Space power needs and forecasted technologies for the 1990s and beyond

International Nuclear Information System (INIS)

Buden, D.; Albert, T.

1987-01-01

A new generation of reactors for electric power will be available for space missions to satisfy military and civilian needs in the 1990s and beyond. To ensure a useful product, nuclear power plant development must be cognizant of other space power technologies. Major advances in solar and chemical technologies need to be considered in establishing the goals of future nuclear power plants. In addition, the mission needs are evolving into new regimes. Civilian and military power needs are forecasted to exceed anything used in space to date. Technology trend forecasts have been mapped as a function of time for solar, nuclear, chemical, and storage systems to illustrate areas where each technology provides minimum mass. Other system characteristics may dominate the usefulness of a technology on a given mission. This paper will discuss some of these factors, as well as forecast future military and civilian power needs and the status of technologies for the 1990s and 2000s. 6 references

Forecasting Technological Discontinuities in the ICT Industry

DEFF Research Database (Denmark)

Hoisl, Karin; Stelzer, Tobias; Biala, Stefanie

2015-01-01

in the ICT industry. The conjoint approach allows for a simulation of the forecasting process and considers utility trade-offs. The results show that for both types of experts the perceived benefit of users most highly contributes to predicting technological discontinuities. Internal experts assign more...

Succeeding in deep water by combining technology qualification and production forecasting

Energy Technology Data Exchange (ETDEWEB)

Hussain, A.; Oiungen, B.; Raposo, C. [Det Norske Veritas (DNV), Rio de Janeiro, RJ (Brazil)

2008-07-01

All the easy oil and gas is gone, and, as a result the Oil and Gas industry is continuously targeting deeper and more remote fields. The exploration and development of deep water oil and gas fields is associated with enormous costs and multiple uncertainties with regard to equipment reliability and performance. Proper risk management can be used to evaluate the impact of these uncertainties thereby helping to ensure optimal business performance of the future assets, as well as helping the decision maker target investment towards areas where the financial impact will be the greatest. This paper reviews the principles of Technology Qualification and Production Forecasting methodology, both of which are risk management solutions with a proven track record for deep water field developments. (author)

Forecasting systemic impact in financial networks

NARCIS (Netherlands)

Hautsch, N.; Schaumburg, J.; Schienle, M.

2014-01-01

We propose a methodology for forecasting the systemic impact of financial institutions in interconnected systems. Utilizing a five-year sample including the 2008/9 financial crisis, we demonstrate how the approach can be used for the timely systemic risk monitoring of large European banks and

Case studies of extended model-based flood forecasting: prediction of dike strength and flood impacts

Science.gov (United States)

Stuparu, Dana; Bachmann, Daniel; Bogaard, Tom; Twigt, Daniel; Verkade, Jan; de Bruijn, Karin; de Leeuw, Annemargreet

2017-04-01

Flood forecasts, warning and emergency response are important components in flood risk management. Most flood forecasting systems use models to translate weather predictions to forecasted discharges or water levels. However, this information is often not sufficient for real time decisions. A sound understanding of the reliability of embankments and flood dynamics is needed to react timely and reduce the negative effects of the flood. Where are the weak points in the dike system? When, how much and where the water will flow? When and where is the greatest impact expected? Model-based flood impact forecasting tries to answer these questions by adding new dimensions to the existing forecasting systems by providing forecasted information about: (a) the dike strength during the event (reliability), (b) the flood extent in case of an overflow or a dike failure (flood spread) and (c) the assets at risk (impacts). This work presents three study-cases in which such a set-up is applied. Special features are highlighted. Forecasting of dike strength. The first study-case focusses on the forecast of dike strength in the Netherlands for the river Rhine branches Waal, Nederrijn and IJssel. A so-called reliability transformation is used to translate the predicted water levels at selected dike sections into failure probabilities during a flood event. The reliability of a dike section is defined by fragility curves - a summary of the dike strength conditional to the water level. The reliability information enhances the emergency management and inspections of embankments. Ensemble forecasting. The second study-case shows the setup of a flood impact forecasting system in Dumfries, Scotland. The existing forecasting system is extended with a 2D flood spreading model in combination with the Delft-FIAT impact model. Ensemble forecasts are used to make use of the uncertainty in the precipitation forecasts, which is useful to quantify the certainty of a forecasted flood event. From global

Impact of onsite solar generation on system load demand forecast

International Nuclear Information System (INIS)

Kaur, Amanpreet; Pedro, Hugo T.C.; Coimbra, Carlos F.M.

2013-01-01

Highlights: • We showed the impact onsite solar generation on system demand load forecast. • Forecast performance degrades by 9% and 3% for 1 h and 15 min forecast horizons. • Error distribution for onsite case is best characterized as t-distribution. • Relation between error, solar penetration and solar variability is characterized. - Abstract: Net energy metering tariffs have encouraged the growth of solar PV in the distribution grid. The additional variability associated with weather-dependent renewable energy creates new challenges for power system operators that must maintain and operate ancillary services to balance the grid. To deal with these issues power operators mostly rely on demand load forecasts. Electric load forecast has been used in power industry for a long time and there are several well established load forecasting models. But the performance of these models for future scenario of high renewable energy penetration is unclear. In this work, the impact of onsite solar power generation on the demand load forecast is analyzed for a community that meets between 10% and 15% of its annual power demand and 3–54% of its daily power demand from a solar power plant. Short-Term Load Forecasts (STLF) using persistence, machine learning and regression-based forecasting models are presented for two cases: (1) high solar penetration and (2) no penetration. Results show that for 1-h and 15-min forecasts the accuracy of the models drops by 9% and 3% with high solar penetration. Statistical analysis of the forecast errors demonstrate that the error distribution is best characterized as a t-distribution for the high penetration scenario. Analysis of the error distribution as a function of daily solar penetration for different levels of variability revealed that the solar power variability drives the forecast error magnitude whereas increasing penetration level has a much smaller contribution. This work concludes that the demand forecast error distribution

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...

An Approach to Assess Observation Impact Based on Observation-Minus-Forecast Residuals

Science.gov (United States)

Todling, Ricardo

2009-01-01

Langland and Baker (2004) introduced an approach to assess the impact of observations on the forecasts. In that, a state-space aspect of the forecast is defined and a procedure is derived that relates changes in the aspect with changes in the initial conditions associated with the assimilation of observations) ultimately providing information about the impact of individual observations on the forecast. Some features of the approach are to be noted. The typical choice of forecast aspect employed in related works is rather arbitrary and leads to an incomplete assessment of the observing system. Furthermore, the state-space forecast aspect requires availability of a verification state that should ideally be uncorrelated with the forecast but in practice is not. Lastly, the approach involves the adjoint operator of the entire data assimilation system and as such it is constrained by the validity of this operator. In this presentation, an observation-space metric is used that, for a relatively time-homogeneous observing system, allows inferring observation impact on the forecast without some of the limitations above. Specifically, using observation-minus-forecast residuals leads to an approach with the following features: (i) it suggests a rather natural choice of forecast aspect, directly linked to the analysis system and providing full assessment of the observations; (ii) it naturally avoids introducing undesirable correlations in the forecast aspect by verifying against the observations; and (iii) it does not involve linearization and use of adjoints; therefore being applicable to any length of forecast. The state and observation-space approaches might be complementary to some degree, and involve different limitations and complexities. Illustrations are given using the NASA GEOS-5 data.

On the market impact of wind energy forecasts

International Nuclear Information System (INIS)

Jonsson, Tryggvi; Pinson, Pierre; Madsen, Henrik

2010-01-01

This paper presents an analysis of how day-ahead electricity spot prices are affected by day-ahead wind power forecasts. Demonstration of this relationship is given as a test case for the Western Danish price area of the Nord Pool's Elspot market. Impact on the average price behaviour is investigated as well as that on the distributional properties of the price. By using a non-parametric regression model to assess the effects of wind power forecasts on the average behaviour, the non-linearities and time variations in the relationship are captured well and the effects are shown to be quite substantial. Furthermore, by evaluating the distributional properties of the spot prices under different scenarios, the impact of the wind power forecasts on the price distribution is proved to be considerable. The conditional price distribution is moreover shown to be non-Gaussian. This implies that forecasting models for electricity spot prices for which parameters are estimated by a least squares techniques will not have Gaussian residuals. Hence the widespread assumption of Gaussian residuals from electricity spot price models is shown to be inadequate for these model types. The revealed effects are likely to be observable and qualitatively similar in other day-ahead electricity markets significantly penetrated by wind power. (author)

[Guidelines for budget impact analysis of health technologies in Brazil].

Science.gov (United States)

Ferreira-Da-Silva, Andre Luis; Ribeiro, Rodrigo Antonini; Santos, Vânia Cristina Canuto; Elias, Flávia Tavares Silva; d'Oliveira, Alexandre Lemgruber Portugal; Polanczyk, Carisi Anne

2012-07-01

Budget impact analysis (BIA) provides operational financial forecasts to implement new technologies in healthcare systems. There were no previous specific recommendations to conduct such analyses in Brazil. This paper reviews BIA methods for health technologies and proposes BIA guidelines for the public and private Brazilian healthcare system. The following recommendations were made: adopt the budget administrator's perspective; use a timeframe of 1 to 5 years; compare reference and alternative scenarios; consider the technology's rate of incorporation; estimate the target population by either an epidemiological approach or measured demand; consider restrictions on technologies' indication or factors that increase the demand for them; consider direct and averted costs; do not adjust for inflation or discounts; preferably, integrate information on a spreadsheet; calculate the incremental budget impact between scenarios; and summarize information in a budget impact report.

Forecasting with quantitative methods the impact of special events in time series

OpenAIRE

Nikolopoulos, Konstantinos

2010-01-01

Abstract Quantitative methods are very successful for producing baseline forecasts of time series; however these models fail to forecast neither the timing nor the impact of special events such as promotions or strikes. In most of the cases the timing of such events is not known so they are usually referred as shocks (economics) or special events (forecasting). Sometimes the timing of such events is known a priori (i.e. a future promotion); but even then the impact of the forthcom...

A patent survey case: how could technological forecasting help cosmetic chemists with product innovation?

Science.gov (United States)

Domicio Da Silva Souza, Ivan; Juliana Pinheiro, Bárbara; Passarini Takahashi, Vania

2012-01-01

Patents represent a free and open source of data for studying innovation and forecasting technological trends. Thus, we suggest that new discussions about the role of patent information are needed. To illustrate the relevance of this issue, we performed a survey of patents involving skin care products, which were granted by the United States Patent and Trademark Office (USPTO) between 2006 and 2010, to identify opportunities for innovation and technological trends. We quantified the use of technologies in 333 patents. We plotted a life cycle of technologies related to natural ingredients. We also determined the cross impact of the technologies identified. We observed technologies related to processes applied to cosmetics (2.2%), functional packaging and applicators (2.9%), excipients and active compounds (21.5%), and cosmetic preparations (73.5%). Further, 21.6% of the patents were related to the use of natural ingredients. Several opportunities for innovation were discussed throughout this paper, for example, the use of peptides as active compounds or intracellular carriers (only 3.9% of the technologies in cosmetic preparations). We also observed technological cross impacts that suggested a trend toward multifunctional cosmetics, among others. Patent surveys may help researchers with product innovation because they allow us to identify available and unexplored technologies and turn them into whole new concepts.

An Interval Estimation Method of Patent Keyword Data for Sustainable Technology Forecasting

Directory of Open Access Journals (Sweden)

Daiho Uhm

2017-11-01

Full Text Available Technology forecasting (TF is forecasting the future state of a technology. It is exciting to know the future of technologies, because technology changes the way we live and enhances the quality of our lives. In particular, TF is an important area in the management of technology (MOT for R&D strategy and new product development. Consequently, there are many studies on TF. Patent analysis is one method of TF because patents contain substantial information regarding developed technology. The conventional methods of patent analysis are based on quantitative approaches such as statistics and machine learning. The most traditional TF methods based on patent analysis have a common problem. It is the sparsity of patent keyword data structured from collected patent documents. After preprocessing with text mining techniques, most frequencies of technological keywords in patent data have values of zero. This problem creates a disadvantage for the performance of TF, and we have trouble analyzing patent keyword data. To solve this problem, we propose an interval estimation method (IEM. Using an adjusted Wald confidence interval called the Agresti–Coull confidence interval, we construct our IEM for efficient TF. In addition, we apply the proposed method to forecast the technology of an innovative company. To show how our work can be applied in the real domain, we conduct a case study using Apple technology.

The Impact of Forecasting on Strategic Planning and Decision Making

African Journals Online (AJOL)

The Impact of Forecasting on Strategic Planning and Decision Making: An Exploratory ... lead to the failure to achieve projected performance. ... the use of multiple regression analysis model to forecast the stock market activities of each sector ... making, and that these decisions must be congruent with the company strategy.

Review of methods for forecasting the market penetration of new technologies

International Nuclear Information System (INIS)

Gilshannon, S.T.; Brown, D.R.

1996-12-01

In 1993 the DOE Office of Energy Efficiency and Renewable Energy (EE) initiated a program called Quality Metrics. Quality Metrics was developed to measure the costs and benefits of technologies being developed by EE R ampersand D programs. The impact of any new technology is directly related to its adoption by the market. The techniques employed to project market adoption are critical to measuring a new technology's impact. Our purpose was to review current market penetration theories and models and develop a recommended approach for evaluating the market penetration of DOE technologies. The following commonly cited innovation diffusion theories were reviewed to identify analytical approaches relevant to new energy technologies: (1) the normal noncumulative adopter distribution method, (2) the Bass Model, (3) the Mansfield-Blackman Model, (4) the Fisher-Pry Model, (5) a meta-analysis of innovation diffusion studies. Of the theories reviewed, the Bass and Mansfield-Blackman models were found most applicable to forecasting the market penetration of electricity supply technologies. Their algorithms require input estimates which characterize the technology adoption behavior of the electricity supply industry. But, inadequate work has been done to quantify the technology adoption characteristics of this industry. The following energy technology market penetration models were also reviewed: (1) DOE's Renewable Energy Penetration (REP) Model, (2) DOE's Electricity Capacity Planning Submodule of the National Energy Modeling System (NEMS), (3) the Assessment of Energy Technologies (ASSET) model by Regional Economic Research, Inc., (4) the Market TREK model by the Electric Power Research Institute (EPRI). The two DOE models were developed for electricity generation technologies whereas the Regional Economic Research and EPRI models were designed for demand- side energy technology markets. Therefore, the review and evaluation focused on the DOE models

PATTERNS OF SCIENTIFIC AND TECHNOLOGICAL DEVELOPMENT AND THEIR USE IN FORECASTING

Directory of Open Access Journals (Sweden)

N. I. Komkov

2010-01-01

Full Text Available In article laws of scientifically-technological development are considered. Their number concern traditional, base and new, formed. Possibilities and ways of the account of the listed laws are shown at forecasting of prospects of scientifically-technological development.

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...

Verification of ECMWF and ECMWF/MACC's global and direct irradiance forecasts with respect to solar electricity production forecasts

Directory of Open Access Journals (Sweden)

M. Schroedter-Homscheidt

2017-02-01

Full Text Available The successful electricity grid integration of solar energy into day-ahead markets requires at least hourly resolved 48 h forecasts. Technologies as photovoltaics and non-concentrating solar thermal technologies make use of global horizontal irradiance (GHI forecasts, while all concentrating technologies both from the photovoltaic and the thermal sector require direct normal irradiances (DNI. The European Centre for Medium-Range Weather Forecasts (ECMWF has recently changed towards providing direct as well as global irradiances. Additionally, the MACC (Monitoring Atmospheric Composition & Climate near-real time services provide daily analysis and forecasts of aerosol properties in preparation of the upcoming European Copernicus programme. The operational ECMWF/IFS (Integrated Forecast System forecast system will in the medium term profit from the Copernicus service aerosol forecasts. Therefore, within the MACC‑II project specific experiment runs were performed allowing for the assessment of the performance gain of these potential future capabilities. Also the potential impact of providing forecasts with hourly output resolution compared to three-hourly resolved forecasts is investigated. The inclusion of the new aerosol climatology in October 2003 improved both the GHI and DNI forecasts remarkably, while the change towards a new radiation scheme in 2007 only had minor and partly even unfavourable impacts on the performance indicators. For GHI, larger RMSE (root mean square error values are found for broken/overcast conditions than for scattered cloud fields. For DNI, the findings are opposite with larger RMSE values for scattered clouds compared to overcast/broken cloud situations. The introduction of direct irradiances as an output parameter in the operational IFS version has not resulted in a general performance improvement with respect to biases and RMSE compared to the widely used Skartveit et al. (1998 global to direct irradiance

A formulation of multidimensional growth models for the assessment and forecast of technology attributes

Science.gov (United States)

Danner, Travis W.

. A lone researcher may be able to identify the physical limitation to a single attribute of a simple system; however, as system complexity and the number of attributes increases, the attention of researchers from multiple fields of study is required. Thus, limit identification is itself an area of research and development requiring some level of investment. Whether estimated by physics or regression-based approaches, predicted limits will always have some degree of uncertainty. This research takes the approach of quantifying the impact of that uncertainty on model forecasts rather than heavily endorsing a single technique to limit identification. In addition to formulating the multidimensional growth model, this research provides a systematic procedure for applying that model to specific technology architectures. Researchers and decision-makers are able to investigate the potential for additional improvement within that technology architecture and to estimate the expected cost of each incremental improvement relative to the cost of past improvements. In this manner, multidimensional growth models provide the necessary information to set reasonable program goals for the further evolution of a particular technological approach or to establish the need for revolutionary approaches in light of the constraining limits of conventional approaches.

Extravehicular Activity Technology Development Status and Forecast

Science.gov (United States)

Chullen, Cinda; Westheimer, David T.

2011-01-01

The goal of NASA s current EVA technology effort is to further develop technologies that will be used to demonstrate a robust EVA system that has application for a variety of future missions including microgravity and surface EVA. Overall the objectives will be to reduce system mass, reduce consumables and maintenance, increase EVA hardware robustness and life, increase crew member efficiency and autonomy, and enable rapid vehicle egress and ingress. Over the past several years, NASA realized a tremendous increase in EVA system development as part of the Exploration Technology Development Program and the Constellation Program. The evident demand for efficient and reliable EVA technologies, particularly regenerable technologies was apparent under these former programs and will continue to be needed as future mission opportunities arise. The technological need for EVA in space has been realized over the last several decades by the Gemini, Apollo, Skylab, Space Shuttle, and the International Space Station (ISS) programs. EVAs were critical to the success of these programs. Now with the ISS extension to 2028 in conjunction with a current forecasted need of at least eight EVAs per year, the EVA hardware life and limited availability of the Extravehicular Mobility Units (EMUs) will eventually become a critical issue. The current EMU has successfully served EVA demands by performing critical operations to assemble the ISS and provide repairs of satellites such as the Hubble Space Telescope. However, as the life of ISS and the vision for future mission opportunities are realized, a new EVA systems capability will be needed and the current architectures and technologies under development offer significant improvements over the current flight systems. In addition to ISS, potential mission applications include EVAs for missions to Near Earth Objects (NEO), Phobos, or future surface missions. Surface missions could include either exploration of the Moon or Mars. Providing an

Past speculations of the future: a review of the methods used for forecasting emerging health technologies.

Science.gov (United States)

Doos, Lucy; Packer, Claire; Ward, Derek; Simpson, Sue; Stevens, Andrew

2016-03-10

Forecasting can support rational decision-making around the introduction and use of emerging health technologies and prevent investment in technologies that have limited long-term potential. However, forecasting methods need to be credible. We performed a systematic search to identify the methods used in forecasting studies to predict future health technologies within a 3-20-year timeframe. Identification and retrospective assessment of such methods potentially offer a route to more reliable prediction. Systematic search of the literature to identify studies reported on methods of forecasting in healthcare. People are not needed in this study. The authors searched MEDLINE, EMBASE, PsychINFO and grey literature sources, and included articles published in English that reported their methods and a list of identified technologies. Studies reporting methods used to predict future health technologies within a 3-20-year timeframe with an identified list of individual healthcare technologies. Commercially sponsored reviews, long-term futurology studies (with over 20-year timeframes) and speculative editorials were excluded. 15 studies met our inclusion criteria. Our results showed that the majority of studies (13/15) consulted experts either alone or in combination with other methods such as literature searching. Only 2 studies used more complex forecasting tools such as scenario building. The methodological fundamentals of formal 3-20-year prediction are consistent but vary in details. Further research needs to be conducted to ascertain if the predictions made were accurate and whether accuracy varies by the methods used or by the types of technologies identified. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Technology data characterizing refrigeration in commercial buildings: Application to end-use forecasting with COMMEND 4.0

Energy Technology Data Exchange (ETDEWEB)

Sezgen, O.; Koomey, J.G.

1995-12-01

In the United States, energy consumption is increasing most rapidly in the commercial sector. Consequently, the commercial sector is becoming an increasingly important target for state and federal energy policies and also for utility-sponsored demand side management (DSM) programs. The rapid growth in commercial-sector energy consumption also makes it important for analysts working on energy policy and DSM issues to have access to energy end-use forecasting models that include more detailed representations of energy-using technologies in the commercial sector. These new forecasting models disaggregate energy consumption not only by fuel type, end use, and building type, but also by specific technology. The disaggregation of the refrigeration end use in terms of specific technologies, however, is complicated by several factors. First, the number of configurations of refrigeration cases and systems is quite large. Also, energy use is a complex function of the refrigeration-case properties and the refrigeration-system properties. The Electric Power Research Institute`s (EPRI`s) Commercial End-Use Planning System (COMMEND 4.0) and the associated data development presented in this report attempt to address the above complications and create a consistent forecasting framework. Expanding end-use forecasting models so that they address individual technology options requires characterization of the present floorstock in terms of service requirements, energy technologies used, and cost-efficiency attributes of the energy technologies that consumers may choose for new buildings and retrofits. This report describes the process by which we collected refrigeration technology data. The data were generated for COMMEND 4.0 but are also generally applicable to other end-use forecasting frameworks for the commercial sector.

Technological developments in real-time operational hydrologic forecasting in the United States

Science.gov (United States)

Hudlow, Michael D.

1988-09-01

The hydrologic forecasting service of the United States spans applications and scales ranging from those associated with the issuance of flood and flash warnings to those pertaining to seasonal water supply forecasts. New technological developments (underway in or planned by the National Weather Service (NWS) in support of the Hydrologic Program) are carried out as combined efforts by NWS headquarters and field personnel in cooperation with other organizations. These developments fall into two categories: hardware and software systems technology, and hydrometeorological analysis and prediction technology. Research, development, and operational implementation in progress in both of these areas are discussed. Cornerstones of an overall NWS modernization effort include implementation of state-of-the-art data acquisition systems (including the Next Generation Weather Radar) and communications and computer processing systems. The NWS Hydrologic Service will capitalize on these systems and will incorporate results from specific hydrologic projects including collection and processing of multivariate data sets, conceptual hydrologic modeling systems, integrated hydrologic modeling systems with meteorological interfaces and automatic updating of model states, and extended streamflow prediction techniques. The salient aspects of ongoing work in these areas are highlighted in this paper, providing some perspective on the future U.S. hydrologic forecasting service and its transitional period into the 1990s.

Research on forecast technology of mine gas emission based on fuzzy data mining (FDM)

Energy Technology Data Exchange (ETDEWEB)

Xu Chang-kai; Wang Yao-cai; Wang Jun-wei [CUMT, Xuzhou (China). School of Information and Electrical Engineering

2004-07-01

The safe production of coalmine can be further improved by forecasting the quantity of gas emission based on the real-time data and historical data which the gas monitoring system has saved. By making use of the advantages of data warehouse and data mining technology for processing large quantity of redundancy data, the method and its application of forecasting mine gas emission quantity based on FDM were studied. The constructing fuzzy resembling relation and clustering analysis were proposed, which the potential relationship inside the gas emission data may be found. The mode finds model and forecast model were presented, and the detailed approach to realize this forecast was also proposed, which have been applied to forecast the gas emission quantity efficiently.

Extravehicular Activity (EVA) Technology Development Status and Forecast

Science.gov (United States)

Chullen, Cinda; Westheimer, David T.

2010-01-01

Beginning in Fiscal Year (FY) 2011, Extravehicular activity (EVA) technology development became a technology foundational domain under a new program Enabling Technology Development and Demonstration. The goal of the EVA technology effort is to further develop technologies that will be used to demonstrate a robust EVA system that has application for a variety of future missions including microgravity and surface EVA. Overall the objectives will be reduce system mass, reduce consumables and maintenance, increase EVA hardware robustness and life, increase crew member efficiency and autonomy, and enable rapid vehicle egress and ingress. Over the past several years, NASA realized a tremendous increase in EVA system development as part of the Exploration Technology Development Program and the Constellation Program. The evident demand for efficient and reliable EVA technologies, particularly regenerable technologies was apparent under these former programs and will continue to be needed as future mission opportunities arise. The technological need for EVA in space has been realized over the last several decades by the Gemini, Apollo, Skylab, Space Shuttle, and the International Space Station (ISS) programs. EVAs were critical to the success of these programs. Now with the ISS extension to 2028 in conjunction with a current forecasted need of at least eight EVAs per year, the EVA technology life and limited availability of the EMUs will become a critical issue eventually. The current Extravehicular Mobility Unit (EMU) has vastly served EVA demands by performing critical operations to assemble the ISS and provide repairs of satellites such as the Hubble Space Telescope. However, as the life of ISS and the vision for future mission opportunities are realized, a new EVA systems capability could be an option for the future mission applications building off of the technology development over the last several years. Besides ISS, potential mission applications include EVAs for

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.

AIRS Impact on the Analysis and Forecast Track of Tropical Cyclone Nargis in a Global Data Assimilation and Forecasting System

Science.gov (United States)

Reale, O.; Lau, W.K.; Susskind, J.; Brin, E.; Liu, E.; Riishojgaard, L. P.; Rosenburg, R.; Fuentes, M.

2009-01-01

Tropical cyclones in the northern Indian Ocean pose serious challenges to operational weather forecasting systems, partly due to their shorter lifespan and more erratic track, compared to those in the Atlantic and the Pacific. Moreover, the automated analyses of cyclones over the northern Indian Ocean, produced by operational global data assimilation systems (DASs), are generally of inferior quality than in other basins. In this work it is shown that the assimilation of Atmospheric Infrared Sounder (AIRS) temperature retrievals under partial cloudy conditions can significantly impact the representation of the cyclone Nargis (which caused devastating loss of life in Myanmar in May 2008) in a global DAS. Forecasts produced from these improved analyses by a global model produce substantially smaller track errors. The impact of the assimilation of clear-sky radiances on the same DAS and forecasting system is positive, but smaller than the one obtained by ingestion of AIRS retrievals, possibly due to poorer coverage.

