the full potential exploitation of multi-GNSS water vapour estimates on a wide range of temporal and spatial scales, from real-time severe weather monitoring and forecasting to climate research. The Action will work in close collaboration with the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN), GNSS Precipitable Water Task Team (TT). GRUAN is a global reference observing network, designed to meet climate requirements and to fill a major void in the current global observing system. GRUAN observations will provide long-term, high-quality data to determine climatic trends and to constrain and validate data from space-based remote sensors. Ground-based GNSS PW was identified as a Priority 1 measurement for GRUAN, and the GNSS-PW TT's goal is to develop explicit guidance on hardware, software and data management practices to obtain GNSS PW

U.S. Department of Energy Workshop Report: Solar Resources and Forecasting

Energy Technology Data Exchange (ETDEWEB)

Stoffel, T.

2012-06-01

This report summarizes the technical presentations, outlines the core research recommendations, and augments the information of the Solar Resources and Forecasting Workshop held June 20-22, 2011, in Golden, Colorado. The workshop brought together notable specialists in atmospheric science, solar resource assessment, solar energy conversion, and various stakeholders from industry and academia to review recent developments and provide input for planning future research in solar resource characterization, including measurement, modeling, and forecasting.

A systematic review of health manpower forecasting models.

NARCIS (Netherlands)

Martins-Coelho, G.; Greuningen, M. van; Barros, H.; Batenburg, R.

2011-01-01

Context: Health manpower planning (HMP) aims at matching health manpower (HM) supply to the population’s health requirements. To achieve this, HMP needs information on future HM supply and requirement (S&R). This is estimated by several different forecasting models (FMs). In this paper, we review

Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast

Directory of Open Access Journals (Sweden)

Jinyin Ye

2016-01-01

Full Text Available TIGGE (THORPEX International Grand Global Ensemble was a major part of the THORPEX (Observing System Research and Predictability Experiment. It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability forecast, extend the lead time of the flood forecast, and gain more time for decision-makers to make the right decision. In this study, precipitation ensemble forecast products from ECMWF, NCEP, and CMA are used to drive distributed hydrologic model TOPX. We focus on Yi River catchment and aim to build a flood forecast and early warning system. The results show that the meteorologic-hydrologic coupled model can satisfactorily predict the flow-process of four flood events. The predicted occurrence time of peak discharges is close to the observations. However, the magnitude of the peak discharges is significantly different due to various performances of the ensemble prediction systems. The coupled forecasting model can accurately predict occurrence of the peak time and the corresponding risk probability of peak discharge based on the probability distribution of peak time and flood warning, which can provide users a strong theoretical foundation and valuable information as a promising new approach.

A multiscale forecasting method for power plant fleet management

Science.gov (United States)

Chen, Hongmei

In recent years the electric power industry has been challenged by a high level of uncertainty and volatility brought on by deregulation and globalization. A power producer must minimize the life cycle cost while meeting stringent safety and regulatory requirements and fulfilling customer demand for high reliability. Therefore, to achieve true system excellence, a more sophisticated system-level decision-making process with a more accurate forecasting support system to manage diverse and often widely dispersed generation units as a single, easily scaled and deployed fleet system in order to fully utilize the critical assets of a power producer has been created as a response. The process takes into account the time horizon for each of the major decision actions taken in a power plant and develops methods for information sharing between them. These decisions are highly interrelated and no optimal operation can be achieved without sharing information in the overall process. The process includes a forecasting system to provide information for planning for uncertainty. A new forecasting method is proposed, which utilizes a synergy of several modeling techniques properly combined at different time-scales of the forecasting objects. It can not only take advantages of the abundant historical data but also take into account the impact of pertinent driving forces from the external business environment to achieve more accurate forecasting results. Then block bootstrap is utilized to measure the bias in the estimate of the expected life cycle cost which will actually be needed to drive the business for a power plant in the long run. Finally, scenario analysis is used to provide a composite picture of future developments for decision making or strategic planning. The decision-making process is applied to a typical power producer chosen to represent challenging customer demand during high-demand periods. The process enhances system excellence by providing more accurate market

Forecasting Housing Approvals in Australia: Do Forecasters Herd?

DEFF Research Database (Denmark)

Stadtmann, Georg; Pierdzioch; Rülke

2012-01-01

Price trends in housing markets may reflect herding of market participants. A natural question is whether such herding, to the extent that it occurred, reflects herding in forecasts of professional forecasters. Using more than 6,000 forecasts of housing approvals for Australia, we did not find...

A new forecast presentation tool for offshore contractors

Science.gov (United States)

Jørgensen, M.

2009-09-01

Contractors working off shore are often very sensitive to both sea and weather conditions, and it's essential that they have easy access to reliable information on coming conditions to enable planning of when to start or shut down offshore operations to avoid loss of life and materials. Danish Meteorological Institute, DMI, recently, in cooperation with business partners in the field, developed a new application to accommodate that need. The "Marine Forecast Service” is a browser based forecast presentation tool. It provides an interface for the user to enable easy and quick access to all relevant meteorological and oceanographic forecasts and observations for a given area of interest. Each customer gains access to the application via a standard login/password procedure. Once logged in, the user can inspect animated forecast maps of parameters like wind, gust, wave height, swell and current among others. Supplementing the general maps, the user can choose to look at forecast graphs for each of the locations where the user is running operations. These forecast graphs can also be overlaid with the user's own in situ observations, if such exist. Furthermore, the data from the graphs can be exported as data files that the customer can use in his own applications as he desires. As part of the application, a forecaster's view on the current and near future weather situation is presented to the user as well, adding further value to the information presented through maps and graphs. Among other features of the product, animated radar and satellite images could be mentioned. And finally the application provides the possibility of a "second opinion” through traditional weather charts from another recognized provider of weather forecasts. The presentation will provide more detailed insights into the contents of the applications as well as some of the experiences with the product.

Forecasting freight flows

DEFF Research Database (Denmark)

Lyk-Jensen, Stéphanie

2011-01-01

Trade patterns and transport markets are changing as a result of the growth and globalization of international trade, and forecasting future freight flow has to rely on trade forecasts. Forecasting freight flows is critical for matching infrastructure supply to demand and for assessing investment...... constitute a valuable input to freight models for forecasting future capacity problems.......Trade patterns and transport markets are changing as a result of the growth and globalization of international trade, and forecasting future freight flow has to rely on trade forecasts. Forecasting freight flows is critical for matching infrastructure supply to demand and for assessing investment...

A Semantic Web-Based Authoring Tool to Facilitate the Planning of Collaborative Learning Scenarios Compliant with Learning Theories

Science.gov (United States)

Isotani, Seiji; Mizoguchi, Riichiro; Isotani, Sadao; Capeli, Olimpio M.; Isotani, Naoko; de Albuquerque, Antonio R. P. L.; Bittencourt, Ig. I.; Jaques, Patricia

2013-01-01

When the goal of group activities is to support long-term learning, the task of designing well-thought-out collaborative learning (CL) scenarios is an important key to success. To help students adequately acquire and develop their knowledge and skills, a teacher can plan a scenario that increases the probability for learning to occur. Such a…

Robust forecast comparison

OpenAIRE

Jin, Sainan; Corradi, Valentina; Swanson, Norman

2015-01-01

Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. This paper addresses this issue by using a novel criterion for forecast evaluation which is based on the entire distribution of forecast errors. We introduce the concepts of general-loss (GL) forecast superiority and convex-loss (CL) forecast superiority, and we establish a ...

MOS BASED FORECAST OF 6-HOURLY AREA PRECIPITATION

Czech Academy of Sciences Publication Activity Database

Sokol, Zbyněk

2006-01-01

Roč. 50, č. 1 (2006), s. 105-120 ISSN 0039-3169 R&D Projects: GA AV ČR IBS3042101 Institutional research plan: CEZ:AV0Z30420517 Keywords : precipitation forecast * regression * statistical postprocessing * MOS Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 0.603, year: 2006

Health workforce governance and integration: the fit between planning and system.

NARCIS (Netherlands)

Batenburg, R.

2014-01-01

Background: The EU Joint Action on Health Workforce Planning and Forecasting has taken up the challenge to let countries share and exchange practices in health workforce planning. It appears however, that not many countries actually apply (needs-based forecasting) models to support this. But does

Generation Mix Study Focusing on Nuclear Power by Practical Peak Forecast

International Nuclear Information System (INIS)

Shin, Jung Ho; Roh, Myung Sub

2013-01-01

The excessive underestimation can lead to a range of problem; expansion of LNG plant requiring short construction period, the following increase of electricity price, low reserve margin and inefficient configuration of power source. With regard to nuclear power, the share of the stable and economic base load plant, nuclear power, can reduce under the optimum level. Amongst varied factors which contribute to the underestimate, immoderate target for demand side management (DSM) including double deduction of the constraint amount by DSM from peak demand forecast is one of the causes. The hypothesis in this study is that the better optimum generation mix including the adequate share of nuclear power can be obtained under the condition of the peak demand forecast without deduction of DSM target because this forecast is closer to the actual peak demand. In this study, the hypothesis is verified with comparison between peak demand forecast before (or after) DSM target application and the actual peak demand in the 3 rd through 5 th BPE from 2006 to 2010. Furthermore, this research compares and analyzes several generation mix in 2027 focusing on the nuclear power by a few conditions using the WASP-IV program on the basis of the 6 th BPE in 2013. According to the comparative analysis on the peak demand forecast and actual peak demand from 2006 to 2010, the peak demand forecasts without the deduction of the DSM target is closer to the actual peak demand than the peak demand forecasts considering the DSM target in the 3 th , 4 th , 5 th entirely. In addition, the generation mix until 2027 is examined by the WASP-IV. As a result of the program run, when considering the peak demand forecast without DSM reflection, since the base load plants including nuclear power take up adequate proportion, stable and economic supply of electricity can be achieved. On the contrary, in case of planning based on the peak demand forecast with DSM reflected and then compensating the shortage by

The implementation of NEMS GFS Aerosol Component (NGAC) Version 1.0 for global dust forecasting at NOAA/NCEP.

Science.gov (United States)

Lu, Cheng-Hsuan; da Silva, Arlindo; Wang, Jun; Moorthi, Shrinivas; Chin, Mian; Colarco, Peter; Tang, Youhua; Bhattacharjee, Partha S; Chen, Shen-Po; Chuang, Hui-Ya; Juang, Hann-Ming Henry; McQueen, Jeffery; Iredell, Mark

2016-01-01

The NOAA National Centers for Environmental Prediction (NCEP) implemented NEMS GFS Aerosol Component (NGAC) for global dust forecasting in collaboration with NASA Goddard Space Flight Center (GSFC). NGAC Version 1.0 has been providing 5 day dust forecasts at 1°×1° resolution on a global scale, once per day at 00:00 Coordinated Universal Time (UTC), since September 2012. This is the first global system capable of interactive atmosphere aerosol forecasting at NCEP. The implementation of NGAC V1.0 reflects an effective and efficient transitioning of NASA research advances to NCEP operations, paving the way for NCEP to provide global aerosol products serving a wide range of stakeholders as well as to allow the effects of aerosols on weather forecasts and climate prediction to be considered.

Forecast Inaccuracies in Power Plant Projects From Project Managers' Perspectives

Science.gov (United States)

Sanabria, Orlando

Guided by organizational theory, this phenomenological study explored the factors affecting forecast preparation and inaccuracies during the construction of fossil fuel-fired power plants in the United States. Forecast inaccuracies can create financial stress and uncertain profits during the project construction phase. A combination of purposeful and snowball sampling supported the selection of participants. Twenty project managers with over 15 years of experience in power generation and project experience across the United States were interviewed within a 2-month period. From the inductive codification and descriptive analysis, 5 themes emerged: (a) project monitoring, (b) cost control, (c) management review frequency, (d) factors to achieve a precise forecast, and (e) factors causing forecast inaccuracies. The findings of the study showed the factors necessary to achieve a precise forecast includes a detailed project schedule, accurate labor cost estimates, monthly project reviews and risk assessment, and proper utilization of accounting systems to monitor costs. The primary factors reported as causing forecast inaccuracies were cost overruns by subcontractors, scope gaps, labor cost and availability of labor, and equipment and material cost. Results of this study could improve planning accuracy and the effective use of resources during construction of power plants. The study results could contribute to social change by providing a framework to project managers to lessen forecast inaccuracies, and promote construction of power plants that will generate employment opportunities and economic development.

The Value, Protocols, and Scientific Ethics of Earthquake Forecasting

Science.gov (United States)

Jordan, Thomas H.

2013-04-01

provide public sources of information on short-term probabilities that are authoritative, scientific, open, and timely. Alert procedures should be negotiated with end-users to facilitate decisions at different levels of society, based in part on objective analysis of costs and benefits but also on less tangible aspects of value-of-information, such as gains in psychological preparedness and resilience. Unfortunately, in most countries, operational forecasting systems do not conform to such high standards, and earthquake scientists are often called upon to advise the public in roles that exceed their civic authority, expertise in risk communication, and situational knowledge. Certain ethical principles are well established; e.g., announcing unreliable predictions in public forums should be avoided, because bad information can be dangerous. But what are the professional responsibilities of earthquake scientists during seismic crises, especially when the public information through official channels is thought to be inadequate or incorrect? How much should these responsibilities be discounted in the face of personal liability? How should scientists contend with highly uncertain forecasts? To what degree should the public be involved in controversies about forecasting results? No simple answers to these questions can be offered, but the need for answers can be reduced by improving operational forecasting systems. This will require more substantial, and more trustful, collaborations between scientists, civil authorities, and public stakeholders.

A simple approach to measure transmissibility and forecast incidence.

Science.gov (United States)

Nouvellet, Pierre; Cori, Anne; Garske, Tini; Blake, Isobel M; Dorigatti, Ilaria; Hinsley, Wes; Jombart, Thibaut; Mills, Harriet L; Nedjati-Gilani, Gemma; Van Kerkhove, Maria D; Fraser, Christophe; Donnelly, Christl A; Ferguson, Neil M; Riley, Steven

2018-03-01

Outbreaks of novel pathogens such as SARS, pandemic influenza and Ebola require substantial investments in reactive interventions, with consequent implementation plans sometimes revised on a weekly basis. Therefore, short-term forecasts of incidence are often of high priority. In light of the recent Ebola epidemic in West Africa, a forecasting exercise was convened by a network of infectious disease modellers. The challenge was to forecast unseen "future" simulated data for four different scenarios at five different time points. In a similar method to that used during the recent Ebola epidemic, we estimated current levels of transmissibility, over variable time-windows chosen in an ad hoc way. Current estimated transmissibility was then used to forecast near-future incidence. We performed well within the challenge and often produced accurate forecasts. A retrospective analysis showed that our subjective method for deciding on the window of time with which to estimate transmissibility often resulted in the optimal choice. However, when near-future trends deviated substantially from exponential patterns, the accuracy of our forecasts was reduced. This exercise highlights the urgent need for infectious disease modellers to develop more robust descriptions of processes - other than the widespread depletion of susceptible individuals - that produce non-exponential patterns of incidence. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Demand forecast of turbines in the offshore wind power industry

DEFF Research Database (Denmark)

Martinez-Neri, Ivan

2014-01-01

How important is it for a manufacturing company to be able to predict the demand of their products? How much will it lose in inventory costs due to a bad forecasting technique? And what if the product in question is composed of more than 100,000 parts and costs millions of euros a piece......? This article summarises the reasoning followed by a European manufacturer to determine the demand curve of finished offshore wind turbines and how to forecast it for the purpose of production planning....

Short-Term Load Forecast in Electric Energy System in Bulgaria

Directory of Open Access Journals (Sweden)

Irina Asenova

2010-01-01

Full Text Available As the accuracy of the electricity load forecast is crucial in providing better cost effective risk management plans, this paper proposes a Short Term Electricity Load Forecast (STLF model with high forecasting accuracy. Two kind of neural networks, Multilayer Perceptron network model and Radial Basis Function network model, are presented and compared using the mean absolute percentage error. The data used in the models are electricity load historical data. Even though the very good performance of the used model for the load data, weather parameters, especially the temperature, take important part for the energy predicting which is taken into account in this paper. A comparative evaluation between a traditional statistical method and artificial neural networks is presented.