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 the Success of Implementing Sensors Advanced Manufacturing Technology

Directory of Open Access Journals (Sweden)

Cheng-Shih Su

2014-08-01

Full Text Available This paper is presented fuzzy preference relations approach to forecast the success of implementing sensors advanced manufacturing technology (AMT. In the manufacturing environment, performance measurement is based on different quantitative and qualitative factors. This study proposes an analytic hierarchical prediction model based on fuzzy preference relations to help the organizations become aware of the essential factors affecting the AMT implementation, forecasting the chance of successful implementing sensors AMT, as well as identifying the actions necessary before implementing sensors AMT. Then predicted success/failure values are obtained to enable organizations to decide whether to initiate sensors AMT, inhibit adoption or take remedial actions to increase the possibility of successful sensors AMT initiatives. This proposed approach is demonstrated with a real case study involving six influential factors assessed by nine evaluators solicited from a semiconductor engineering incorporation located in Taiwan.

The Impact of Jumps and Leverage in Forecasting Co-Volatility

NARCIS (Netherlands)

M. Asai (Manabu); M.J. McAleer (Michael)

2015-01-01

markdownabstract__Abstract__ The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage effects. We modify the jump-robust two time scale covariance estimator of Boudt and Zhang (2013) such that the estimated matrix is positive definite. Using this

Forecasting surface water flooding hazard and impact in real-time

Science.gov (United States)

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

2016-04-01

Across the world, there is increasing demand for more robust and timely forecast and alert information on Surface Water Flooding (SWF). Within a UK context, the government Pitt Review into the Summer 2007 floods provided recommendations and impetus to improve the understanding of SWF risk for both off-line design and real-time forecasting and warning. Ongoing development and trial of an end-to-end real-time SWF system is being progressed through the recently formed Natural Hazards Partnership (NHP) with delivery to the Flood Forecasting Centre (FFC) providing coverage over England & Wales. The NHP is a unique forum that aims to deliver coordinated assessments, research and advice on natural hazards for governments and resilience communities across the UK. Within the NHP, a real-time Hazard Impact Model (HIM) framework has been developed that includes SWF as one of three hazards chosen for initial trialling. The trial SWF HIM system uses dynamic gridded surface-runoff estimates from the Grid-to-Grid (G2G) hydrological model to estimate the SWF hazard. National datasets on population, infrastructure, property and transport are available to assess impact severity for a given rarity of SWF hazard. Whilst the SWF hazard footprint is calculated in real-time using 1, 3 and 6 hour accumulations of G2G surface runoff on a 1 km grid, it has been possible to associate these with the effective rainfall design profiles (at 250m resolution) used as input to a detailed flood inundation model (JFlow+) run offline to produce hazard information resolved to 2m resolution. This information is contained in the updated Flood Map for Surface Water (uFMfSW) held by the Environment Agency. The national impact datasets can then be used with the uFMfSW SWF hazard dataset to assess impacts at this scale and severity levels of potential impact assigned at 1km and for aggregated county areas in real-time. The impact component is being led by the Health and Safety Laboratory (HSL) within the NHP

Applying the Repertory Grid Method for Technology Forecasting: Civil Unmanned Aviation Systems for Germany

Directory of Open Access Journals (Sweden)

Eimecke Jörgen

2017-09-01

Full Text Available Multistage expert surveys like the Delphi method are proven concepts for technology forecasting that enable the prediction of content-related and temporal development in fields of innovation (e.g., [1, 2]. Advantages of these qualitative multistage methods are a simple and easy to understand concept while still delivering valid results [3]. Nevertheless, the literature also points out certain disadvantages especially in large-scale technology forecasts in particularly abstract fields of innovation [4]. The proposed approach highlights the usefulness of the repertory grid method as an alternative for technology forecasting and as a first step for preference measurement. The basic approach from Baier and Kohler [5] is modified in-so-far that an online survey reduces the cognitive burden for the experts and simplifies the data collection process. Advantages over alternative approaches through its simple structure and through combining qualitative and quantitative methods are shown and an adaption on an actual field of innovation – civil drones in Germany – is done. The measurement of a common terminology for all experts minimizes misunderstandings during the interview and the achievement of an inter-individual comparable level of abstraction is forced by the laddering technique [6] during the interview.

The Impact of Jumps and Leverage in Forecasting Co-Volatility

NARCIS (Netherlands)

M. Asai (Manabu); M.J. McAleer (Michael)

2015-01-01

markdownabstract__Abstract__ The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage effects. We modify the jump-robust two time scale covariance estimator of Boudt and Zhang (2013)such that the estimated matrix is positive definite. Using this approach we

Technological forecasting a long time of the scientific-technological development of the nuclear fusion

International Nuclear Information System (INIS)

Schettert, Plinio G.; Oliveira, Wagner S.; Aquino, Afonso R.

2009-01-01

With base in the introduction in long time of the nuclear fusion inside of a system of viable energy, taking in consideration economic factors, would imply on investment in a long period. The objective of this project utilizing the method of the Delphi technique is the technological forecast a long time of the scientific-technological development of the nuclear fusion and its impact. This research project will be carried through different stages of improvement of variables. A questionnaire based on information and analysis of the literature validated for specialists in nuclear fusion becomes this project a tool in the elaboration future of a database contends variables on the theme nuclear fusion and its perspectives. The database will be composed for the answers and suggestions obtained, with exploratory and extrapolatory elements, on the theme a great number of specialists involving in the nuclear fusion area. The database is analyzed for the configuration of variables that represent elements as scientific-technological factors, economical, political, social and environmental among others. As final result of the research with the Delphi technique, different scenes obtained with the variables will be indicated by convergent factors or not on the approached perspectives. The analysis of the data will be possible through of improve of statistical analysis tools. This is the first analyzes of the answers. The questionnaire was validated with nuclear fusion specialists from the Institute of Physics of the University of Sao Paulo in Brazil and the Center of Nuclear Fusion of the Technical University of Lisbon in Portugal. (author)

Approaches, techniques, and information technology systems in the restaurants and foodservice industry: a qualitative study in sales forecasting.

OpenAIRE

Green, Yvette N. J.; Weaver, Pamela A.

2008-01-01

This is a study of the approaches, techniques, and information technology systems utilized for restaurant sales forecasting in the full-service restaurant segment. Companies were examined using a qualitative research methods design and long interviews to gather information on approaches, techniques, and technology systems utilized in the sales forecasting process. The results of the interviews were presented along with ensuing discussion.

Innovative activity of high-technology companies as assessment and forecasting object

Directory of Open Access Journals (Sweden)

A. E. Sklyarov

2016-01-01

Full Text Available Innovation activities, as well as innovations, are closely related meanings, and like many others economical definitions, have a broad range of meanings. Main characteristics and attributes of innovation involves new or significantly improved product, that’s being used, or in other words, found its application, and innovative activities – activities focused on realization of innovations. In this article, innovations are mainly considered in terms of high-technology production, evidence from Russian space industry. There are 5 basic stages of lifecycle of innovative project in considered industry: initiation, development, realization, expansion, consumption. Practically, third or fourth, or even both of these stages, often missing because there is no need of them. R&D activities, or even further serial production, based on previous developments, is an innovation activity, because these activities are stages of innovative projects lifecycle itself. Then it seems legit, to draw a conclusion, that in terms of high-technology production, company’s primary activity equals innovative activity. Basic characteristics of innovative activity of high-technology companies as assessment and forecasting object involves high level of uncertainty at every stage of projects lifecycle, high dependency on funding level of this activity, and high level and erratic structure of risk. All the above mentioned, means that assessment and forecasting of innovative activity of high-technology companies, needs development of its own methodological tools for each industry.

Problems of Forecast

OpenAIRE

Kucharavy , Dmitry; De Guio , Roland

2005-01-01

International audience; The ability to foresee future technology is a key task of Innovative Design. The paper focuses on the obstacles to reliable prediction of technological evolution for the purpose of Innovative Design. First, a brief analysis of problems for existing forecasting methods is presented. The causes for the complexity of technology prediction are discussed in the context of reduction of the forecast errors. Second, using a contradiction analysis, a set of problems related to ...

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

Issues in midterm analysis and forecasting, 1996

International Nuclear Information System (INIS)

1996-08-01

This document consists of papers which cover topics in analysis and modeling that underlie the Annual Energy Outlook 1996. Topics include: The Potential Impact of Technological Progress on U.S. Energy Markets; The Outlook for U.S. Import Dependence; Fuel Economy, Vehicle Choice, and Changing Demographics, and Annual Energy Outlook Forecast Evaluation

Artificial Intelligence as a Business Forecasting and Error Handling Tool

OpenAIRE

Md. Tabrez Quasim; Rupak Chattopadhyay

2015-01-01

 Any business enterprise must rely a lot on how well it can predict the future happenings. To cope up with the modern global customer demand, technological challenges, market competitions etc., any organization is compelled to foresee the future having maximum impact and least chances of errors. The traditional forecasting approaches have some limitations. That is why the business world is adopting the modern Artificial Intelligence based forecasting techniques. This paper has tried to presen...

Modeling and Forecasting the Impact of Major Technological and Infrastructural Changes on Travel Demand

OpenAIRE

El Zarwi, Feras

2017-01-01

The transportation system is undergoing major technological and infrastructural changes, such as the introduction of autonomous vehicles, high speed rail, carsharing, ridesharing, flying cars, drones, and other app-driven on-demand services. While the changes are imminent, the impact on travel behavior is uncertain, as is the role of policy in shaping the future. Literature shows that even under the most optimistic scenarios, society’s environmental goals cannot be met by technology, operatio...

AIRS Impact on Analysis and Forecast of an Extreme Rainfall Event (Indus River Valley 2010) with a Global Data Assimilation and Forecast System

Science.gov (United States)

Reale, O.; Lau, W. K.; Susskind, J.; Rosenberg, R.

2011-01-01

A set of data assimilation and forecast experiments are performed with the NASA Global data assimilation and forecast system GEOS-5, to compare the impact of different approaches towards assimilation of Advanced Infrared Spectrometer (AIRS) data on the precipitation analysis and forecast skill. The event chosen is an extreme rainfall episode which occurred in late July 11 2010 in Pakistan, causing massive floods along the Indus River Valley. Results show that the assimilation of quality-controlled AIRS temperature retrievals obtained under partly cloudy conditions produce better precipitation analyses, and substantially better 7-day forecasts, than assimilation of clear-sky radiances. The improvement of precipitation forecast skill up to 7 day is very significant in the tropics, and is caused by an improved representation, attributed to cloudy retrieval assimilation, of two contributing mechanisms: the low-level moisture advection, and the concentration of moisture over the area in the days preceding the precipitation peak.

The impact of scatterometer wind data on global weather forecasting

Science.gov (United States)

Atlas, D.; Baker, W. E.; Kalnay, E.; Halem, M.; Woiceshyn, P. M.; Peteherych, S.

1984-01-01

The impact of SEASAT-A scatterometer (SASS) winds on coarse resolution atmospheric model forecasts was assessed. The scatterometer provides high resolution winds, but each wind can have up to four possible directions. One wind direction is correct; the remainder are ambiguous or "aliases'. In general, the effect of objectively dealiased-SASS data was found to be negligible in the Northern Hemisphere. In the Southern Hemisphere, the impact was larger and primarily beneficial when vertical temperature profile radiometer (VTPR) data was excluded. However, the inclusion of VTPR data eliminates the positive impact, indicating some redundancy between the two data sets.

The impact of implementing a demand forecasting system into a low-income country's supply chain.

Science.gov (United States)

Mueller, Leslie E; Haidari, Leila A; Wateska, Angela R; Phillips, Roslyn J; Schmitz, Michelle M; Connor, Diana L; Norman, Bryan A; Brown, Shawn T; Welling, Joel S; Lee, Bruce Y

2016-07-12

To evaluate the potential impact and value of applications (e.g. adjusting ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country's vaccine supply chain with different levels of population change to urban areas. Using our software, HERMES, we generated a detailed discrete event simulation model of Niger's entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. Demand forecasting systems have the potential to greatly improve vaccine demand fulfilment, and decrease logistics cost/dose when implemented with storage and transportation increases. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements. Copyright �

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

An improved market penetration model for wind energy technology forecasting

International Nuclear Information System (INIS)

Lund, P.D.

1995-01-01

An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)

An improved market penetration model for wind energy technology forecasting

Energy Technology Data Exchange (ETDEWEB)

Lund, P D [Helsinki Univ. of Technology, Espoo (Finland). Advanced Energy Systems

1996-12-31

An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)

An improved market penetration model for wind energy technology forecasting

Energy Technology Data Exchange (ETDEWEB)

Lund, P.D. [Helsinki Univ. of Technology, Espoo (Finland). Advanced Energy Systems

1995-12-31

An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)

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...

Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system

Science.gov (United States)

Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic

2014-01-01

The finite resolution of general circulation models of the coupled atmosphere–ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere–ocean climate system in operational forecast mode, and the latest seasonal forecasting system—System 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981–2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden–Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid

The Impact of Implementing a Demand Forecasting System into a Low-Income Country’s Supply Chain

Science.gov (United States)

Mueller, Leslie E.; Haidari, Leila A.; Wateska, Angela R.; Phillips, Roslyn J.; Schmitz, Michelle M.; Connor, Diana L.; Norman, Bryan A.; Brown, Shawn T.; Welling, Joel S.; Lee, Bruce Y.

2016-01-01

OBJECTIVE To evaluate the potential impact and value of applications (e.g., ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country’s vaccine supply chain with different levels of population change to urban areas. MATERIALS AND METHODS Using our software, HERMES, we generated a detailed discrete event simulation model of Niger’s entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. RESULTS Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. DISCUSSION The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. CONCLUSION Demand forecasting systems have the potential to greatly improve vaccine demand fulfillment, and decrease logistics cost/dose when implemented with storage and transportation increases direct vaccines. Simulation modeling can demonstrate the potential

Observation impact studies with the Mercator Ocean analysis and forecasting systems

Science.gov (United States)

Remy, E. D.; Le Traon, P. Y.; Lellouche, J. M.; Drevillon, M.; Turpin, V.; Benkiran, M.

2016-02-01

Mercator Ocean produces and delivers in real-time ocean analysis and forecasts on a daily basis. The quality of the analysis highly relies on the availability and quality of the assimilated observations.Tools are developed to estimate the impact of the present network and to help designing the future evolutions of the observing systems in the context of near real time production of ocean analysis and forecasts. OSE and OSSE are the main approaches used in this context. They allow the assessment of the efficiency of a given data set to constrain the ocean model circulation through the data assimilation process. Illustrations will mainly focus on the present and future evolution of the Argo observation network and altimetry constellation, including the potential impact of future SWOT data. Our systems show clear sensitivities to observation array changes, mainly depending on the specified observation error and regional dynamic. Impact on non observed variables can be important and are important to evaluate. Dedicated diagnostics has to be define to measure the improvements bring by each data set. Alternative approaches to OSE and OSSE are also explored: approximate computation of DFS will be presented and discussed. Limitations of each approach will be discussed in the context of real time operation.

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.

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

Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy

International Nuclear Information System (INIS)

Jain, Rishee K.; Smith, Kevin M.; Culligan, Patricia J.; Taylor, John E.

2014-01-01

Highlights: • We develop a building energy forecasting model using support vector regression. • Model is applied to data from a multi-family residential building in New York City. • We extend sensor based energy forecasting to multi-family residential buildings. • We examine the impact temporal and spatial granularity has on model accuracy. • Optimal granularity occurs at the by floor in hourly temporal intervals. - Abstract: Buildings are the dominant source of energy consumption and environmental emissions in urban areas. Therefore, the ability to forecast and characterize building energy consumption is vital to implementing urban energy management and efficiency initiatives required to curb emissions. Advances in smart metering technology have enabled researchers to develop “sensor based” approaches to forecast building energy consumption that necessitate less input data than traditional methods. Sensor-based forecasting utilizes machine learning techniques to infer the complex relationships between consumption and influencing variables (e.g., weather, time of day, previous consumption). While sensor-based forecasting has been studied extensively for commercial buildings, there is a paucity of research applying this data-driven approach to the multi-family residential sector. In this paper, we build a sensor-based forecasting model using Support Vector Regression (SVR), a commonly used machine learning technique, and apply it to an empirical data-set from a multi-family residential building in New York City. We expand our study to examine the impact of temporal (i.e., daily, hourly, 10 min intervals) and spatial (i.e., whole building, by floor, by unit) granularity have on the predictive power of our single-step model. Results indicate that sensor based forecasting models can be extended to multi-family residential buildings and that the optimal monitoring granularity occurs at the by floor level in hourly intervals. In addition to implications for

Value of Forecaster in the Loop

Science.gov (United States)

2014-09-01

forecast system IFR instrument flight rules IMC instrument meteorological conditions LAMP Localized Aviation Model Output Statistics Program METOC...obtaining valuable experience. Additional factors have impacted the Navy weather forecast process. There has been a the realignment of the meteorology...forecasts that are assessed, it may be a relatively small number that have direct impact on the decision-making process. Whether the value is minimal or

Forecast Based Financing for Managing Weather and Climate Risks to Reduce Potential Disaster Impacts

Science.gov (United States)

Arrighi, J.

2017-12-01

There is a critical window of time to reduce potential impacts of a disaster after a forecast for heightened risk is issued and before an extreme event occurs. The concept of Forecast-based Financing focuses on this window of opportunity. Through advanced preparation during system set-up, tailored methodologies are used to 1) analyze a range of potential extreme event forecasts, 2) identify emergency preparedness measures that can be taken when factoring in forecast lead time and inherent uncertainty and 3) develop standard operating procedures that are agreed on and tied to guaranteed funding sources to facilitate emergency measures led by the Red Cross or government actors when preparedness measures are triggered. This presentation will focus on a broad overview of the current state of theory and approaches used in developing a forecast-based financing systems - with a specific focus on hydrologic events, case studies of success and challenges in various contexts where this approach is being piloted, as well as what is on the horizon to be further explored and developed from a research perspective as the application of this approach continues to expand.

Forecasting the future of biodiversity

DEFF Research Database (Denmark)

Fitzpatrick, M. C.; Sanders, Nate; Ferrier, Simon

2011-01-01

, but their application to forecasting climate change impacts on biodiversity has been limited. Here we compare forecasts of changes in patterns of ant biodiversity in North America derived from ensembles of single-species models to those from a multi-species modeling approach, Generalized Dissimilarity Modeling (GDM...... climate change impacts on biodiversity....

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

Solar Storm GIC Forecasting: Solar Shield Extension Development of the End-User Forecasting System Requirements

Science.gov (United States)

Pulkkinen, A.; Mahmood, S.; Ngwira, C.; Balch, C.; Lordan, R.; Fugate, D.; Jacobs, W.; Honkonen, I.

2015-01-01

A NASA Goddard Space Flight Center Heliophysics Science Division-led team that includes NOAA Space Weather Prediction Center, the Catholic University of America, Electric Power Research Institute (EPRI), and Electric Research and Management, Inc., recently partnered with the Department of Homeland Security (DHS) Science and Technology Directorate (S&T) to better understand the impact of Geomagnetically Induced Currents (GIC) on the electric power industry. This effort builds on a previous NASA-sponsored Applied Sciences Program for predicting GIC, known as Solar Shield. The focus of the new DHS S&T funded effort is to revise and extend the existing Solar Shield system to enhance its forecasting capability and provide tailored, timely, actionable information for electric utility decision makers. To enhance the forecasting capabilities of the new Solar Shield, a key undertaking is to extend the prediction system coverage across Contiguous United States (CONUS), as the previous version was only applicable to high latitudes. The team also leverages the latest enhancements in space weather modeling capacity residing at Community Coordinated Modeling Center to increase the Technological Readiness Level, or Applications Readiness Level of the system http://www.nasa.gov/sites/default/files/files/ExpandedARLDefinitions4813.pdf.

Impact of MODIS High-Resolution Sea-Surface Temperatures on WRF Forecasts at NWS Miami, FL

Science.gov (United States)

Case, Jonathan L.; LaCasse, Katherine M.; Dembek, Scott R.; Santos, Pablo; Lapenta, William M.

2007-01-01

Over the past few years,studies at the Short-term Prediction Research and Transition (SPoRT) Center have suggested that the use of Moderate Resolution Imaging Spectroradiometer (MODIS) composite sea-surface temperature (SST) products in regional weather forecast models can have a significant positive impact on short-term numerical weather prediction in coastal regions. The recent paper by LaCasse et al. (2007, Monthly Weather Review) highlights lower atmospheric differences in regional numerical simulations over the Florida offshore waters using 2-km SST composites derived from the MODIS instrument aboard the polar-orbiting Aqua and Terra Earth Observing System satellites. To help quantify the value of this impact on NWS Weather Forecast Offices (WFOs), the SPoRT Center and the NWS WFO at Miami, FL (MIA) are collaborating on a project to investigate the impact of using the high-resolution MODIS SST fields within the Weather Research and Forecasting (WRF) prediction system. The scientific hypothesis being tested is: More accurate specification of the lower-boundary forcing within WRF will result in improved land/sea fluxes and hence, more accurate evolution of coastal mesoscale circulations and the associated sensible weather elements. The NWS MIA is currently running the WRF system in real-time to support daily forecast operations, using the National Centers for Environmental Prediction Nonhydrostatic Mesoscale Model dynamical core within the NWS Science and Training Resource Center's Environmental Modeling System (EMS) software; The EMS is a standalone modeling system capable of downloading the necessary daily datasets, and initializing, running and displaying WRF forecasts in the NWS Advanced Weather Interactive Processing System (AWIPS) with little intervention required by forecasters. Twenty-seven hour forecasts are run daily with start times of 0300,0900, 1500, and 2100 UTC on a domain with 4-km grid spacing covering the southern half of Florida and the far

Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis

Directory of Open Access Journals (Sweden)

Juan Pardo

2013-09-01

Full Text Available The small medium large system (SMLsystem is a house built at the Universidad CEU Cardenal Herrera (CEU-UCH for participation in the Solar Decathlon 2013 competition. Several technologies have been integrated to reduce power consumption. One of these is a forecasting system based on artificial neural networks (ANNs, which is able to predict indoor temperature in the near future using captured data by a complex monitoring system as the input. A study of the impact on forecasting performance of different covariate combinations is presented in this paper. Additionally, a comparison of ANNs with the standard statistical forecasting methods is shown. The research in this paper has been focused on forecasting the indoor temperature of a house, as it is directly related to HVAC—heating, ventilation and air conditioning—system consumption. HVAC systems at the SMLsystem house represent 53:89% of the overall power consumption. The energy used to maintain temperature was measured to be 30%–38:9% of the energy needed to lower it. Hence, these forecasting measures allow the house to adapt itself to future temperature conditions by using home automation in an energy-efficient manner. Experimental results show a high forecasting accuracy and therefore, they might be used to efficiently control an HVAC system.

TECHNOLOGICAL IMPERATIVE IMPACT OF GLOBALIZATION ON INTERNATIONAL AGRICULTURAL ECONOMY

Directory of Open Access Journals (Sweden)

А. Кozlova

2014-09-01

Full Text Available The article highlights the factors influencing agricultural production towards global market. The study consists basic fundamental imperatives of globalization on the agricultural sector in international economic relations. The article analyzes the strategic priorities of the international agricultural sector, which includes financial and credit support, legal aspects, processes and integration of organizational structures. Technological imperatives require a large structural and institutional turn in the Ukrainian economy on the basis of current trends in the global economy, scientific and technical potential. There is a growing importance of organizing and conducting international level in the field of technological forecasting. This type of prediction is considered as backbone component in strategic forecasting and economic development programming.

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...

Assessing the accuracy of forecasting: applying standard diagnostic assessment tools to a health technology early warning system.

Science.gov (United States)

Simpson, Sue; Hyde, Chris; Cook, Alison; Packer, Claire; Stevens, Andrew

2004-01-01

Early warning systems are an integral part of many health technology assessment programs. Despite this finding, to date, there have been no quantitative evaluations of the accuracy of predictions made by these systems. We report a study evaluating the accuracy of predictions made by the main United Kingdom early warning system. As prediction of impact is analogous to diagnosis, a method normally applied to determine the accuracy of diagnostic tests was used. The sensitivity, specificity, and predictive values of the National Horizon Scanning Centre's prediction methods were estimated with reference to an (imperfect) gold standard, that is, expert opinion of impact 3 to 5 years after prediction. The sensitivity of predictions was 71 percent (95 percent confidence interval [CI], 0.36-0.92), and the specificity was 73 percent (95 percent CI, 0.64-0.8). The negative predictive value was 98 percent (95 percent CI, 0.92-0.99), and the positive predictive value was 14 percent (95 percent CI, 0.06-0.3). Forecasting is difficult, but the results suggest that this early warning system's predictions have an acceptable level of accuracy. However, there are caveats. The first is that early warning systems may themselves reduce the impact of a technology, as helping to control adoption and diffusion is their main purpose. The second is that the use of an imperfect gold standard may bias the results. As early warning systems are viewed as an increasingly important component of health technology assessment and decision making, their outcomes must be evaluated. The method used here should be investigated further and the accuracy of other early warning systems explored.

Satellites, tweets, forecasts: the future of flood disaster management?

Science.gov (United States)

Dottori, Francesco; Kalas, Milan; Lorini, Valerio; Wania, Annett; Pappenberger, Florian; Salamon, Peter; Ramos, Maria Helena; Cloke, Hannah; Castillo, Carlos

2017-04-01

Floods have devastating effects on lives and livelihoods around the world. Structural flood defence measures such as dikes and dams can help protect people. However, it is the emerging science and technologies for flood disaster management and preparedness, such as increasingly accurate flood forecasting systems, high-resolution satellite monitoring, rapid risk mapping, and the unique strength of social media information and crowdsourcing, that are most promising for reducing the impacts of flooding. Here, we describe an innovative framework which integrates in real-time two components of the Copernicus Emergency mapping services, namely the European Flood Awareness System and the satellite-based Rapid Mapping, with new procedures for rapid risk assessment and social media and news monitoring. The integrated framework enables improved flood impact forecast, thanks to the real-time integration of forecasting and monitoring components, and increases the timeliness and efficiency of satellite mapping, with the aim of capturing flood peaks and following the evolution of flooding processes. Thanks to the proposed framework, emergency responders will have access to a broad range of timely and accurate information for more effective and robust planning, decision-making, and resource allocation.

The impact of underwater glider observations in the forecast of Hurricane Gonzalo (2014)

Science.gov (United States)

Goni, G. J.; Domingues, R. M.; Kim, H. S.; Domingues, R. M.; Halliwell, G. R., Jr.; Bringas, F.; Morell, J. M.; Pomales, L.; Baltes, R.

2017-12-01

The tropical Atlantic basin is one of seven global regions where tropical cyclones (TC) are commonly observed to originate and intensify from June to November. On average, approximately 12 TCs travel through the region every year, frequently affecting coastal, and highly populated areas. In an average year, 2 to 3 of them are categorized as intense hurricanes. Given the appropriate atmospheric conditions, TC intensification has been linked to ocean conditions, such as increased ocean heat content and enhanced salinity stratification near the surface. While errors in hurricane track forecasts have been reduced during the last years, errors in intensity forecasts remain mostly unchanged. Several studies have indicated that the use of in situ observations has the potential to improve the representation of the ocean to correctly initialize coupled hurricane intensity forecast models. However, a sustained in situ ocean observing system in the tropical North Atlantic Ocean and Caribbean Sea dedicated to measuring subsurface thermal and salinity fields in support of TC intensity studies and forecasts has yet to be implemented. Autonomous technologies offer new and cost-effective opportunities to accomplish this objective. We highlight here a partnership effort that utilize underwater gliders to better understand air-sea processes during high wind events, and are particularly geared towards improving hurricane intensity forecasts. Results are presented for Hurricane Gonzalo (2014), where glider observations obtained in the tropical Atlantic: Helped to provide an accurate description of the upper ocean conditions, that included the presence of a low salinity barrier layer; Allowed a detailed analysis of the upper ocean response to hurricane force winds of Gonzalo; Improved the initialization of the ocean in a coupled ocean-atmosphere numerical model; and together with observations from other ocean observing platforms, substantially reduced the error in intensity forecast

Forecasting inflation based on the consumer price index, taking into account the impact of seasonal factors

Directory of Open Access Journals (Sweden)

A. K. Sapova

2017-01-01

Full Text Available The consumer price index is a key indicator of the inflation level in Russia. It is important for the Central Bank and Government in decision-making process. There is a strong need for high-quality analysis and accurate forecast of this index. Modelling and forecasting of consumer price index as a key indicator of inflation are relevant issues in current macroeconomic conditions. The article is dedicated to development of quality short-term forecast of consumer inflation level, with the impact of seasonal factor. Two classes of models (vector autoregression and time series models are considered. It was shown that vector autoregression model of the dependency between consumer price index and nominal effective exchange rate is worse for the proposes of inflation forecast then non-linear model with structural components and conventional heteroscedasticity. The practical significance of this work is that the developed approach to the forecasting of the consumer price index adjusted of seasonal factor can be very helpful for the purpose of proper assessment and regulation of inflation.

Ethical issues in forecasting of natural hazards

Science.gov (United States)

Tinti, Stefano

2014-05-01

Natural hazards have by definition a large impact on the society and, therefore, since the beginning of science one of the major aspiration of mankind has been the prediction of natural calamities in the attempt to avoid or to mitigate their effects. In modern societies where science and technology have gained a foundational role, forecasts and predictions have become part of the every-day life and may also influence state policies and economic development. And in parallel with the growing importance of forecasting, even ethical problems for forecasters and for forecasters communities have started to appear. In this work two of the many geo-ethical issues are considered mostly: 1) how to cope with uncertainties that are inherently associated with any forecast statement; 2) how to handle predictions in scientific journals and scientific conferences The former issue is mainly related to the impact of predictions on the general public and on managers and operators in the civil protection field. Forecasters operate in specific contexts that 1) may change from country to country, depending on the local adopted best practices, but also, which is more constraining, on the local legal regulations and laws; 2) may change from discipline to discipline according to the development of the specific knowhow and the range of the forecast (from minutes to centuries) The second issue has to do with the communication of the scientific results on predictions and on prediction methods to the audience mainly composed of scientists, and involves one of the basic elements of science. In principle, scientists should use scientific communication means (papers in scientific journals, conferences, …) to illustrate results that are sound and certain, or the methods by means of which they conduct their research. But scientists involved in predictions have inherently to do with uncertainties, and, since there is no common agreement on how to deal with them, there is the risk that scientific

Tailored vs Black-Box Models for Forecasting Hourly Average Solar Irradiance

Czech Academy of Sciences Publication Activity Database

Brabec, Marek; Paulescu, M.; Badescu, V.

2015-01-01

Roč. 111, January (2015), s. 320-331 ISSN 0038-092X R&D Projects: GA MŠk LD12009 Grant - others:European Cooperation in Science and Technology(XE) COST ES1002 Institutional support: RVO:67985807 Keywords : solar irradiance * forecasting * tilored statistical models Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.685, year: 2015

Foresight and Forecasts

DEFF Research Database (Denmark)

Kilbourn, Kyle; Bay, Marie Brøndum

In predicting areas of growth, public innovation projects may rely on optimistic visions of technology still in development as a way of ensuring novelty for funding. This paper explores what happens when forecasts of robotic technology meets the practice of sterile supply in a preliminary stage...

Contribution to forecast of environmental impact, in the long run, for fuel cells of low and average temperature using the Delphi methodology

International Nuclear Information System (INIS)

Ribeiro, Maria Alice Morato; Oliveira, Wagner dos Santos

2007-01-01

Assessing future energy systems is of major importance for providing information on potential environmental awareness of some life cycle stages of innovative technologies, for determining competitive advantages compared to conventional technologies and for developing scenarios of future. Today, intense activity of R and D in cells is verified in fuel cells, practiced in centers of research, university, and laboratories of great companies, what it seems to indicate the use in wide scale of these generating right-handers of energy, before long. The work has a main objective, in the long run, to make a forecast of the environmental impact of low and average temperature fuel cells, analyzing all the stages of their useful life and final disposal of the materials that constitute them, using the Delphi methodology. The results of the environmental impact evaluation of the main materials used in the stacks are presented, considering their manufacture, operation and final disposal after their useful life ends. (author)

How accurate are forecasts of costs of energy? A methodological contribution

International Nuclear Information System (INIS)

Siddons, Craig; Allan, Grant; McIntyre, Stuart

2015-01-01

Forecasts of the cost of energy are typically presented as point estimates; however forecasts are seldom accurate, which makes it important to understand the uncertainty around these point estimates. The scale of the differences between forecasts and outturns (i.e. contemporary estimates) of costs may have important implications for government decisions on the appropriate form (and level) of support, modelling energy scenarios or industry investment appraisal. This paper proposes a methodology to assess the accuracy of cost forecasts. We apply this to levelised costs of energy for different generation technologies due to the availability of comparable forecasts and contemporary estimates, however the same methodology could be applied to the components of levelised costs, such as capital costs. The estimated “forecast errors” capture the accuracy of previous forecasts and can provide objective bounds to the range around current forecasts for such costs. The results from applying this method are illustrated using publicly available data for on- and off-shore wind, Nuclear and CCGT technologies, revealing the possible scale of “forecast errors” for these technologies. - Highlights: • A methodology to assess the accuracy of forecasts of costs of energy is outlined. • Method applied to illustrative data for four electricity generation technologies. • Results give an objective basis for sensitivity analysis around point estimates.

Impacts of Amazonia biomass burning aerosols assessed from short-range weather forecasts

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S. R. Kolusu

2015-11-01

Full Text Available The direct radiative impacts of biomass burning aerosols (BBA on meteorology are investigated using short-range forecasts from the Met Office Unified Model (MetUM over South America during the South American Biomass Burning Analysis (SAMBBA. The impacts are evaluated using a set of three simulations: (i no aerosols, (ii with monthly mean aerosol climatologies and (iii with prognostic aerosols modelled using the Coupled Large-scale Aerosol Simulator for Studies In Climate (CLASSIC scheme. Comparison with observations show that the prognostic CLASSIC scheme provides the best representation of BBA. The impacts of BBA are quantified over central and southern Amazonia from the first and second day of 2-day forecasts during 14 September–3 October 2012. On average, during the first day of the forecast, including prognostic BBA reduces the clear-sky net radiation at the surface by 15 ± 1 W m−2 and reduces net top-of-atmosphere (TOA radiation by 8 ± 1 W m−2, with a direct atmospheric warming of 7 ± 1 W m−2. BBA-induced reductions in all-sky radiation are smaller in magnitude: 9.0 ± 1 W m−2 at the surface and 4.0 ± 1 W m−2 at TOA. In this modelling study the BBA therefore exert an overall cooling influence on the Earth–atmosphere system, although some levels of the atmosphere are directly warmed by the absorption of solar radiation. Due to the reduction of net radiative flux at the surface, the mean 2 m air temperature is reduced by around 0.1 ± 0.02 °C. The BBA also cools the boundary layer (BL but warms air above by around 0.2 °C due to the absorption of shortwave radiation. The overall impact is to reduce the BL depth by around 19 ± 8 m. These differences in heating lead to a more anticyclonic circulation at 700 hPa, with winds changing by around 0.6 m s−1. Inclusion of climatological or prognostic BBA in the MetUM makes a small but significant improvement in forecasts of temperature and relative humidity, but improvements were

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

either an intermediate forecast between the extremes of the ensemble spread or a manually selected forecast based on a meteorologists advice. 2. Downstream catchments with low influence of weather forecast In downstream catchments with strong human impact on discharge (e.g. by reservoir operation) and large influence of upstream gauge observation quality on forecast quality, the 'overall error' may in most cases be larger than the combination of the 'model error' and an ensemble spread. Therefore, the overall forecast uncertainty bounds are calculated differently: a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. Here, additionally the corresponding inflow hydrograph from all upstream catchments must be used. b) As for an upstream catchment, the uncertainty range is determined by combination of 'model error' and the ensemble member forecasts c) In addition, the 'overall error' is superimposed on the 'lead forecast'. For reasons of consistency, the lead forecast must be based on the same meteorological forecast in the downstream and all upstream catchments. d) From the resulting two uncertainty ranges (one from the ensemble forecast and 'model error', one from the 'lead forecast' and 'overall error'), the envelope is taken as the most prudent uncertainty range. In sum, the uncertainty associated with each forecast run is calculated and communicated to the public in the form of 10% and 90% percentiles. As in part I of this study, the methodology as well as the useful- or uselessness of the resulting uncertainty ranges will be presented and discussed by typical examples.

Impact of streamflow data assimilation and length of the verification period on the quality of short-term ensemble hydrologic forecasts

Science.gov (United States)

Randrianasolo, A.; Thirel, G.; Ramos, M. H.; Martin, E.

2014-11-01

Data assimilation has gained wide recognition in hydrologic forecasting due mainly to its capacity to improve the quality of short-term forecasts. In this study, a comparative analysis is conducted to assess the impact of discharge data assimilation on the quality of streamflow forecasts issued by two different modeling conceptualizations of catchment response. The sensitivity of the performance metrics to the length of the verification period is also investigated. The hydrological modeling approaches are: the coupled physically-based hydro-meteorological model SAFRAN-ISBA-MODCOU, a distributed model with a data assimilation procedure that uses streamflow measurements to assess the initial state of soil water content that optimizes discharge simulations, and the lumped soil moisture-accounting type rainfall-runoff model GRP, which assimilates directly the last observed discharge to update the state of the routing store. The models are driven by the weather ensemble prediction system PEARP of Météo-France, which is based on the global spectral ARPEGE model zoomed over France. It runs 11 perturbed members for a forecast range of 60 h. Forecast and observed data are available for 86 catchments over a 17-month period (March 2005-July 2006) for both models and for 82 catchments over a 52-month period (April 2005-July 2009) for the GRP model. The first dataset is used to investigate the impact of streamflow data assimilation on forecast quality, while the second is used to evaluate the impact of the length of the verification period on the assessment of forecast quality. Forecasts are compared to daily observed discharges and scores are computed for lead times 24 h and 48 h. Results indicate an overall good performance of both hydrological models forced by the PEARP ensemble predictions when the models are run with their data assimilation procedures. In general, when data assimilation is performed, the quality of the forecasts increases: median differences between

Forecasting the space weather impact

DEFF Research Database (Denmark)

Crosby, N. B.; Veronig, A.; Robbrecht, E.

2012-01-01

The FP7 COronal Mass Ejections and Solar Energetic Particles (COMESEP) project is developing tools for forecasting geomagnetic storms and solar energetic particle (SEP) radiation storms. By analysis of historical data, complemented by the extensive data coverage of solar cycle 23, the key ingredi...

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.

Probabilistic Latent Semantic Analyses (PLSA in Bibliometric Analysis for Technology Forecasting

Directory of Open Access Journals (Sweden)

Wang Zan

2007-03-01

Full Text Available Due to the availability of internet-based abstract services and patent databases, bibliometric analysis has become one of key technology forecasting approaches. Recently, latent semantic analysis (LSA has been applied to improve the accuracy in document clustering. In this paper, a new LSA method, probabilistic latent semantic analysis (PLSA which uses probabilistic methods and algebra to search latent space in the corpus is further applied in document clustering. The results show that PLSA is more accurate than LSA and the improved iteration method proposed by authors can simplify the computing process and improve the computing efficiency

Integration of wind generation forecasts. Volume 2

International Nuclear Information System (INIS)

Ahlstrom, M.; Zavadil, B.; Jones, L.

2005-01-01

WindLogics is a company that specializes in atmospheric modelling, visualization and fine-scale forecasting systems for the wind power industry. A background of the organization was presented. The complexities of wind modelling were discussed. Issues concerning location and terrain, shear, diurnal and interannual variability were reviewed. It was suggested that wind power producers should aim to be mainstream, and that variability should be considered as intrinsic to fuel supply. Various utility operating impacts were outlined. Details of an Xcel NSP wind integration study were presented, as well as a studies conducted in New York state and Colorado. It was concluded that regulations and load following impacts with wind energy integration are modest. Overall impacts are dominated by costs incurred to accommodate wind generation variability and uncertainty in the day-ahead time frame. Cost impacts can be reduced with adjustments to operating strategies, improvements in wind forecasting and access to real-time markets. Details of WindLogic's wind energy forecast system were presented, as well as examples of day ahead and hour ahead forecasts and wind speed and power forecasts. Screenshots of control room integration, EMS integration and simulations were presented. Details of a utility-scale wind energy forecasting system funded by Xcel Renewable Development Fund (RDF) were also presented. The goal of the system was to optimize the way that wind forecast information is integrated into the control room environment. Project components were outlined. It was concluded that accurate day-ahead forecasting can lead to significant asset optimization. It was recommended that wind plants share data, and aim to resolve issues concerning grid codes and instrumentation. refs., tabs., figs

Financial impact of errors in business forecasting: a comparative study of linear models and neural networks

Directory of Open Access Journals (Sweden)

Claudimar Pereira da Veiga

2012-08-01

Full Text Available The importance of demand forecasting as a management tool is a well documented issue. However, it is difficult to measure costs generated by forecasting errors and to find a model that assimilate the detailed operation of each company adequately. In general, when linear models fail in the forecasting process, more complex nonlinear models are considered. Although some studies comparing traditional models and neural networks have been conducted in the literature, the conclusions are usually contradictory. In this sense, the objective was to compare the accuracy of linear methods and neural networks with the current method used by the company. The results of this analysis also served as input to evaluate influence of errors in demand forecasting on the financial performance of the company. The study was based on historical data from five groups of food products, from 2004 to 2008. In general, one can affirm that all models tested presented good results (much better than the current forecasting method used, with mean absolute percent error (MAPE around 10%. The total financial impact for the company was 6,05% on annual sales.

Impact of AIRS Thermodynamic Profile on Regional Weather Forecast

Science.gov (United States)

Chou, Shih-Hung; Zavodsky, Brad; Jedlovee, Gary

2010-01-01

Prudent assimilation of AIRS thermodynamic profiles and quality indicators can improve initial conditions for regional weather models. AIRS-enhanced analysis has warmer and moister PBL. Forecasts with AIRS profiles are generally closer to NAM analyses than CNTL. Assimilation of AIRS leads to an overall QPF improvement in 6-h accumulated precipitation forecasts. Including AIRS profiles in assimilation process enhances the moist instability and produces stronger updrafts and a better precipitation forecast than the CNTL run.

Uncertainty propagation for flood forecasting in the Alps: different views and impacts from MAP D-PHASE

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M. W. Rotach

2012-08-01

Full Text Available D-PHASE was a Forecast Demonstration Project of the World Weather Research Programme (WWRP related to the Mesoscale Alpine Programme (MAP. Its goal was to demonstrate the reliability and quality of operational forecasting of orographically influenced (determined precipitation in the Alps and its consequences on the distribution of run-off characteristics. A special focus was, of course, on heavy-precipitation events.

The D-PHASE Operations Period (DOP ran from June to November~2007, during which an end-to-end forecasting system was operated covering many individual catchments in the Alps, with their water authorities, civil protection organizations or other end users. The forecasting system's core piece was a Visualization Platform where precipitation and flood warnings from some 30 atmospheric and 7 hydrological models (both deterministic and probabilistic and corresponding model fields were displayed in uniform and comparable formats. Also, meteograms, nowcasting information and end user communication was made available to all the forecasters, users and end users. D-PHASE information was assessed and used by some 50 different groups ranging from atmospheric forecasters to civil protection authorities or water management bodies.

In the present contribution, D-PHASE is briefly presented along with its outstanding scientific results and, in particular, the lessons learnt with respect to uncertainty propagation. A focus is thereby on the transfer of ensemble prediction information into the hydrological community and its use with respect to other aspects of societal impact. Objective verification of forecast quality is contrasted to subjective quality assessments during the project (end user workshops, questionnaires and some general conclusions concerning forecast demonstration projects are drawn.

Long-term forecast 2010; Laangsiktsprognos 2010

Energy Technology Data Exchange (ETDEWEB)

2011-07-01

This report presents the energy forecast to the year 2030, and two different sensitivity scenarios. The forecast is based on existing instruments, which means that the report's findings should not be considered a proper forecast of the future energy use, but as an impact assessment of existing policy instruments, given different circumstances such as economic growth and fuel prices

Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMS

Directory of Open Access Journals (Sweden)

Sheng-Chi Yang

2014-01-01

Full Text Available During an extreme event, having accurate inflow forecasting with enough lead time helps reservoir operators decrease the impact of floods downstream. Furthermore, being able to efficiently operate reservoirs could help maximize flood protection while saving water for drier times of the year. This study combines ensemble quantitative precipitation forecasts and a hydrological model to provide a 3-day reservoir inflow in the Shihmen Reservoir, Taiwan. A total of six historical typhoons were used for model calibration, validation, and application. An understanding of cascaded uncertainties from the numerical weather model through the hydrological model is necessary for a better use for forecasting. This study thus conducted an assessment of forecast uncertainty on magnitude and timing of peak and cumulative inflows. It found that using the ensemble-mean had less uncertainty than randomly selecting individual member. The inflow forecasts with shorter length of cumulative time had a higher uncertainty. The results showed that using the ensemble precipitation forecasts with the hydrological model would have the advantage of extra lead time and serve as a valuable reference for operating reservoirs.

The Impact of Cross-track Infrared Sounder (CrIS) Cloud-Cleared Radiances on Hurricane Joaquin (2015) and Matthew (2016) Forecasts

Science.gov (United States)

Wang, Pei; Li, Jun; Li, Zhenglong; Lim, Agnes H. N.; Li, Jinlong; Schmit, Timothy J.; Goldberg, Mitchell D.

2017-12-01

Hyperspectral infrared (IR) sounders provide high vertical resolution atmospheric sounding information that can improve the forecast skill in numerical weather prediction. Commonly, only clear radiances are assimilated, because IR sounder observations are highly affected by clouds. A cloud-clearing (CC) technique, which removes the cloud effects from an IR cloudy field of view (FOV) and derives the cloud-cleared radiances (CCRs) or clear-sky equivalent radiances, can be an alternative yet effective way to take advantage of the thermodynamic information from cloudy skies in data assimilation. This study develops a Visible Infrared Imaging Radiometer Suite (VIIRS)-based CC method for deriving Cross-track Infrared Sounder (CrIS) CCRs under partially cloudy conditions. Due to the lack of absorption bands on VIIRS, two important quality control steps are implemented in the CC process. Validation using VIIRS clear radiances indicates that the CC method can effectively obtain the CrIS CCRs for FOVs with partial cloud cover. To compare the impacts from assimilation of CrIS original radiances and CCRs, three experiments are carried out on two storm cases, Hurricane Joaquin (2015) and Hurricane Matthew (2016), using Gridpoint Statistical Interpolation assimilation system and Weather Research and Forecasting-Advanced Research Version models. At the analysis time, more CrIS observations are assimilated when using CrIS CCRs than with CrIS original radiances. Comparing temperature, specific humidity, and U/V winds with radiosondes indicates that the data impacts are growing larger with longer time forecasts (beyond 72 h forecast). Hurricane track forecasts also show improvements from the assimilation of CrIS CCRs due to better weather system forecasts. The impacts of CCRs on intensity are basically neutral with mixed positive and negative results.

Using Adaptive Neural-Fuzzy Inference Systems (ANFIS for Demand Forecasting and an Application

Directory of Open Access Journals (Sweden)

Onur Doğan

2016-06-01

Full Text Available Due to the rapid increase in global competition among organizations and companies, rational approaches in decision making have become indispensable for organizations in today’s world. Establishing a safe and robust path through uncertainties and risks depends on the decision units’ ability of using scientific methods as well as technology. Demand forecasting is known to be one of the most critical problems in organizations.  A company which supports its demand forecasting mechanism with scientific methodologies could increase its productivity and efficiency in all other functions. New methods, such as fuzzy logic and artificial neural networks are frequently being used as a decision-making mechanism in organizations and companies recently.  In this study, it is aimed to solve a critical demand forecasting problem with ANFIS. In the first phase of the study, the factors which impact demand forecasting are determined, and then a database of the model is established using these factors. It has been shown that ANFIS could be used for demand forecasting.

Assessing the Impact of Surface and Upper-Air Observations on the Forecast Skill of the ACCESS Numerical Weather Prediction Model over Australia

Directory of Open Access Journals (Sweden)

Sergei Soldatenko

2018-01-01

Full Text Available The impact of the Australian Bureau of Meteorology’s in situ observations (land and sea surface observations, upper air observations by radiosondes, pilot balloons, wind profilers, and aircraft observations on the short-term forecast skill provided by the ACCESS (Australian Community Climate and Earth-System Simulator global numerical weather prediction (NWP system is evaluated using an adjoint-based method. This technique makes use of the adjoint perturbation forecast model utilized within the 4D-Var assimilation system, and is able to calculate the individual impact of each assimilated observation in a cycling NWP system. The results obtained show that synoptic observations account for about 60% of the 24-h forecast error reduction, with the remainder accounted for by aircraft (12.8%, radiosondes (10.5%, wind profilers (3.9%, pilot balloons (2.8%, buoys (1.7% and ships (1.2%. In contrast, the largest impact per observation is from buoys and aircraft. Overall, all observation types have a positive impact on the 24-h forecast skill. Such results help to support the decision-making process regarding the evolution of the observing network, particularly at the national level. Consequently, this 4D-Var-based approach has great potential as a tool to assist the design and running of an efficient and effective observing network.

New technology for using meteorological information in forest insect pest forecast and warning systems.

Science.gov (United States)

Qin, Jiang-Lin; Yang, Xiu-Hao; Yang, Zhong-Wu; Luo, Ji-Tong; Lei, Xiu-Feng

2017-12-01

Near surface air temperature and rainfall are major weather factors affecting forest insect dynamics. The recent developments in remote sensing retrieval and geographic information system spatial analysis techniques enable the utilization of weather factors to significantly enhance forest pest forecasting and warning systems. The current study focused on building forest pest digital data structures as a platform of correlation analysis between weather conditions and forest pest dynamics for better pest forecasting and warning systems using the new technologies. The study dataset contained 3 353 425 small polygons with 174 defined attributes covering 95 counties of Guangxi province of China currently registering 292 forest pest species. Field data acquisition and information transfer systems were established with four software licences that provided 15-fold improvement compared to the systems currently used in China. Nine technical specifications were established including codes of forest districts, pest species and host tree species, and standard practices of forest pest monitoring and information management. Attributes can easily be searched using ArcGIS9.3 and/or the free QGIS2.16 software. Small polygons with pest relevant attributes are a new tool of precision farming and detailed forest insect pest management that are technologically advanced. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

Directory of Open Access Journals (Sweden)

Dehua Zheng

2017-12-01

Full Text Available The power generated by wind generators is usually associated with uncertainties, due to the intermittency of wind speed and other weather variables. This creates a big challenge for transmission system operators (TSOs and distribution system operators (DSOs in terms of connecting, controlling and managing power networks with high-penetration wind energy. Hence, in these power networks, accurate wind power forecasts are essential for their reliable and efficient operation. They support TSOs and DSOs in enhancing the control and management of the power network. In this paper, a novel two-stage hybrid approach based on the combination of the Hilbert-Huang transform (HHT, genetic algorithm (GA and artificial neural network (ANN is proposed for day-ahead wind power forecasting. The approach is composed of two stages. The first stage utilizes numerical weather prediction (NWP meteorological information to predict wind speed at the exact site of the wind farm. The second stage maps actual wind speed vs. power characteristics recorded by SCADA. Then, the wind speed forecast in the first stage for the future day is fed to the second stage to predict the future day’s wind power. Comparative selection of input-data parameter sets for the forecasting model and impact analysis of input-data dependency on forecasting accuracy have also been studied. The proposed approach achieves significant forecasting accuracy improvement compared with three other artificial intelligence-based forecasting approaches and a benchmark model using the smart persistence method.

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)

AMSR2 all-sky radiance assimilation and its impact on the analysis and forecast of Hurricane Sandy with a limited-area data assimilation system

Directory of Open Access Journals (Sweden)

Chun Yang

2016-06-01

Full Text Available A method to assimilate all-sky radiances from the Advanced Microwave Scanning Radiometer 2 (AMSR2 was developed within the Weather Research and Forecasting (WRF model's data assimilation (WRFDA system. The four essential elements are: (1 extending the community radiative transform model's (CRTM interface to include hydrometeor profiles; (2 using total water Qt as the moisture control variable; (3 using a warm-rain physics scheme for partitioning the Qt increment into individual increments of water vapour, cloud liquid water and rain; and (4 adopting a symmetric observation error model for all-sky radiance assimilation.Compared to a benchmark experiment with no AMSR2 data, the impact of assimilating clear-sky or all-sky AMSR2 radiances on the analysis and forecast of Hurricane Sandy (2012 was assessed through analysis/forecast cycling experiments using WRF and WRFDA's three-dimensional variational (3DVAR data assimilation scheme. With more cloud/precipitation-affected data being assimilated around tropical cyclone (TC core areas in the all-sky AMSR2 assimilation experiment, better analyses were obtained in terms of the TC's central sea level pressure (CSLP, warm-core structure and cloud distribution. Substantial (>20 % error reduction in track and CSLP forecasts was achieved from both clear-sky and all-sky AMSR2 assimilation experiments, and this improvement was consistent from the analysis time to 72-h forecasts. Moreover, the all-sky assimilation experiment consistently yielded better track and CSLP forecasts than the clear-sky did for all forecast lead times, due to a better analysis in the TC core areas. Positive forecast impact from assimilating AMSR2 radiances is also seen when verified against the European Center for Medium-Range Weather Forecasts (ECMWF analysis and the Stage IV precipitation analysis, with an overall larger positive impact from the all-sky assimilation experiment.

Wind and load forecast error model for multiple geographically distributed forecasts

Energy Technology Data Exchange (ETDEWEB)

Makarov, Yuri V.; Reyes-Spindola, Jorge F.; Samaan, Nader; Diao, Ruisheng; Hafen, Ryan P. [Pacific Northwest National Laboratory, Richland, WA (United States)

2010-07-01

The impact of wind and load forecast errors on power grid operations is frequently evaluated by conducting multi-variant studies, where these errors are simulated repeatedly as random processes based on their known statistical characteristics. To simulate these errors correctly, we need to reflect their distributions (which do not necessarily follow a known distribution law), standard deviations. auto- and cross-correlations. For instance, load and wind forecast errors can be closely correlated in different zones of the system. This paper introduces a new methodology for generating multiple cross-correlated random processes to produce forecast error time-domain curves based on a transition probability matrix computed from an empirical error distribution function. The matrix will be used to generate new error time series with statistical features similar to observed errors. We present the derivation of the method and some experimental results obtained by generating new error forecasts together with their statistics. (orig.)