Electronic Collaboration Logbook

CERN Multimedia

CERN. Geneva

2012-01-01

At FNAL, we developed an Electronic Collaboration Logbook (ECL) application which is used by about 20 different collaborations, experiments and groups at FNAL. ECL is the latest iteration of the project formerly known as Control Room Logbook (CRL). We have been working on mobile (IOS and Android) clients for ECL. We will present history, current status and future plans of the project, as well as design, implementation and support solutions made by the project.

Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon

Directory of Open Access Journals (Sweden)

Yildiz Baran

2018-01-01

Full Text Available Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For example, in order to optimize residential PV generation and electricity consumption and plan battery charge-discharge regimes by scheduling household appliances, forecasts need to target and be tailored to individual household electricity loads. The recent uptake of smart meters allows easier access to electricity readings at very fine resolutions; hence, it is possible to utilize this source of available data to create forecast models. In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM, are implemented to forecast individual household loads. Well-known machine learning models such as artificial neural networks (ANN, support vector machines (SVM and Least-Square SVM are implemented within the SMBM framework and their performance is compared. The analysed household stock consists of 14 households from the state of New South Wales, Australia, with at least a year worth of 5 min. resolution data. In order for the results to be comparable between different households, our study first investigates household load profiles according to their volatility and reveals the relationship between load standard deviation and forecast performance. The analysis extends previous research by evaluating forecasts over four different data resolution; 5, 15, 30 and 60 min, each resolution analysed for four different horizons; 1, 6, 12 and 24 h ahead. Both, data resolution and forecast horizon, proved to have significant impact on the forecast performance and the obtained results provide important insights for the operation of various smart grid applications. Finally, it is shown that the load profile of some

Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon

Science.gov (United States)

Yildiz, Baran; Bilbao, Jose I.; Dore, Jonathon; Sproul, Alistair B.

2018-05-01

Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For example, in order to optimize residential PV generation and electricity consumption and plan battery charge-discharge regimes by scheduling household appliances, forecasts need to target and be tailored to individual household electricity loads. The recent uptake of smart meters allows easier access to electricity readings at very fine resolutions; hence, it is possible to utilize this source of available data to create forecast models. In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM), are implemented to forecast individual household loads. Well-known machine learning models such as artificial neural networks (ANN), support vector machines (SVM) and Least-Square SVM are implemented within the SMBM framework and their performance is compared. The analysed household stock consists of 14 households from the state of New South Wales, Australia, with at least a year worth of 5 min. resolution data. In order for the results to be comparable between different households, our study first investigates household load profiles according to their volatility and reveals the relationship between load standard deviation and forecast performance. The analysis extends previous research by evaluating forecasts over four different data resolution; 5, 15, 30 and 60 min, each resolution analysed for four different horizons; 1, 6, 12 and 24 h ahead. Both, data resolution and forecast horizon, proved to have significant impact on the forecast performance and the obtained results provide important insights for the operation of various smart grid applications. Finally, it is shown that the load profile of some households vary

Distributed HUC-based modeling with SUMMA for ensemble streamflow forecasting over large regional domains.

Science.gov (United States)

Saharia, M.; Wood, A.; Clark, M. P.; Bennett, A.; Nijssen, B.; Clark, E.; Newman, A. J.

2017-12-01

Most operational streamflow forecasting systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow require an experienced human forecaster. But this approach faces challenges surrounding process reproducibility, hindcasting capability, and extension to large domains. The operational hydrologic community is increasingly moving towards `over-the-loop' (completely automated) large-domain simulations yet recent developments indicate a widespread lack of community knowledge about the strengths and weaknesses of such systems for forecasting. A realistic representation of land surface hydrologic processes is a critical element for improving forecasts, but often comes at the substantial cost of forecast system agility and efficiency. While popular grid-based models support the distributed representation of land surface processes, intermediate-scale Hydrologic Unit Code (HUC)-based modeling could provide a more efficient and process-aligned spatial discretization, reducing the need for tradeoffs between model complexity and critical forecasting requirements such as ensemble methods and comprehensive model calibration. The National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the USACE to implement, assess, and demonstrate real-time, over-the-loop distributed streamflow forecasting for several large western US river basins and regions. In this presentation, we present early results from short to medium range hydrologic and streamflow forecasts for the Pacific Northwest (PNW). We employ a real-time 1/16th degree daily ensemble model forcings as well as downscaled Global Ensemble Forecasting System (GEFS) meteorological forecasts. These datasets drive an intermediate-scale configuration of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model, which represents the PNW using over 11,700 HUCs. The system produces not only streamflow forecasts (using the Mizu

The economic value of accurate wind power forecasting to utilities

Energy Technology Data Exchange (ETDEWEB)

Watson, S J [Rutherford Appleton Lab., Oxfordshire (United Kingdom); Giebel, G; Joensen, A [Risoe National Lab., Dept. of Wind Energy and Atmospheric Physics, Roskilde (Denmark)

1999-03-01

With increasing penetrations of wind power, the need for accurate forecasting is becoming ever more important. Wind power is by its very nature intermittent. For utility schedulers this presents its own problems particularly when the penetration of wind power capacity in a grid reaches a significant level (>20%). However, using accurate forecasts of wind power at wind farm sites, schedulers are able to plan the operation of conventional power capacity to accommodate the fluctuating demands of consumers and wind farm output. The results of a study to assess the value of forecasting at several potential wind farm sites in the UK and in the US state of Iowa using the Reading University/Rutherford Appleton Laboratory National Grid Model (NGM) are presented. The results are assessed for different types of wind power forecasting, namely: persistence, optimised numerical weather prediction or perfect forecasting. In particular, it will shown how the NGM has been used to assess the value of numerical weather prediction forecasts from the Danish Meteorological Institute model, HIRLAM, and the US Nested Grid Model, which have been `site tailored` by the use of the linearized flow model WA{sup s}P and by various Model output Statistics (MOS) and autoregressive techniques. (au)

Forecasting Monthly Electricity Demands by Wavelet Neuro-Fuzzy System Optimized by Heuristic Algorithms

Directory of Open Access Journals (Sweden)

Jeng-Fung Chen

2018-02-01

Full Text Available Electricity load forecasting plays a paramount role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate planning and prediction of electricity load are therefore vital. In this study, a novel approach for forecasting monthly electricity demands by wavelet transform and a neuro-fuzzy system is proposed. Firstly, the most appropriate inputs are selected and a dataset is constructed. Then, Haar wavelet transform is utilized to decompose the load data and eliminate noise. In the model, a hierarchical adaptive neuro-fuzzy inference system (HANFIS is suggested to solve the curse-of-dimensionality problem. Several heuristic algorithms including Gravitational Search Algorithm (GSA, Cuckoo Optimization Algorithm (COA, and Cuckoo Search (CS are utilized to optimize the clustering parameters which help form the rule base, and adaptive neuro-fuzzy inference system (ANFIS optimize the parameters in the antecedent and consequent parts of each sub-model. The proposed approach was applied to forecast the electricity load of Hanoi, Vietnam. The constructed models have shown high forecasting performances based on the performance indices calculated. The results demonstrate the validity of the approach. The obtained results were also compared with those of several other well-known methods including autoregressive integrated moving average (ARIMA and multiple linear regression (MLR. In our study, the wavelet CS-HANFIS model outperformed the others and provided more accurate forecasting.

Forecasting Skill

Science.gov (United States)

1981-01-01

for the third and fourth day precipitation forecasts. A marked improvement was shown for the consensus 24 hour precipitation forecast, and small... Zuckerberg (1980) found a small long term skill increase in forecasts of heavy snow events for nine eastern cities. Other National Weather Service...and maximum temperature) are each awarded marks 2, 1, or 0 according to whether the forecast is correct, 8 - *- -**■*- ———"—- - -■ t0m 1 MM—IB I

Reservoir Management using seasonal forecasts in Lake Kariba and Lake Kahora Bassa: Initial results and plans

CSIR Research Space (South Africa)

Muchuru, S

2012-06-01

Full Text Available Seasonal forecasting as a tool to improve on reservoir management in Zimbabwe is presented. The focus of the talk is on predicting rainfall extremes over the Lake Kariba catchments. The forecast systems to do the predictions and the levels of skill...

2015 Plan. Project 1: methodology and planning process of the Brazilian electric sector expansion

International Nuclear Information System (INIS)

1993-10-01

The Planning Process of Brazilian Electric Sector Expansion, their normative aspects, instruments, main agents and the planning cycles are described. The methodology of expansion planning is shown, with the interactions of several study areas, electric power market and the used computer models. The forecasts of methodology evolution is also presented. (C.G.C.)

A New Two-Stage Approach to Short Term Electrical Load Forecasting

Directory of Open Access Journals (Sweden)

Dragan Tasić

2013-04-01

Full Text Available In the deregulated energy market, the accuracy of load forecasting has a significant effect on the planning and operational decision making of utility companies. Electric load is a random non-stationary process influenced by a number of factors which make it difficult to model. To achieve better forecasting accuracy, a wide variety of models have been proposed. These models are based on different mathematical methods and offer different features. This paper presents a new two-stage approach for short-term electrical load forecasting based on least-squares support vector machines. With the aim of improving forecasting accuracy, one more feature was added to the model feature set, the next day average load demand. As this feature is unknown for one day ahead, in the first stage, forecasting of the next day average load demand is done and then used in the model in the second stage for next day hourly load forecasting. The effectiveness of the presented model is shown on the real data of the ISO New England electricity market. The obtained results confirm the validity advantage of the proposed approach.

Forecasting Temporal Dynamics of Cutaneous Leishmaniasis in Northeast Brazil

Science.gov (United States)

Lewnard, Joseph A.; Jirmanus, Lara; Júnior, Nivison Nery; Machado, Paulo R.; Glesby, Marshall J.; Ko, Albert I.; Carvalho, Edgar M.; Schriefer, Albert; Weinberger, Daniel M.

2014-01-01

Introduction Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. Methodology/Principal Findings We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. Significance These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets. PMID:25356734

Energy demand forecasting method based on international statistical data

International Nuclear Information System (INIS)

Glanc, Z.; Kerner, A.

1997-01-01

Poland is in a transition phase from a centrally planned to a market economy; data collected under former economic conditions do not reflect a market economy. Final energy demand forecasts are based on the assumption that the economic transformation in Poland will gradually lead the Polish economy, technologies and modes of energy use, to the same conditions as mature market economy countries. The starting point has a significant influence on the future energy demand and supply structure: final energy consumption per capita in 1992 was almost half the average of OECD countries; energy intensity, based on Purchasing Power Parities (PPP) and referred to GDP, is more than 3 times higher in Poland. A method of final energy demand forecasting based on regression analysis is described in this paper. The input data are: output of macroeconomic and population growth forecast; time series 1970-1992 of OECD countries concerning both macroeconomic characteristics and energy consumption; and energy balance of Poland for the base year of the forecast horizon. (author). 1 ref., 19 figs, 4 tabs

Energy demand forecasting method based on international statistical data

Energy Technology Data Exchange (ETDEWEB)

Glanc, Z; Kerner, A [Energy Information Centre, Warsaw (Poland)

1997-09-01

Poland is in a transition phase from a centrally planned to a market economy; data collected under former economic conditions do not reflect a market economy. Final energy demand forecasts are based on the assumption that the economic transformation in Poland will gradually lead the Polish economy, technologies and modes of energy use, to the same conditions as mature market economy countries. The starting point has a significant influence on the future energy demand and supply structure: final energy consumption per capita in 1992 was almost half the average of OECD countries; energy intensity, based on Purchasing Power Parities (PPP) and referred to GDP, is more than 3 times higher in Poland. A method of final energy demand forecasting based on regression analysis is described in this paper. The input data are: output of macroeconomic and population growth forecast; time series 1970-1992 of OECD countries concerning both macroeconomic characteristics and energy consumption; and energy balance of Poland for the base year of the forecast horizon. (author). 1 ref., 19 figs, 4 tabs.

Stationarity test with a direct test for heteroskedasticity in exchange rate forecasting models

Science.gov (United States)

Khin, Aye Aye; Chau, Wong Hong; Seong, Lim Chee; Bin, Raymond Ling Leh; Teng, Kevin Low Lock

2017-05-01

Global economic has been decreasing in the recent years, manifested by the greater exchange rates volatility on international commodity market. This study attempts to analyze some prominent exchange rate forecasting models on Malaysian commodity trading: univariate ARIMA, ARCH and GARCH models in conjunction with stationarity test on residual diagnosis direct testing of heteroskedasticity. All forecasting models utilized the monthly data from 1990 to 2015. Given a total of 312 observations, the data used to forecast both short-term and long-term exchange rate. The forecasting power statistics suggested that the forecasting performance of ARIMA (1, 1, 1) model is more efficient than the ARCH (1) and GARCH (1, 1) models. For ex-post forecast, exchange rate was increased from RM 3.50 per USD in January 2015 to RM 4.47 per USD in December 2015 based on the baseline data. For short-term ex-ante forecast, the analysis results indicate a decrease in exchange rate on 2016 June (RM 4.27 per USD) as compared with 2015 December. A more appropriate forecasting method of exchange rate is vital to aid the decision-making process and planning on the sustainable commodities' production in the world economy.

Evaluating information in multiple horizon forecasts. The DOE's energy price forecasts

International Nuclear Information System (INIS)

Sanders, Dwight R.; Manfredo, Mark R.; Boris, Keith

2009-01-01

The United States Department of Energy's (DOE) quarterly price forecasts for energy commodities are examined to determine the incremental information provided at the one-through four-quarter forecast horizons. A direct test for determining information content at alternative forecast horizons, developed by Vuchelen and Gutierrez [Vuchelen, J. and Gutierrez, M.-I. 'A Direct Test of the Information Content of the OECD Growth Forecasts.' International Journal of Forecasting. 21(2005):103-117.], is used. The results suggest that the DOE's price forecasts for crude oil, gasoline, and diesel fuel do indeed provide incremental information out to three-quarters ahead, while natural gas and electricity forecasts are informative out to the four-quarter horizon. In contrast, the DOE's coal price forecasts at two-, three-, and four-quarters ahead provide no incremental information beyond that provided for the one-quarter horizon. Recommendations of how to use these results for making forecast adjustments is also provided. (author)

Facilitating Collaboration in Online Groups

Directory of Open Access Journals (Sweden)

Geralyn E Stephens

2017-02-01

Full Text Available Demonstrating the ability to collaborate effectively is essential for students moving into 21st century workplaces. Employers are expecting new hires to already possess group-work skills and will seek evidence of their ability to cooperate, collaborate, and complete projects with colleagues, including remotely or at a distance. Instructional activities and assignments that provide students with a variety of ways to engage each other have a direct and immediate effect on their academic performance. This paper shares the Facilitating Collaboration in Online Groups (FCOG instructional planning strategy. The strategy is designed for faculty use and familiarizes students with the process and technology necessary to collaborate effectively in online classroom groups. The strategy utilizes proven teaching techniques to maximize student-student and student-content relationships. Each of the four (4 sequential phases in the FCOG instructional planning strategy are discussed: 1 Creating Groups, 2 Establishing Expectations, 3 Communication Tools, and 4 Assignments and Activities. The discussion also contains implementation suggestions as well as examples of instructional assignments and activities that provide students with a variety of ways to collaborate to reach the learning outcomes.

Machine learning based switching model for electricity load forecasting

Energy Technology Data Exchange (ETDEWEB)

Fan, Shu; Lee, Wei-Jen [Energy Systems Research Center, The University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen, Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan)

2008-06-15

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma. (author)

Machine learning based switching model for electricity load forecasting

Energy Technology Data Exchange (ETDEWEB)

Fan Shu [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan); Lee, Weijen [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States)], E-mail: wlee@uta.edu

2008-06-15

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma.

Machine learning based switching model for electricity load forecasting

International Nuclear Information System (INIS)

Fan Shu; Chen Luonan; Lee, Weijen

2008-01-01

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma

Hourly weather forecasts for gas turbine power generation

Directory of Open Access Journals (Sweden)

G. Giunta

2017-06-01

Full Text Available An hourly short-term weather forecast can optimize processes in Combined Cycle Gas Turbine (CCGT plants by helping to reduce imbalance charges on the national power grid. Consequently, a reliable meteorological prediction for a given power plant is crucial for obtaining competitive prices for the electric market, better planning and stock management, sales and supplies of energy sources. The paper discusses the short-term hourly temperature forecasts, at lead time day+1 and day+2, over a period of thirteen months in 2012 and 2013 for six Italian CCGT power plants of 390 MW each (260 MW from the gas turbine and 130 MW from the steam turbine. These CCGT plants are placed in three different Italian climate areas: the Po Valley, the Adriatic coast, and the North Tyrrhenian coast. The meteorological model applied in this study is the eni-Kassandra Meteo Forecast (e‑kmf™, a multi-model approach system to provide probabilistic forecasts with a Kalman filter used to improve accuracy of local temperature predictions. Performance skill scores, computed by the output data of the meteorological model, are compared with local observations, and used to evaluate forecast reliability. In the study, the approach has shown good overall scores encompassing more than 50,000 hourly temperature values. Some differences from one site to another, due to local meteorological phenomena, can affect the short-term forecast performance, with consequent impacts on gas-to-power production and related negative imbalances. For operational application of the methodology in CCGT power plant, the benefits and limits have been successfully identified.