Ensemble hydromoeteorological forecasting in Denmark

DEFF Research Database (Denmark)

Lucatero Villasenor, Diana

forecasts where a dampening of the differences of precipitation quality occurs. Seasonal meteorological forecasts are possible due to changes of large scale patterns of the ocean and land, such as el Niño, that evolve at a much slower pace than the atmosphere, which can have an impact on its evolution later...

An experimental system for flood risk forecasting at global scale

Science.gov (United States)

Alfieri, L.; Dottori, F.; Kalas, M.; Lorini, V.; Bianchi, A.; Hirpa, F. A.; Feyen, L.; Salamon, P.

2016-12-01

Global flood forecasting and monitoring systems are nowadays a reality and are being applied by an increasing range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasts, combining streamflow estimations with expected inundated areas and flood impacts. To this end, we have developed an experimental procedure for near-real time flood mapping and impact assessment based on the daily forecasts issued by the Global Flood Awareness System (GloFAS). The methodology translates GloFAS streamflow forecasts into event-based flood hazard maps based on the predicted flow magnitude and the forecast lead time and a database of flood hazard maps with global coverage. Flood hazard maps are then combined with exposure and vulnerability information to derive flood risk. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To further increase the reliability of the proposed methodology we integrated model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification of impact forecasts. The preliminary tests provided good results and showed the potential of the developed real-time operational procedure in helping emergency response and management. In particular, the link with social media is crucial for improving the accuracy of impact predictions.

Ensemble Forecasts with Useful Skill-Spread Relationships for African meningitis and Asia Streamflow Forecasting

Science.gov (United States)

Hopson, T. M.

2014-12-01

One potential benefit of an ensemble prediction system (EPS) is its capacity to forecast its own forecast error through the ensemble spread-error relationship. In practice, an EPS is often quite limited in its ability to represent the variable expectation of forecast error through the variable dispersion of the ensemble, and perhaps more fundamentally, in its ability to provide enough variability in the ensembles dispersion to make the skill-spread relationship even potentially useful (irrespective of whether the EPS is well-calibrated or not). In this paper we examine the ensemble skill-spread relationship of an ensemble constructed from the TIGGE (THORPEX Interactive Grand Global Ensemble) dataset of global forecasts and a combination of multi-model and post-processing approaches. Both of the multi-model and post-processing techniques are based on quantile regression (QR) under a step-wise forward selection framework leading to ensemble forecasts with both good reliability and sharpness. The methodology utilizes the ensemble's ability to self-diagnose forecast instability to produce calibrated forecasts with informative skill-spread relationships. A context for these concepts is provided by assessing the constructed ensemble in forecasting district-level humidity impacting the incidence of meningitis in the meningitis belt of Africa, and in forecasting flooding events in the Brahmaputra and Ganges basins of South Asia.

CSIR Technology Impact 1998

CSIR Research Space (South Africa)

CSIR

1998-01-01

Full Text Available stream_source_info CSIR Technology Impact 1998.pdf.txt stream_content_type text/plain stream_size 23 Content-Encoding ISO-8859-1 stream_name CSIR Technology Impact 1998.pdf.txt Content-Type text/plain; charset=ISO-8859-1 ...

Demand forecast model based on CRM

Science.gov (United States)

Cai, Yuancui; Chen, Lichao

2006-11-01

With interiorizing day by day management thought that regarding customer as the centre, forecasting customer demand becomes more and more important. In the demand forecast of customer relationship management, the traditional forecast methods have very great limitation because much uncertainty of the demand, these all require new modeling to meet the demands of development. In this paper, the notion is that forecasting the demand according to characteristics of the potential customer, then modeling by it. The model first depicts customer adopting uniform multiple indexes. Secondly, the model acquires characteristic customers on the basis of data warehouse and the technology of data mining. The last, there get the most similar characteristic customer by their comparing and forecast the demands of new customer by the most similar characteristic customer.

Novel methodology for pharmaceutical expenditure forecast.

Science.gov (United States)

Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Rémuzat, Cécile; Urbinati, Duccio; Kornfeld, Åsa; Mzoughi, Olfa; Toumi, Mondher

2014-01-01

The value appreciation of new drugs across countries today features a disruption that is making the historical data that are used for forecasting pharmaceutical expenditure poorly reliable. Forecasting methods rarely addressed uncertainty. The objective of this project was to propose a methodology to perform pharmaceutical expenditure forecasting that integrates expected policy changes and uncertainty (developed for the European Commission as the 'EU Pharmaceutical expenditure forecast'; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). 1) Identification of all pharmaceuticals going off-patent and new branded medicinal products over a 5-year forecasting period in seven European Union (EU) Member States. 2) Development of a model to estimate direct and indirect impacts (based on health policies and clinical experts) on savings of generics and biosimilars. Inputs were originator sales value, patent expiry date, time to launch after marketing authorization, price discount, penetration rate, time to peak sales, and impact on brand price. 3) Development of a model for new drugs, which estimated sales progression in a competitive environment. Clinical expected benefits as well as commercial potential were assessed for each product by clinical experts. Inputs were development phase, marketing authorization dates, orphan condition, market size, and competitors. 4) Separate analysis of the budget impact of products going off-patent and new drugs according to several perspectives, distribution chains, and outcomes. 5) Addressing uncertainty surrounding estimations via deterministic and probabilistic sensitivity analysis. This methodology has proven to be effective by 1) identifying the main parameters impacting the variations in pharmaceutical expenditure forecasting across countries: generics discounts and penetration, brand price after patent loss, reimbursement rate, the penetration of biosimilars and discount price, distribution chains, and the time

What Do Experts Know About Forecasting Journal Quality? A Comparison with ISI Research Impact in Finance?

NARCIS (Netherlands)

C-L. Chang (Chia-Lin); M.J. McAleer (Michael)

2013-01-01

textabstractExperts possess knowledge and information that are not publicly available. The paper is concerned with forecasting academic journal quality and research impact using a survey of international experts from a national project on ranking academic finance journals in Taiwan. A comparison is

What Do Experts Know About Forecasting Journal Quality? A Comparison with ISI Research Impact in Finance

NARCIS (Netherlands)

C-L. Chang (Chia-Lin); M.J. McAleer (Michael)

2013-01-01

textabstractExperts possess knowledge and information that are not publicly available. The paper is concerned with forecasting academic journal quality and research impact using a survey of international experts from a national project on ranking academic finance journals in Taiwan. A comparison is

An Assessment of the Subseasonal Forecast Performance in the Extended Global Ensemble Forecast System (GEFS)

Science.gov (United States)

Sinsky, E.; Zhu, Y.; Li, W.; Guan, H.; Melhauser, C.

2017-12-01

Optimal forecast quality is crucial for the preservation of life and property. Improving monthly forecast performance over both the tropics and extra-tropics requires attention to various physical aspects such as the representation of the underlying SST, model physics and the representation of the model physics uncertainty for an ensemble forecast system. This work focuses on the impact of stochastic physics, SST and the convection scheme on forecast performance for the sub-seasonal scale over the tropics and extra-tropics with emphasis on the Madden-Julian Oscillation (MJO). A 2-year period is evaluated using the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS). Three experiments with different configurations than the operational GEFS were performed to illustrate the impact of the stochastic physics, SST and convection scheme. These experiments are compared against a control experiment (CTL) which consists of the operational GEFS but its integration is extended from 16 to 35 days. The three configurations are: 1) SPs, which uses a Stochastically Perturbed Physics Tendencies (SPPT), Stochastic Perturbed Humidity (SHUM) and Stochastic Kinetic Energy Backscatter (SKEB); 2) SPs+SST_bc, which uses a combination of SPs and a bias-corrected forecast SST from the NCEP Climate Forecast System Version 2 (CFSv2); and 3) SPs+SST_bc+SA_CV, which combines SPs, a bias-corrected forecast SST and a scale aware convection scheme. When comparing to the CTL experiment, SPs shows substantial improvement. The MJO skill has improved by about 4 lead days during the 2-year period. Improvement is also seen over the extra-tropics due to the updated stochastic physics, where there is a 3.1% and a 4.2% improvement during weeks 3 and 4 over the northern hemisphere and southern hemisphere, respectively. Improvement is also seen when the bias-corrected CFSv2 SST is combined with SPs. Additionally, forecast performance enhances when the scale aware

Integrating observation and statistical forecasts over sub-Saharan Africa to support Famine Early Warning

Science.gov (United States)

Funk, Chris; Verdin, James P.; Husak, Gregory

2007-01-01

Famine early warning in Africa presents unique challenges and rewards. Hydrologic extremes must be tracked and anticipated over complex and changing climate regimes. The successful anticipation and interpretation of hydrologic shocks can initiate effective government response, saving lives and softening the impacts of droughts and floods. While both monitoring and forecast technologies continue to advance, discontinuities between monitoring and forecast systems inhibit effective decision making. Monitoring systems typically rely on high resolution satellite remote-sensed normalized difference vegetation index (NDVI) and rainfall imagery. Forecast systems provide information on a variety of scales and formats. Non-meteorologists are often unable or unwilling to connect the dots between these disparate sources of information. To mitigate these problem researchers at UCSB's Climate Hazard Group, NASA GIMMS and USGS/EROS are implementing a NASA-funded integrated decision support system that combines the monitoring of precipitation and NDVI with statistical one-to-three month forecasts. We present the monitoring/forecast system, assess its accuracy, and demonstrate its application in food insecure sub-Saharan Africa.

Structural change and forecasting long-run energy prices

International Nuclear Information System (INIS)

Bernard, J.T.; Khalaf, L.

2004-01-01

Fluctuating energy prices have a significant impact on the economies of industrialized nations. A recent study has shown a strong non-linear relationship between changes in oil prices and growth in gross domestic product (GDP). In order to forecast the behaviour of energy prices, a complete model must take into account domestic and international supply and demand conditions, market regulations, technological advances and geopolitics. In 1999, Pindyck suggested that for long-term forecasting, a simple model should be adopted where prices grow in real terms and at a fixed rate. This paper tests the statistical significance of Pindyck's suggested class of econometric equations that model the behaviour of long-run real energy prices. The models assume mean-reverting prices with continuous and random changes in their level and trend. They are estimated using Kalman filtering. The authors used simulation-based procedures to address the issue of non-standard test statistics and nuisance parameters. Results were reported for a standard Monte Carlo test and a maximized Monte Carlo test. Results shown statistically significant instabilities for coal and natural gas prices, but not for crude oil prices. Various models were differentiated using out-of-sample forecasting exercises. 25 refs., 3 tabs

Now, Here's the Weather Forecast...

Science.gov (United States)

Richardson, Mathew

2013-01-01

The Met Office has a long history of weather forecasting, creating tailored weather forecasts for customers across the world. Based in Exeter, the Met Office is also home to the Met Office Hadley Centre, a world-leading centre for the study of climate change and its potential impacts. Climate information from the Met Office Hadley Centre is used…

Cognitive methodology for forecasting oil and gas industry using pattern-based neural information technologies

Science.gov (United States)

Gafurov, O.; Gafurov, D.; Syryamkin, V.

2018-05-01

The paper analyses a field of computer science formed at the intersection of such areas of natural science as artificial intelligence, mathematical statistics, and database theory, which is referred to as "Data Mining" (discovery of knowledge in data). The theory of neural networks is applied along with classical methods of mathematical analysis and numerical simulation. The paper describes the technique protected by the patent of the Russian Federation for the invention “A Method for Determining Location of Production Wells during the Development of Hydrocarbon Fields” [1–3] and implemented using the geoinformation system NeuroInformGeo. There are no analogues in domestic and international practice. The paper gives an example of comparing the forecast of the oil reservoir quality made by the geophysicist interpreter using standard methods and the forecast of the oil reservoir quality made using this technology. The technical result achieved shows the increase of efficiency, effectiveness, and ecological compatibility of development of mineral deposits and discovery of a new oil deposit.

A model for Long-term Industrial Energy Forecasting (LIEF)

Energy Technology Data Exchange (ETDEWEB)

Ross, M. [Lawrence Berkeley Lab., CA (United States)]|[Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics]|[Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.; Hwang, R. [Lawrence Berkeley Lab., CA (United States)

1992-02-01

The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model`s parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

A model for Long-term Industrial Energy Forecasting (LIEF)

Energy Technology Data Exchange (ETDEWEB)

Ross, M. (Lawrence Berkeley Lab., CA (United States) Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.); Hwang, R. (Lawrence Berkeley Lab., CA (United States))

1992-02-01

The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model's parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

Forecasting of Processes in Complex Systems for Real-World Problems

Czech Academy of Sciences Publication Activity Database

Pelikán, Emil

2014-01-01

Roč. 24, č. 6 (2014), s. 567-589 ISSN 1210-0552 Institutional support: RVO:67985807 Keywords : complex systems * data assimilation * ensemble forecasting * forecasting * global solar radiation * judgmental forecasting * multimodel forecasting * pollution Subject RIV: IN - Informatics, Computer Science Impact factor: 0.479, year: 2014

Forecasting behavior in smart homes based on sleep and wake patterns.

Science.gov (United States)

Williams, Jennifer A; Cook, Diane J

2017-01-01

The goal of this research is to use smart home technology to assist people who are recovering from injuries or coping with disabilities to live independently. We introduce an algorithm to model and forecast wake and sleep behaviors that are exhibited by the participant. Furthermore, we propose that sleep behavior is impacted by and can be modeled from wake behavior, and vice versa. This paper describes the Behavior Forecasting (BF) algorithm. BF consists of 1) defining numeric values that reflect sleep and wake behavior, 2) forecasting wake and sleep values from past behavior, 3) analyzing the effect of wake behavior on sleep and vice versa, and 4) improving prediction performance by using both wake and sleep scores. The BF method was evaluated with data collected from 20 smart homes. We found that regardless of the forecasting method utilized, wake behavior and sleep behavior can be modeled with a minimum accuracy of 84%. Additionally, normalizing the wake and sleep scores drastically improves the accuracy to 99%. The results show that we can effectively model wake and sleep behaviors in a smart environment. Furthermore, wake behaviors can be predicted from sleep behaviors and vice versa.

Projecting technology change to improve space technology planning and systems management

Science.gov (United States)

Walk, Steven Robert

2011-04-01

Projecting technology performance evolution has been improving over the years. Reliable quantitative forecasting methods have been developed that project the growth, diffusion, and performance of technology in time, including projecting technology substitutions, saturation levels, and performance improvements. These forecasts can be applied at the early stages of space technology planning to better predict available future technology performance, assure the successful selection of technology, and improve technology systems management strategy. Often what is published as a technology forecast is simply scenario planning, usually made by extrapolating current trends into the future, with perhaps some subjective insight added. Typically, the accuracy of such predictions falls rapidly with distance in time. Quantitative technology forecasting (QTF), on the other hand, includes the study of historic data to identify one of or a combination of several recognized universal technology diffusion or substitution patterns. In the same manner that quantitative models of physical phenomena provide excellent predictions of system behavior, so do QTF models provide reliable technological performance trajectories. In practice, a quantitative technology forecast is completed to ascertain with confidence when the projected performance of a technology or system of technologies will occur. Such projections provide reliable time-referenced information when considering cost and performance trade-offs in maintaining, replacing, or migrating a technology, component, or system. This paper introduces various quantitative technology forecasting techniques and illustrates their practical application in space technology and technology systems management.

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...

Potential predictability and forecast skill in ensemble climate forecast: the skill-persistence rule

Science.gov (United States)

Jin, Y.; Rong, X.; Liu, Z.

2017-12-01

This study investigates the factors that impact the forecast skill for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill of sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further examined using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but can be distorted by the sampling error and non-AR1 processes.

A preliminary study of the impact of the ERS 1 C band scatterometer wind data on the European Centre for Medium-Range Weather Forecasts global data assimilation system

Science.gov (United States)

Hoffman, Ross N.

1993-01-01

A preliminary assessment of the impact of the ERS 1 scatterometer wind data on the current European Centre for Medium-Range Weather Forecasts analysis and forecast system has been carried out. Although the scatterometer data results in changes to the analyses and forecasts, there is no consistent improvement or degradation. Our results are based on comparing analyses and forecasts from assimilation cycles. The two sets of analyses are very similar except for the low level wind fields over the ocean. Impacts on the analyzed wind fields are greater over the southern ocean, where other data are scarce. For the most part the mass field increments are too small to balance the wind increments. The effect of the nonlinear normal mode initialization on the analysis differences is quite small, but we observe that the differences tend to wash out in the subsequent 6-hour forecast. In the Northern Hemisphere, analysis differences are very small, except directly at the scatterometer locations. Forecast comparisons reveal large differences in the Southern Hemisphere after 72 hours. Notable differences in the Northern Hemisphere do not appear until late in the forecast. Overall, however, the Southern Hemisphere impacts are neutral. The experiments described are preliminary in several respects. We expect these data to ultimately prove useful for global data assimilation.

Forecasting in Complex Systems

Science.gov (United States)

Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Turcotte, D. L.; Donnellan, A.

2014-12-01

Complex nonlinear systems are typically characterized by many degrees of freedom, as well as interactions between the elements. Interesting examples can be found in the areas of earthquakes and finance. In these two systems, fat tails play an important role in the statistical dynamics. For earthquake systems, the Gutenberg-Richter magnitude-frequency is applicable, whereas for daily returns for the securities in the financial markets are known to be characterized by leptokurtotic statistics in which the tails are power law. Very large fluctuations are present in both systems. In earthquake systems, one has the example of great earthquakes such as the M9.1, March 11, 2011 Tohoku event. In financial systems, one has the example of the market crash of October 19, 1987. Both were largely unexpected events that severely impacted the earth and financial systems systemically. Other examples include the M9.3 Andaman earthquake of December 26, 2004, and the Great Recession which began with the fall of Lehman Brothers investment bank on September 12, 2013. Forecasting the occurrence of these damaging events has great societal importance. In recent years, national funding agencies in a variety of countries have emphasized the importance of societal relevance in research, and in particular, the goal of improved forecasting technology. Previous work has shown that both earthquakes and financial crashes can be described by a common Landau-Ginzburg-type free energy model. These metastable systems are characterized by fat tail statistics near the classical spinodal. Correlations in these systems can grow and recede, but do not imply causation, a common source of misunderstanding. In both systems, a common set of techniques can be used to compute the probabilities of future earthquakes or crashes. In this talk, we describe the basic phenomenology of these systems and emphasize their similarities and differences. We also consider the problem of forecast validation and verification

Forecasting system predicts presence of sea nettles in Chesapeake Bay

Science.gov (United States)

Brown, Christopher W.; Hood, Raleigh R.; Li, Zhen; Decker, Mary Beth; Gross, Thomas F.; Purcell, Jennifer E.; Wang, Harry V.

Outbreaks of noxious biota, which occur in both aquatic and terrestrial systems, can have considerable negative economic impacts. For example, an increasing frequency of harmful algal blooms worldwide has negatively affected the tourism industry in many regions. Such impacts could be mitigated if the conditions that give rise to these outbreaks were known and could be monitored. Recent advances in technology and communications allow us to continuously measure and model many environmental factors that are responsible for outbreaks of certain noxious organisms. A new prototype ecological forecasting system predicts the likelihood of occurrence of the sea nettle (Chrysaora quinquecirrha), a stinging jellyfish, in the Chesapeake Bay.

Forecasting transportation impacts upon land use

Energy Technology Data Exchange (ETDEWEB)

Wendt, P F [ed.

1976-01-01

Chapter titles include: introduction and overview; national and regional econometric models; growth and change in the Georgia regional economies; a Delphi approach to land use forecasting; land suitability and land use analysis; the interaction of transportation and land use; and, summary and conclusions. Lists of tables and figures are also included. (JGB)

An Improved Approach for Forecasting Ecological Impacts from Future Drilling in Unconventional Shale Oil and Gas Plays.

Science.gov (United States)

Wolaver, Brad D; Pierre, Jon Paul; Ikonnikova, Svetlana A; Andrews, John R; McDaid, Guinevere; Ryberg, Wade A; Hibbitts, Toby J; Duran, Charles M; Labay, Benjamin J; LaDuc, Travis J

2018-04-13

Directional well drilling and hydraulic fracturing has enabled energy production from previously inaccessible resources, but caused vegetation conversion and landscape fragmentation, often in relatively undisturbed habitats. We improve forecasts of future ecological impacts from unconventional oil and gas play developments using a new, more spatially-explicit approach. We applied an energy production outlook model, which used geologic and economic data from thousands of wells and three oil price scenarios, to map future drilling patterns and evaluate the spatial distribution of vegetation conversion and habitat impacts. We forecast where future well pad construction may be most intense, illustrating with an example from the Eagle Ford Shale Play of Texas. We also illustrate the ecological utility of this approach using the Spot-tailed Earless Lizard (Holbrookia lacerata) as the focal species, which historically occupied much of the Eagle Ford and awaits a federal decision for possible Endangered Species Act protection. We found that ~17,000-45,500 wells would be drilled 2017‒2045 resulting in vegetation conversion of ~26,485-70,623 ha (0.73-1.96% of pre-development vegetation), depending on price scenario ($40-$80/barrel). Grasslands and row crop habitats were most affected (2.30 and 2.82% areal vegetation reduction). Our approach improves forecasts of where and to what extent future energy development in unconventional plays may change land-use and ecosystem services, enabling natural resource managers to anticipate and direct on-the-ground conservation actions to places where they will most effectively mitigate ecological impacts of well pads and associated infrastructure.

Impact of GFZ's Effective Angular Momentum Forecasts on Polar Motion Prediction

Science.gov (United States)

Dill, Robert; Dobslaw, Henryk

2017-04-01

The Earth System Modelling group at GeoForschungsZentrum (GFZ) Potsdam offers now 6-day forecasts of Earth rotation excitation due to atmospheric, oceanic, and hydrologic angular momentum changes that are consistent with its 40 years-long EAM series. Those EAM forecasts are characterized by an improved long-term consistency due to the introduction of a time-invariant high-resolution reference topography into the AAM processing that accounts for occasional NWP model changes. In addition, all tidal signals from both atmosphere and ocean have been separated, and the temporal resolution of both AAM and OAM has been increased to 3 hours. Analysis of an extended set of EAM short-term hindcasts revealed positive prediction skills for up to 6 days into the future when compared to a persistent forecast. Whereas UT1 predictions in particular rely on an accurate AAM forecast, skillfull polar motion prediction requires high-quality OAM forecasts as well. We will present in this contribution the results from a multi-year hindcast experiment, demonstrating that the polar motion prediction as currently available from Bulletin A can be improved in particular for lead-times between 2 and 5 days by incorporating OAM forecasts. We will also report about early results obtained at Observatoire de Paris to predict polar motion from the integration of GFZ's 6-day EAM forecasts into the Liouville equation in a routine setting, that fully takes into account the operational latencies of all required input products.

Analysis of the economic impact of different Chinese climate policy options based on a CGE model incorporating endogenous technological change

International Nuclear Information System (INIS)

Wang Ke; Wang Can; Chen Jining

2009-01-01

Abatement cost is the main concern for climate change mitigation and the key factor for mitigation cost is technological change. This study established an integrated economic, energy, environmental, dynamic, computable general equilibrium (CGE) model representing endogenous technological change for China's climate change policy analysis. This study analyzed and compared the economic impact of different approaches to mitigation commitments as well as the potential role of technological change in the formulation of mitigation targets and commitments, taking into account China's climate policy-making needs based on the current international climate negotiation process. The results show that, absolute emission limits similar to the Kyoto Protocol will seriously impede the future economic development of China, while the impact of an 80% reduction in carbon intensity, forecast for 2050 based on the 2005 level, is relatively small. Technological change can promote economic growth, improve energy efficiency and reduce carbon intensity per unit of output through the substitution of production factors. Consequently it can reduce marginal abatement cost and related GDP loss by mitigation. At the same time it can increase mitigation potentials and extend the emission reduction amount, showing that consideration of the impact of technological change when deciding the emission reduction targets is necessary.

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...

Electrical Load Survey and Forecast for a Decentralized Hybrid ...

African Journals Online (AJOL)

Electrical Load Survey and Forecast for a Decentralized Hybrid Power System at Elebu, Kwara State, Nigeria. ... Nigerian Journal of Technology ... The paper reports the results of electrical load demand and forecast for Elebu rural community ...

Tsunami Forecast for Galapagos Islands

Science.gov (United States)

Renteria, W.

2012-04-01

The objective of this study is to present a model for the short-term and long-term tsunami forecast for Galapagos Islands. For both cases the ComMIT/MOST(Titov,et al 2011) numerical model and methodology have been used. The results for the short-term model has been compared with the data from Lynett et al, 2011 surveyed from the impacts of the March/11 in the Galapagos Islands. For the case of long-term forecast, several scenarios have run along the Pacific, an extreme flooding map is obtained, the method is considered suitable for places with poor or without tsunami impact information, but under tsunami risk geographic location.

Evaluation of technology modifications required to apply clean coal technologies in Russian utilities. Final report

Energy Technology Data Exchange (ETDEWEB)

NONE

1995-12-01

The report describes the following: overview of the Russian power industry; electric power equipment of Russia; power industry development forecast for Russia; clean coal technology demonstration program of the US Department of Energy; reduction of coal TPS (thermal power station) environmental impacts in Russia; and base options of advanced coal thermal power plants. Terms of the application of clean coal technology at Russian TPS are discussed in the Conclusions.

The Impact of Ocean Data Assimilation on Seasonal-to-Interannual Forecasts: A Case Study of the 2006 El Nino Event

Science.gov (United States)

Yang, Shu-Chih; Rienecker, Michele; Keppenne, Christian

2010-01-01

This study investigates the impact of four different ocean analyses on coupled forecasts of the 2006 El Nino event. Forecasts initialized in June 2006 using ocean analyses from an assimilation that uses flow-dependent background error covariances are compared with those using static error covariances that are not flow dependent. The flow-dependent error covariances reflect the error structures related to the background ENSO instability and are generated by the coupled breeding method. The ocean analyses used in this study result from the assimilation of temperature and salinity, with the salinity data available from Argo floats. Of the analyses, the one using information from the coupled bred vectors (BV) replicates the observed equatorial long wave propagation best and exhibits more warming features leading to the 2006 El Nino event. The forecasts initialized from the BV-based analysis agree best with the observations in terms of the growth of the warm anomaly through two warming phases. This better performance is related to the impact of the salinity analysis on the state evolution in the equatorial thermocline. The early warming is traced back to salinity differences in the upper ocean of the equatorial central Pacific, while the second warming, corresponding to the mature phase, is associated with the effect of the salinity assimilation on the depth of the thermocline in the western equatorial Pacific. The series of forecast experiments conducted here show that the structure of the salinity in the initial conditions is important to the forecasts of the extension of the warm pool and the evolution of the 2006 El Ni o event.

Best approach to impact assessment is to use empirically based or simulation models to forecast impacts. Environmental Sciences Division Publication No. 1538

International Nuclear Information System (INIS)

Christensen, S.W.

1980-01-01

This paper advocates the utility of mathematical models, as contrasted with statistical procedures and processional judgment, for assessing environmental impacts. While it would be desirable to use statistical tests to detect and estimate impacts, this is generally difficult or impossible to do, even with existing sources of impact. Empirical modeling, supported by statistical analyses when possible, is proffered as the logical alternative. Next, for purposes of forecasting impacts, the use of models as opposed to professional judgment or experience is considered. The conclusion is reached that, while models cannot answer all of the relevant questions, they can be used effectively and can address problems that are beyond the reach of statistical methods

Forecasting Propagation and Evolution of CMEs in an Operational Setting: What Has Been Learned

Science.gov (United States)

Zheng, Yihua; Macneice, Peter; Odstrcil, Dusan; Mays, M. L.; Rastaetter, Lutz; Pulkkinen, Antti; Taktakishvili, Aleksandre; Hesse, Michael; Kuznetsova, M. Masha; Lee, Hyesook;

The case for better PV forecasting

DEFF Research Database (Denmark)

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

2016-01-01

Rising levels of PV penetration mean increasingly sophisticated forecasting technologies are needed to maintain grid stability and maximise the economic value of PV systems. The Grid Integration working group of the European Technology and Innovation Platform – Photovoltaics (ETIP PV) shares the ...

Cellular automata-based forecasting of the impact of accidental fire and toxic dispersion in process industries

International Nuclear Information System (INIS)

Sarkar, Chinmoy; Abbasi, S.A.

2006-01-01

The strategies to prevent accidents from occurring in a process industry, or to minimize the harm if an accident does take place, always revolve around forecasting the likely accidents and their impacts. Based on the likely frequency and severity of the accidents, resources are committed towards preventing the accidents. Nearly all techniques of ranking hazardous units, be it the hazard and operability studies, fault tree analysis, hazard indice, etc. - qualitative as well as quantitative - depend essentially on the assessment of the likely frequency and the likely harm accidents in different units may cause. This fact makes it exceedingly important that the forecasting the accidents and their likely impact is done as accurately as possible. In the present study we introduce a new approach to accident forecasting based on the discrete modeling paradigm of cellular automata. In this treatment an accident is modeled as a self-evolving phenomena, the impact of which is strongly influenced by the size, nature, and position of the environmental components which lie in the vicinity of the accident site. The outward propagation of the mass, energy and momentum from the accident epicenter is modeled as a fast diffusion process occurring in discrete space-time coordinates. The quantum of energy and material that would flow into each discrete space element (cell) due to the accidental release is evaluated and the degree of vulnerability posed to the receptors if present in the cell is measured at the end of each time element. This approach is able to effectively take into account the modifications in the flux of energy and material which occur as a result of the heterogeneous environment prevailing between the accident epicenter and the receptor. Consequently, more realistic accident scenarios are generated than possible with the prevailing techniques. The efficacy of the approach has been illustrated with case studies

An independent system operator's perspective on operational ramp forecasting

Energy Technology Data Exchange (ETDEWEB)

Porter, G. [New Brunswick System Operator, Fredericton, NB (Canada)

2010-07-01

One of the principal roles of the power system operator is to select the most economical resources to reliably supply electric system power needs. Operational wind power production forecasts are required by system operators in order to understand the impact of ramp event forecasting on dispatch functions. A centralized dispatch approach can contribute to a more efficient use of resources that traditional economic dispatch methods. Wind ramping events can have a significant impact on system reliability. Power systems can have constrained or robust transmission systems, and may also be islanded or have large connections to neighbouring systems. Power resources can include both flexible and inflexible generation resources. Wind integration tools must be used by system operators to improve communications and connections with wind power plants. Improved wind forecasting techniques are also needed. Sensitivity to forecast errors is dependent on current system conditions. System operators require basic production forecasts, probabilistic forecasts, and event forecasts. Forecasting errors were presented as well as charts outlining the implications of various forecasts. tabs., figs.

Forecasting Behavior in Smart Homes Based on Sleep and Wake Patterns

Science.gov (United States)

Williams, Jennifer A.; Cook, Diane J.

2017-01-01

Background The goal of this research is to use smart home technology to assist people who are recovering from injuries or coping with disabilities to live independently. Objective We introduce an algorithm to model and forecast wake and sleep behaviors that are exhibited by the participant. Furthermore, we propose that sleep behavior is impacted by and can be modeled from wake behavior, and vice versa. Methods This paper describes the Behavior Forecasting (BF) algorithm. BF consists of 1) defining numeric values that reflect sleep and wake behavior, 2) forecasting wake and sleep values from past behavior, 3) analyzing the effect of wake behavior on sleep and vice versa, and 4) improving prediction performance by using both wake and sleep scores. Results The BF method was evaluated with data collected from 20 smart homes. We found that regardless of the forecasting method utilized, wake behavior and sleep behavior can be modeled with a minimum accuracy of 84%. Additionally, normalizing the wake and sleep scores drastically improves the accuracy to 99%. Conclusions The results show that we can effectively model wake and sleep behaviors in a smart environment. Furthermore, wake behaviors can be predicted from sleep behaviors and vice versa. PMID:27689555

Impacts of calibration strategies and ensemble methods on ensemble flood forecasting over Lanjiang basin, Southeast China

Science.gov (United States)

Liu, Li; Xu, Yue-Ping

2017-04-01

Ensemble flood forecasting driven by numerical weather prediction products is becoming more commonly used in operational flood forecasting applications.In this study, a hydrological ensemble flood forecasting system based on Variable Infiltration Capacity (VIC) model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated.The hydrological model is optimized by parallel programmed ɛ-NSGAII multi-objective algorithm and two respectively parameterized models are determined to simulate daily flows and peak flows coupled with a modular approach.The results indicatethat the ɛ-NSGAII algorithm permits more efficient optimization and rational determination on parameter setting.It is demonstrated that the multimodel ensemble streamflow mean have better skills than the best singlemodel ensemble mean (ECMWF) and the multimodel ensembles weighted on members and skill scores outperform other multimodel ensembles. For typical flood event, it is proved that the flood can be predicted 3-4 days in advance, but the flows in rising limb can be captured with only 1-2 days ahead due to the flash feature. With respect to peak flows selected by Peaks Over Threshold approach, the ensemble means from either singlemodel or multimodels are generally underestimated as the extreme values are smoothed out by ensemble process.

The System of Inventory Forecasting in PT. XYZ by using the Method of Holt Winter Multiplicative

Science.gov (United States)

Shaleh, W.; Rasim; Wahyudin

2018-01-01

Problems at PT. XYZ currently only rely on manual bookkeeping, then the cost of production will swell and all investments invested to be less to predict sales and inventory of goods. If the inventory prediction of goods is to large, then the cost of production will swell and all investments invested to be less efficient. Vice versa, if the inventory prediction is too small it will impact on consumers, so that consumers are forced to wait for the desired product. Therefore, in this era of globalization, the development of computer technology has become a very important part in every business plan. Almost of all companies, both large and small, use computer technology. By utilizing computer technology, people can make time in solving complex business problems. Computer technology for companies has become an indispensable activity to provide enhancements to the business services they manage but systems and technologies are not limited to the distribution model and data processing but the existing system must be able to analyze the possibilities of future company capabilities. Therefore, the company must be able to forecast conditions and circumstances, either from inventory of goods, force, or profits to be obtained. To forecast it, the data of total sales from December 2014 to December 2016 will be calculated by using the method of Holt Winters, which is the method of time series prediction (Multiplicative Seasonal Method) it is seasonal data that has increased and decreased, also has 4 equations i.e. Single Smoothing, Trending Smoothing, Seasonal Smoothing and Forecasting. From the results of research conducted, error value in the form of MAPE is below 1%, so it can be concluded that forecasting with the method of Holt Winter Multiplicative.

Regional Precipitation Forecast with Atmospheric InfraRed Sounder (AIRS) Profile Assimilation

Science.gov (United States)

Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

2010-01-01

Advanced technology in hyperspectral sensors such as the Atmospheric InfraRed Sounder (AIRS; Aumann et al. 2003) on NASA's polar orbiting Aqua satellite retrieve higher vertical resolution thermodynamic profiles than their predecessors due to increased spectral resolution. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, they can serve as a complement to radiosondes in both space and time. These retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models. Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. (2006) concluded that AIRS radiances significantly improved 500 hPa anomaly correlations in medium-range forecasts of the Global Forecast System (GFS) model. McCarty et al. (2009) demonstrated similar forecast improvement in 0-48 hour forecasts in an offline version of the operational North American Mesoscale (NAM) model when AIRS radiances were assimilated at the regional scale. Reale et al. (2008) showed improvements to Northern Hemisphere 500 hPa height anomaly correlations in NASA's Goddard Earth Observing System Model, Version 5 (GEOS-5) global system with the inclusion of partly cloudy AIRS temperature profiles. Singh et al. (2008) assimilated AIRS temperature and moisture profiles into a regional modeling system for a study of a heavy rainfall event during the summer monsoon season in Mumbai, India. This paper describes an approach to assimilate AIRS temperature and moisture profiles into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimensional variational (3DVAR) assimilation system (WRF-Var; Barker et al. 2004). Section 2 describes the AIRS instrument and how the quality indicators are used to intelligently select the highest-quality data for assimilation

Incorporating forecast uncertainties into EENS for wind turbine studies

Energy Technology Data Exchange (ETDEWEB)

Toh, G.K.; Gooi, H.B. [School of EEE, Nanyang Technological University, Singapore 639798 (Singapore)

2011-02-15

The rapid increase in wind power generation around the world has stimulated the development of applicable technologies to model the uncertainties of wind power resulting from the stochastic nature of wind and fluctuations of demand for integration of wind turbine generators (WTGs). In this paper the load and wind power forecast errors are integrated into the expected energy not served (EENS) formulation through determination of probabilities using the normal distribution approach. The effects of forecast errors and wind energy penetration in the power system are traversed. The impact of wind energy penetration on system reliability, total cost for energy and reserve procurement is then studied for a conventional power system. The results show a degradation of system reliability with significant wind energy penetration in the generation system. This work provides a useful insight into system reliability and economics for the independent system operator (ISO) to deploy energy/reserve providers when WTGs are integrated into the existing power system. (author)

Bayesian flood forecasting methods: A review

Science.gov (United States)

Han, Shasha; Coulibaly, Paulin

2017-08-01

Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been

Operational Impact of Data Collected from the Global Hawk Unmanned Aircraft During SHOUT

Science.gov (United States)

Wick, G. A.; Dunion, J. P.; Sippel, J.; Cucurull, L.; Aksoy, A.; Kren, A.; Christophersen, H.; Black, P.

2017-12-01

The primary scientific goal of the Sensing Hazards with Operational Unmanned Technology (SHOUT) Project was to determine the potential utility of observations from high-altitude, long-endurance unmanned aircraft systems such as the Global Hawk (GH) aircraft to improve operational forecasts of high-impact weather events or mitigate potential degradation of forecasts in the event of a future gap in satellite coverage. Hurricanes and tropical cyclones are among the most potentially destructive high-impact weather events and pose a major forecasting challenge to NOAA. Major winter storms over the Pacific Ocean, including atmospheric river events, which make landfall and bring strong winds and extreme precipitation to the West Coast and Alaska are also important to forecast accurately because of their societal impact in those parts of the country. In response, the SHOUT project supported three field campaigns with the GH aircraft and dedicated data impact studies exploring the potential for the real-time data from the aircraft to improve the forecasting of both tropical cyclones and landfalling Pacific storms. Dropsonde observations from the GH aircraft were assimilated into the operational Hurricane Weather Research and Forecasting (HWRF) and Global Forecast System (GFS) models. The results from several diverse but complementary studies consistently demonstrated significant positive forecast benefits spanning the regional and global models. Forecast skill improvements within HWRF reached up to about 9% for track and 14% for intensity. Within GFS, track skill improvements for multi-storm averages exceeded 10% and improvements for individual storms reached over 20% depending on forecast lead time. Forecasted precipitation was also improved. Impacts for Pacific winter storms were smaller but still positive. The results are highly encouraging and support the potential for operational utilization of data from a platform like the GH. This presentation summarizes the

Regional PV power estimation and forecast to mitigate the impact of high photovoltaic penetration on electric grid.

Science.gov (United States)

Pierro, Marco; De Felice, Matteo; Maggioni, Enrico; Moser, David; Perotto, Alessandro; Spada, Francesco; Cornaro, Cristina

2017-04-01

The growing photovoltaic generation results in a stochastic variability of the electric demand that could compromise the stability of the grid and increase the amount of energy reserve and the energy imbalance cost. On regional scale, solar power estimation and forecast is becoming essential for Distribution System Operators, Transmission System Operator, energy traders, and aggregators of generation. Indeed the estimation of regional PV power can be used for PV power supervision and real time control of residual load. Mid-term PV power forecast can be employed for transmission scheduling to reduce energy imbalance and related cost of penalties, residual load tracking, trading optimization, secondary energy reserve assessment. In this context, a new upscaling method was developed and used for estimation and mid-term forecast of the photovoltaic distributed generation in a small area in the north of Italy under the control of a local DSO. The method was based on spatial clustering of the PV fleet and neural networks models that input satellite or numerical weather prediction data (centered on cluster centroids) to estimate or predict the regional solar generation. It requires a low computational effort and very few input information should be provided by users. The power estimation model achieved a RMSE of 3% of installed capacity. Intra-day forecast (from 1 to 4 hours) obtained a RMSE of 5% - 7% while the one and two days forecast achieve to a RMSE of 7% and 7.5%. A model to estimate the forecast error and the prediction intervals was also developed. The photovoltaic production in the considered region provided the 6.9% of the electric consumption in 2015. Since the PV penetration is very similar to the one observed at national level (7.9%), this is a good case study to analyse the impact of PV generation on the electric grid and the effects of PV power forecast on transmission scheduling and on secondary reserve estimation. It appears that, already with 7% of PV

Institutionalized Technological Foresight

DEFF Research Database (Denmark)

Koch, Christian; Hansen, Hans Henrik; Stissing Jensen, Jens

2008-01-01

, and they become dependent of national and other institutional foresights. Since 2000 the Danish ministry of Science, Technology and Innovation has tendered nine technology foresights. These could be used by SMEs as supplementary to internal decision making. One also expects to see these foresights placed firmly...... in the national strategy of coordinating and strengthening policy on research, technology and innovation in one superministry. The paper evaluates the methods, impact and role in policy making of these technological foresights. The particular role of institutionalized public foresight in relation to enterprise......Technology and knowledge monitoring is a continual challenge especially for small and medium size enterprises. Technological foresight seemingly offers important parts of this crucial monitoring. The SMEs rarely possess sufficient resources to do systematic foresights, or forecasts however...

Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools

OpenAIRE

Seto, Karen C.; Güneralp, Burak; Hutyra, Lucy R.

2012-01-01

Urban land-cover change threatens biodiversity and affects ecosystem productivity through loss of habitat, biomass, and carbon storage. However, despite projections that world urban populations will increase to nearly 5 billion by 2030, little is known about future locations, magnitudes, and rates of urban expansion. Here we develop spatially explicit probabilistic forecasts of global urban land-cover change and explore the direct impacts on biodiversity hotspots and tropical carbon biomass. ...

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....

Development and validation of a regional coupled forecasting system for S2S forecasts

Science.gov (United States)

Sun, R.; Subramanian, A. C.; Hoteit, I.; Miller, A. J.; Ralph, M.; Cornuelle, B. D.

2017-12-01

Accurate and efficient forecasting of oceanic and atmospheric circulation is essential for a wide variety of high-impact societal needs, including: weather extremes; environmental protection and coastal management; management of fisheries, marine conservation; water resources; and renewable energy. Effective forecasting relies on high model fidelity and accurate initialization of the models with observed state of the ocean-atmosphere-land coupled system. A regional coupled ocean-atmosphere model with the Weather Research and Forecasting (WRF) model and the MITGCM ocean model coupled using the ESMF (Earth System Modeling Framework) coupling framework is developed to resolve mesoscale air-sea feedbacks. The regional coupled model allows oceanic mixed layer heat and momentum to interact with the atmospheric boundary layer dynamics at the mesoscale and submesoscale spatiotemporal regimes, thus leading to feedbacks which are otherwise not resolved in coarse resolution global coupled forecasting systems or regional uncoupled forecasting systems. The model is tested in two scenarios in the mesoscale eddy rich Red Sea and Western Indian Ocean region as well as mesoscale eddies and fronts of the California Current System. Recent studies show evidence for air-sea interactions involving the oceanic mesoscale in these two regions which can enhance predictability on sub seasonal timescale. We will present results from this newly developed regional coupled ocean-atmosphere model for forecasts over the Red Sea region as well as the California Current region. The forecasts will be validated against insitu observations in the region as well as reanalysis fields.

Tracking the New Technology. A Summary of the Second Invitational Postsecondary Educational Review Panel.

Science.gov (United States)

Sterling, C. H.

The second educational technology review panel addressed problems with forecasting the trends and impact of newer telecomunications technologies in both home and institution of higher education settings, and the role of several grant programs in educational technology. Paul Mertins provided an update on a National Center for Education Statistics…

GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting

Directory of Open Access Journals (Sweden)

Lintao Yang

2018-01-01

Full Text Available With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH technology. The proposed algorithm consists of three main stages: (1 training the basic classifier; (2 selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3 training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection and GMDH-U (GMDH-based semi-supervised feature selection for customer classification models.

Energy Forecasts and their Attendant Risks

International Nuclear Information System (INIS)

Greggio, Rodolphe; Maffei, Benoit

2017-01-01

As Jean-Marie Chevalier stresses in this issue, it is currently quite tricky to pronounce on how energy prices will move over time or to predict how energy production systems will change. Further support for that view comes from this article by Rodolphe Greggio and Benoit Maffei. They have looked into the way long-term energy forecasts are made and their conclusion is that, as things stand, they are doomed to fail. The main underlying reason for this is the difficulty of making reliable predictions about how energy demand will evolve, since it is the product of exogenous developments that are unknowable in the long term (demographic growth, economic growth, productivity, energy efficiency etc.). A number of forecasting errors with regard to technological breaks have also played a role: a 'golden age' for natural gas was forecast too early; the date of 'peak oil' has shifted around wildly; a peak with regard to ore deposits has not had the impact originally anticipated; and the decline of nuclear power has turned out to require qualification. Ultimately, all this is the product of various political and geopolitical factors linked to the respective energy assets of the different countries and their strategies for achieving energy independence: e.g. the USA and shale gas, France and nuclear power, Germany and renewables. Quite clearly, the current context suggests that a gradual transition from carbon-based to renewable energies is the order of the day, but it is far from easy to predict on precisely what timescale, in what proportions and on what geographical scale this might occur. (authors)

Three-dimensional visualization of ensemble weather forecasts – Part 1: The visualization tool Met.3D (version 1.0

Directory of Open Access Journals (Sweden)

M. Rautenhaus

2015-07-01

Full Text Available We present "Met.3D", a new open-source tool for the interactive three-dimensional (3-D visualization of numerical ensemble weather predictions. The tool has been developed to support weather forecasting during aircraft-based atmospheric field campaigns; however, it is applicable to further forecasting, research and teaching activities. Our work approaches challenging topics related to the visual analysis of numerical atmospheric model output – 3-D visualization, ensemble visualization and how both can be used in a meaningful way suited to weather forecasting. Met.3D builds a bridge from proven 2-D visualization methods commonly used in meteorology to 3-D visualization by combining both visualization types in a 3-D context. We address the issue of spatial perception in the 3-D view and present approaches to using the ensemble to allow the user to assess forecast uncertainty. Interactivity is key to our approach. Met.3D uses modern graphics technology to achieve interactive visualization on standard consumer hardware. The tool supports forecast data from the European Centre for Medium Range Weather Forecasts (ECMWF and can operate directly on ECMWF hybrid sigma-pressure level grids. We describe the employed visualization algorithms, and analyse the impact of the ECMWF grid topology on computing 3-D ensemble statistical quantities. Our techniques are demonstrated with examples from the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment campaign.

The challenge of forecasting impacts of flash floods: test of a simplified hydraulic approach and validation based on insurance claim data

Science.gov (United States)

Le Bihan, Guillaume; Payrastre, Olivier; Gaume, Eric; Moncoulon, David; Pons, Frédéric

2017-11-01

Up to now, flash flood monitoring and forecasting systems, based on rainfall radar measurements and distributed rainfall-runoff models, generally aimed at estimating flood magnitudes - typically discharges or return periods - at selected river cross sections. The approach presented here goes one step further by proposing an integrated forecasting chain for the direct assessment of flash flood possible impacts on inhabited areas (number of buildings at risk in the presented case studies). The proposed approach includes, in addition to a distributed rainfall-runoff model, an automatic hydraulic method suited for the computation of flood extent maps on a dense river network and over large territories. The resulting catalogue of flood extent maps is then combined with land use data to build a flood impact curve for each considered river reach, i.e. the number of inundated buildings versus discharge. These curves are finally used to compute estimated impacts based on forecasted discharges. The approach has been extensively tested in the regions of Alès and Draguignan, located in the south of France, where well-documented major flash floods recently occurred. The article presents two types of validation results. First, the automatically computed flood extent maps and corresponding water levels are tested against rating curves at available river gauging stations as well as against local reference or observed flood extent maps. Second, a rich and comprehensive insurance claim database is used to evaluate the relevance of the estimated impacts for some recent major floods.

CSIR Technology Impact 2000

CSIR Research Space (South Africa)

CSIR

2000-01-01

Full Text Available This millennium issue of Technology Impact celebrates the CSIR's contributions during the transitional 1999/2000 year. It presents a rich canvas portrays technology solutions and information which have touched the lives of people both within...

Considerations in forecasting the demand for carbon sequestration and biotic storage technologies

Energy Technology Data Exchange (ETDEWEB)

Trexler, M.C. [Trexler and Associates, Inc., Portland, OR (United States)

1997-12-31

The Intergovernmental Panel on Climate Change (IPCC) has identified forestry and other land-use based mitigation measures as possible sources and sinks of greenhouse gases. An overview of sequestration and biotic storage is presented, and the potential impacts of the use of carbon sequestration as a mitigation technology are briefly noted. Carbon sequestration is also compare to other mitigation technologies. Biotic mitigation technologies are concluded to be a legitimate and potentially important part of greenhouse gas mitigation due to their relatively low costs, ancillary benefits, and climate impact. However, not all biotic mitigation techniques perfectly match the idealized definition of a mitigation measure, and policies are becoming increasingly biased against biotic technologies.

Impact of Public Aggregate Wind Forecasts on Electricity Market Outcomes

DEFF Research Database (Denmark)

Exizidis, Lazaros; Kazempour, Jalal; Pinson, Pierre

2017-01-01

Following a call to foster a transparent and more competitive market, member states of the European transmission system operator are required to publish, among other information, aggregate wind power forecasts. The publication of the latter information is expected to benefit market participants...... by offering better knowledge of the market operation, leading subsequently to a more competitive energy market. Driven by the above regulation, we consider an equilibrium study to address how public information of aggregate wind power forecasts can potentially affect market results, social welfare as well...... as the profits of participating power producers. We investigate, therefore, a joint day-ahead energy and reserve auction, where producers offer their conventional power strategically based on a complementarity approach and their wind power at generation cost based on a forecast. In parallel, an iterative game...

The distribution of wind power forecast errors from operational systems

Energy Technology Data Exchange (ETDEWEB)

Hodge, Bri-Mathias; Ela, Erik; Milligan, Michael

2011-07-01

Wind power forecasting is one important tool in the integration of large amounts of renewable generation into the electricity system. Wind power forecasts from operational systems are not perfect, and thus, an understanding of the forecast error distributions can be important in system operations. In this work, we examine the errors from operational wind power forecasting systems, both for a single wind plant and for an entire interconnection. The resulting error distributions are compared with the normal distribution and the distribution obtained from the persistence forecasting model at multiple timescales. A model distribution is fit to the operational system forecast errors and the potential impact on system operations highlighted through the generation of forecast confidence intervals. (orig.)

CSIR Technology Impact 1996

CSIR Research Space (South Africa)

CSIR

1996-01-01

Full Text Available This issue of Technology Impact offers a brief snapshot of CSIR activities during the year under review by highlighting a number of innovative projects and initiatives in these areas. It presents a rich canvas portrays technology solutions...

CSIR Technology Impact 1994

CSIR Research Space (South Africa)

CSIR

1994-01-01

Full Text Available This issue of Technology Impact offers a brief snapshot of CSIR activities during the year under review by highlighting a number of innovative projects and initiatives in these areas. It presents a rich canvas portrays technology solutions...

CSIR Technology Impact 1999

CSIR Research Space (South Africa)

CSIR

1999-01-01

Full Text Available This issue of Technology Impact offers a brief snapshot of CSIR activities during the year under review by highlighting a number of innovative projects and initiatives in these areas. It presents a rich canvas portrays technology solutions...

CSIR Technology Impact 1993

CSIR Research Space (South Africa)

CSIR

1993-01-01

Full Text Available This issue of Technology Impact offers a brief snapshot of CSIR activities during the year under review by highlighting a number of innovative projects and initiatives in these areas. It presents a rich canvas portrays technology solutions...

Structured, Physically Inspired (Gray Box) Models Versus Black Box Modeling for Forecasting the Output Power of Photovoltaic Plants

Czech Academy of Sciences Publication Activity Database

Paulescu, M.; Brabec, Marek; Boata, R.; Badescu, V.

2017-01-01

Roč. 121, 15 February (2017), s. 792-802 ISSN 0360-5442 Grant - others:European Cooperation in Science and Technology(XE) COST ES1002 Institutional support: RVO:67985807 Keywords : photovoltaic plant * output power * forecasting * fuzzy model * generalized additive model Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 4.520, year: 2016

The Impact of Distributed Generation Systems in the Load Forecasting

OpenAIRE

Benedicto Llorens, Juan Manuel

2009-01-01

Projecte fet en col.laboració amb l'Instituto Superior Tecnico. Universidade Técnica de Lisboa Load forecasting is vitally important for the electric industry in the deregulated economy. It has many applications including energy purchasing and generation, load switching, contract evaluation and infrastructure development. Because of this, a large variety of mathematical methods have been developed for load forecasting. In addition, the large-scale integration of wind power, now...

FORECAST: Regulatory effects cost analysis software manual -- Version 4.1. Revision 1

International Nuclear Information System (INIS)

Lopez, B.; Sciacca, F.W.

1996-07-01

The FORECAST program was developed to facilitate the preparation of the value-impact portion of NRC regulatory analyses. This PC program integrates the major cost and benefit considerations that may result from a proposed regulatory change. FORECAST automates much of the calculations typically needed in a regulatory analysis and thus reduces the time and labor required to perform these analyses. More importantly, its integrated and consistent treatment of the different value-impact considerations should help assure comprehensiveness, uniformity, and accuracy in the preparation of NRC regulatory analyses. The Current FORECAST Version 4.1 has been upgraded from the previous version and now includes an uncertainty package, an automatic cost escalation package, and other improvements. In addition, it now explicitly addresses public health impacts, occupational health impacts, onsite property damage, and government costs. Thus, FORECAST Version 4.1 can treat all attributes normally quantified in a regulatory analysis

Residential/commercial market for energy technologies

Energy Technology Data Exchange (ETDEWEB)

Glesk, M M

1979-08-01

The residential/commercial market sector, particularly as it relates to energy technologies, is described. Buildings account for about 25% of the total energy consumed in the US. Market response to energy technologies is influenced by several considerations. Some considerations discussed are: industry characteristics; market sectors; energy-consumption characeristics; industry forecasts; and market influences. Market acceptance may be slow or nonexistent, the technology may have little impact on energy consumption, and redesign or modification may be necessary to overcome belatedly perceived market barriers. 7 figures, 20 tables.