Air Quality Forecasts Using the NASA GEOS Model

Science.gov (United States)

Keller, Christoph A.; Knowland, K. Emma; Nielsen, Jon E.; Orbe, Clara; Ott, Lesley; Pawson, Steven; Saunders, Emily; Duncan, Bryan; Follette-Cook, Melanie; Liu, Junhua;

The new Met Office strategy for seasonal forecasts

Science.gov (United States)

Hewson, T. D.

2012-04-01

during the presentation. Another key component of the 3-month outlook is the focus it places on potential hazards and impacts. To date specific references have been made to snow and ice disruption, to replenishment expectation for regions suffering water supply shortages, and to windstorm frequency. This aspect will be discussed, showing also some subjective verification. In future we hope to extend the 3-month outlook framework to other parts of the world, notably Africa, a region where the Met Office, with DfID support, is working collaboratively to improve real-time long range forecasts. Brief reference will also be made to such activities.

Ticket consumption forecast for Brazilian championship games

Directory of Open Access Journals (Sweden)

Adriana Bruscato Bortoluzzo

Full Text Available Abstract For the efficiency of sales and marketing management of athletic clubs, it is crucial to find a way to appropriately estimate the level of demand for sporting events. More precise estimates allow for an appropriate financial and operational plan and a higher quality of service delivered to the fans. The focus of this study is to analyze and forecast the ticket consumption for soccer games in Brazilian stadiums. We compare the results of the regression model with normally distributed errors (benchmark, the TOBIT model and the Gamma generalized linear model. The models include explanatory variables related to the economic environment, product quality, as well as monetary and non-monetary incentives that people are given to attend sporting events at stadiums. We show that most of these variables are statistically significant to explain the amount of fans that go to stadiums. We used different measures of accuracy to evaluate the performance of demand forecasts and concluded that Gamma generalized linear model presented better results to forecast the ticket consumption for Brazilian championship games, when compared to a benchmark.

Electric power demand forecasting using interval time series. A comparison between VAR and iMLP

International Nuclear Information System (INIS)

Garcia-Ascanio, Carolina; Mate, Carlos

2010-01-01

Electric power demand forecasts play an essential role in the electric industry, as they provide the basis for making decisions in power system planning and operation. A great variety of mathematical methods have been used for demand forecasting. The development and improvement of appropriate mathematical tools will lead to more accurate demand forecasting techniques. In order to forecast the monthly electric power demand per hour in Spain for 2 years, this paper presents a comparison between a new forecasting approach considering vector autoregressive (VAR) forecasting models applied to interval time series (ITS) and the iMLP, the multi-layer perceptron model adapted to interval data. In the proposed comparison, for the VAR approach two models are fitted per every hour, one composed of the centre (mid-point) and radius (half-range), and another one of the lower and upper bounds according to the interval representation assumed by the ITS in the learning set. In the case of the iMLP, only the model composed of the centre and radius is fitted. The other interval representation composed of the lower and upper bounds is obtained from the linear combination of the two. This novel approach, obtaining two bivariate models each hour, makes possible to establish, for different periods in the day, which interval representation is more accurate. Furthermore, the comparison between two different techniques adapted to interval time series allows us to determine the efficiency of these models in forecasting electric power demand. It is important to note that the iMLP technique has been selected for the comparison, as it has shown its accuracy in forecasting daily electricity price intervals. This work shows the ITS forecasting methods as a potential tool that will lead to a reduction in risk when making power system planning and operational decisions. (author)

National Forecast Charts

Science.gov (United States)

code. Press enter or select the go button to submit request Local forecast by "City, St" or Prediction Center on Twitter NCEP Quarterly Newsletter WPC Home Analyses and Forecasts National Forecast to all federal, state, and local government web resources and services. National Forecast Charts

Hydrologic Forecasting in the 21st Century: Challenges and Directions of Research

Science.gov (United States)

Restrepo, P.; Schaake, J.

2009-04-01

from forest fires or levee breaches, and slow, as the result of watershed reforestation, reforestation or urban development; 5) an expanded suite of products, including soil moisture and temperature forecasts, and water quality constituents; and 6) a comprehensive verification system to assess the effectiveness of the other 5 goals. To this end, the research plan places an emphasis on research of models with parameters that can be derived from physical watershed characteristics. Purely physically based models may be unattainable or impractical, and, therefore, models resulting from a combination of physically and conceptually approached processes may be required With respect to the hydrometeorological forcings the research plan emphasizes the development of improved precipitation estimation techniques through the synthesis of radar, rain gauge, satellite, and numerical weather prediction model output, particularly in those areas where ground-based sensors are inadequate to detect spatial variability in precipitation. Better estimation and forecasting of precipitation are most likely to be achieved by statistical merging of remote-sensor observations and forecasts from high-resolution numerical prediction models. Enhancements to the satellite-based precipitation products will include use of TRMM precipitation data in preparation for information to be supplied by the Global Precipitation Mission satellites not yet deployed. Because of a growing need for services in water resources, including low-flow forecasts for water supply customers, we will be directing research into coupled surface-groundwater models that will eventually replace the groundwater component of the existing models, and will be part of the new generation of models. Finally, the research plan covers the directions of research for probabilistic forecasting using ensembles, data assimilation and the verification and validation of both deterministic and probabilistic forecasts.

Analyzing Effect of System Inertia on Grid Frequency Forecasting Usnig Two Stage Neuro-Fuzzy System

Science.gov (United States)

Chourey, Divyansh R.; Gupta, Himanshu; Kumar, Amit; Kumar, Jitesh; Kumar, Anand; Mishra, Anup

2018-04-01

Frequency forecasting is an important aspect of power system operation. The system frequency varies with load-generation imbalance. Frequency variation depends upon various parameters including system inertia. System inertia determines the rate of fall of frequency after the disturbance in the grid. Though, inertia of the system is not considered while forecasting the frequency of power system during planning and operation. This leads to significant errors in forecasting. In this paper, the effect of inertia on frequency forecasting is analysed for a particular grid system. In this paper, a parameter equivalent to system inertia is introduced. This parameter is used to forecast the frequency of a typical power grid for any instant of time. The system gives appreciable result with reduced error.

Integrating Collaboration, Adaptive Management, and Scenario-Planning: Experiences at Las Cienegas National Conservation Area

Directory of Open Access Journals (Sweden)

Jeremy K. Caves

2013-09-01

Full Text Available There is growing recognition that public lands cannot be managed as islands; rather, land management must address the ecological, social, and temporal complexity that often spans jurisdictions and traditional planning horizons. Collaborative decision making and adaptive management (CAM have been promoted as methods to reconcile competing societal demands and respond to complex ecosystem dynamics. We detail the experiences of land managers and stakeholders in using CAM at Las Cienegas National Conservation Area (LCNCA, a highly valued site under the jurisdiction of the Bureau of Land Management (BLM. The CAM process at Las Cienegas is marked by strong stakeholder engagement, with four core elements: (1 shared watershed goals with measurable resource objectives; (2 relevant and reliable scientific information; (3 mechanisms to incorporate new information into decision making; and (4 shared learning to improve both the process and management actions. The combination of stakeholder engagement and adaptive management has led to agreement on contentious issues, more innovative solutions, and more effective land management. However, the region is now experiencing rapid changes outside managers' control, including climate change, human population growth, and reduced federal budgets, with large but unpredictable impacts on natural resources. Although the CAM experience provides a strong foundation for making the difficult and contentious management decisions that such changes are likely to require, neither collaboration nor adaptive management provides a sufficient structure for addressing the externalities that drive uncontrollable and unpredictable change. As a result, LCNCA is exploring two specific modifications to CAM that may better address emerging challenges, including: (1 creating nested resource objectives to distinguish between those objectives that may be crucial to maintaining ecological resilience from those that may hinder a flexible

Managing collaborative innovation in public bureaucracies

DEFF Research Database (Denmark)

Agger, Annika; Sørensen, Eva

2018-01-01

Public planners are increasingly recruited to manage collaborative innovation processes, but there is hardly any research on how they deal with the tensions they encounter in managing collaborative innovation in the institutional context of a public bureaucracy. Drawing on emerging theories...... of collaborative planning, network management and public innovation, the article develops a taxonomy of tasks related to managing collaborative innovation, identifies potential tensions between these tasks and the institutional logic of public bureaucracies and investigates how these tensions are experienced...

Exploring the interactions between forecast accuracy, risk perception and perceived forecast reliability in reservoir operator's decision to use forecast

Science.gov (United States)

Shafiee-Jood, M.; Cai, X.

2017-12-01

Advances in streamflow forecasts at different time scales offer a promise for proactive flood management and improved risk management. Despite the huge potential, previous studies have found that water resources managers are often not willing to incorporate streamflow forecasts information in decisions making, particularly in risky situations. While low accuracy of forecasts information is often cited as the main reason, some studies have found that implementation of streamflow forecasts sometimes is impeded by institutional obstacles and behavioral factors (e.g., risk perception). In fact, a seminal study by O'Connor et al. (2005) found that risk perception is the strongest determinant of forecast use while managers' perception about forecast reliability is not significant. In this study, we aim to address this issue again. However, instead of using survey data and regression analysis, we develop a theoretical framework to assess the user-perceived value of streamflow forecasts. The framework includes a novel behavioral component which incorporates both risk perception and perceived forecast reliability. The framework is then used in a hypothetical problem where reservoir operator should react to probabilistic flood forecasts with different reliabilities. The framework will allow us to explore the interactions among risk perception and perceived forecast reliability, and among the behavioral components and information accuracy. The findings will provide insights to improve the usability of flood forecasts information through better communication and education.

NSF's Perspective on Space Weather Research for Building Forecasting Capabilities

Science.gov (United States)

Bisi, M. M.; Pulkkinen, A. A.; Bisi, M. M.; Pulkkinen, A. A.; Webb, D. F.; Oughton, E. J.; Azeem, S. I.

2017-12-01

Space weather research at the National Science Foundation (NSF) is focused on scientific discovery and on deepening knowledge of the Sun-Geospace system. The process of maturation of knowledge base is a requirement for the development of improved space weather forecast models and for the accurate assessment of potential mitigation strategies. Progress in space weather forecasting requires advancing in-depth understanding of the underlying physical processes, developing better instrumentation and measurement techniques, and capturing the advancements in understanding in large-scale physics based models that span the entire chain of events from the Sun to the Earth. This presentation will provide an overview of current and planned programs pertaining to space weather research at NSF and discuss the recommendations of the Geospace Section portfolio review panel within the context of space weather forecasting capabilities.

Seasonal UK Drought Forecasting using Statistical Methods

Science.gov (United States)

Richardson, Doug; Fowler, Hayley; Kilsby, Chris; Serinaldi, Francesco

2016-04-01

In the UK drought is a recurrent feature of climate with potentially large impacts on public water supply. Water companies' ability to mitigate the impacts of drought by managing diminishing availability depends on forward planning and it would be extremely valuable to improve forecasts of drought on monthly to seasonal time scales. By focusing on statistical forecasting methods, this research aims to provide techniques that are simpler, faster and computationally cheaper than physically based models. In general, statistical forecasting is done by relating the variable of interest (some hydro-meteorological variable such as rainfall or streamflow, or a drought index) to one or more predictors via some formal dependence. These predictors are generally antecedent values of the response variable or external factors such as teleconnections. A candidate model is Generalised Additive Models for Location, Scale and Shape parameters (GAMLSS). GAMLSS is a very flexible class allowing for more general distribution functions (e.g. highly skewed and/or kurtotic distributions) and the modelling of not just the location parameter but also the scale and shape parameters. Additionally GAMLSS permits the forecasting of an entire distribution, allowing the output to be assessed in probabilistic terms rather than simply the mean and confidence intervals. Exploratory analysis of the relationship between long-memory processes (e.g. large-scale atmospheric circulation patterns, sea surface temperatures and soil moisture content) and drought should result in the identification of suitable predictors to be included in the forecasting model, and further our understanding of the drivers of UK drought.

Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Nebula Cloud Computing Environment

Science.gov (United States)

Molthan, Andrew L.; Case, Jonathan L.; Venner, Jason; Moreno-Madrinan, Max. J.; Delgado, Francisco

2012-01-01

Over the past two years, scientists in the Earth Science Office at NASA fs Marshall Space Flight Center (MSFC) have explored opportunities to apply cloud computing concepts to support near real ]time weather forecast modeling via the Weather Research and Forecasting (WRF) model. Collaborators at NASA fs Short ]term Prediction Research and Transition (SPoRT) Center and the SERVIR project at Marshall Space Flight Center have established a framework that provides high resolution, daily weather forecasts over Mesoamerica through use of the NASA Nebula Cloud Computing Platform at Ames Research Center. Supported by experts at Ames, staff at SPoRT and SERVIR have established daily forecasts complete with web graphics and a user interface that allows SERVIR partners access to high resolution depictions of weather in the next 48 hours, useful for monitoring and mitigating meteorological hazards such as thunderstorms, heavy precipitation, and tropical weather that can lead to other disasters such as flooding and landslides. This presentation will describe the framework for establishing and providing WRF forecasts, example applications of output provided via the SERVIR web portal, and early results of forecast model verification against available surface ] and satellite ]based observations.

Overview of Hydrometeorologic Forecasting Procedures at BC Hydro

Science.gov (United States)

McCollor, D.

2004-12-01

Energy utility companies must balance production from limited sources with increasing demand from industrial, business, and residential consumers. The utility planning process requires a balanced, efficient, and effective distribution of energy from source to consumer. Therefore utility planners must consider the impact of weather on energy production and consumption. Hydro-electric companies should be particularly tuned to weather because their source of energy is water, and water supply depends on precipitation. BC Hydro operates as the largest hydro-electric company in western Canada, managing over 30 reservoirs within the province of British Columbia, and generating electricity for 1.6 million people. BC Hydro relies on weather forecasts of watershed precipitation and temperature to drive hydrologic reservoir inflow models and of urban temperatures to meet energy demand requirements. Operations and planning specialists in the company rely on current, value-added weather forecasts for extreme high-inflow events, daily reservoir operations planning, and long-term water resource management. Weather plays a dominant role for BC Hydro financial planners in terms of sensitive economic responses. For example, a two percent change in hydropower generation, due in large part to annual precipitation patterns, results in an annual net change of \\50 million in earnings. A five percent change in temperature produces a \\5 million change in yearly earnings. On a daily basis, significant precipitation events or temperature extremes involve potential profit/loss decisions in the tens of thousands of dollars worth of power generation. These factors are in addition to environmental and societal costs that must be considered equally as part of a triple bottom line reporting structure. BC Hydro water resource managers require improved meteorological information from recent advancements in numerical weather prediction. At BC Hydro, methods of providing meteorological forecast data

Understanding Farmers’ Forecast Use from Their Beliefs, Values, Social Norms, and Perceived Obstacles

Science.gov (United States)

Hu, Qi; Pytlik Zillig, Lisa M.; Lynne, Gary D.; Tomkins, Alan J.; Waltman, William J.; Hayes, Michael J.; Hubbard, Kenneth G.; Artikov, Ikrom; Hoffman, Stacey J.; Wilhite, Donald A.