Six rules for accurate effective forecasting.

Science.gov (United States)

Saffo, Paul

2007-01-01

The primary goal of forecasting is to identify the full range of possibilities facing a company, society, or the world at large. In this article, Saffo demythologizes the forecasting process to help executives become sophisticated and participative consumers of forecasts, rather than passive absorbers. He illustrates how to use forecasts to at once broaden understanding of possibilities and narrow the decision space within which one must exercise intuition. The events of 9/11, for example, were a much bigger surprise than they should have been. After all, airliners flown into monuments were the stuff of Tom Clancy novels in the 1990s, and everyone knew that terrorists had a very personal antipathy toward the World Trade Center. So why was 9/11 such a surprise? What can executives do to avoid being blind-sided by other such wild cards, be they radical shifts in markets or the seemingly sudden emergence of disruptive technologies? In describing what forecasters are trying to achieve, Saffo outlines six simple, commonsense rules that smart managers should observe as they embark on a voyage of discovery with professional forecasters. Map a cone of uncertainty, he advises, look for the S curve, embrace the things that don't fit, hold strong opinions weakly, look back twice as far as you look forward, and know when not to make a forecast.

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).

The challenge of forecasting impacts of flash floods: test of a simplified hydraulic approach and validation based on insurance claim data

Directory of Open Access Journals (Sweden)

G. Le Bihan

2017-11-01

Full Text Available Up to now, flash flood monitoring and forecasting systems, based on rainfall radar measurements and distributed rainfall–runoff models, generally aimed at estimating flood magnitudes – typically discharges or return periods – at selected river cross sections. The approach presented here goes one step further by proposing an integrated forecasting chain for the direct assessment of flash flood possible impacts on inhabited areas (number of buildings at risk in the presented case studies. The proposed approach includes, in addition to a distributed rainfall–runoff model, an automatic hydraulic method suited for the computation of flood extent maps on a dense river network and over large territories. The resulting catalogue of flood extent maps is then combined with land use data to build a flood impact curve for each considered river reach, i.e. the number of inundated buildings versus discharge. These curves are finally used to compute estimated impacts based on forecasted discharges. The approach has been extensively tested in the regions of Alès and Draguignan, located in the south of France, where well-documented major flash floods recently occurred. The article presents two types of validation results. First, the automatically computed flood extent maps and corresponding water levels are tested against rating curves at available river gauging stations as well as against local reference or observed flood extent maps. Second, a rich and comprehensive insurance claim database is used to evaluate the relevance of the estimated impacts for some recent major floods.

The impact of the internet on community pharmacy practice: a comparison of a Delphi panel's forecast with emerging trends.

Science.gov (United States)

Holmes, Erin R; Tipton, David J; Desselle, Shane P

2002-01-01

The purpose of this study was to forecast the impact of Internet pharmacy commerce on various stakeholders. A panel of experts assembled from a list of academicians in the social and administrative pharmaceutical sciences participated in a three-iteration Delphi procedure. Feedback from the aggregate responses of the panel was used to construct questionnaires employed in subsequent iterations of the Delphi. The panel converged to form a consensus upon a variety of issues. They forecasted the attainment of a 10-15% share in the market of prescription and over-the-counter pharmaceuticals through on-line purchases, the formation of strategic alliances among stakeholders in the drug distribution process, a shift in marketing strategies by brick-and-mortar stores, an increase in the prevalence of niching among pharmacy service providers and a subsequent growth in the implementation of cognitive services throughout the industry. With few exceptions, the forecast produced by the Delphi panel appears to be coming to fruition.

Acceleration, Transport, Forecasting and Impact of solar energetic particles in the framework of the 'HESPERIA' HORIZON 2020 project

Science.gov (United States)

Malandraki, Olga; Klein, Karl-Ludwig; Vainio, Rami; Agueda, Neus; Nunez, Marlon; Heber, Bernd; Buetikofer, Rolf; Sarlanis, Christos; Crosby, Norma

2017-04-01

High-energy solar energetic particles (SEPs) emitted from the Sun are a major space weather hazard motivating the development of predictive capabilities. In this work, the current state of knowledge on the origin and forecasting of SEP events will be reviewed. Subsequently, we will present the EU HORIZON2020 HESPERIA (High Energy Solar Particle Events foRecastIng and Analysis) project, its structure, its main scientific objectives and forecasting operational tools, as well as the added value to SEP research both from the observational as well as the SEP modelling perspective. The project addresses through multi-frequency observations and simulations the chain of processes from particle acceleration in the corona, particle transport in the magnetically complex corona and interplanetary space to the detection near 1 AU. Furthermore, publicly available software to invert neutron monitor observations of relativistic SEPs to physical parameters that can be compared with space-borne measurements at lower energies is provided for the first time by HESPERIA. In order to achieve these goals, HESPERIA is exploiting already available large datasets stored in databases such as the neutron monitor database (NMDB) and SEPServer that were developed under EU FP7 projects from 2008 to 2013. Forecasting results of the two novel SEP operational forecasting tools published via the consortium server of 'HESPERIA' will be presented, as well as some scientific key results on the acceleration, transport and impact on Earth of high-energy particles. Acknowledgement: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 637324.

Particle-Reinforced Aluminum Matrix Composites (AMCs—Selected Results of an Integrated Technology, User, and Market Analysis and Forecast

Directory of Open Access Journals (Sweden)

Anja Schmidt

2018-02-01

Full Text Available The research and development of new materials such as particle-reinforced aluminum matrix composites (AMCs will only result in a successful innovation if these materials show significant advantages not only from a technological, but also from an economic point of view. Against this background, in the Collaborative Research Center SFB 692, the concept of an integrated technology, user, and market analysis and forecast has been developed as a means for assessing the technological and commercial potential of new materials in early life cycle stages. After briefly describing this concept, it is applied to AMCs and the potential field of manufacturing aircraft components. Results show not only technological advances, but also considerable economic potential—the latter one primarily resulting from the possible weight reduction being enabled by the increased yield strength of the new material.

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....

Forecasting and Warning of Tropical Cyclones in China

Directory of Open Access Journals (Sweden)

Bangzhong Wang

2007-10-01

Full Text Available With the development of the global economy, the impact of tropical cyclones has become far-reaching. Thus they are a fundamental issue to be addressed both nationally and globally. The socio-economic impact is particularly noticeable in developing countries, especially China. This paper begins with the effects of cyclones on regional and global economies. Then a brief introduction to the past and current situations and progress in cyclones forecasting and warning in China are presented. Finally the paper gives recommendations about improving and perfecting the tropical cyclone forecasting and warning systems.

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.

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.

Forecasting techno-social systems: how physics and computing help to fight off global pandemics

Science.gov (United States)

Vespignani, Alessandro

2010-03-01

The crucial issue when planning for adequate public health interventions to mitigate the spread and impact of epidemics is risk evaluation and forecast. This amount to the anticipation of where, when and how strong the epidemic will strike. In the last decade advances in performance in computer technology, data acquisition, statistical physics and complex networks theory allow the generation of sophisticated simulations on supercomputer infrastructures to anticipate the spreading pattern of a pandemic. For the first time we are in the position of generating real time forecast of epidemic spreading. I will review the history of the current H1N1 pandemic, the major road-blocks the community has faced in its containment and mitigation and how physics and computing provide predictive tools that help us to battle epidemics.

Evaluating the Effectiveness of DART® Buoy Networks Based on Forecast Accuracy

Science.gov (United States)

Percival, Donald B.; Denbo, Donald W.; Gica, Edison; Huang, Paul Y.; Mofjeld, Harold O.; Spillane, Michael C.; Titov, Vasily V.

2018-03-01

A performance measure for a DART® tsunami buoy network has been developed. DART® buoys are used to detect tsunamis, but the full potential of the data they collect is realized through accurate forecasts of inundations caused by the tsunamis. The performance measure assesses how well the network achieves its full potential through a statistical analysis of simulated forecasts of wave amplitudes outside an impact site and a consideration of how much the forecasts are degraded in accuracy when one or more buoys are inoperative. The analysis uses simulated tsunami amplitude time series collected at each buoy from selected source segments in the Short-term Inundation Forecast for Tsunamis database and involves a set for 1000 forecasts for each buoy/segment pair at sites just offshore of selected impact communities. Random error-producing scatter in the time series is induced by uncertainties in the source location, addition of real oceanic noise, and imperfect tidal removal. Comparison with an error-free standard leads to root-mean-square errors (RMSEs) for DART® buoys located near a subduction zone. The RMSEs indicate which buoy provides the best forecast (lowest RMSE) for sections of the zone, under a warning-time constraint for the forecasts of 3 h. The analysis also shows how the forecasts are degraded (larger minimum RMSE among the remaining buoys) when one or more buoys become inoperative. The RMSEs provide a way to assess array augmentation or redesign such as moving buoys to more optimal locations. Examples are shown for buoys off the Aleutian Islands and off the West Coast of South America for impact sites at Hilo HI and along the US West Coast (Crescent City CA and Port San Luis CA, USA). A simple measure (coded green, yellow or red) of the current status of the network's ability to deliver accurate forecasts is proposed to flag the urgency of buoy repair.

Evaluating the Effectiveness of DART® Buoy Networks Based on Forecast Accuracy

Science.gov (United States)

Percival, Donald B.; Denbo, Donald W.; Gica, Edison; Huang, Paul Y.; Mofjeld, Harold O.; Spillane, Michael C.; Titov, Vasily V.

2018-04-01

A performance measure for a DART® tsunami buoy network has been developed. DART® buoys are used to detect tsunamis, but the full potential of the data they collect is realized through accurate forecasts of inundations caused by the tsunamis. The performance measure assesses how well the network achieves its full potential through a statistical analysis of simulated forecasts of wave amplitudes outside an impact site and a consideration of how much the forecasts are degraded in accuracy when one or more buoys are inoperative. The analysis uses simulated tsunami amplitude time series collected at each buoy from selected source segments in the Short-term Inundation Forecast for Tsunamis database and involves a set for 1000 forecasts for each buoy/segment pair at sites just offshore of selected impact communities. Random error-producing scatter in the time series is induced by uncertainties in the source location, addition of real oceanic noise, and imperfect tidal removal. Comparison with an error-free standard leads to root-mean-square errors (RMSEs) for DART® buoys located near a subduction zone. The RMSEs indicate which buoy provides the best forecast (lowest RMSE) for sections of the zone, under a warning-time constraint for the forecasts of 3 h. The analysis also shows how the forecasts are degraded (larger minimum RMSE among the remaining buoys) when one or more buoys become inoperative. The RMSEs provide a way to assess array augmentation or redesign such as moving buoys to more optimal locations. Examples are shown for buoys off the Aleutian Islands and off the West Coast of South America for impact sites at Hilo HI and along the US West Coast (Crescent City CA and Port San Luis CA, USA). A simple measure (coded green, yellow or red) of the current status of the network's ability to deliver accurate forecasts is proposed to flag the urgency of buoy repair.

The weather forecasting in Colombia: Science plus Art

International Nuclear Information System (INIS)

Gonzalez Marentes, Humberto

2006-01-01

The presentation intends to show briefly and rapidly the progress weather forecasting science has undergone times until today. Undoubtedly, there has been an impressive technological advances, more data better models, better representations of the physics of the atmosphere; however for the case of the low latitude countries, there are still some problems to resolve concerning the local prediction that deserve more research and more data to be included in the models. As these limitations subsist, the subjective knowledge and the experience of the duty forecaster remain valuable. The presentation is also useful to summarize how IDEAM prepares short weather forecasts

Value of Improved Wind Power Forecasting in the Western Interconnection (Poster)

Energy Technology Data Exchange (ETDEWEB)

Hodge, B.

2013-12-01

Wind power forecasting is a necessary and important technology for incorporating wind power into the unit commitment and dispatch process. It is expected to become increasingly important with higher renewable energy penetration rates and progress toward the smart grid. There is consensus that wind power forecasting can help utility operations with increasing wind power penetration; however, there is far from a consensus about the economic value of improved forecasts. This work explores the value of improved wind power forecasting in the Western Interconnection of the United States.

An Integrated Modeling Framework Forecasting Ecosystem Exposure-- A Systems Approach to the Cumulative Impacts of Multiple Stressors

Science.gov (United States)

Johnston, J. M.

2013-12-01

Freshwater habitats provide fishable, swimmable and drinkable resources and are a nexus of geophysical and biological processes. These processes in turn influence the persistence and sustainability of populations, communities and ecosystems. Climate change and landuse change encompass numerous stressors of potential exposure, including the introduction of toxic contaminants, invasive species, and disease in addition to physical drivers such as temperature and hydrologic regime. A systems approach that includes the scientific and technologic basis of assessing the health of ecosystems is needed to effectively protect human health and the environment. The Integrated Environmental Modeling Framework 'iemWatersheds' has been developed as a consistent and coherent means of forecasting the cumulative impact of co-occurring stressors. The Framework consists of three facilitating technologies: Data for Environmental Modeling (D4EM) that automates the collection and standardization of input data; the Framework for Risk Assessment of Multimedia Environmental Systems (FRAMES) that manages the flow of information between linked models; and the Supercomputer for Model Uncertainty and Sensitivity Evaluation (SuperMUSE) that provides post-processing and analysis of model outputs, including uncertainty and sensitivity analysis. Five models are linked within the Framework to provide multimedia simulation capabilities for hydrology and water quality processes: the Soil Water Assessment Tool (SWAT) predicts surface water and sediment runoff and associated contaminants; the Watershed Mercury Model (WMM) predicts mercury runoff and loading to streams; the Water quality Analysis and Simulation Program (WASP) predicts water quality within the stream channel; the Habitat Suitability Index (HSI) model scores physicochemical habitat quality for individual fish species; and the Bioaccumulation and Aquatic System Simulator (BASS) predicts fish growth, population dynamics and bioaccumulation

On the effect of model parameters on forecast objects

Science.gov (United States)

Marzban, Caren; Jones, Corinne; Li, Ning; Sandgathe, Scott

2018-04-01

Many physics-based numerical models produce a gridded, spatial field of forecasts, e.g., a temperature map. The field for some quantities generally consists of spatially coherent and disconnected objects. Such objects arise in many problems, including precipitation forecasts in atmospheric models, eddy currents in ocean models, and models of forest fires. Certain features of these objects (e.g., location, size, intensity, and shape) are generally of interest. Here, a methodology is developed for assessing the impact of model parameters on the features of forecast objects. The main ingredients of the methodology include the use of (1) Latin hypercube sampling for varying the values of the model parameters, (2) statistical clustering algorithms for identifying objects, (3) multivariate multiple regression for assessing the impact of multiple model parameters on the distribution (across the forecast domain) of object features, and (4) methods for reducing the number of hypothesis tests and controlling the resulting errors. The final output of the methodology is a series of box plots and confidence intervals that visually display the sensitivities. The methodology is demonstrated on precipitation forecasts from a mesoscale numerical weather prediction model.

Forecasting Value-at-Risk Under Temporal and Portfolio Aggregation

NARCIS (Netherlands)

H.J.W.G. Kole (Erik); T.D. Markwat (Thijs); A. Opschoor (Anne); D.J.C. van Dijk (Dick)

2016-01-01

textabstractWe examine the impact of temporal and portfolio aggregation on the quality of Value-at-Risk (VaR) forecasts over a horizon of ten trading days for a well-diversified portfolio of stocks, bonds and alternative investments. The VaR forecasts are constructed based on daily, weekly or

Environmental noise forecasting based on support vector machine

Science.gov (United States)

Fu, Yumei; Zan, Xinwu; Chen, Tianyi; Xiang, Shihan

2018-01-01

As an important pollution source, the noise pollution is always the researcher's focus. Especially in recent years, the noise pollution is seriously harmful to the human beings' environment, so the research about the noise pollution is a very hot spot. Some noise monitoring technologies and monitoring systems are applied in the environmental noise test, measurement and evaluation. But, the research about the environmental noise forecasting is weak. In this paper, a real-time environmental noise monitoring system is introduced briefly. This monitoring system is working in Mianyang City, Sichuan Province. It is monitoring and collecting the environmental noise about more than 20 enterprises in this district. Based on the large amount of noise data, the noise forecasting by the Support Vector Machine (SVM) is studied in detail. Compared with the time series forecasting model and the artificial neural network forecasting model, the SVM forecasting model has some advantages such as the smaller data size, the higher precision and stability. The noise forecasting results based on the SVM can provide the important and accuracy reference to the prevention and control of the environmental noise.

Impact of advanced technology microwave sounder data in the NCMRWF 4D-VAR data assimilation system

Science.gov (United States)

Rani, S. Indira; Srinivas, D.; Mallick, Swapan; George, John P.

2016-05-01

This study demonstrates the added benefits of assimilating the Advanced Technology Microwave Sounder (ATMS) radiances from the Suomi-NPP satellite in the NCMRWF Unified Model (NCUM). ATMS is a cross-track scanning microwave radiometer inherited the legacy of two very successful instrument namely, Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS). ATMS has 22 channels: 11 temperature sounding channels around 50-60 GHz oxygen band and 6 moisture sounding channels around the 183GHz water vapour band in addition to 5 channels sensitive to the surface in clear conditions, or to water vapour, rain, and cloud when conditions are not clear (at 23, 31, 50, 51 and 89 GHz). Before operational assimilation of any new observation by NWP centres it is standard practice to assess data quality with respect to NWP model background (short-forecast) fields. Quality of all channels is estimated against the model background and the biases are computed and compared against that from the similar observations. The impact of the ATMS data on global analyses and forecasts is tested by adding the ATMS data in the NCUM Observation Processing system (OPS) and 4D-Var variational assimilation (VAR) system. This paper also discusses the pre-operational numerical experiments conducted to assess the impact of ATMS radiances in the NCUM assimilation system. It is noted that the performance of ATMS is stable and it contributes to the performance of the model, complimenting observations from other instruments.

The intersections between TRIZ and forecasting methodology

Directory of Open Access Journals (Sweden)

Georgeta BARBULESCU

2010-12-01

Full Text Available The authors’ intention is to correlate the basic knowledge in using the TRIZ methodology (Theory of Inventive Problem Solving or in Russian: Teoriya Resheniya Izobretatelskikh Zadatch as a problem solving tools meant to help the decision makers to perform more significant forecasting exercises. The idea is to identify the TRIZ features and instruments (40 inventive principles, i.e. for putting in evidence the noise and signal problem, for trend identification (qualitative and quantitative tendencies and support tools in technological forecasting, to make the decision-makers able to refine and to increase the level of confidence in the forecasting results. The interest in connecting TRIZ to forecasting methodology, nowadays, relates to the massive application of TRIZ methods and techniques for engineering system development world-wide and in growing application of TRIZ’s concepts and paradigms for improvements of non-engineering systems (including the business and economic applications.

The SPoRT-WRF: Evaluating the Impact of NASA Datasets on Convective Forecasts

Science.gov (United States)

Zavodsky, Bradley; Kozlowski, Danielle; Case, Jonathan; Molthan, Andrew

2012-01-01

Short-term Prediction Research and Transition (SPoRT) seeks to improve short-term, regional weather forecasts using unique NASA products and capabilities SPoRT has developed a unique, real-time configuration of the NASA Unified Weather Research and Forecasting (WRF)WRF (ARW) that integrates all SPoRT modeling research data: (1) 2-km SPoRT Sea Surface Temperature (SST) Composite, (2) 3-km LIS with 1-km Greenness Vegetation Fraction (GVFs) (3) 45-km AIRS retrieved profiles. Transitioned this real-time forecast to NOAA's Hazardous Weather Testbed (HWT) as deterministic model at Experimental Forecast Program (EFP). Feedback from forecasters/participants and internal evaluation of SPoRT-WRF shows a cool, dry bias that appears to suppress convection likely related to methodology for assimilation of AIRS profiles Version 2 of the SPoRT-WRF will premier at the 2012 EFP and include NASA physics, cycling data assimilation methodology, better coverage of precipitation forcing, and new GVFs

An Electrical Energy Consumption Monitoring and Forecasting System

Directory of Open Access Journals (Sweden)

J. L. Rojas-Renteria

2016-10-01

Full Text Available Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations. Detailed monitoring has proved to be valuable for both power companies and consumers. Further, as smart grid technology is bound to result to increasingly flexible rates, an accurate forecast is bound to prove valuable in the future. In this paper, a monitoring and forecasting system is investigated. The monitoring system was installed in an actual building and the recordings were used to design and evaluate the forecasting system, based on an artificial neural network. Results show that the system can provide detailed monitoring and also an accurate forecast for a building’s consumption.

Interactive Vegetation Phenology, Soil Moisture, and Monthly Temperature Forecasts

Science.gov (United States)

Koster, R. D.; Walker, G. K.

2015-01-01

The time scales that characterize the variations of vegetation phenology are generally much longer than those that characterize atmospheric processes. The explicit modeling of phenological processes in an atmospheric forecast system thus has the potential to provide skill to subseasonal or seasonal forecasts. We examine this possibility here using a forecast system fitted with a dynamic vegetation phenology model. We perform three experiments, each consisting of 128 independent warm-season monthly forecasts: 1) an experiment in which both soil moisture states and carbon states (e.g., those determining leaf area index) are initialized realistically, 2) an experiment in which the carbon states are prescribed to climatology throughout the forecasts, and 3) an experiment in which both the carbon and soil moisture states are prescribed to climatology throughout the forecasts. Evaluating the monthly forecasts of air temperature in each ensemble against observations, as well as quantifying the inherent predictability of temperature within each ensemble, shows that dynamic phenology can indeed contribute positively to subseasonal forecasts, though only to a small extent, with an impact dwarfed by that of soil moisture.

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.

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 waste compositions: A case study on plastic waste of electronic display housings.

Science.gov (United States)

Peeters, Jef R; Vanegas, Paul; Kellens, Karel; Wang, Feng; Huisman, Jaco; Dewulf, Wim; Duflou, Joost R

2015-12-01

Because of the rapid succession of technological developments, the architecture and material composition of many products used in daily life have drastically changed over the last decades. As a result, well-adjusted recycling technologies need to be developed and installed to cope with these evolutions. This is essential to guarantee continued access to materials and to reduce the ecological impact of our material consumption. However, limited information is currently available on the material composition of arising waste streams and even less on how these waste streams will evolve. Therefore, this paper presents a methodology to forecast trends in the material composition of waste streams. To demonstrate the applicability and value of the proposed methodology, it is applied to forecast the evolution of plastic housing waste from flat panel display (FPD) TVs, FPD monitors, cathode ray tube (CRT) TVs and CRT monitors. The results of the presented forecasts indicate that a wide variety of plastic types and additives, such as flame retardants, are found in housings of similar products. The presented case study demonstrates that the proposed methodology allows the identification of trends in the evolution of the material composition of waste streams. In addition, it is demonstrated that the recycling sector will need to adapt its processes to deal with the increasing complexity of plastics of end-of-life electronic displays while respecting relevant directives. Copyright © 2015 Elsevier Ltd. All rights reserved.

Forecasting new product diffusion using both patent citation and web search traffic.

Science.gov (United States)

Lee, Won Sang; Choi, Hyo Shin; Sohn, So Young

2018-01-01

Accurate demand forecasting for new technology products is a key factor in the success of a business. We propose a way to forecasting a new product's diffusion through technology diffusion and interest diffusion. Technology diffusion and interest diffusion are measured by the volume of patent citations and web search traffic, respectively. We apply the proposed method to forecast the sales of hybrid cars and industrial robots in the US market. The results show that that technology diffusion, as represented by patent citations, can explain long-term sales for hybrid cars and industrial robots. On the other hand, interest diffusion, as represented by web search traffic, can help to improve the predictability of market sales of hybrid cars in the short-term. However, interest diffusion is difficult to explain the sales of industrial robots due to the different market characteristics. Finding indicates our proposed model can relatively well explain the diffusion of consumer goods.

Smart Irrigation From Soil Moisture Forecast Using Satellite And Hydro -Meteorological Modelling

Science.gov (United States)

Corbari, Chiara; Mancini, Marco; Ravazzani, Giovanni; Ceppi, Alessandro; Salerno, Raffaele; Sobrino, Josè

2017-04-01

Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. The SIM project funded by EU in the framework of the WaterWorks2014 - Water Joint Programming Initiative aims at developing an operational tool for real-time forecast of crops irrigation water requirements to support parsimonious water management and to optimize irrigation scheduling providing real-time and forecasted soil moisture behavior at high spatial and temporal resolutions with forecast horizons from few up to thirty days. This study discusses advances in coupling satellite driven soil water balance model and meteorological forecast as support for precision irrigation use comparing different case studies in Italy, in the Netherlands, in China and Spain, characterized by different climatic conditions, water availability, crop types and irrigation techniques and water distribution rules. Herein, the applications in two operative farms in vegetables production in the South of Italy where semi-arid climatic conditions holds, two maize fields in Northern Italy in a more water reach environment with flood irrigation will be presented. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations. Discussion on the methodology approach is presented, comparing for a reanalysis periods the forecast system outputs with observed soil moisture and crop water needs proving the reliability of the forecasting system and its benefits. The real-time visualization of the implemented system is also presented through web-dashboards.

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.

Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools.

Science.gov (United States)

Seto, Karen C; Güneralp, Burak; Hutyra, Lucy R

2012-10-02

Urban land-cover change threatens biodiversity and affects ecosystem productivity through loss of habitat, biomass, and carbon storage. However, despite projections that world urban populations will increase to nearly 5 billion by 2030, little is known about future locations, magnitudes, and rates of urban expansion. Here we develop spatially explicit probabilistic forecasts of global urban land-cover change and explore the direct impacts on biodiversity hotspots and tropical carbon biomass. If current trends in population density continue and all areas with high probabilities of urban expansion undergo change, then by 2030, urban land cover will increase by 1.2 million km(2), nearly tripling the global urban land area circa 2000. This increase would result in considerable loss of habitats in key biodiversity hotspots, with the highest rates of forecasted urban growth to take place in regions that were relatively undisturbed by urban development in 2000: the Eastern Afromontane, the Guinean Forests of West Africa, and the Western Ghats and Sri Lanka hotspots. Within the pan-tropics, loss in vegetation biomass from areas with high probability of urban expansion is estimated to be 1.38 PgC (0.05 PgC yr(-1)), equal to ∼5% of emissions from tropical deforestation and land-use change. Although urbanization is often considered a local issue, the aggregate global impacts of projected urban expansion will require significant policy changes to affect future growth trajectories to minimize global biodiversity and vegetation carbon losses.

Demand Forecasting in the Smart Grid Paradigm: Features and Challenges

Energy Technology Data Exchange (ETDEWEB)

Khodayar, Mohammad E.; Wu, Hongyu

2015-07-01

Demand forecasting faces challenges that include a large volume of data, increasing number of factors that affect the demand profile, uncertainties in the generation profile of the distributed and renewable generation resources and lack of historical data. A hierarchical demand forecasting framework can incorporate the new technologies, customer behaviors and preferences, and environmental factors.