2006-09-01

Although the accuracy of weather and climate forecasts is continuously improving and new information retrieved from climate data is adding to the understanding of climate variation, use of the forecasts and climate information by farmers in farming decisions has changed little. This lack of change may result from knowledge barriers and psychological, social, and economic factors that undermine farmer motivation to use forecasts and climate information. According to the theory of planned behavior (TPB), the motivation to use forecasts may arise from personal attitudes, social norms, and perceived control or ability to use forecasts in specific decisions. These attributes are examined using data from a survey designed around the TPB and conducted among farming communities in the region of eastern Nebraska and the western U.S. Corn Belt. There were three major findings: 1) the utility and value of the forecasts for farming decisions as perceived by farmers are, on average, around 3.0 on a 0 7 scale, indicating much room to improve attitudes toward the forecast value. 2) The use of forecasts by farmers to influence decisions is likely affected by several social groups that can provide “expert viewpoints” on forecast use. 3) A major obstacle, next to forecast accuracy, is the perceived identity and reliability of the forecast makers. Given the rapidly increasing number of forecasts in this growing service business, the ambiguous identity of forecast providers may have left farmers confused and may have prevented them from developing both trust in forecasts and skills to use them. These findings shed light on productive avenues for increasing the influence of forecasts, which may lead to greater farming productivity. In addition, this study establishes a set of reference points that can be used for comparisons with future studies to quantify changes in forecast use and influence.

Deterministic and heuristic models of forecasting spare parts demand

Directory of Open Access Journals (Sweden)

Ivan S. Milojević

2012-04-01

Full Text Available Knowing the demand of spare parts is the basis for successful spare parts inventory management. Inventory management has two aspects. The first one is operational management: acting according to certain models and making decisions in specific situations which could not have been foreseen or have not been encompassed by models. The second aspect is optimization of the model parameters by means of inventory management. Supply items demand (asset demand is the expression of customers' needs in units in the desired time and it is one of the most important parameters in the inventory management. The basic task of the supply system is demand fulfillment. In practice, demand is expressed through requisition or request. Given the conditions in which inventory management is considered, demand can be: - deterministic or stochastic, - stationary or nonstationary, - continuous or discrete, - satisfied or unsatisfied. The application of the maintenance concept is determined by the technological level of development of the assets being maintained. For example, it is hard to imagine that the concept of self-maintenance can be applied to assets developed and put into use 50 or 60 years ago. Even less complex concepts cannot be applied to those vehicles that only have indicators of engine temperature - those that react only when the engine is overheated. This means that the maintenance concepts that can be applied are the traditional preventive maintenance and the corrective maintenance. In order to be applied in a real system, modeling and simulation methods require a completely regulated system and that is not the case with this spare parts supply system. Therefore, this method, which also enables the model development, cannot be applied. Deterministic models of forecasting are almost exclusively related to the concept of preventive maintenance. Maintenance procedures are planned in advance, in accordance with exploitation and time resources. Since the timing

Review of the Strategic Plan for International Collaboration on Fusion Science and Technology Research. Fusion Energy Sciences Advisory Committee (FESAC)

International Nuclear Information System (INIS)

1998-01-01

The United States Government has employed international collaborations in magnetic fusion energy research since the program was declassified in 1958. These collaborations have been successful not only in producing high quality scientific results that have contributed to the advancement of fusion science and technology, they have also allowed us to highly leverage our funding. Thus, in the 1980s, when the funding situation made it necessary to reduce the technical breadth of the U.S. domestic program, these highly leveraged collaborations became key strategic elements of the U.S. program, allowing us to maintain some degree of technical breadth. With the recent, nearly complete declassification of inertial confinement fusion, the use of some international collaboration is expected to be introduced in the related inertial fusion energy research activities as well. The United States has been a leader in establishing and fostering collaborations that have involved scientific and technological exchanges, joint planning, and joint work at fusion facilities in the U.S. and worldwide. These collaborative efforts have proven mutually beneficial to the United States and our partners. International collaborations are a tool that allows us to meet fusion program goals in the most effective way possible. Working with highly qualified people from other countries and other cultures provides the collaborators with an opportunity to see problems from new and different perspectives, allows solutions to arise from the diversity of the participants, and promotes both collaboration and friendly competition. In short, it provides an exciting and stimulating environment resulting in a synergistic effect that is good for science and good for the people of the world.

Forecasts of county-level land uses under three future scenarios: a technical document supporting the Forest Service 2010 RPA Assessment

Science.gov (United States)

David N. Wear

2011-01-01

Accurately forecasting future forest conditions and the implications for ecosystem services depends on understanding land use dynamics. In support of the 2010 Renewable Resources Planning Act (RPA) Assessment, we forecast changes in land uses for the coterminous United States in response to three scenarios. Our land use models forecast urbanization in response to the...

Generation Mix Study Focusing on Nuclear Power by Practical Peak Forecast

Energy Technology Data Exchange (ETDEWEB)

Shin, Jung Ho; Roh, Myung Sub [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)

2013-10-15

The excessive underestimation can lead to a range of problem; expansion of LNG plant requiring short construction period, the following increase of electricity price, low reserve margin and inefficient configuration of power source. With regard to nuclear power, the share of the stable and economic base load plant, nuclear power, can reduce under the optimum level. Amongst varied factors which contribute to the underestimate, immoderate target for demand side management (DSM) including double deduction of the constraint amount by DSM from peak demand forecast is one of the causes. The hypothesis in this study is that the better optimum generation mix including the adequate share of nuclear power can be obtained under the condition of the peak demand forecast without deduction of DSM target because this forecast is closer to the actual peak demand. In this study, the hypothesis is verified with comparison between peak demand forecast before (or after) DSM target application and the actual peak demand in the 3{sup rd} through 5{sup th} BPE from 2006 to 2010. Furthermore, this research compares and analyzes several generation mix in 2027 focusing on the nuclear power by a few conditions using the WASP-IV program on the basis of the 6{sup th} BPE in 2013. According to the comparative analysis on the peak demand forecast and actual peak demand from 2006 to 2010, the peak demand forecasts without the deduction of the DSM target is closer to the actual peak demand than the peak demand forecasts considering the DSM target in the 3{sup th}, 4{sup th}, 5{sup th} entirely. In addition, the generation mix until 2027 is examined by the WASP-IV. As a result of the program run, when considering the peak demand forecast without DSM reflection, since the base load plants including nuclear power take up adequate proportion, stable and economic supply of electricity can be achieved. On the contrary, in case of planning based on the peak demand forecast with DSM reflected and then

The impact of short term traffic forecasting on the effectiveness of vehicles routes planning in urban areas

Energy Technology Data Exchange (ETDEWEB)

Kubek, D.

2016-07-01

An impossibility to foresee in advance the accurate traffic parameters in face of dynamism phenomena in complex transportation system is a one of the major source of uncertainty. The paper presents an approach to robust optimization of logistics vehicle routes in urban areas on the basis of estimated short-term traffic time forecasts in a selected area of the urban road network. The forecast values of optimization parameters have been determined using the spectral analysis model, taking into account the forecast uncertainty degree. The robust counterparts approach of uncertain bi-criteria shortest path problem formulation is used to determining the robust routes for logistics vehicles in the urban network. The uncertainty set is created on the basis of forecast travel times in chosen sections, estimated by means of spectral analysis. The advantages and the characteristics are exemplified in the actual Krakow road network. The obtained data have been compared with classic approach wherein it is assumed that the optimization parameters are certain and accurate. The results obtained in the simulation example indicate that use of forecasting techniques with robust optimization models has a positive impact on the quality of final solutions. (Author)

Gold sales forecasting: The Box-Jenkins methodology

Directory of Open Access Journals (Sweden)

Johannes Tshepiso Tsoku

2017-02-01

Full Text Available The study employs the Box-Jenkins Methodology to forecast South African gold sales. For a resource economy like South Africa where metals and minerals account for a high proportion of GDP and export earnings, the decline in gold sales is very disturbing. Box-Jenkins time series technique was used to perform time series analysis of monthly gold sales for the period January 2000 to June 2013 with the following steps: model identification, model estimation, diagnostic checking and forecasting. Furthermore, the prediction accuracy is tested using mean absolute percentage error (MAPE. From the analysis, a seasonal ARIMA(4,1,4×(0,1,112 was found to be the “best fit model” with an MAPE value of 11% indicating that the model is fit to be used to predict or forecast future gold sales for South Africa. In addition, the forecast values show that there will be a decrease in the overall gold sales for the first six months of 2014. It is hoped that the study will help the public and private sectors to understand the gold sales or output scenario and later plan the gold mining activities in South Africa. Furthermore, it is hoped that this research paper has demonstrated the significance of Box-Jenkins technique for this area of research and that they will be applied in the future.

Forecasting in strategic international marketing. Conception for and application to the construction of large-scale pollution abatement installations

International Nuclear Information System (INIS)

Tressin, J.M.

1992-01-01

Division of labour continues to progress worldwide, particularly in the European economy, where it is an accompainment to the steady growth of transnational trade relations. In this situation forecasts attain great importance for companies facing structural decisions. This holds not only for large international concerns but also increasingly for medium-sized companies. The methodological approaches adopted by such companies are primarily aimed at reducing the risk of wrong decisions by gathering information and incorporating forecasts in their planning and decision-making processes. The present article describes various forecasting methods, exemplifying them for the area of commercial-sized building projects. Due to the great complexity of such installations and discontinuities in developments in international markets, quantitative forecasting methods and models are only of limited value here. In contrast to this, qualitative methods such as those provided by expert systems and contemporary dp tools for data collection and compression have attained great importance for the design of practiable and reliable forecasting systems. The present study thus lays have the interface between international marketing and corporate planning and decision-making processes. (orig.) [de

Strategies for Managing Conflict in the Collaboration Process.

Science.gov (United States)

Ivarie, Judith J.

Approaches to managing conflict in the collaborative process are discussed, along with the need for collaboration in schools. Collaboration by teachers, administrators, parents, and others can help identify problems, consider relevant data, plan and implement interventions, and evaluate results. However, the knowledge, experience, and values of…

Forecasting and Technology Management: Statistical Theory and Methodological Issues

DEFF Research Database (Denmark)

Madsen, Henning

of directions and targets for a R and D project, monitoring of a given area by a public agency, and evaluation of the future competitive situation for a company. This paper gives a brief introduction to the field of technological forecasting especially in relation to the strategic planning process...

Inter-Institutional Collaboration and Team Teaching.

Science.gov (United States)

Gatliff, Bee; Wendel, Frederick C.

1998-01-01

Inter-institutional collaboration and team teaching can enhance distance education. Of particular interest to those who are new to distance education or collaborative relationships, this article discusses several issues that should be considered in the planning process to avoid potential roadblocks and to maximize returns. (Author/AEF)

The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt

Directory of Open Access Journals (Sweden)

Cécile Viboud

2018-03-01

Full Text Available Infectious disease forecasting is gaining traction in the public health community; however, limited systematic comparisons of model performance exist. Here we present the results of a synthetic forecasting challenge inspired by the West African Ebola crisis in 2014–2015 and involving 16 international academic teams and US government agencies, and compare the predictive performance of 8 independent modeling approaches. Challenge participants were invited to predict 140 epidemiological targets across 5 different time points of 4 synthetic Ebola outbreaks, each involving different levels of interventions and “fog of war” in outbreak data made available for predictions. Prediction targets included 1–4 week-ahead case incidences, outbreak size, peak timing, and several natural history parameters. With respect to weekly case incidence targets, ensemble predictions based on a Bayesian average of the 8 participating models outperformed any individual model and did substantially better than a null auto-regressive model. There was no relationship between model complexity and prediction accuracy; however, the top performing models for short-term weekly incidence were reactive models with few parameters, fitted to a short and recent part of the outbreak. Individual model outputs and ensemble predictions improved with data accuracy and availability; by the second time point, just before the peak of the epidemic, estimates of final size were within 20% of the target. The 4th challenge scenario − mirroring an uncontrolled Ebola outbreak with substantial data reporting noise − was poorly predicted by all modeling teams. Overall, this synthetic forecasting challenge provided a deep understanding of model performance under controlled data and epidemiological conditions. We recommend such “peace time” forecasting challenges as key elements to improve coordination and inspire collaboration between modeling groups ahead of the next pandemic threat

Science implementation of Forecast Mekong for food and environmental security

Science.gov (United States)

Turnipseed, D. Phil

2012-01-01

Forecast Mekong is a significant international thrust under the Delta Research and Global Observation Network (DRAGON) of the U.S. Geological Survey (USGS) and was launched in 2009 by the U.S. Department of State and the Foreign Ministers of Cambodia, Laos, Thailand, and Vietnam under U.S. Department of State Secretary Hillary R. Clinton's Lower Mekong Initiative to enhance U.S. engagement with countries of the Lower Mekong River Basin in the areas of environment, health, education, and infrastructure. Since 2009, the USGS has worked closely with the U.S. Department of State; personnel from Cambodia, Laos, Thailand, and Vietnam; nongovernmental organizations; and academia to collect and use research and data from the Lower Mekong River Basin to provide hands-on results that will help decisionmakers in future planning and design for restoration, conservation, and management efforts in the Lower Mekong River Basin. In 2012 Forecast Mekong is highlighting the increasing cooperation between the United States and Lower Mekong River Basin countries in the areas of food and environmental security. Under the DRAGON, Forecast Mekong continues work in interactive data integration, modeling, and visualization system by initiating three-dimensional bathymetry and river flow data along with a pilot study of fish distribution, population, and migratory patterns in the Lower Mekong River Basin. When fully developed by the USGS, in partnership with local governments and universities throughout the Mekong River region, Forecast Mekong will provide valuable planning tools to visualize the consequences of climate change and river management.

Reducing uncertainty in load forecasts and using real options for improving capacity dispatch management through the utilization of weather and hydrologic forecasts

International Nuclear Information System (INIS)

Davis, T.

2004-01-01

The effect of weather on electricity markets was discussed with particular focus on reducing weather uncertainty by improving short term weather forecasts. The implications of weather for hydroelectric power dispatch and use were also discussed. Although some errors in weather forecasting can result in economic benefits, most errors are associated with more costs than benefits. This presentation described how a real options analysis can make weather a favorable option. Four case studies were presented for exploratory data analysis of regional weather phenomena. These included: (1) the 2001 California electricity crisis, (2) the delta breeze effects on the California ISO, (3) the summer 2002 weather forecast error for ISO New England, and (4) the hydro plant asset valuation using weather uncertainty. It was concluded that there is a need for more economic methodological studies on the effect of weather on energy markets and costs. It was suggested that the real options theory should be applied to weather planning and utility applications. tabs., figs

Building on a national health information technology strategic plan for long-term and post-acute care: comments by the Long Term Post Acute Care Health Information Technology Collaborative.

Science.gov (United States)

Alexander, Gregory L; Alwan, Majd; Batshon, Lynne; Bloom, Shawn M; Brennan, Richard D; Derr, John F; Dougherty, Michelle; Gruhn, Peter; Kirby, Annessa; Manard, Barbara; Raiford, Robin; Serio, Ingrid Johnson

2011-07-01

The LTPAC (Long Term Post Acute Care) Health Information Technology (HIT) Collaborative consists of an alliance of long-term services and post-acute care stakeholders. Members of the collaborative are actively promoting HIT innovations in long-term care settings because IT adoption for health care institutions in the United States has become a high priority. One method used to actively promote HIT is providing expert comments on important documents addressing HIT adoption. Recently, the Office of the National Coordinator for HIT released a draft of the Federal Health Information Technology Strategic Plan 2011-2015 for public comment. The following brief is intended to inform about recommendations and comments made by the Collaborative on the strategic plan. Copyright 2011, SLACK Incorporated.

Development of a web-based pharmaceutical care plan to facilitate collaboration between healthcare providers and patients

Directory of Open Access Journals (Sweden)

Marlies ME Geurts

2014-02-01

Full Text Available Background To facilitate collaboration between different healthcare providers and to exchange patient data we developed a paper-based tool, which also enabled to plan interventions and follow-up activities: the PCP. Interviews with participating healthcare providers concluded the PCP was a very useful tool to collect and share patient data. A disadvantage was the time spent to collect all information. We therefore developed our PCP into a web-based tool: the web-based PCP (W-PCP.Objectives Development of a W-PCP to (1 provide healthcare providers with information from pharmacist- and GP computer systems and (2 facilitate collaboration between healthcare providers and patients.Method The W-PCP was used in three research lines, two in primary care and one in a hospital setting. Outcomes measures were defined as satisfaction about efficiency and effectiveness during data sharing and documentation in providing care and conducting medication reviews using the W-PCP.First experiences concerning the use of W-PCP in a primary care setting were collected by a questionnaire and interviews with pharmacists and GPs using the W-PCP.Results A questionnaire was sent to 38 healthcare providers. 17 healthcare providers returned the questionnaire. The use of W-PCP resulted in positive experiences from participating healthcare providers. On the basis of experiences and requirements collected, the application will be further developed.Conclusions The W-PCP application can potentially support successful collaboration between different healthcare providers and patients, which is important for medication therapy management. With this application, a successful collaboration between different healthcare providers and patients could be achieved.

Timeline Collaboration

DEFF Research Database (Denmark)

Bohøj, Morten; Borchorst, Nikolaj Gandrup; Bouvin, Niels Olof

2010-01-01

This paper explores timelines as a web-based tool for collaboration between citizens and municipal caseworkers. The paper takes its outset in a case study of planning and control of parental leave; a process that may involve surprisingly many actors. As part of the case study, a web-based timeline...

How seasonal forecast could help a decision maker: an example of climate service for water resource management

Science.gov (United States)

Viel, Christian; Beaulant, Anne-Lise; Soubeyroux, Jean-Michel; Céron, Jean-Pierre

2016-04-01

The FP7 project EUPORIAS was a great opportunity for the climate community to co-design with stakeholders some original and innovative climate services at seasonal time scales. In this framework, Météo-France proposed a prototype that aimed to provide to water resource managers some tailored information to better anticipate the coming season. It is based on a forecasting system, built on a refined hydrological suite, forced by a coupled seasonal forecast model. It particularly delivers probabilistic river flow prediction on river basins all over the French territory. This paper presents the work we have done with "EPTB Seine Grands Lacs" (EPTB SGL), an institutional stakeholder in charge of the management of 4 great reservoirs on the upper Seine Basin. First, we present the co-design phase, which means the translation of classical climate outputs into several indices, relevant to influence the stakeholder's decision making process (DMP). And second, we detail the evaluation of the impact of the forecast on the DMP. This evaluation is based on an experiment realised in collaboration with the stakeholder. Concretely EPTB SGL has replayed some past decisions, in three different contexts: without any forecast, with a forecast A and with a forecast B. One of forecast A and B really contained seasonal forecast, the other only contained random forecasts taken from past climate. This placebo experiment, realised in a blind test, allowed us to calculate promising skill scores of the DMP based on seasonal forecast in comparison to a classical approach based on climatology, and to EPTG SGL current practice.

Statistical Correction of Air Temperature Forecasts for City and Road Weather Applications

Science.gov (United States)

Mahura, Alexander; Petersen, Claus; Sass, Bent; Gilet, Nicolas

2014-05-01

that the CPU time required for the operational procedure is relatively short (less than 15 minutes including a large time spent for interpolation). These also showed that in order to start correction of forecasts there is no need to have a long-term pre-historical data (containing forecasts and observations) and, at least, a couple of weeks will be sufficient when a new observational station is included and added to the forecast point. Note for the road weather application, the operationalization of the statistical correction of the road surface temperature forecasts (for the RWM system daily hourly runs covering forecast length up to 5 hours ahead) for the Danish road network (for about 400 road stations) was also implemented, and it is running in a test mode since Sep 2013. The method can also be applied for correction of the dew point temperature and wind speed (as a part of observations/ forecasts at synoptical stations), where these both meteorological parameters are parts of the proposed system of equations. The evaluation of the method performance for improvement of the wind speed forecasts is planned as well, with considering possibilities for the wind direction improvements (which is more complex due to multi-modal types of such data distribution). The method worked for the entire domain of mainland Denmark (tested for 60 synoptical and 395 road stations), and hence, it can be also applied for any geographical point within this domain, as through interpolation to about 100 cities' locations (for Danish national byvejr forecasts). Moreover, we can assume that the same method can be used in other geographical areas. The evaluation for other domains (with a focus on Greenland and Nordic countries) is planned. In addition, a similar approach might be also tested for statistical correction of concentrations of chemical species, but such approach will require additional elaboration and evaluation.

Interval forecasting of cyber-attacks on industrial control systems

Science.gov (United States)

Ivanyo, Y. M.; Krakovsky, Y. M.; Luzgin, A. N.

2018-03-01

At present, cyber-security issues of industrial control systems occupy one of the key niches in a state system of planning and management Functional disruption of these systems via cyber-attacks may lead to emergencies related to loss of life, environmental disasters, major financial and economic damage, or disrupted activities of cities and settlements. There is then an urgent need to develop protection methods against cyber-attacks. This paper studied the results of cyber-attack interval forecasting with a pre-set intensity level of cyber-attacks. Interval forecasting is the forecasting of one interval from two predetermined ones in which a future value of the indicator will be obtained. For this, probability estimates of these events were used. For interval forecasting, a probabilistic neural network with a dynamic updating value of the smoothing parameter was used. A dividing bound of these intervals was determined by a calculation method based on statistical characteristics of the indicator. The number of cyber-attacks per hour that were received through a honeypot from March to September 2013 for the group ‘zeppo-norcal’ was selected as the indicator.

Broad Scale Monitoring in the US Forest Service: Institutional Challenges and Collaborative Opportunites for Improving Planning and Decision-Making in an Era of Climate Change

Science.gov (United States)

Wurtzebach, Z.

2016-12-01

In 2012, the United States Forest Service promulgated new rules to guide Forest planning efforts in accordance with the National Forest Management Act (NFMA). One important component of the 2012 rule is a requirement for Regionally coordinated cross-boundary "broad scale" monitoring strategies that are designed to inform and facilitate Forest-level adaptive management and planning. This presentation will examine institutional challenges and opportunites for developing effective broad scale monitoring strategies identified in 90 interviews with USFS staff and partner organizations, and collaborative workshops held in Colorado, Wyoming, Arizona, and New Mexico. Internal barriers to development include funding and human resource constraints, organizational culture, problematic incentives and accountability structures, data management issues, and administrative barriers to collaboration. However, we also identify several opportunities for leveraging interagency collaboration, facilitating multi-level coordination, generating efficiencies in data collection and analysis, and improving strategies for reporting and communication to Forest level decision-makers and relevant stakeholders.

Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting

Directory of Open Access Journals (Sweden)

Yunxuan Dong

2017-04-01

Full Text Available The process of modernizing smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems, and, in order to develop a more reliable, flexible, efficient and resilient grid, electrical load forecasting is not only an important key but is still a difficult and challenging task as well. In this paper, a short-term electrical load forecasting model, with a unit for feature learning named Pyramid System and recurrent neural networks, has been developed and it can effectively promote the stability and security of the power grid. Nine types of methods for feature learning are compared in this work to select the best one for learning target, and two criteria have been employed to evaluate the accuracy of the prediction intervals. Furthermore, an electrical load forecasting method based on recurrent neural networks has been formed to achieve the relational diagram of historical data, and, to be specific, the proposed techniques are applied to electrical load forecasting using the data collected from New South Wales, Australia. The simulation results show that the proposed hybrid models can not only satisfactorily approximate the actual value but they are also able to be effective tools in the planning of smart grids.

Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

Directory of Open Access Journals (Sweden)

Radziukynas V.

2016-04-01

Full Text Available The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011 and planned wind power capacities (the year 2023.

Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

Science.gov (United States)

Radziukynas, V.; Klementavičius, A.

2016-04-01

The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011) and planned wind power capacities (the year 2023).

Experiences from coordinated national-level landslide and flood forecasting in Norway

Science.gov (United States)

Krøgli, Ingeborg; Fleig, Anne; Glad, Per; Dahl, Mads-Peter; Devoli, Graziella; Colleuille, Hervé

2015-04-01

While flood forecasting at national level is quite well established and operational in many countries worldwide, landslide forecasting at national level is still seldom. Examples of coordinated flood and landslide forecasting are even rarer. Most of the time flood and landslide forecasters work separately (investigating, defining thresholds, and developing models) and most of the time without communication with each other. One example of coordinated operational early warning systems (EWS) for flooding and shallow landslides is found at the Norwegian Water Resources and Energy Directorate (NVE) in Norway. In this presentation we give an introduction to the two separate but tightly collaborative EWSs and to the coordination of these. The two EWSs are being operated from the same office, every day using similar hydro-meteorological prognosis and hydrological models. Prognosis and model outputs on e.g. discharge, snow melt, soil water content and exceeded landslide thresholds are evaluated in a web based decision-making tool (xgeo.no). The experts performing forecasts are hydrologists, geologists and physical geographers. A similar warning scale, based on colors (green, yellow, orange and red) is used for both EWSs, however thresholds for flood and landslide warning levels are defined differently. Also warning areas may not necessary be the same for both hazards and depending on the specific meteorological event, duration of the warning periods can differ. We present how knowledge, models and tools, but also human and economic resources are being shared between the two EWSs. Moreover, we discuss challenges faced in the communication of warning messages using recent flood and landslide events as examples.

Short-term forecasting of Czech quarterly GDP using monthly indicators

Czech Academy of Sciences Publication Activity Database

Arnoštová, K.; Havrlant, D.; Růžička, L.; Tóth, Peter

2011-01-01

Roč. 61, č. 6 (2011), s. 566-583 ISSN 0015-1920 Institutional research plan: CEZ:MSM0021620846 Keywords : GDP forecasting * bridge models * principal components Subject RIV: AH - Economics Impact factor: 0.346, year: 2011 http://journal.fsv.cuni.cz/storage/1235_toth.pdf

Study to forecast and determine characteristics of world satellite communications market

Science.gov (United States)

Filep, R. T.; Schnapf, A.; Fordyce, S. W.

1983-01-01

The world commercial communications satellite market during the spring and summer of 1983 was examined and characteristics and forecasts of the market extending to the year 2000 were developed. Past, present and planned satellites were documented in relation to frequencies, procurement and launch dates, costs, transponders, and prime contractor. Characteristics of the market are outlined for the periods 1965 - 1985, 1986 - 1989, and 1990 - 2000. Market share forecasts, discussions of potential competitors in various world markets, and profiles of major communication satellite manufacturing and user countries are documented.

Social science to improve fuels management: a synthesis of research on collaboration.

Science.gov (United States)

Victoria Sturtevant; Margaret Ann Moote; Pamela Jakes; Anthony S. Cheng

2005-01-01

A series of syntheses were commissioned by the USDA Forest Service to aid in fuels mitigation project planning. This synthesis focuses on collaboration research, and offers knowledge and tools to improve collaboration in the planning and implementation of wildland fire and fuels management projects. It covers a variety of topics including benefits of collaboration,...

Inflation Forecast Contracts

OpenAIRE

Gersbach, Hans; Hahn, Volker

2012-01-01

We introduce a new type of incentive contract for central bankers: inflation forecast contracts, which make central bankers’ remunerations contingent on the precision of their inflation forecasts. We show that such contracts enable central bankers to influence inflation expectations more effectively, thus facilitating more successful stabilization of current inflation. Inflation forecast contracts improve the accuracy of inflation forecasts, but have adverse consequences for output. On balanc...

Estimating the Value of Improved Distributed Photovoltaic Adoption Forecasts for Utility Resource Planning

Energy Technology Data Exchange (ETDEWEB)

Gagnon, Pieter [National Renewable Energy Lab. (NREL), Golden, CO (United States); Barbose, Galen L. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Stoll, Brady [National Renewable Energy Lab. (NREL), Golden, CO (United States); Ehlen, Ali [National Renewable Energy Lab. (NREL), Golden, CO (United States); Zuboy, Jarret [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mai, Trieu [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mills, Andrew D. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2018-05-15

Misforecasting the adoption of customer-owned distributed photovoltaics (DPV) can have operational and financial implications for utilities; forecasting capabilities can be improved, but generally at a cost. This paper informs this decision-space by using a suite of models to explore the capacity expansion and operation of the Western Interconnection over a 15-year period across a wide range of DPV growth rates and misforecast severities. The system costs under a misforecast are compared against the costs under a perfect forecast, to quantify the costs of misforecasting. Using a simplified probabilistic method applied to these modeling results, an analyst can make a first-order estimate of the financial benefit of improving a utility’s forecasting capabilities, and thus be better informed about whether to make such an investment. For example, under our base assumptions, a utility with 10 TWh per year of retail electric sales who initially estimates that DPV growth could range from 2% to 7.5% of total generation over the next 15 years could expect total present-value savings of approximately $4 million if they could reduce the severity of misforecasting to within ±25%. Utility resource planners can compare those savings against the costs needed to achieve that level of precision, to guide their decision on whether to make an investment in tools or resources.

Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

Directory of Open Access Journals (Sweden)

Anand Krishnan Prakash

2018-04-01

Full Text Available Robust and accurate building energy load forecasting is important for helping building managers and utilities to plan, budget, and strategize energy resources in advance. With recent prevalent adoption of smart-meters in buildings, a significant amount of building energy consumption data became available. Many studies have developed physics-based white box models and data-driven black box models to predict building energy consumption; however, they require extensive prior knowledge about building system, need a large set of training data, or lack robustness to different forecasting scenarios. In this paper, we introduce a new building energy forecasting method based on Gaussian Process Regression (GPR that incorporates physical insights about load data characteristics to improve accuracy while reducing training requirements. The GPR is a non-parametric regression method that models the data as a joint Gaussian distribution with mean and covariance functions and forecast using the Bayesian updating. We model the covariance function of the GPR to reflect the data patterns in different forecasting horizon scenarios, as prior knowledge. Our method takes advantage of the modeling flexibility and computational efficiency of the GPR while benefiting from the physical insights to further improve the training efficiency and accuracy. We evaluate our method with three field datasets from two university campuses (Carnegie Mellon University and Stanford University for both short- and long-term load forecasting. The results show that our method performs more accurately, especially when the training dataset is small, compared to other state-of-the-art forecasting models (up to 2.95 times smaller prediction error.

Incorporating regional growth into forecasts of greenhouse gas emissions from project-level residential and commercial development

International Nuclear Information System (INIS)

Rowangould, Dana; Eldridge, Melody; Niemeier, Deb

2013-01-01

To better understand the greenhouse gas (GHG) implications of land use planning decisions, regional planning organizations have developed tools to forecast the emissions from project-level residential and commercial development. This paper reviews the state of GHG emissions forecasting methods for project-level development. We argue that when forecasting changes in regional emissions it is important to make explicit what is assumed about a project′s effect on the population of residents and businesses in the region. We present five regional growth assumptions capturing the range of ways that project-level development might influence (i) construction and occupancy of similar developments elsewhere in a region and (ii) relocation of the initial activities that occur on-site before the project is built. We show that current forecasting tools inconsistently address the latter when they are interpreted as forecasted changes in regional emissions. Using a case study in Yolo County, California we demonstrate that forecasted changes in regional emissions are greatly affected by the regional growth assumption. In the absence of information about which regional growth assumption is accurate, we provide guidelines for selection of a conservative regional growth assumption. - Highlights: • Current tools inconsistently forecast GHG emissions from project-level development. • We outline five assumptions about how projects may affect regional growth. • Our assumptions capture a range of economic and population effects of projects. • Our case study shows that growth assumptions greatly affect regional GHG estimates. • We provide guidelines for selecting a conservative regional growth assumption

Objective Lightning Forecasting at Kennedy Space Center and Cape Canaveral Air Force Station using Cloud-to-Ground Lightning Surveillance System Data

Science.gov (United States)

Lambert, Winfred; Wheeler, Mark; Roeder, William

2005-01-01

The 45th Weather Squadron (45 WS) at Cape Canaveral Air-Force Station (CCAFS)ln Florida issues a probability of lightning occurrence in their daily 24-hour and weekly planning forecasts. This information is used for general planning of operations at CCAFS and Kennedy Space Center (KSC). These facilities are located in east-central Florida at the east end of a corridor known as 'Lightning Alley', an indication that lightning has a large impact on space-lift operations. Much of the current lightning probability forecast is based on a subjective analysis of model and observational data and an objective forecast tool developed over 30 years ago. The 45 WS requested that a new lightning probability forecast tool based on statistical analysis of more recent historical warm season (May-September) data be developed in order to increase the objectivity of the daily thunderstorm probability forecast. The resulting tool is a set of statistical lightning forecast equations, one for each month of the warm season, that provide a lightning occurrence probability for the day by 1100 UTC (0700 EDT) during the warm season.