Unstructured Grid Adaptation: Status, Potential Impacts, and Recommended Investments Toward CFD Vision 2030

Science.gov (United States)

Park, Michael A.; Krakos, Joshua A.; Michal, Todd; Loseille, Adrien; Alonso, Juan J.

2016-01-01

Unstructured grid adaptation is a powerful tool to control discretization error for Computational Fluid Dynamics (CFD). It has enabled key increases in the accuracy, automation, and capacity of some fluid simulation applications. Slotnick et al. provides a number of case studies in the CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences to illustrate the current state of CFD capability and capacity. The authors forecast the potential impact of emerging High Performance Computing (HPC) environments forecast in the year 2030 and identify that mesh generation and adaptivity continue to be significant bottlenecks in the CFD work flow. These bottlenecks may persist because very little government investment has been targeted in these areas. To motivate investment, the impacts of improved grid adaptation technologies are identified. The CFD Vision 2030 Study roadmap and anticipated capabilities in complementary disciplines are quoted to provide context for the progress made in grid adaptation in the past fifteen years, current status, and a forecast for the next fifteen years with recommended investments. These investments are specific to mesh adaptation and impact other aspects of the CFD process. Finally, a strategy is identified to diffuse grid adaptation technology into production CFD work flows.

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

Improving weather forecasts for wind energy applications

Science.gov (United States)

Kay, Merlinde; MacGill, Iain

2010-08-01

Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms-1 and around 25 ms-1. A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

Improving weather forecasts for wind energy applications

International Nuclear Information System (INIS)

Kay, Merlinde; MacGill, Iain

2010-01-01

Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms -1 and around 25 ms -1 . A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

FORECAST: Regulatory effects cost analysis software annual

International Nuclear Information System (INIS)

Lopez, B.; Sciacca, F.W.

1991-11-01

Over the past several years the NRC has developed a generic cost methodology for the quantification of cost/economic impacts associated with a wide range of new or revised regulatory requirements. This methodology has been developed to aid the NRC in preparing Regulatory Impact Analyses (RIAs). These generic costing methods can be useful in quantifying impacts both to industry and to the NRC. The FORECAST program was developed to facilitate the use of the generic costing methodology. This PC program integrates the major cost considerations that may be required because of a regulatory change. FORECAST automates much of the calculations typically needed in an RIA and thus reduces the time and labor required to perform these analysis. More importantly, its integrated and consistent treatment of the different cost elements should help assure comprehensiveness, uniformity, and accuracy in the preparation of needed cost estimates

Modeling timelines for translational science in cancer; the impact of technological maturation.

Directory of Open Access Journals (Sweden)

Laura M McNamee

Full Text Available This work examines translational science in cancer based on theories of innovation that posit a relationship between the maturation of technologies and their capacity to generate successful products. We examined the growth of technologies associated with 138 anticancer drugs using an analytical model that identifies the point of initiation of exponential growth and the point at which growth slows as the technology becomes established. Approval of targeted and biological products corresponded with technological maturation, with first approval averaging 14 years after the established point and 44 years after initiation of associated technologies. The lag in cancer drug approvals after the increases in cancer funding and dramatic scientific advances of the 1970s thus reflects predictable timelines of technology maturation. Analytical models of technological maturation may be used for technological forecasting to guide more efficient translation of scientific discoveries into cures.

Evaluating the impact of improvements to the FLAMBE smoke source model on forecasts of aerosol distribution from NAAPS

Science.gov (United States)

Hyer, E. J.; Reid, J. S.

2006-12-01

As more forecast models aim to include aerosol and chemical species, there is a need for source functions for biomass burning emissions that are accurate, robust, and operable in real-time. NAAPS is a global aerosol forecast model running every six hours and forecasting distributions of biomass burning, industrial sulfate, dust, and sea salt aerosols. This model is run operationally by the U.S. Navy as an aid to planning. The smoke emissions used as input to the model are calculated from the data collected by the FLAMBE system, driven by near-real-time active fire data from GOES WF_ABBA and MODIS Rapid Response. The smoke source function uses land cover data to predict properties of detected fires based on literature data from experimental burns. This scheme is very sensitive to the choice of land cover data sets. In areas of rapid land cover change, the use of static land cover data can produce artifactual changes in emissions unrelated to real changes in fire patterns. In South America, this change may be as large as 40% over five years. We demonstrate the impact of a modified land cover scheme on FLAMBE emissions and NAAPS forecasts, including a fire size algorithm developed using MODIS burned area data. We also describe the effects of corrections to emissions estimates for cloud and satellite coverage. We outline areas where existing data sources are incomplete and improvements are required to achieve accurate modeling of biomass burning emissions in real time.

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.

THE ACCURACY OF EARNINGS FORECAST AND POST-IPO EARNINGS MANAGEMENT

Directory of Open Access Journals (Sweden)

Yanthi Hutagaol

2017-03-01

Full Text Available Prior studies showed that before IPO, many companies conducted earnings management in order to attractpotential investors through impressive earnings figures. This study aimed to investigate the tendency of earningsmanagement practice post - IPO. This practice of earnings management was motivated to preserve managers’reputation in achieving their earnings forecasts. Using a total of 165 IPOs in IDX during year 2000-2010, thisstudy employed descriptive analyses to identify the earnings management differences within the sample. A crosssectionanalysis was conducted to test the difference of earnings management indicator among the forecasters.Then, controlling for audit quality, ownership, firm size, and firm leverage, a regression analysis was performedto test the impact of earnings forecasts accuracy on the earnings management. The result of this research showedthat there was an indication that the forecasters conducted more earnings management than the non-forecasters.The study found that forecast accuracy was significantly related to managers’ behavior to manage post-IPOearnings. Further analysis showed that optimistic forecasters tended to engage more in more earning managementthan conservative forecasters. The cross section analysis confirmed that optimistic earnings forecast strengthenedthe relationship of forecast accuracy and post-IPO earnings management, while high audit quality failed toweaken it.

The Impacts of Satellite Remotely Sensed Winds and Total Precipitable Vapour in WRF Tropical Cyclone Track Forecasts

Directory of Open Access Journals (Sweden)

Diandong Ren

2016-01-01

Full Text Available This study assesses the impact assimilating the scatterometer near-surface wind observations and total precipitable water from the SSMI, into WRF on genesis and track forecasting of four tropical cyclones (TCs. These TCs are selected to be representative of different intensity categories and basins. Impact is via a series of data denial experiments that systematically exclude the remote sensed information. Compared with the control case, in which only the final analysis atmospheric variables are used to initialize and provide the lateral boundary conditions, the data assimilation runs performed consistently better, but with very different skill levels for the different TCs. Eliassen-Palm flux analyses are employed. It is confirmed that if a polar orbital satellite footprint passes over the TC’s critical genesis region, the forecast will profit most from assimilating the remotely sensed information. If the critical genesis region lies within an interorbital gap then, regardless of how strong the TC later becomes (e.g., Katrina 2005, the improvement from assimilating near-surface winds and total precipitable water in the model prediction is severely limited. This underpins the need for a synergy of data from different scatterometers/radiometers. Other approaches are suggested to improve the accuracy in the prediction of TC genesis and tracks.

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...

The Impact of In-Service Technology Training Programmes on Technology Teachers

Science.gov (United States)

Gumbo, Mishack; Makgato, Moses; Muller, Helene

2012-01-01

The aim of this paper is to assess the impact the Advanced Certificate in Education (ACE) in-service technology training program has on technology teachers' knowledge and understanding of technology. The training of technology teachers is an initiative toward teachers' professional development within the mathematics, science, and technology sphere…

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...

Do location specific forecasts pose a new challenge for communicating uncertainty?

Science.gov (United States)

Abraham, Shyamali; Bartlett, Rachel; Standage, Matthew; Black, Alison; Charlton-Perez, Andrew; McCloy, Rachel

2015-04-01

In the last decade, the growth of local, site-specific weather forecasts delivered by mobile phone or website represents arguably the fastest change in forecast consumption since the beginning of Television weather forecasts 60 years ago. In this study, a street-interception survey of 274 members of the public a clear first preference for narrow weather forecasts above traditional broad weather forecasts is shown for the first time, with a clear bias towards this preference for users under 40. The impact of this change on the understanding of forecast probability and intensity information is explored. While the correct interpretation of the statement 'There is a 30% chance of rain tomorrow' is still low in the cohort, in common with previous studies, a clear impact of age and educational attainment on understanding is shown, with those under 40 and educated to degree level or above more likely to correctly interpret it. The interpretation of rainfall intensity descriptors ('Light', 'Moderate', 'Heavy') by the cohort is shown to be significantly different to official and expert assessment of the same descriptors and to have large variance amongst the cohort. However, despite these key uncertainties, members of the cohort generally seem to make appropriate decisions about rainfall forecasts. There is some evidence that the decisions made are different depending on the communication format used, and the cohort expressed a clear preference for tabular over graphical weather forecast presentation.

Cross-impacts analysis development and energy policy analysis applications

Energy Technology Data Exchange (ETDEWEB)

Roop, J.M.; Scheer, R.M.; Stacey, G.S.

1986-12-01

Purpose of this report is to describe the cross-impact analysis process and microcomputer software developed for the Office of Policy, Planning, and Analysis (PPA) of DOE. First introduced in 1968, cross-impact analysis is a technique that produces scenarios of future conditions and possibilities. Cross-impact analysis has several unique attributes that make it a tool worth examining, especially in the current climate when the outlook for the economy and several of the key energy markets is uncertain. Cross-impact analysis complements the econometric, engineering, systems dynamics, or trend approaches already in use at DOE. Cross-impact analysis produces self-consistent scenarios in the broadest sense and can include interaction between the economy, technology, society and the environment. Energy policy analyses that couple broad scenarios of the future with detailed forecasting can produce more powerful results than scenario analysis or forecasts can produce alone.

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...

Do we need demographic data to forecast plant population dynamics?

Science.gov (United States)

Tredennick, Andrew T.; Hooten, Mevin B.; Adler, Peter B.

2017-01-01

Rapid environmental change has generated growing interest in forecasts of future population trajectories. Traditional population models built with detailed demographic observations from one study site can address the impacts of environmental change at particular locations, but are difficult to scale up to the landscape and regional scales relevant to management decisions. An alternative is to build models using population-level data that are much easier to collect over broad spatial scales than individual-level data. However, it is unknown whether models built using population-level data adequately capture the effects of density-dependence and environmental forcing that are necessary to generate skillful forecasts.Here, we test the consequences of aggregating individual responses when forecasting the population states (percent cover) and trajectories of four perennial grass species in a semi-arid grassland in Montana, USA. We parameterized two population models for each species, one based on individual-level data (survival, growth and recruitment) and one on population-level data (percent cover), and compared their forecasting accuracy and forecast horizons with and without the inclusion of climate covariates. For both models, we used Bayesian ridge regression to weight the influence of climate covariates for optimal prediction.In the absence of climate effects, we found no significant difference between the forecast accuracy of models based on individual-level data and models based on population-level data. Climate effects were weak, but increased forecast accuracy for two species. Increases in accuracy with climate covariates were similar between model types.In our case study, percent cover models generated forecasts as accurate as those from a demographic model. For the goal of forecasting, models based on aggregated individual-level data may offer a practical alternative to data-intensive demographic models. Long time series of percent cover data already exist

Low flows: mechanisms, forecasts and climate change impacts

NARCIS (Netherlands)

Demirel, M.C.

2014-01-01

A research on low flows may seem controversial for a “wet” country protected by dykes and barriers. However, low flows in dry summers such as in 1976, 1985 and 2003 indicate that it may happen also in the Rhine basin. Improved medium-range and seasonal low fow forecasts are important as there is an

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.

Impact of Moist Physics Complexity on Tropical Cyclone Simulations from the Hurricane Weather Research and Forecast System

Science.gov (United States)

Kalina, E. A.; Biswas, M.; Newman, K.; Grell, E. D.; Bernardet, L.; Frimel, J.; Carson, L.

2017-12-01

The parameterization of moist physics in numerical weather prediction models plays an important role in modulating tropical cyclone structure, intensity, and evolution. The Hurricane Weather Research and Forecast system (HWRF), the National Oceanic and Atmospheric Administration's operational model for tropical cyclone prediction, uses the Scale-Aware Simplified Arakawa-Schubert (SASAS) cumulus scheme and a modified version of the Ferrier-Aligo (FA) microphysics scheme to parameterize moist physics. The FA scheme contains a number of simplifications that allow it to run efficiently in an operational setting, which includes prescribing values for hydrometeor number concentrations (i.e., single-moment microphysics) and advecting the total condensate rather than the individual hydrometeor species. To investigate the impact of these simplifying assumptions on the HWRF forecast, the FA scheme was replaced with the more complex double-moment Thompson microphysics scheme, which individually advects cloud ice, cloud water, rain, snow, and graupel. Retrospective HWRF forecasts of tropical cyclones that occurred in the Atlantic and eastern Pacific ocean basins from 2015-2017 were then simulated and compared to those produced by the operational HWRF configuration. Both traditional model verification metrics (i.e., tropical cyclone track and intensity) and process-oriented metrics (e.g., storm size, precipitation structure, and heating rates from the microphysics scheme) will be presented and compared. The sensitivity of these results to the cumulus scheme used (i.e., the operational SASAS versus the Grell-Freitas scheme) also will be examined. Finally, the merits of replacing the moist physics schemes that are used operationally with the alternatives tested here will be discussed from a standpoint of forecast accuracy versus computational resources.

Analysis of the market penetration of clean coal technologies and its impacts in China's electricity sector

International Nuclear Information System (INIS)

Wang, Hao; Nakata, Toshihiko

2009-01-01

This paper discusses policy instruments for promoting the market penetration of clean coal technologies (CCTs) into China's electricity sector and the evaluation of corresponding effects. Based on the reality that coal will remain the predominant fuel to generate electricity and conventional pulverized coal boiler power plants have serious impacts on environment degradation, development of clean coal technologies could be one alternative to meet China's fast growing demand of electricity as well as protect the already fragile environment. A multi-period market equilibrium model is applied and an electricity model of China is established to forecast changes in the electricity system up to 2030s. Three policy instruments: SO 2 emission charge, CO 2 emission charge and implementing subsidies are considered in this research. The results show that all instruments cause a significant shift in China's electricity structure, promote CCTs' competitiveness and lead China to gain great benefit in both resource saving and environment improvement. Since resource security and environment degradation are becoming primary concerns in China, policies that could help to gain generations' market share of advanced coal-based technologies such as CCTs' is suitable for the current situation of China's electricity sector. (author)

The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations. The Southern Study Area, Final Report

Energy Technology Data Exchange (ETDEWEB)

Freedman, Jeffrey M. [AWS Truepower, LLC, Albany, NY (United States); Manobianco, John [MESO, Inc., Troy, NY (United States); Schroeder, John [Texas Tech Univ., Lubbock, TX (United States). National Wind Inst.; Ancell, Brian [Texas Tech Univ., Lubbock, TX (United States). Atmospheric Science Group; Brewster, Keith [Univ. of Oklahoma, Norman, OK (United States). Center for Analysis and Prediction of Storms; Basu, Sukanta [North Carolina State Univ., Raleigh, NC (United States). Dept. of Marine, Earth, and Atmospheric Sciences; Banunarayanan, Venkat [ICF International (United States); Hodge, Bri-Mathias [National Renewable Energy Lab. (NREL), Golden, CO (United States); Flores, Isabel [Electricity Reliability Council of Texas (United States)

2014-04-30

This Final Report presents a comprehensive description, findings, and conclusions for the Wind Forecast Improvement Project (WFIP) -- Southern Study Area (SSA) work led by AWS Truepower (AWST). This multi-year effort, sponsored by the Department of Energy (DOE) and National Oceanographic and Atmospheric Administration (NOAA), focused on improving short-term (15-minute - 6 hour) wind power production forecasts through the deployment of an enhanced observation network of surface and remote sensing instrumentation and the use of a state-of-the-art forecast modeling system. Key findings from the SSA modeling and forecast effort include: 1. The AWST WFIP modeling system produced an overall 10 - 20% improvement in wind power production forecasts over the existing Baseline system, especially during the first three forecast hours; 2. Improvements in ramp forecast skill, particularly for larger up and down ramps; 3. The AWST WFIP data denial experiments showed mixed results in the forecasts incorporating the experimental network instrumentation; however, ramp forecasts showed significant benefit from the additional observations, indicating that the enhanced observations were key to the model systems’ ability to capture phenomena responsible for producing large short-term excursions in power production; 4. The OU CAPS ARPS simulations showed that the additional WFIP instrument data had a small impact on their 3-km forecasts that lasted for the first 5-6 hours, and increasing the vertical model resolution in the boundary layer had a greater impact, also in the first 5 hours; and 5. The TTU simulations were inconclusive as to which assimilation scheme (3DVAR versus EnKF) provided better forecasts, and the additional observations resulted in some improvement to the forecasts in the first 1 - 3 hours.

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....

Recent advances in flood forecasting and flood risk assessment

Directory of Open Access Journals (Sweden)

G. Arduino

2005-01-01

Full Text Available Recent large floods in Europe have led to increased interest in research and development of flood forecasting systems. Some of these events have been provoked by some of the wettest rainfall periods on record which has led to speculation that such extremes are attributable in some measure to anthropogenic global warming and represent the beginning of a period of higher flood frequency. Whilst current trends in extreme event statistics will be difficult to discern, conclusively, there has been a substantial increase in the frequency of high floods in the 20th century for basins greater than 2x105 km2. There is also increasing that anthropogenic forcing of climate change may lead to an increased probability of extreme precipitation and, hence, of flooding. There is, therefore, major emphasis on the improvement of operational flood forecasting systems in Europe, with significant European Community spending on research and development on prototype forecasting systems and flood risk management projects. This Special Issue synthesises the most relevant scientific and technological results presented at the International Conference on Flood Forecasting in Europe held in Rotterdam from 3-5 March 2003. During that meeting 150 scientists, forecasters and stakeholders from four continents assembled to present their work and current operational best practice and to discuss future directions of scientific and technological efforts in flood prediction and prevention. The papers presented at the conference fall into seven themes, as follows.

Forecast communication through the newspaper Part 2: perceptions of uncertainty

Science.gov (United States)

Harris, Andrew J. L.

2015-04-01

In the first part of this review, I defined the media filter and how it can operate to frame and blame the forecaster for losses incurred during an environmental disaster. In this second part, I explore the meaning and role of uncertainty when a forecast, and its basis, is communicated through the response and decision-making chain to the newspaper, especially during a rapidly evolving natural disaster which has far-reaching business, political, and societal impacts. Within the media-based communication system, there remains a fundamental disconnect of the definition of uncertainty and the interpretation of the delivered forecast between various stakeholders. The definition and use of uncertainty differs especially between scientific, media, business, and political stakeholders. This is a serious problem for the scientific community when delivering forecasts to the public though the press. As reviewed in Part 1, the media filter can result in a negative frame, which itself is a result of bias, slant, spin, and agenda setting introduced during passage of the forecast and its uncertainty through the media filter. The result is invariably one of anger and fury, which causes loss of credibility and blaming of the forecaster. Generation of a negative frame can be aided by opacity of the decision-making process that the forecast is used to support. The impact of the forecast will be determined during passage through the decision-making chain where the precautionary principle and cost-benefit analysis, for example, will likely be applied. Choice of forecast delivery format, vehicle of communication, syntax of delivery, and lack of follow-up measures can further contribute to causing the forecast and its role to be misrepresented. Follow-up measures to negative frames may include appropriately worded press releases and conferences that target forecast misrepresentation or misinterpretation in an attempt to swing the slant back in favor of the forecaster. Review of

Accurate Medium-Term Wind Power Forecasting in a Censored Classification Framework

DEFF Research Database (Denmark)

Dahl, Christian M.; Croonenbroeck, Carsten

2014-01-01

We provide a wind power forecasting methodology that exploits many of the actual data's statistical features, in particular both-sided censoring. While other tools ignore many of the important “stylized facts” or provide forecasts for short-term horizons only, our approach focuses on medium......-term forecasts, which are especially necessary for practitioners in the forward electricity markets of many power trading places; for example, NASDAQ OMX Commodities (formerly Nord Pool OMX Commodities) in northern Europe. We show that our model produces turbine-specific forecasts that are significantly more...... accurate in comparison to established benchmark models and present an application that illustrates the financial impact of more accurate forecasts obtained using our methodology....

Forecasting Dry Bulk Freight Index with Improved SVM

Directory of Open Access Journals (Sweden)

Qianqian Han

2014-01-01

Full Text Available An improved SVM model is presented to forecast dry bulk freight index (BDI in this paper, which is a powerful tool for operators and investors to manage the market trend and avoid price risking shipping industry. The BDI is influenced by many factors, especially the random incidents in dry bulk market, inducing the difficulty in forecasting of BDI. Therefore, to eliminate the impact of random incidents in dry bulk market, wavelet transform is adopted to denoise the BDI data series. Hence, the combined model of wavelet transform and support vector machine is developed to forecast BDI in this paper. Lastly, the BDI data in 2005 to 2012 are presented to test the proposed model. The 84 prior consecutive monthly BDI data are the inputs of the model, and the last 12 monthly BDI data are the outputs of model. The parameters of the model are optimized by genetic algorithm and the final model is conformed through SVM training. This paper compares the forecasting result of proposed method and three other forecasting methods. The result shows that the proposed method has higher accuracy and could be used to forecast the short-term trend of the BDI.

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

Forecasting how residential urban form affects the regional carbon savings and costs of retrofitting and decentralized energy supply

International Nuclear Information System (INIS)

Hargreaves, Anthony; Cheng, Vicky; Deshmukh, Sandip; Leach, Matthew; Steemers, Koen

2017-01-01

Highlights: • An innovative model for testing combinations of spatial planning and decentralised energy supply. • An improved method of modelling the spatial variability of energy consumption per dwelling type. • Shows how spatial planning would affect the future carbon reduction of decentralised supply. • Forecasts the future carbon reduction and costs of retrofitting and decentralised supply. • A method of forecasting how residential space would affect the suitability of decentralised supply. - Abstract: Low carbon energy supply technologies are increasingly used at the building and community scale and are an important part of the government decarbonisation strategy. However, with their present state of development and costs, many of these decentralised technologies rely on public subsidies to be financially viable. It is questionable whether they are cost effective compared to other ways of reducing carbon emissions, such as decarbonisation of conventional supply and improving the energy efficiency of dwellings. Previous studies have found it difficult to reliably estimate the future potential of decentralised supply because this depends on the available residential space which varies greatly within a city region. To address this problem, we used an integrated modelling framework that converted the residential density forecasts of a regional model into a representation of the building dimensions and land of the future housing stock. This included a method of estimating the variability of the dwellings and residential land. We present the findings of a case study of the wider south east regions of England that forecasted the impacts of energy efficiency and decentralised supply scenarios to year 2031. Our novel and innovative method substantially improves the spatial estimates of energy consumption compared to building energy models that only use standard dwelling typologies. We tested the impact of an alternative spatial planning policy on the future

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.

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.

Impact of a Diagnostic Pressure Equation Constraint on Tornadic Supercell Thunderstorm Forecasts Initialized Using 3DVAR Radar Data Assimilation

Directory of Open Access Journals (Sweden)

Guoqing Ge

2013-01-01

Full Text Available A diagnostic pressure equation constraint has been incorporated into a storm-scale three-dimensional variational (3DVAR data assimilation system. This diagnostic pressure equation constraint (DPEC is aimed to improve dynamic consistency among different model variables so as to produce better data assimilation results and improve the subsequent forecasts. Ge et al. (2012 described the development of DPEC and testing of it with idealized experiments. DPEC was also applied to a real supercell case, but only radial velocity was assimilated. In this paper, DPEC is further applied to two real tornadic supercell thunderstorm cases, where both radial velocity and radar reflectivity data are assimilated. The impact of DPEC on radar data assimilation is examined mainly based on the storm forecasts. It is found that the experiments using DPEC generally predict higher low-level vertical vorticity than the experiments not using DPEC near the time of observed tornadoes. Therefore, it is concluded that the use of DPEC improves the forecast of mesocyclone rotation within supercell thunderstorms. The experiments using different weighting coefficients generate similar results. This suggests that DPEC is not very sensitive to the weighting coefficients.

The technological singularity and exponential medicine

Directory of Open Access Journals (Sweden)

Iraj Nabipour

2016-01-01

Full Text Available The "technological singularity" is forecasted to occur in 2045. It is a point when non-biological intelligence becomes more intelligent than humans and each generation of intelligent machines re-designs itself smarter. Beyond this point, there is a symbiosis between machines and humans. This co-existence will produce incredible impacts on medicine that its sparkles could be seen in healthcare industry and the future medicine since 2025. Ray Kurzweil, the great futurist, suggested that three revolutions in science and technology consisting genetic and molecular science, nanotechnology, and robotic (artificial intelligence provided an exponential growth rate for medicine. The "exponential medicine" is going to create more disruptive technologies in healthcare industry. The exponential medicine shifts the paradigm of medical philosophy and produces significant impacts on the healthcare system and patient-physician relationship.   

DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA

Directory of Open Access Journals (Sweden)

J. Rhee

2016-06-01

Full Text Available The reduction of drought impacts may be achieved through sustainable drought management and proactive measures against drought disaster. Accurate and timely provision of drought information is essential. In this study, drought forecasting models to provide high-resolution drought information based on drought indicators for ungauged areas were developed. The developed models predict drought indices of the 6-month Standardized Precipitation Index (SPI6 and the 6-month Standardized Precipitation Evapotranspiration Index (SPEI6. An interpolation method based on multiquadric spline interpolation method as well as three machine learning models were tested. Three machine learning models of Decision Tree, Random Forest, and Extremely Randomized Trees were tested to enhance the provision of drought initial conditions based on remote sensing data, since initial conditions is one of the most important factors for drought forecasting. Machine learning-based methods performed better than interpolation methods for both classification and regression, and the methods using climatology data outperformed the methods using long-range forecast. The model based on climatological data and the machine learning method outperformed overall.

Impact of chemical lateral boundary conditions in a regional air quality forecast model on surface ozone predictions during stratospheric intrusions

Science.gov (United States)

Pendlebury, Diane; Gravel, Sylvie; Moran, Michael D.; Lupu, Alexandru

2018-02-01

A regional air quality forecast model, GEM-MACH, is used to examine the conditions under which a limited-area air quality model can accurately forecast near-surface ozone concentrations during stratospheric intrusions. Periods in 2010 and 2014 with known stratospheric intrusions over North America were modelled using four different ozone lateral boundary conditions obtained from a seasonal climatology, a dynamically-interpolated monthly climatology, global air quality forecasts, and global air quality reanalyses. It is shown that the mean bias and correlation in surface ozone over the course of a season can be improved by using time-varying ozone lateral boundary conditions, particularly through the correct assignment of stratospheric vs. tropospheric ozone along the western lateral boundary (for North America). Part of the improvement in surface ozone forecasts results from improvements in the characterization of near-surface ozone along the lateral boundaries that then directly impact surface locations near the boundaries. However, there is an additional benefit from the correct characterization of the location of the tropopause along the western lateral boundary such that the model can correctly simulate stratospheric intrusions and their associated exchange of ozone from stratosphere to troposphere. Over a three-month period in spring 2010, the mean bias was seen to improve by as much as 5 ppbv and the correlation by 0.1 depending on location, and on the form of the chemical lateral boundary condition.