Analysis and forecasting of nonresidential electricity consumption in Romania

Energy Technology Data Exchange (ETDEWEB)

Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio [Dipartimento di Ingegneria Aerospaziale e Meccanica, Seconda Universita degli Studi di Napoli, Via Roma 29, 81031 Aversa (CE) (Italy); Minea, Alina A. [Faculty of Materials Science and Engineering, Technical University Gh. Asachi from Iasi, Bd. D. Mangeron, No. 59, Iasi (Romania)

2010-11-15

Electricity consumption forecast has fundamental importance in the energy planning of a country. In this paper, we present an analysis and two forecast models for nonresidential electricity consumption in Romania. A first part of the paper is dedicated to the estimation of GDP and price elasticities of consumption. Nonresidential short run GDP and price elasticities are found to be approximately 0.136 and -0.0752, respectively, whereas long run GDP and price elasticities are equal to 0.496 and -0.274 respectively. The second part of the study is dedicated to the forecasting of nonresidential electricity consumption up to year 2020. A Holt-Winters exponential smoothing method and a trigonometric grey model with rolling mechanism (TGMRM) are employed for the consumption prediction. The two models lead to similar results, with an average deviation less than 5%. This deviation is to be considered acceptable in relation to the time horizon considered in the present study. (author)

Analysis and forecasting of nonresidential electricity consumption in Romania

International Nuclear Information System (INIS)

Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio; Minea, Alina A.

2010-01-01

Electricity consumption forecast has fundamental importance in the energy planning of a country. In this paper, we present an analysis and two forecast models for nonresidential electricity consumption in Romania. A first part of the paper is dedicated to the estimation of GDP and price elasticities of consumption. Nonresidential short run GDP and price elasticities are found to be approximately 0.136 and -0.0752, respectively, whereas long run GDP and price elasticities are equal to 0.496 and -0.274 respectively. The second part of the study is dedicated to the forecasting of nonresidential electricity consumption up to year 2020. A Holt-Winters exponential smoothing method and a trigonometric grey model with rolling mechanism (TGMRM) are employed for the consumption prediction. The two models lead to similar results, with an average deviation less than 5%. This deviation is to be considered acceptable in relation to the time horizon considered in the present study. (author)

An Operational Short-Term Forecasting System for Regional Hydropower Management

Science.gov (United States)

Gronewold, A.; Labuhn, K. A.; Calappi, T. J.; MacNeil, A.

2017-12-01

The Niagara River is the natural outlet of Lake Erie and drains four of the five Great lakes. The river is used to move commerce and is home to both sport fishing and tourism industries. It also provides nearly 5 million kilowatts of hydropower for approximately 3.9 million homes. Due to a complex international treaty and the necessity of balancing water needs for an extensive tourism industry, the power entities operating on the river require detailed and accurate short-term river flow forecasts to maximize power output. A new forecast system is being evaluated that takes advantage of several previously independent components including the NOAA Lake Erie operational Forecast System (LEOFS), a previously developed HEC-RAS model, input from the New York Power Authority(NYPA) and Ontario Power Generation (OPG) and lateral flow forecasts for some of the tributaries provided by the NOAA Northeast River Forecast Center (NERFC). The Corps of Engineers updated the HEC-RAS model of the upper Niagara River to use the output forcing from LEOFS and a planned Grass Island Pool elevation provided by the power entities. The entire system has been integrated at the NERFC; it will be run multiple times per day with results provided to the Niagara River Control Center operators. The new model helps improve discharge forecasts by better accounting for dynamic conditions on Lake Erie. LEOFS captures seiche events on the lake that are often several meters of displacement from still water level. These seiche events translate into flow spikes that HEC-RAS routes downstream. Knowledge of the peak arrival time helps improve operational decisions at the Grass Island Pool. This poster will compare and contrast results from the existing operational flow forecast and the new integrated LEOFS/HEC-RAS forecast. This additional model will supply the Niagara River Control Center operators with multiple forecasts of flow to help improve forecasting under a wider variety of conditions.

Application of Medium and Seasonal Flood Forecasts for Agriculture Damage Assessment

Science.gov (United States)

Fakhruddin, Shamsul; Ballio, Francesco; Menoni, Scira

2015-04-01

Early warning is a key element for disaster risk reduction. In recent decades, major advancements have been made in medium range and seasonal flood forecasting. This progress provides a great opportunity to reduce agriculture damage and improve advisories for early action and planning for flood hazards. This approach can facilitate proactive rather than reactive management of the adverse consequences of floods. In the agricultural sector, for instance, farmers can take a diversity of options such as changing cropping patterns, applying fertilizer, irrigating and changing planting timing. An experimental medium range (1-10 day) and seasonal (20-25 days) flood forecasting model has been developed for Thailand and Bangladesh. It provides 51 sets of discharge ensemble forecasts of 1-10 days with significant persistence and high certainty and qualitative outlooks for 20-25 days. This type of forecast could assist farmers and other stakeholders for differential preparedness activities. These ensembles probabilistic flood forecasts have been customized based on user-needs for community-level application focused on agriculture system. The vulnerabilities of agriculture system were calculated based on exposure, sensitivity and adaptive capacity. Indicators for risk and vulnerability assessment were conducted through community consultations. The forecast lead time requirement, user-needs, impacts and management options for crops were identified through focus group discussions, informal interviews and community surveys. This paper illustrates potential applications of such ensembles for probabilistic medium range and seasonal flood forecasts in a way that is not commonly practiced globally today.

Global Drought Services: Collaborations Toward an Information System for Early Warning

Science.gov (United States)

Hayes, M. J.; Pulwarty, R. S.; Svoboda, M.

2014-12-01

Drought is a hazard that lends itself well to diligent, sustained monitoring and early warning. However, unlike most hazards, the fact that droughts typically evolve slowly, can last for months or years and cover vast areas spanning multiple political boundaries/jurisdictions and economic sectors can make it a daunting task to monitor, develop plans for, and identify appropriate, proactive mitigation strategies. The National Drought Mitigation Center (NDMC) and National Integrated Drought Information System (NIDIS) have been working together to reduce societal vulnerability to drought by helping decision makers at all levels to: 1) implement drought early warning/forecasting and decision support systems; 2) support and advocate for better collection of, and understanding of drought impacts; and 3) increase long-term resilience to drought through proactive planning. The NDMC and NIDIS risk management approach has been the basis from which many partners around the world are developing a collaboration and coordination nexus with an ultimate goal of building comprehensive global drought early warning information systems (GDEWIS). The core emphasis of this model is on developing and applying useful and usable information that can be integrated and transferred freely to other regions around the globe. The High-Level Ministerial Declaration on Drought, the Integrated Drought Management Programme (IDMP) co-led by the WMO and the Global Water Partnership (GWP), and the Global Framework for Climate Services are drawing extensively from the integrated NDMC-NIDIS risk management framework. This presentation will describe, in detail, the various drought resources, tools, services, and collaborations already being provided and undertaken at the national and regional scales by the NDMC, NIDIS, and their partners. The presentation will be forward-looking, identifying improvements in existing and proposed mechanisms to help strengthen national and international drought early

Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting

International Nuclear Information System (INIS)

Zhang, Wen Yu; Hong, Wei-Chiang; Dong, Yucheng; Tsai, Gary; Sung, Jing-Tian; Fan, Guo-feng

2012-01-01

The electric load forecasting is complicated, and it sometimes reveals cyclic changes due to cyclic economic activities or climate seasonal nature, such as hourly peak in a working day, weekly peak in a business week, and monthly peak in a demand planned year. Hybridization of support vector regression (SVR) with chaotic sequence and evolutionary algorithms has successfully been applied to improve forecasting accuracy, and to effectively avoid trapping in a local optimum. However, it has not been widely explored to employ SVR-based model to deal with cyclic electric load forecasting. This paper will firstly investigate the potentiality of a novel hybrid algorithm, namely chaotic genetic algorithm-simulated annealing algorithm (CGASA), with an SVR model to improve load forecasting accurate performance. In which, the proposed CGASA employs internal randomness of chaotic iterations to overcome premature local optimum. Secondly, the seasonal mechanism will then be applied to well adjust the cyclic load tendency. Finally, a numerical example from an existed reference is employed to compare the forecasting performance of the proposed SSVRCGASA model. The forecasting results show that the SSVRCGASA model yields more accurate forecasting results than ARIMA and TF-ε-SVR-SA models. -- Highlights: ► Hybridizing the seasonal adjustment mechanism into an SVR model. ► Employing chaotic sequence to improve the premature convergence of genetic algorithm and simulated annealing algorithm. ► Successfully providing significant accurate monthly load demand forecasting.

Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression

KAUST Repository

Taieb, Souhaib Ben; Huser, Raphaë l; Hyndman, Rob J.; Genton, Marc G.

2016-01-01

volatile and less predictable. There is a need within the energy industry for probabilistic forecasts of household electricity consumption to quantify the uncertainty of future electricity demand in order to undertake appropriate planning of generation

Modelling and Forecasting Cruise Tourism Demand to İzmir by Different Artificial Neural Network Architectures

Directory of Open Access Journals (Sweden)

Murat Cuhadar

2014-03-01

Full Text Available Abstract Cruise ports emerged as an important sector for the economy of Turkey bordered on three sides by water. Forecasting cruise tourism demand ensures better planning, efficient preparation at the destination and it is the basis for elaboration of future plans. In the recent years, new techniques such as; artificial neural networks were employed for developing of the predictive models to estimate tourism demand. In this study, it is aimed to determine the forecasting method that provides the best performance when compared the forecast accuracy of Multi-layer Perceptron (MLP, Radial Basis Function (RBF and Generalized Regression neural network (GRNN to estimate the monthly inbound cruise tourism demand to İzmir via the method giving best results. We used the total number of foreign cruise tourist arrivals as a measure of inbound cruise tourism demand and monthly cruise tourist arrivals to İzmir Cruise Port in the period of January 2005 ‐December 2013 were utilized to appropriate model. Experimental results showed that radial basis function (RBF neural network outperforms multi-layer perceptron (MLP and the generalised regression neural networks (GRNN in terms of forecasting accuracy. By the means of the obtained RBF neural network model, it has been forecasted the monthly inbound cruise tourism demand to İzmir for the year 2014.

An Interprofessional Approach to Business Planning: A Model of Collaboration

Science.gov (United States)

Ross, Cory; Alexander, Kathleen; Gritsyuk, Renata; Morrin, Arleen; Tan, Jackie

2011-01-01

George Brown College is among the leaders in the interprofessional health-care education movement in Canada. Interprofessional Education (IPE) and Collaborative Practice occur "when students from two or more professions learn about, from and with each other to enable effective collaboration and improve health outcomes." According to the…

Developing guidelines for incorporating managing demand into WSDOT planning and programming: transportation demand management guidance for corridor planning studies.

Science.gov (United States)

2016-02-01

The Washington State Department of Transportation (WSDOT) regional planning programs address current and forecasted deficiencies of State highways through the conduct of corridor studies. This Guidance for the conduct of corridor planning studies is ...

Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew.

Science.gov (United States)

Pasetto, Damiano; Finger, Flavio; Camacho, Anton; Grandesso, Francesco; Cohuet, Sandra; Lemaitre, Joseph C; Azman, Andrew S; Luquero, Francisco J; Bertuzzo, Enrico; Rinaldo, Andrea

2018-05-01

Computational models of cholera transmission can provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc. Here, we report the efforts of a team from academia, field research and humanitarian organizations to model in near real-time the Haitian cholera outbreak after Hurricane Matthew in October 2016, to assess risk and to quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Our first projection, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak. Future efforts should thus draw together multi

Operational flash flood forecasting platform based on grid technology

Science.gov (United States)

Thierion, V.; Ayral, P.-A.; Angelini, V.; Sauvagnargues-Lesage, S.; Nativi, S.; Payrastre, O.

2009-04-01

Flash flood events of south of France such as the 8th and 9th September 2002 in the Grand Delta territory caused important economic and human damages. Further to this catastrophic hydrological situation, a reform of flood warning services have been initiated (set in 2006). Thus, this political reform has transformed the 52 existing flood warning services (SAC) in 22 flood forecasting services (SPC), in assigning them territories more hydrological consistent and new effective hydrological forecasting mission. Furthermore, national central service (SCHAPI) has been created to ease this transformation and support local services in their new objectives. New functioning requirements have been identified: - SPC and SCHAPI carry the responsibility to clearly disseminate to public organisms, civil protection actors and population, crucial hydrologic information to better anticipate potential dramatic flood event, - a new effective hydrological forecasting mission to these flood forecasting services seems essential particularly for the flash floods phenomenon. Thus, models improvement and optimization was one of the most critical requirements. Initially dedicated to support forecaster in their monitoring mission, thanks to measuring stations and rainfall radar images analysis, hydrological models have to become more efficient in their capacity to anticipate hydrological situation. Understanding natural phenomenon occuring during flash floods mainly leads present hydrological research. Rather than trying to explain such complex processes, the presented research try to manage the well-known need of computational power and data storage capacities of these services. Since few years, Grid technology appears as a technological revolution in high performance computing (HPC) allowing large-scale resource sharing, computational power using and supporting collaboration across networks. Nowadays, EGEE (Enabling Grids for E-science in Europe) project represents the most important

Least square regression based integrated multi-parameteric demand modeling for short term load forecasting

International Nuclear Information System (INIS)

Halepoto, I.A.; Uqaili, M.A.

2014-01-01

Nowadays, due to power crisis, electricity demand forecasting is deemed an important area for socioeconomic development and proper anticipation of the load forecasting is considered essential step towards efficient power system operation, scheduling and planning. In this paper, we present STLF (Short Term Load Forecasting) using multiple regression techniques (i.e. linear, multiple linear, quadratic and exponential) by considering hour by hour load model based on specific targeted day approach with temperature variant parameter. The proposed work forecasts the future load demand correlation with linear and non-linear parameters (i.e. considering temperature in our case) through different regression approaches. The overall load forecasting error is 2.98% which is very much acceptable. From proposed regression techniques, Quadratic Regression technique performs better compared to than other techniques because it can optimally fit broad range of functions and data sets. The work proposed in this paper, will pave a path to effectively forecast the specific day load with multiple variance factors in a way that optimal accuracy can be maintained. (author)

Magnetogram Forecast: An All-Clear Space Weather Forecasting System

Science.gov (United States)

Barghouty, Nasser; Falconer, David

2015-01-01

Solar flares and coronal mass ejections (CMEs) are the drivers of severe space weather. Forecasting the probability of their occurrence is critical in improving space weather forecasts. The National Oceanic and Atmospheric Administration (NOAA) currently uses the McIntosh active region category system, in which each active region on the disk is assigned to one of 60 categories, and uses the historical flare rates of that category to make an initial forecast that can then be adjusted by the NOAA forecaster. Flares and CMEs are caused by the sudden release of energy from the coronal magnetic field by magnetic reconnection. It is believed that the rate of flare and CME occurrence in an active region is correlated with the free energy of an active region. While the free energy cannot be measured directly with present observations, proxies of the free energy can instead be used to characterize the relative free energy of an active region. The Magnetogram Forecast (MAG4) (output is available at the Community Coordinated Modeling Center) was conceived and designed to be a databased, all-clear forecasting system to support the operational goals of NASA's Space Radiation Analysis Group. The MAG4 system automatically downloads nearreal- time line-of-sight Helioseismic and Magnetic Imager (HMI) magnetograms on the Solar Dynamics Observatory (SDO) satellite, identifies active regions on the solar disk, measures a free-energy proxy, and then applies forecasting curves to convert the free-energy proxy into predicted event rates for X-class flares, M- and X-class flares, CMEs, fast CMEs, and solar energetic particle events (SPEs). The forecast curves themselves are derived from a sample of 40,000 magnetograms from 1,300 active region samples, observed by the Solar and Heliospheric Observatory Michelson Doppler Imager. Figure 1 is an example of MAG4 visual output

Medium Range Forecasts Representation (and Long Range Forecasts?)

Science.gov (United States)

Vincendon, J.-C.

2009-09-01

The progress of the numerical forecasts urges us to interest us in more and more distant ranges. We thus supply more and more forecasts with term of some days. Nevertheless, precautions of use are necessary to give the most reliable and the most relevant possible information. Available in a TV bulletin or on quite other support (Internet, mobile phone), the interpretation and the representation of a medium range forecast (5 - 15 days) must be different from those of a short range forecast. Indeed, the "foresee-ability” of a meteorological phenomenon decreases gradually in the course of the ranges, it decreases all the more quickly that the phenomenon is of small scale. So, at the end of some days, the probability character of a forecast becomes very widely dominating. That is why in Meteo-France the forecasts of D+4 to D+7 are accompanied with a confidence index since around ten years. It is a figure between 1 and 5: the more we approach 5, the more the confidence in the supplied forecast is good. In the practice, an indication is supplied for period D+4 / D+5, the other one for period D+6 / D+7, every day being able to benefit from a different forecast, that is be represented in a independent way. We thus supply a global tendency over 24 hours with less and less precise symbols as the range goes away. Concrete examples will be presented. From now on two years, we also publish forecasts to D+8 / J+9, accompanied with a sign of confidence (" good reliability " or " to confirm "). These two days are grouped together on a single map because for us, the described tendency to this term is relevant on a duration about 48 hours with a spatial scale slightly superior to the synoptic scale. So, we avoid producing more than two zones of types of weather over France and we content with giving an evolution for the temperatures (still, in increase or in decline). Newspapers began to publish this information, it should soon be the case of televisions. It is particularly

Tertiary Students' Intention to e-Collaborate for Group Projects: Exploring the Missing Link from an Extended Theory of Planned Behaviour Model

Science.gov (United States)

Cheng, Eddie W. L.; Chu, Samuel K. W.; Ma, Carol S. M.

2016-01-01

With the emergence of web technologies, students can conduct their group projects via virtual platforms, which enable online collaboration. However, students' lack of intention to use web technologies for conducting group work has recently been highlighted. Based on the theory of planned behaviour (TPB), this paper developed and examined an…

Collaborative engagement experiment

Science.gov (United States)

Mullens, Katherine; Troyer, Bradley; Wade, Robert; Skibba, Brian; Dunn, Michael

2006-05-01

Unmanned ground and air systems operating in collaboration have the potential to provide future Joint Forces a significant capability for operations in complex terrain. Collaborative Engagement Experiment (CEE) is a consolidation of separate Air Force, Army and Navy collaborative efforts within the Joint Robotics Program (JRP) to provide a picture of the future of unmanned warfare. The Air Force Research Laboratory (AFRL), Material and Manufacturing Directorate, Aerospace Expeditionary Force Division, Force Protection Branch (AFRL/MLQF), The Army Aviation and Missile Research, Development and Engineering Center (AMRDEC) Joint Technology Center (JTC)/Systems Integration Laboratory (SIL), and the Space and Naval Warfare Systems Center - San Diego (SSC San Diego) are conducting technical research and proof of principle experiments for an envisioned operational concept for extended range, three dimensional, collaborative operations between unmanned systems, with enhanced situational awareness for lethal operations in complex terrain. This paper describes the work by these organizations to date and outlines some of the plans for future work.

A concept for collaborative supply chain planning

DEFF Research Database (Denmark)

Alfnes, E.; Dreyer, H.C.; Hvolby, Hans-Henrik

2012-01-01

The main challenge for many manufacturers is the increased complexity of the supply chain, and as supply chains get more complicated enterprises require better tools for supply chain planning and execution. Many vendors offer systems to plan and control in-house operations, whereas mainly large...... vendors such as Oracle, SAP and I2 offer supply chain planning systems. This limits the ability for SMEs to exploit the supply chain planning options. This paper is part of a research project carried out to develop a new supply chain add-on for Microsoft AX, a very common system for SMEs. The paper...... discusses current supply chain planning solutions and presents a more simple and adaptive concept to be used in both SMEs and larger enterprises....

The strategy of professional forecasting

DEFF Research Database (Denmark)

Ottaviani, Marco; Sørensen, Peter Norman

2006-01-01

We develop and compare two theories of professional forecasters’ strategic behavior. The first theory, reputational cheap talk, posits that forecasters endeavor to convince the market that they are well informed. The market evaluates their forecasting talent on the basis of the forecasts...... and the realized state. If the market expects forecasters to report their posterior expectations honestly, then forecasts are shaded toward the prior mean. With correct market expectations, equilibrium forecasts are imprecise but not shaded. The second theory posits that forecasters compete in a forecasting...... contest with pre-specified rules. In a winner-take-all contest, equilibrium forecasts are excessively differentiated...

THE ACCURACY OF DEMAND FORECAST MODELS AS A CRITICAL FACTOR IN THE FINANCIAL PERFORMANCE OF THE FOOD INDUSTRY

Directory of Open Access Journals (Sweden)

Cássia Rita Pereira Da Veiga

2010-11-01

Full Text Available Every organization needs to balance their production capacities with demand. The role of demand forecasting is to assist in the organization's strategic planning; this process allows administrators to anticipate the future and plot an appropriate course of action. On its own, however, a system of demand forecasting is not enough. It is the quality of information obtained by this system which enables the organization to achieve better operational planning. In this context, this paper presents case study research to: (a define the quantitative model to forecast demand with greater accuracy; and (b to verify the influence of accuracy in demand forecasting on financial performance. This is an ex-post facto descriptive inquiry with a time series in which we made use of historical data from five groups of products over the period 2004–2008. The results suggest that if a company employs the ARIMA model for groups A, B, and E; the Holt model for group D; and the Winter model for group C, revenues will increase by approximately $1,600,000 annually. Key-words: Accuracy. Demand forecasting. Financial performance. 

Post LANDSAT D Advanced Concept Evaluation (PLACE). [with emphasis on mission planning, technological forecasting, and user requirements

Science.gov (United States)

1977-01-01

An outline is given of the mission objectives and requirements, system elements, system concepts, technology requirements and forecasting, and priority analysis for LANDSAT D. User requirements and mission analysis and technological forecasting are emphasized. Mission areas considered include agriculture, range management, forestry, geology, land use, water resources, environmental quality, and disaster assessment.

Are Forecast Updates Progressive?

NARCIS (Netherlands)

C-L. Chang (Chia-Lin); Ph.H.B.F. Franses (Philip Hans); M.J. McAleer (Michael)

2010-01-01

textabstractMacro-economic forecasts typically involve both a model component, which is replicable, as well as intuition, which is non-replicable. Intuition is expert knowledge possessed by a forecaster. If forecast updates are progressive, forecast updates should become more accurate, on average,

Forecasting the Revenues of Local Public Health Departments in the Shadows of the "Great Recession".

Science.gov (United States)

Reschovsky, Andrew; Zahner, Susan J

2016-01-01

The ability of local health departments (LHD) to provide core public health services depends on a reliable stream of revenue from federal, state, and local governments. This study investigates the impact of the "Great Recession" on major sources of LHD revenues and develops a fiscal forecasting model to predict revenues to LHDs in one state over the period 2012 to 2014. Economic forecasting offers a new financial planning tool for LHD administrators and local government policy makers. This study represents a novel research application for these econometric methods. Detailed data on revenues by source for each LHD in Wisconsin were taken from annual surveys conducted by the Wisconsin Department of Health Services over an 8-year period (2002-2009). A forecasting strategy appropriate for each revenue source was developed resulting in "base case" estimates. An analysis of the sensitivity of these revenue forecasts to a set of alternative fiscal policies by the federal, state, and local governments was carried out. The model forecasts total LHD revenues in 2012 of $170.5 million (in 2010 dollars). By 2014, inflation-adjusted revenues will decline by $8 million, a reduction of 4.7%. Because of population growth, per capita real revenues of LHDs are forecast to decline by 6.6% between 2012 and 2014. There is a great deal of uncertainty about the future of federal funding in support of local public health. A doubling of the reductions in federal grants scheduled under current law would result in an additional $4.4 million decline in LHD revenues in 2014. The impact of the Great Recession continues to haunt LHDs. Multiyear revenue forecasting offers a new financial tool to help LHDs better plan for an environment of declining resources. New revenue sources are needed if sharp drops in public health service delivery are to be avoided.

Tsunami Forecasting: The 10 August 2009 Andaman tsunami Demonstrates Progress

Science.gov (United States)

Titov, Vasily; Moore, Christopher; Uslu, Burak; Kanoglu, Utku

2010-05-01

The 10 August 2009 Andaman non-destructive tsunami in the Indian Ocean demonstrated advances in creating a tsunami-resilient global society. Following the Indian Ocean tsunami on 26 December 2004, scientists at the National Oceanic and Atmospheric Administration Center for Tsunami Research (NCTR) at the Pacific Marine Environmental Laboratory (PMEL) developed an interface for its validated and verified tsunami numerical model Method of Splitting Tsunamis (MOST). MOST has been benchmarked substantially through analytical solutions, experimental results and field measurements (Synolakis et al., 2008). MOST and its interface the Community Model Interface for Tsunami (ComMIT) are distributed through extensive capacity-building sessions for the Indian Ocean nations using UNESCO/Intergovernmental Oceanographic Commission (IOC), AusAID, and USAID funding. Over one hundred-sixty scientists have been trained in tsunami inundation mapping, leading to the first generation of inundation models for many Indian Ocean shorelines. During the 10 August 2009 Andaman tsunami event, NCTR scientists exercised the forecast system in research mode using the first generation inundation models developed during ComMIT trainings. Assimilating key data from a Kingdom of Thailand tsunameter, coastal tsunami amplitudes were predicted in Indonesia, Thailand, and India coastlines, before the first tsunami arrival, using models developed by ComMIT trainees. Since its first test in 2003, one more time, NCTR's forecasting methodology proved the effectiveness of operational tsunami forecasting using real-time deep-ocean data assimilated into forecast models (Wei et al., 2008 and Titov, 2009). The 2009 Andaman tsunami demonstrated that operational tsunami forecasting tools are now available and coupled with inundation mapping tools can be effective and can reduce false alarms. International collaboration is required to fully utilize this technology's potential. Enhanced educational efforts both at

A review of operational, regional-scale, chemical weather forecasting models in Europe

Czech Academy of Sciences Publication Activity Database

Kukkonen, J.; Olsson, T.; Schultz, D.M.; Baklanov, A.; Klein, T.; Miranda, A.I.; Monteiro, A.; Hirtl, M.; Tarvainen, V.; Boy, M.; Peuch, V.H.; PoupKou, A.; Kioutsioukis, I.; Finardi, S.; Sofiev, M.; Sokhi, R.; Lehtinen, K.E.J.; Karatzas, K.; San José, R.; Astitha, M.; Kallos, G.; Schaap, M.; Reimer, E.; Jakobs, H.; Eben, Kryštof

2012-01-01

Roč. 12, - (2012), s. 1-87 ISSN 1680-7316 Institutional research plan: CEZ:AV0Z10300504 Keywords : chemical weather * numerical models * operational forecasting * air Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 5.510, year: 2012

Relating Tropical Cyclone Track Forecast Error Distributions with Measurements of Forecast Uncertainty

Science.gov (United States)

2016-03-01

CYCLONE TRACK FORECAST ERROR DISTRIBUTIONS WITH MEASUREMENTS OF FORECAST UNCERTAINTY by Nicholas M. Chisler March 2016 Thesis Advisor...March 2016 3. REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE RELATING TROPICAL CYCLONE TRACK FORECAST ERROR DISTRIBUTIONS...WITH MEASUREMENTS OF FORECAST UNCERTAINTY 5. FUNDING NUMBERS 6. AUTHOR(S) Nicholas M. Chisler 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES

Linking seasonal climate forecasts with crop models in Iberian Peninsula

Science.gov (United States)

Capa, Mirian; Ines, Amor; Baethgen, Walter; Rodriguez-Fonseca, Belen; Han, Eunjin; Ruiz-Ramos, Margarita

2015-04-01

Translating seasonal climate forecasts into agricultural production forecasts could help to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse conditions. In this study, we use seasonal rainfall forecasts and crop models to improve predictability of wheat yield in the Iberian Peninsula (IP). Additionally, we estimate economic margins and production risks associated with extreme scenarios of seasonal rainfall forecast. This study evaluates two methods for disaggregating seasonal climate forecasts into daily weather data: 1) a stochastic weather generator (CondWG), and 2) a forecast tercile resampler (FResampler). Both methods were used to generate 100 (with FResampler) and 110 (with CondWG) weather series/sequences for three scenarios of seasonal rainfall forecasts. Simulated wheat yield is computed with the crop model CERES-wheat (Ritchie and Otter, 1985), which is included in Decision Support System for Agrotechnology Transfer (DSSAT v.4.5, Hoogenboom et al., 2010). Simulations were run at two locations in northeastern Spain where the crop model was calibrated and validated with independent field data. Once simulated yields were obtained, an assessment of farmer's gross margin for different seasonal climate forecasts was accomplished to estimate production risks under different climate scenarios. This methodology allows farmers to assess the benefits and risks of a seasonal weather forecast in IP prior to the crop growing season. The results of this study may have important implications on both, public (agricultural planning) and private (decision support to farmers, insurance companies) sectors. Acknowledgements Research by M. Capa-Morocho has been partly supported by a PICATA predoctoral fellowship of the Moncloa Campus of International Excellence (UCM-UPM) and MULCLIVAR project (CGL2012-38923-C02-02) References Hoogenboom, G. et al., 2010. The Decision

Improving the principles of short-term electric load forecasting of the Irkutsk region

Directory of Open Access Journals (Sweden)

Kornilov Vladimir

2017-01-01

Full Text Available Forecasting of electric load (EL is an important task for both electric power entities and large consumers of electricity [1]. Large consumers are faced with the need to compose applications for the planned volume of EL, and the deviation of subsequent real consumption from previously announced leads to the appearance of penalties from the wholesale market. In turn, electricity producers are interested in forecasting the demand for electricity for prompt response to its fluctuations and for the purpose of optimal infrastructure development. The most difficult and urgent task is the hourly forecasting of EL, which is extremely important for the successful solution of problems of optimization of generating capacities, minimization of power losses, dispatching control, security assessment of power supply, etc. Ultimately, such forecasts allow optimizing the cash costs for electricity and fuel or water consumption during generation. This paper analyzes the experience of the branch of JSC "SO UPS" Irkutsk Regional Dispatch Office of the procedure for short-term forecasting of the EL of the Irkutsk region.

Collaboration process for integrated social and health care strategy implementation

Directory of Open Access Journals (Sweden)

Jukka Korpela

2012-05-01

Full Text Available Objective:  To present collaboration process for creating a roadmap for the implementation of a strategy for integrated health and social care. The developed collaboration process includes multiple phases and uses electronic group decision support system technology (GDSS.Method: A case study done in the South Karelia District of Social and Health Services in Finland during 2010 - 2011. An expert panel of 13 participants was used in the planning process of the strategy implementation. The participants were interviewed and observed during the case study.Results: As a practical result, a roadmap for integrated health and social care strategy implementation has been developed. The strategic roadmap includes detailed plans of several projects which are needed for successful integration strategy implementation. As an academic result, a collaboration process to create such a roadmap has been developed.Conclusions: The collaboration process and technology seem to suit the planning process well. The participants of the meetings were satisfied with the collaboration process and the GDSS technology. The strategic roadmap was accepted by the participants, which indicates satisfaction with the developed process.

Collaboration process for integrated social and health care strategy implementation.

Science.gov (United States)

Korpela, Jukka; Elfvengren, Kalle; Kaarna, Tanja; Tepponen, Merja; Tuominen, Markku

2012-01-01

To present a collaboration process for creating a roadmap for the implementation of a strategy for integrated health and social care. The developed collaboration process includes multiple phases and uses electronic group decision support system technology (GDSS). A case study done in the South Karelia District of Social and Health Services in Finland during 2010-2011. An expert panel of 13 participants was used in the planning process of the strategy implementation. The participants were interviewed and observed during the case study. As a practical result, a roadmap for integrated health and social care strategy implementation has been developed. The strategic roadmap includes detailed plans of several projects which are needed for successful integration strategy implementation. As an academic result, a collaboration process to create such a roadmap has been developed. The collaboration process and technology seem to suit the planning process well. The participants of the meetings were satisfied with the collaboration process and the GDSS technology. The strategic roadmap was accepted by the participants, which indicates satisfaction with the developed process.

The Role of Collaborative Advantage for Analyzing the Effect of Supply Chain Collaboration on Firm Performance

Directory of Open Access Journals (Sweden)

Huriye Yılmaz

2017-06-01

Full Text Available Collaboration plays a critical role in a globalized, rapidly changing and competitive world, as the resources of an individual company are limited to compete with the challenges of the era. Supply chain collaboration is defined as a partnership process where two or more autonomous firms work closely to plan and execute supply chain operations towards common goals and mutual benefits. Supply chain collaboration results in collaborative advantage, the strategic benefits gained over competitors through supply chain partnering, and these both increase firm performance of the partners. In this research, the effect of supply chain collaboration on firm performance has been investigated by distributing a survey to Turkish companies which have been responded by 150. The role of collaborative advantage in this relation has also been measured. The results of the research suggest that there is a positive correlation between supply chain collaboration and collaborative advantage. The results also prove that supply chain collaboration positively affects firm performance. It is also proven that the mediator role of collaborative advantage on the effect of supply chain collaboration on firm performance is statistically significant.

Drought Forecasting by SPI Index and ANFIS Model Using Fuzzy C-mean Clustering

Directory of Open Access Journals (Sweden)

mehdi Komasi

2013-08-01

Full Text Available Drought is the interaction between environment and water cycle in the world and affects natural environment of an area when it persists for a longer period. So, developing a suitable index to forecast the spatial and temporal distribution of drought plays an important role in the planning and management of natural resources and water resource systems. In this article, firstly, the drought concept and drought indexes were introduced and then the fuzzy neural networks and fuzzy C-mean clustering were applied to forecast drought via standardized precipitation index (SPI. The results of this research indicate that the SPI index is more capable than the other indexes such as PDSI (Palmer Drought Severity Index, PAI (Palfai Aridity Index and etc. in drought forecasting process. Moreover, application of adaptive nero-fuzzy network accomplished by C-mean clustering has high efficiency in the drought forecasting.

Review of Collaborative Tools for Planning and Engineering

Science.gov (United States)

2007-10-01

intergrating simulations and diverse collaborative activities, enhance productivity, generate products, faster, automation, integration, multiple...required. Identifiers, such as equipment model designation, trade name, military project code name, geographic location may also be included. If

Grey Forecast Rainfall with Flow Updating Algorithm for Real-Time Flood Forecasting

Directory of Open Access Journals (Sweden)

Jui-Yi Ho

2015-04-01

Full Text Available The dynamic relationship between watershed characteristics and rainfall-runoff has been widely studied in recent decades. Since watershed rainfall-runoff is a non-stationary process, most deterministic flood forecasting approaches are ineffective without the assistance of adaptive algorithms. The purpose of this paper is to propose an effective flow forecasting system that integrates a rainfall forecasting model, watershed runoff model, and real-time updating algorithm. This study adopted a grey rainfall forecasting technique, based on existing hourly rainfall data. A geomorphology-based runoff model can be used for simulating impacts of the changing geo-climatic conditions on the hydrologic response of unsteady and non-linear watershed system, and flow updating algorithm were combined to estimate watershed runoff according to measured flow data. The proposed flood forecasting system was applied to three watersheds; one in the United States and two in Northern Taiwan. Four sets of rainfall-runoff simulations were performed to test the accuracy of the proposed flow forecasting technique. The results indicated that the forecast and observed hydrographs are in good agreement for all three watersheds. The proposed flow forecasting system could assist authorities in minimizing loss of life and property during flood events.

Review of Wind Energy Forecasting Methods for Modeling Ramping Events

Energy Technology Data Exchange (ETDEWEB)

Wharton, S; Lundquist, J K; Marjanovic, N; Williams, J L; Rhodes, M; Chow, T K; Maxwell, R

2011-03-28

Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF.WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.

A trend fixed on firstly and seasonal adjustment model combined with the ε-SVR for short-term forecasting of electricity demand

International Nuclear Information System (INIS)

Wang Jianzhou; Zhu Wenjin; Zhang Wenyu; Sun Donghuai

2009-01-01

Short-term electricity demand forecasting has always been an essential instrument in power system planning and operation by which an electric utility plans and dispatches loading so as to meet system demand. The accuracy of the dispatching system, derived from the accuracy of demand forecasting and the forecasting algorithm used, will determines the economic of the power system operation as well as the stability of the whole society. This paper presents a combined ε-SVR model considering seasonal proportions based on development tendencies from history data. We use one-order moving averages to produce a comparatively smooth data series, taking the averaging period as the interval that can effectively eliminate the seasonal variation. We used the smoothed data series as the training set input for the ε-SVR model and obtained the corresponding forecasting value. Afterward, we accounted for the previously removed seasonal variation. As a case, we forecast northeast electricity demand of China using the new method. We demonstrated that this simple procedure has very satisfactory overall performance by an analysis of variance with relative verification and validation. Significant reductions in forecast errors were achieved.

A trend fixed on firstly and seasonal adjustment model combined with the epsilon-SVR for short-term forecasting of electricity demand

Energy Technology Data Exchange (ETDEWEB)

Wang Jianzhou [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Zhu Wenjin, E-mail: crying.1@hotmail.co [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Zhang Wenyu [College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 (China); Sun Donghuai [Key Laboratory of Western Chinas Environmental Systems (Ministry of Education) College of Earth and Environment Sciences, Lanzhou University, Lanzhou 730000 (China)

2009-11-15

Short-term electricity demand forecasting has always been an essential instrument in power system planning and operation by which an electric utility plans and dispatches loading so as to meet system demand. The accuracy of the dispatching system, derived from the accuracy of demand forecasting and the forecasting algorithm used, will determines the economic of the power system operation as well as the stability of the whole society. This paper presents a combined epsilon-SVR model considering seasonal proportions based on development tendencies from history data. We use one-order moving averages to produce a comparatively smooth data series, taking the averaging period as the interval that can effectively eliminate the seasonal variation. We used the smoothed data series as the training set input for the epsilon-SVR model and obtained the corresponding forecasting value. Afterward, we accounted for the previously removed seasonal variation. As a case, we forecast northeast electricity demand of China using the new method. We demonstrated that this simple procedure has very satisfactory overall performance by an analysis of variance with relative verification and validation. Significant reductions in forecast errors were achieved.

A trend fixed on firstly and seasonal adjustment model combined with the {epsilon}-SVR for short-term forecasting of electricity demand

Energy Technology Data Exchange (ETDEWEB)

Wang, Jianzhou; Zhu, Wenjin [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Zhang, Wenyu [College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 (China); Sun, Donghuai [Key Laboratory of Western Chinas Environmental Systems (Ministry of Education) College of Earth and Environment Sciences, Lanzhou University, Lanzhou 730000 (China)

2009-11-15

Short-term electricity demand forecasting has always been an essential instrument in power system planning and operation by which an electric utility plans and dispatches loading so as to meet system demand. The accuracy of the dispatching system, derived from the accuracy of demand forecasting and the forecasting algorithm used, will determines the economic of the power system operation as well as the stability of the whole society. This paper presents a combined {epsilon}-SVR model considering seasonal proportions based on development tendencies from history data. We use one-order moving averages to produce a comparatively smooth data series, taking the averaging period as the interval that can effectively eliminate the seasonal variation. We used the smoothed data series as the training set input for the {epsilon}-SVR model and obtained the corresponding forecasting value. Afterward, we accounted for the previously removed seasonal variation. As a case, we forecast northeast electricity demand of China using the new method. We demonstrated that this simple procedure has very satisfactory overall performance by an analysis of variance with relative verification and validation. Significant reductions in forecast errors were achieved. (author)

A Novel Hybrid Model for Short-Term Forecasting in PV Power Generation

Directory of Open Access Journals (Sweden)

Yuan-Kang Wu

2014-01-01

Full Text Available The increasing use of solar power as a source of electricity has led to increased interest in forecasting its power output over short-time horizons. Short-term forecasts are needed for operational planning, switching sources, programming backup, reserve usage, and peak load matching. However, the output of a photovoltaic (PV system is influenced by irradiation, cloud cover, and other weather conditions. These factors make it difficult to conduct short-term PV output forecasting. In this paper, an experimental database of solar power output, solar irradiance, air, and module temperature data has been utilized. It includes data from the Green Energy Office Building in Malaysia, the Taichung Thermal Plant of Taipower, and National Penghu University. Based on the historical PV power and weather data provided in the experiment, all factors that influence photovoltaic-generated energy are discussed. Moreover, five types of forecasting modules were developed and utilized to predict the one-hour-ahead PV output. They include the ARIMA, SVM, ANN, ANFIS, and the combination models using GA algorithm. Forecasting results show the high precision and efficiency of this combination model. Therefore, the proposed model is suitable for ensuring the stable operation of a photovoltaic generation system.

Failure Forecasting in Triaxially Stressed Sandstones

Science.gov (United States)

Crippen, A.; Bell, A. F.; Curtis, A.; Main, I. G.

2017-12-01

Precursory signals to fracturing events have been observed to follow power-law accelerations in spatial, temporal, and size distributions leading up to catastrophic failure. In previous studies this behavior was modeled using Voight's relation of a geophysical precursor in order to perform `hindcasts' by solving for failure onset time. However, performing this analysis in retrospect creates a bias, as we know an event happened, when it happened, and we can search data for precursors accordingly. We aim to remove this retrospective bias, thereby allowing us to make failure forecasts in real-time in a rock deformation laboratory. We triaxially compressed water-saturated 100 mm sandstone cores (Pc= 25MPa, Pp = 5MPa, σ = 1.0E-5 s-1) to the point of failure while monitoring strain rate, differential stress, AEs, and continuous waveform data. Here we compare the current `hindcast` methods on synthetic and our real laboratory data. We then apply these techniques to increasing fractions of the data sets to observe the evolution of the failure forecast time with precursory data. We discuss these results as well as our plan to mitigate false positives and minimize errors for real-time application. Real-time failure forecasting could revolutionize the field of hazard mitigation of brittle failure processes by allowing non-invasive monitoring of civil structures, volcanoes, and possibly fault zones.

Short-term residential load forecasting: Impact of calendar effects and forecast granularity

DEFF Research Database (Denmark)

Lusis, Peter; Khalilpour, Kaveh Rajab; Andrew, Lachlan

2017-01-01

forecasting for a single-customer or even down at an appliance level. Access to high resolution data from smart meters has enabled the research community to assess conventional load forecasting techniques and develop new forecasting strategies suitable for demand-side disaggregated loads. This paper studies...... how calendar effects, forecasting granularity and the length of the training set affect the accuracy of a day-ahead load forecast for residential customers. Root mean square error (RMSE) and normalized RMSE were used as forecast error metrics. Regression trees, neural networks, and support vector...... regression yielded similar average RMSE results, but statistical analysis showed that regression trees technique is significantly better. The use of historical load profiles with daily and weekly seasonality, combined with weather data, leaves the explicit calendar effects a very low predictive power...

Forecasting and observability: critical technologies for system operations with high PV penetration

DEFF Research Database (Denmark)

Alet, Pierre-Jean; Efthymiou, Venizelos; Graditi, Giorgio

2016-01-01

– Photovoltaics (ETIP PV) reviews the different use cases for these technologies, their current status, and the need for future developments. Power system operations require a real-time view of PV production for managing power reserves and for feeding shortterm forecasts. They also require forecasts on all......Forecasting and monitoring technologies for photovoltaics are required on different spatial and temporal scales by multiple actors, from the owners of PV systems to transmission system operators. In this paper the Grid integration working group of the European Technology and Innovation Platform...... timescales from the short (for dispatching purposes), where statistical models work best, to the very long (for infrastructure planning), where physics-based models are more accurate. Power system regulations are driving the development of these techniques. This application also provides a good basis...

Adaptation in Collaborative Governance Regimes

Science.gov (United States)

Emerson, Kirk; Gerlak, Andrea K.

2014-10-01

Adaptation and the adaptive capacity of human and environmental systems have been of central concern to natural and social science scholars, many of whom characterize and promote the need for collaborative cross-boundary systems that are seen as flexible and adaptive by definition. Researchers who study collaborative governance systems in the public administration, planning and policy literature have paid less attention to adaptive capacity specifically and institutional adaptation in general. This paper bridges the two literatures and finds four common dimensions of capacity, including structural arrangements, leadership, knowledge and learning, and resources. In this paper, we focus on institutional adaptation in the context of collaborative governance regimes and try to clarify and distinguish collaborative capacity from adaptive capacity and their contributions to adaptive action. We posit further that collaborative capacities generate associated adaptive capacities thereby enabling institutional adaptation within collaborative governance regimes. We develop these distinctions and linkages between collaborative and adaptive capacities with the help of an illustrative case study in watershed management within the National Estuary Program.

Legal Considerations for International Collaborative Research Contract

International Nuclear Information System (INIS)

Lee, D. S.; Oh, K. B.; Kim, H. J.; Lee, J. H.

2007-01-01

Though collaborative research is pure academic activity the research plan and resource allocation for the research are shaped under foam of contract. Thus, legal binding effect and compulsive instrument is adopted at the research contract. This paper aimed at guiding equal collaborative research contract in legal aspect. To reach the goal (1) enforceability and elements of international collaborative contract, (2) damage calculation and related issues with those topics shall be discussed in each section

Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit.

Science.gov (United States)

Capan, Muge; Hoover, Stephen; Jackson, Eric V; Paul, David; Locke, Robert

2016-01-01

Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations. Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach. We used five years of retrospective daily NICU census data for model development (January 2008 - December 2012, N=1827 observations) and one year of data for validation (January - December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics. The census showed a slightly increasing linear trend. Best fitting models included a non-seasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)x(1,1,2)7 and ARIMA(2,1,4)x(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach. Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support short- and long-term census forecasting, and inform staff resource planning.

Nurse forecasting in Europe (RN4CAST): Rationale, design and methodology.

NARCIS (Netherlands)

Sermeus, W.; Aiken, L.H.; Heede, K. Van den; Rafferty, A.M.; Griffiths, P.; Moreno-Casbas, M.T.; Busse, R.; Lindqvist, R.; Scott, A.P.; Bruyneel, L.; Brzostek, T.; Kinnunen, J.; Schubert, M.; Schoonhoven, L.; Zikos, D.

2011-01-01

BACKGROUND: Current human resources planning models in nursing are unreliable and ineffective as they consider volumes, but ignore effects on quality in patient care. The project RN4CAST aims innovative forecasting methods by addressing not only volumes, but quality of nursing staff as well as

Combination of synoptical-analogous and dynamical methods to increase skill score of monthly air temperature forecasts over Northern Eurasia

Science.gov (United States)

Khan, Valentina; Tscepelev, Valery; Vilfand, Roman; Kulikova, Irina; Kruglova, Ekaterina; Tischenko, Vladimir

2016-04-01

Long-range forecasts at monthly-seasonal time scale are in great demand of socio-economic sectors for exploiting climate-related risks and opportunities. At the same time, the quality of long-range forecasts is not fully responding to user application necessities. Different approaches, including combination of different prognostic models, are used in forecast centers to increase the prediction skill for specific regions and globally. In the present study, two forecasting methods are considered which are exploited in operational practice of Hydrometeorological Center of Russia. One of them is synoptical-analogous method of forecasting of surface air temperature at monthly scale. Another one is dynamical system based on the global semi-Lagrangian model SL-AV, developed in collaboration of Institute of Numerical Mathematics and Hydrometeorological Centre of Russia. The seasonal version of this model has been used to issue global and regional forecasts at monthly-seasonal time scales. This study presents results of the evaluation of surface air temperature forecasts generated with using above mentioned synoptical-statistical and dynamical models, and their combination to potentially increase skill score over Northern Eurasia. The test sample of operational forecasts is encompassing period from 2010 through 2015. The seasonal and interannual variability of skill scores of these methods has been discussed. It was noticed that the quality of all forecasts is highly dependent on the inertia of macro-circulation processes. The skill scores of forecasts are decreasing during significant alterations of synoptical fields for both dynamical and empirical schemes. Procedure of combination of forecasts from different methods, in some cases, has demonstrated its effectiveness. For this study the support has been provided by Grant of Russian Science Foundation (№14-37-00053).

Energy reference forecast for 2014

International Nuclear Information System (INIS)

Schlesinger, Michael; Lutz, Christian

2014-01-01

The German Federal Ministry for Economic Affairs and Energy has commissioned three reputed institutions to prepare an energy reference forecast as well as a target scenario up to the year 2050. The results of this survey evidence a substantial need for political action if the goals of the Federal Government's energy concept are to be achieved as planned. In view of the wide range of interests among the players involved as well as the complexity of the demands facing the political leadership from diverse areas of life it appears unlikely that the targets laid down in the energy concept can be realised.

Ovis: A Framework for Visual Analysis of Ocean Forecast Ensembles.

Science.gov (United States)

Höllt, Thomas; Magdy, Ahmed; Zhan, Peng; Chen, Guoning; Gopalakrishnan, Ganesh; Hoteit, Ibrahim; Hansen, Charles D; Hadwiger, Markus

2014-08-01

We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures.Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea.

Calculation of Forecast - Tool of the Decision and the Example of a Shipping Entity DANUBTRANS Galati S.C.