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.

Bayesian quantitative precipitation forecasts in terms of quantiles

Science.gov (United States)

Bentzien, Sabrina; Friederichs, Petra

2014-05-01

Ensemble prediction systems (EPS) for numerical weather predictions on the mesoscale are particularly developed to obtain probabilistic guidance for high impact weather. An EPS not only issues a deterministic future state of the atmosphere but a sample of possible future states. Ensemble postprocessing then translates such a sample of forecasts into probabilistic measures. This study focus on probabilistic quantitative precipitation forecasts in terms of quantiles. Quantiles are particular suitable to describe precipitation at various locations, since no assumption is required on the distribution of precipitation. The focus is on the prediction during high-impact events and related to the Volkswagen Stiftung funded project WEX-MOP (Mesoscale Weather Extremes - Theory, Spatial Modeling and Prediction). Quantile forecasts are derived from the raw ensemble and via quantile regression. Neighborhood method and time-lagging are effective tools to inexpensively increase the ensemble spread, which results in more reliable forecasts especially for extreme precipitation events. Since an EPS provides a large amount of potentially informative predictors, a variable selection is required in order to obtain a stable statistical model. A Bayesian formulation of quantile regression allows for inference about the selection of predictive covariates by the use of appropriate prior distributions. Moreover, the implementation of an additional process layer for the regression parameters accounts for spatial variations of the parameters. Bayesian quantile regression and its spatially adaptive extension is illustrated for the German-focused mesoscale weather prediction ensemble COSMO-DE-EPS, which runs (pre)operationally since December 2010 at the German Meteorological Service (DWD). Objective out-of-sample verification uses the quantile score (QS), a weighted absolute error between quantile forecasts and observations. The QS is a proper scoring function and can be decomposed into

On the relation between forecast precision and trading profitability of financial analysts

DEFF Research Database (Denmark)

Marinelli, Carlo; Weissensteiner, Alex

2014-01-01

We analyze the relation between earnings forecast accuracy and the expected profitability of financial analysts. Modeling forecast errors with a multivariate normal distribution, a complete characterization of the payoff of each analyst is provided. In particular, closed-form expressions for the ......We analyze the relation between earnings forecast accuracy and the expected profitability of financial analysts. Modeling forecast errors with a multivariate normal distribution, a complete characterization of the payoff of each analyst is provided. In particular, closed-form expressions...... for the probability density function, for the expectation, and, more generally, for moments of all orders are obtained. Our analysis shows that the relationship between forecast precision and trading profitability needs not be monotonic, and that the impact of the correlation between the forecasts on the expected...

Logical design of a decision support system to forecast technology, prices and costs for the national communications system

Science.gov (United States)

Williams, K. A.; Partridge, E. C., III

1984-09-01

Originally envisioned as a means to integrate the many systems found throughout the government, the general mission of the NCS continues to be to ensure the survivability of communications during and subsequent to any national emergency. In order to accomplish this mission the NCS is an arrangement of heterogeneous telecommunications systems which are provided by their sponsor Federal agencies. The physical components of Federal telecommunications systems and networks include telephone and digital data switching facilities and primary common user communications centers; Special purpose local delivery message switching and exchange facilities; Government owned or leased radio systems; Technical control facilities which are under exclusive control of a government agency. This thesis describes the logical design of a proposed decision support system for use by the National Communications System in forecasting technology, prices, and costs. It is general in nature and only includes those forecasting models which are suitable for computer implementation. Because it is a logical design it can be coded and applied in many different hardware and/or software configurations.

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...

The impact of weather and ocean forecasting on hydrocarbon production and pollution management in the Gulf of Mexico

International Nuclear Information System (INIS)

Kaiser, Mark J.; Pulsipher, Allan G.

2007-01-01

Over the past 2 years, the vulnerability of offshore production in the Gulf of Mexico (GOM) has been brought to light by extensive damage to oil and gas facilities and pipelines resulting from Hurricanes Ivan, Katrina, and Rita. The occurrences of extreme weather regularly force operators to shut-down production, cease drilling and construction activities, and evacuate personnel. Loop currents and eddies can also impact offshore operations and delay installation and drilling activities and reduce the effectiveness of oil spill response strategies. The purpose of this paper is to describe how weather and ocean forecasting impact production activities and pollution management in the GOM. Physical outcome and decision models in support of production and development activities and oil spill response management are presented, and the expected economic benefits that may result from the implementation of an integrated ocean observation network in the region are summarized. Improved ocean observation systems are expected to reduce the uncertainty of forecasting and to enhance the value of ocean/weather information throughout the Gulf region. The source of benefits and the size of activity from which improved ocean observation benefits may be derived are estimated for energy development and production activities and oil spill response management

Impact of Three-Phase Relative Permeability and Hysteresis Models on Forecasts of Storage Associated With CO2-EOR

Science.gov (United States)

Jia, Wei; McPherson, Brian; Pan, Feng; Dai, Zhenxue; Moodie, Nathan; Xiao, Ting

2018-02-01

Geological CO2 sequestration in conjunction with enhanced oil recovery (CO2-EOR) includes complex multiphase flow processes compared to CO2 storage in deep saline aquifers. Two of the most important factors affecting multiphase flow in CO2-EOR are three-phase relative permeability and associated hysteresis, both of which are difficult to measure and are usually represented by numerical interpolation models. The purpose of this study is to improve understanding of (1) the relative impacts of different three-phase relative permeability models and hysteresis models on CO2 trapping mechanisms, and (2) uncertainty associated with these two factors. Four different three-phase relative permeability models and three hysteresis models were applied to simulations of an active CO2-EOR site, the SACROC unit located in western Texas. To eliminate possible bias of deterministic parameters, we utilized a sequential Gaussian simulation technique to generate 50 realizations to describe heterogeneity of porosity and permeability, based on data obtained from well logs and seismic survey. Simulation results of forecasted CO2 storage suggested that (1) the choice of three-phase relative permeability model and hysteresis model led to noticeable impacts on forecasted CO2 sequestration capacity; (2) impacts of three-phase relative permeability models and hysteresis models on CO2 trapping are small during the CO2-EOR injection period, and increase during the post-EOR CO2 injection period; (3) the specific choice of hysteresis model is more important relative to the choice of three-phase relative permeability model; and (4) using the recommended three-phase WAG (Water-Alternating-Gas) hysteresis model may increase the impact of three-phase relative permeability models and uncertainty due to heterogeneity.

Nambe Pueblo Water Budget and Forecasting model.

Energy Technology Data Exchange (ETDEWEB)

Brainard, James Robert

2009-10-01

This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Water Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.

EU pharmaceutical expenditure forecast.

Science.gov (United States)

Urbinati, Duccio; Rémuzat, Cécile; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

2014-01-01

With constant incentives for healthcare payers to contain their pharmaceutical budgets, forecasting has become critically important. Some countries have, for instance, developed pharmaceutical horizon scanning units. The objective of this project was to build a model to assess the net effect of the entrance of new patented medicinal products versus medicinal products going off-patent, with a defined forecast horizon, on selected European Union (EU) Member States' pharmaceutical budgets. This model took into account population ageing, as well as current and future country-specific pricing, reimbursement, and market access policies (the project was performed for the European Commission; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). In order to have a representative heterogeneity of EU Member States, the following countries were selected for the analysis: France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. A forecasting period of 5 years (2012-2016) was chosen to assess the net pharmaceutical budget impact. A model for generics and biosimilars was developed for each country. The model estimated a separate and combined effect of the direct and indirect impacts of the patent cliff. A second model, estimating the sales development and the risk of development failure, was developed for new drugs. New drugs were reviewed individually to assess their clinical potential and translate it into commercial potential. The forecast was carried out according to three perspectives (healthcare public payer, society, and manufacturer), and several types of distribution chains (retail, hospital, and combined retail and hospital). Probabilistic and deterministic sensitivity analyses were carried out. According to the model, all countries experienced drug budget reductions except Poland (+€41 million). Savings were expected to be the highest in the United Kingdom (-€9,367 million), France (-€5,589 million), and, far behind them

Drought forecasting in Luanhe River basin involving climatic indices

Science.gov (United States)

Ren, Weinan; Wang, Yixuan; Li, Jianzhu; Feng, Ping; Smith, Ronald J.

2017-11-01

Drought is regarded as one of the most severe natural disasters globally. This is especially the case in Tianjin City, Northern China, where drought can affect economic development and people's livelihoods. Drought forecasting, the basis of drought management, is an important mitigation strategy. In this paper, we evolve a probabilistic forecasting model, which forecasts transition probabilities from a current Standardized Precipitation Index (SPI) value to a future SPI class, based on conditional distribution of multivariate normal distribution to involve two large-scale climatic indices at the same time, and apply the forecasting model to 26 rain gauges in the Luanhe River basin in North China. The establishment of the model and the derivation of the SPI are based on the hypothesis of aggregated monthly precipitation that is normally distributed. Pearson correlation and Shapiro-Wilk normality tests are used to select appropriate SPI time scale and large-scale climatic indices. Findings indicated that longer-term aggregated monthly precipitation, in general, was more likely to be considered normally distributed and forecasting models should be applied to each gauge, respectively, rather than to the whole basin. Taking Liying Gauge as an example, we illustrate the impact of the SPI time scale and lead time on transition probabilities. Then, the controlled climatic indices of every gauge are selected by Pearson correlation test and the multivariate normality of SPI, corresponding climatic indices for current month and SPI 1, 2, and 3 months later are demonstrated using Shapiro-Wilk normality test. Subsequently, we illustrate the impact of large-scale oceanic-atmospheric circulation patterns on transition probabilities. Finally, we use a score method to evaluate and compare the performance of the three forecasting models and compare them with two traditional models which forecast transition probabilities from a current to a future SPI class. The results show that the

CSIR Technology Impact 2002

CSIR Research Space (South Africa)

CSIR

2002-01-01

Full Text Available This issue of Technology Impact offers a brief snapshot of CSIR activities during the year under review (1 March 2001 - 28 February 2002) by highlighting a number of innovative projects and initiatives in these areas....

Study of the impact of satellite data in the analysis and forecasting of a SACZ episode using G3DVar

Science.gov (United States)

de Azevedo, H. B.; Goncalves, L.

2013-05-01

Earth observations from satellite have great importance and impact, in particular for operational weather and climate forecast centers in the Southern Hemisphere such as the Center for Weather Forecast and Climate Studies (CPTEC from its Portuguese acronym), a division of the Brazilian National Institute for Space Research (INPE from its Portuguese acronym). It is well known that such data is critical, in particular over Southern Hemisphere oceans where there is a lack of other information sources e.g. radiosonde and aircraft, where satellite data provides excellent spatial coverage with relatively frequent sampling in addition to sweeping continents, deserts, woodlands and other remote areas. Hence, studies like OSEs (Observing Systems Experiments) where we are able to test whether observational input data in an assimilation system degrades or improves analyzes and forecasts, are of great value. Furthermore OSEs applied to satellite observations are expected to show large impact due to its large amount of information compared to conventional observations. OSE also provides useful information on the efficiency of the system and this information can be used to improve the use of one or another observation system in the data assimilation process and to determine its relative importance compared to other observation systems. This is a technique where one or more observation systems are retained in the data assimilation process in order to assess the impact of the inclusion or exclusion of a particular observation on the quality of numerical weather prediction (NWP). One of the difficulties within the NWP nevertheless, is to predict the correct intensity of severe weather systems, which has great impact on the population. Over South America, an important weather system is the South Atlantic Convergence Zone (SACZ), which every year during summer yelds large amounts of rainfall over a band oriented northwest-southeast, extending from the Amazon to the Brazilian

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...

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.

Forecast analysis of the electricity supply-demand balance in France for summer 2013

International Nuclear Information System (INIS)

2013-05-01

Under normal meteorological conditions, and notwithstanding localized risks associated with the vulnerability of certain regions, the forecast outlook for the electricity supply-demand balance in continental France shows no particular risk for the entire summer 2013 period. Special vigilance is maintained in the Provence-Alpes-Cote d'Azur region, given the risk of forest fires and potential outages affecting the dual 400 kV link from Toulon. This assessment is based on the assumption that forecast demand for summer 2013 will remain broadly stable as compared with summer 2012, given public economic indicators, but also that the forecast availability of the French generating fleet will increase by 1100 MW compared with summer 2012. This increased availability is based on information supplied by generators, and notably includes scheduled temporary outages of certain combined cycle gas turbines. Finally, growth in photovoltaic generation (3,700 MW of installed capacity currently in France) is continuing at a sustained pace, leading to a 700 MW increase in the mean availability rate for this generation technology as compared with summer 2012. Moreover, the substantial investments already made by RTE or currently in progress to develop its network (voltage support measures, Cotentin-Maine line, etc.) have had a very positive impact on the reliability of the power system. (authors)

Investigation on the innovative impact hydroforming technology

Science.gov (United States)

Lihui, Lang; Shaohua, Wang; Chunlei, Yang

2013-05-01

Hydroforming has a rapid development recently which has good forming quality and less cost. However, it still cannot meet the requirements of forming complex parts with small features just like convex tables, or bars which are widely employed in automotive and aircraft industries. The impact hydroforming technology means the most features are formed by hydroforming and the small features are rapidly reshaped by high intensity impact energy in a very short time after the traditional hydroforming. The impact pressure rises to the peak in 10ms which belongs to dynamic loading. In this paper, impact hydroforming process is proposed. The generation and transmission of impact hydroforming energy and impact shock wave were studied and simulated. The deformation process of the metal disks under the dynamic impact loading condition presented impact hydroforming is an effective technology to form complex parts with small features.

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 ...

CSIR Technology Impact 1990

CSIR Research Space (South Africa)

CSIR

1990-01-01

Full Text Available Technology impact report, it’s a selection of report on successful co-operation between the CSIR and South African industry in 1989/1990. This publication aims to measure and highlight the success of the CSIR outputs....

Ensemble Streamflow Forecast Improvements in NYC's Operations Support Tool

Science.gov (United States)

Wang, L.; Weiss, W. J.; Porter, J.; Schaake, J. C.; Day, G. N.; Sheer, D. P.

2013-12-01

Like most other water supply utilities, New York City's Department of Environmental Protection (DEP) has operational challenges associated with drought and wet weather events. During drought conditions, DEP must maintain water supply reliability to 9 million customers as well as meet environmental release requirements downstream of its reservoirs. During and after wet weather events, DEP must maintain turbidity compliance in its unfiltered Catskill and Delaware reservoir systems and minimize spills to mitigate downstream flooding. Proactive reservoir management - such as release restrictions to prepare for a drought or preventative drawdown in advance of a large storm - can alleviate negative impacts associated with extreme events. It is important for water managers to understand the risks associated with proactive operations so unintended consequences such as endangering water supply reliability with excessive drawdown prior to a storm event are minimized. Probabilistic hydrologic forecasts are a critical tool in quantifying these risks and allow water managers to make more informed operational decisions. DEP has recently completed development of an Operations Support Tool (OST) that integrates ensemble streamflow forecasts, real-time observations, and a reservoir system operations model into a user-friendly graphical interface that allows its water managers to take robust and defensible proactive measures in the face of challenging system conditions. Since initial development of OST was first presented at the 2011 AGU Fall Meeting, significant improvements have been made to the forecast system. First, the monthly AR1 forecasts ('Hirsch method') were upgraded with a generalized linear model (GLM) utilizing historical daily correlations ('Extended Hirsch method' or 'eHirsch'). The development of eHirsch forecasts improved predictive skill over the Hirsch method in the first week to a month from the forecast date and produced more realistic hydrographs on the tail

Sharing wind power forecasts in electricity markets: A numerical analysis

International Nuclear Information System (INIS)

Exizidis, Lazaros; Kazempour, S. Jalal; Pinson, Pierre; Greve, Zacharie de; Vallée, François

2016-01-01

Highlights: • Information sharing among different agents can be beneficial for electricity markets. • System cost decreases by sharing wind power forecasts between different agents. • Market power of wind producer may increase by sharing forecasts with market operator. • Extensive out-of-sample analysis is employed to draw reliable conclusions. - Abstract: In an electricity pool with significant share of wind power, all generators including conventional and wind power units are generally scheduled in a day-ahead market based on wind power forecasts. Then, a real-time market is cleared given the updated wind power forecast and fixed day-ahead decisions to adjust power imbalances. This sequential market-clearing process may cope with serious operational challenges such as severe power shortage in real-time due to erroneous wind power forecasts in day-ahead market. To overcome such situations, several solutions can be considered such as adding flexible resources to the system. In this paper, we address another potential solution based on information sharing in which market players share their own wind power forecasts with others in day-ahead market. This solution may improve the functioning of sequential market-clearing process through making more informed day-ahead schedules, which reduces the need for balancing resources in real-time operation. This paper numerically evaluates the potential value of sharing forecasts for the whole system in terms of system cost reduction. Besides, its impact on each market player’s profit is analyzed. The framework of this study is based on a stochastic two-stage market setup and complementarity modeling, which allows us to gain further insights into information sharing impacts.

CSIR Technology Impact 2003

CSIR Research Space (South Africa)

CSIR

2003-01-01

Full Text Available This issue of Technology Impact offers a brief snapshot of CSIR activities during the year under review (1 March 2002 - 28 February 2003) by highlighting a number of innovative projects and initiatives in various CSIR business units....

A hybrid approach for probabilistic forecasting of electricity price

DEFF Research Database (Denmark)

Wan, Can; Xu, Zhao; Wang, Yelei

2014-01-01

to the nonstationarities involved in market clearing prices (MCPs), it is rather difficult to accurately predict MCPs in advance. The challenge is getting intensified as more and more renewable energy and other new technologies emerged in smart grids. Therefore transformation from traditional point forecasts...... electricity price forecasting is proposed in this paper. The effectiveness of the proposed hybrid method has been validated through comprehensive tests using real price data from Australian electricity market.......The electricity market plays a key role in realizing the economic prophecy of smart grids. Accurate and reliable electricity market price forecasting is essential to facilitate various decision making activities of market participants in the future smart grid environment. However, due...

Evaluating the Impact of AIRS Observations on Regional Forecasts at the SPoRT Center

Science.gov (United States)

Zavodsky, Bradley

2011-01-01

NASA Short-term Prediction Research and Transition (SPoRT) Center collaborates with operational partners of different sizes and operational goals to improve forecasts using targeted projects and data sets. Modeling and DA activities focus on demonstrating utility of NASA data sets and capabilities within operational systems. SPoRT has successfully assimilated the Atmospheric Infrared Sounder (AIRS) radiance and profile data. A collaborative project is underway with the Joint Center for Satellite Data Assimilation (JCSDA) to use AIRS profiles to better understand the impact of AIRS radiances assimilated within Gridpoint Statistical Interpolation (GSI) in hopes of engaging the operational DA community in a reassessment of assimilation methodologies to more effectively assimilate hyperspectral radiances.

A COMPARATIVE STUDY OF FORECASTING MODELS FOR TREND AND SEASONAL TIME SERIES DOES COMPLEX MODEL ALWAYS YIELD BETTER FORECAST THAN SIMPLE MODELS

Directory of Open Access Journals (Sweden)

Suhartono Suhartono

2005-01-01

Full Text Available Many business and economic time series are non-stationary time series that contain trend and seasonal variations. Seasonality is a periodic and recurrent pattern caused by factors such as weather, holidays, or repeating promotions. A stochastic trend is often accompanied with the seasonal variations and can have a significant impact on various forecasting methods. In this paper, we will investigate and compare some forecasting methods for modeling time series with both trend and seasonal patterns. These methods are Winter's, Decomposition, Time Series Regression, ARIMA and Neural Networks models. In this empirical research, we study on the effectiveness of the forecasting performance, particularly to answer whether a complex method always give a better forecast than a simpler method. We use a real data, that is airline passenger data. The result shows that the more complex model does not always yield a better result than a simpler one. Additionally, we also find the possibility to do further research especially the use of hybrid model by combining some forecasting method to get better forecast, for example combination between decomposition (as data preprocessing and neural network model.

Technology's Impact on Production

Energy Technology Data Exchange (ETDEWEB)

Rachel Amann; Ellis Deweese; Deborah Shipman

2009-06-30

As part of a cooperative agreement with the United States Department of Energy (DOE) - entitled Technology's Impact on Production: Developing Environmental Solutions at the State and National Level - the Interstate Oil and Gas Compact Commission (IOGCC) has been tasked with assisting state governments in the effective, efficient, and environmentally sound regulation of the exploration and production of natural gas and crude oil, specifically in relation to orphaned and abandoned wells and wells nearing the end of productive life. Project goals include: (1) Developing (a) a model framework for prioritization and ranking of orphaned or abandoned well sites; (b) a model framework for disbursement of Energy Policy Act of 2005 funding; and (c) a research study regarding the current status of orphaned wells in the nation. (2) Researching the impact of new technologies on environmental protection from a regulatory perspective. Research will identify and document (a) state reactions to changing technology and knowledge; (b) how those reactions support state environmental conservation and public health; and (c) the impact of those reactions on oil and natural gas production. (3) Assessing emergent technology issues associated with wells nearing the end of productive life. Including: (a) location of orphaned and abandoned well sites; (b) well site remediation; (c) plugging materials; (d) plug placement; (e) the current regulatory environment; and (f) the identification of emergent technologies affecting end of life wells. New Energy Technologies - Regulating Change, is the result of research performed for Tasks 2 and 3.

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method

Energy Technology Data Exchange (ETDEWEB)

Wang, Qin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cui, Mingjian [University of Texas at Dallas; Feng, Cong [University of Texas at Dallas; Wang, Zhenke [University of Texas at Dallas; Zhang, Jie [University of Texas at Dallas

2018-02-01

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power and currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start-time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.

A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting

Directory of Open Access Journals (Sweden)

Ping-Huan Kuo

2018-01-01

Full Text Available One of the most important research topics in smart grid technology is load forecasting, because accuracy of load forecasting highly influences reliability of the smart grid systems. In the past, load forecasting was obtained by traditional analysis techniques such as time series analysis and linear regression. Since the load forecast focuses on aggregated electricity consumption patterns, researchers have recently integrated deep learning approaches with machine learning techniques. In this study, an accurate deep neural network algorithm for short-term load forecasting (STLF is introduced. The forecasting performance of proposed algorithm is compared with performances of five artificial intelligence algorithms that are commonly used in load forecasting. The Mean Absolute Percentage Error (MAPE and Cumulative Variation of Root Mean Square Error (CV-RMSE are used as accuracy evaluation indexes. The experiment results show that MAPE and CV-RMSE of proposed algorithm are 9.77% and 11.66%, respectively, displaying very high forecasting accuracy.

Towards the intrahour forecasting of direct normal irradiance using sky-imaging data.

Science.gov (United States)

Nou, Julien; Chauvin, Rémi; Eynard, Julien; Thil, Stéphane; Grieu, Stéphane

2018-04-01

Increasing power plant efficiency through improved operation is key in the development of Concentrating Solar Power (CSP) technologies. To this end, one of the most challenging topics remains accurately forecasting the solar resource at a short-term horizon. Indeed, in CSP plants, production is directly impacted by both the availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). The present paper deals with a new approach to the intrahour forecasting (the forecast horizon [Formula: see text] is up to [Formula: see text] ahead) of DNI, taking advantage of the fact that this quantity can be split into two terms, i.e. clear-sky DNI and the clear sky index. Clear-sky DNI is forecasted from DNI measurements, using an empirical model (Ineichen and Perez, 2002) combined with a persistence of atmospheric turbidity. Moreover, in the framework of the CSPIMP (Concentrating Solar Power plant efficiency IMProvement) research project, PROMES-CNRS has developed a sky imager able to provide High Dynamic Range (HDR) images. So, regarding the clear-sky index, it is forecasted from sky-imaging data, using an Adaptive Network-based Fuzzy Inference System (ANFIS). A hybrid algorithm that takes inspiration from the classification algorithm proposed by Ghonima et al. (2012) when clear-sky anisotropy is known and from the hybrid thresholding algorithm proposed by Li et al. (2011) in the opposite case has been developed to the detection of clouds. Performance is evaluated via a comparative study in which persistence models - either a persistence of DNI or a persistence of the clear-sky index - are included. Preliminary results highlight that the proposed approach has the potential to outperform these models (both persistence models achieve similar performance) in terms of forecasting accuracy: over the test data used, RMSE (the Root Mean Square Error) is reduced of about [Formula: see text], with [Formula: see text], and [Formula: see

Is the economic value of hydrological forecasts related to their quality? Case study of the hydropower sector.

Science.gov (United States)

Cassagnole, Manon; Ramos, Maria-Helena; Thirel, Guillaume; Gailhard, Joël; Garçon, Rémy

2017-04-01

The improvement of a forecasting system and the evaluation of the quality of its forecasts are recurrent steps in operational practice. However, the evaluation of forecast value or forecast usefulness for better decision-making is, to our knowledge, less frequent, even if it might be essential in many sectors such as hydropower and flood warning. In the hydropower sector, forecast value can be quantified by the economic gain obtained with the optimization of operations or reservoir management rules. Several hydropower operational systems use medium-range forecasts (up to 7-10 days ahead) and energy price predictions to optimize hydropower production. Hence, the operation of hydropower systems, including the management of water in reservoirs, is impacted by weather, climate and hydrologic variability as well as extreme events. In order to assess how the quality of hydrometeorological forecasts impact operations, it is essential to first understand if and how operations and management rules are sensitive to input predictions of different quality. This study investigates how 7-day ahead deterministic and ensemble streamflow forecasts of different quality might impact the economic gains of energy production. It is based on a research model developed by Irstea and EDF to investigate issues relevant to the links between quality and value of forecasts in the optimisation of energy production at the short range. Based on streamflow forecasts and pre-defined management constraints, the model defines the best hours (i.e., the hours with high energy prices) to produce electricity. To highlight the link between forecasts quality and their economic value, we built several synthetic ensemble forecasts based on observed streamflow time series. These inputs are generated in a controlled environment in order to obtain forecasts of different quality in terms of accuracy and reliability. These forecasts are used to assess the sensitivity of the decision model to forecast quality

Impact of seasonal forecast use on agricultural income in a system with varying crop costs and returns: an empirically-grounded simulation

Science.gov (United States)

Gunda, T.; Bazuin, J. T.; Nay, J.; Yeung, K. L.

2017-03-01

Access to seasonal climate forecasts can benefit farmers by allowing them to make more informed decisions about their farming practices. However, it is unclear whether farmers realize these benefits when crop choices available to farmers have different and variable costs and returns; multiple countries have programs that incentivize production of certain crops while other crops are subject to market fluctuations. We hypothesize that the benefits of forecasts on farmer livelihoods will be moderated by the combined impact of differing crop economics and changing climate. Drawing upon methods and insights from both physical and social sciences, we develop a model of farmer decision-making to evaluate this hypothesis. The model dynamics are explored using empirical data from Sri Lanka; primary sources include survey and interview information as well as game-based experiments conducted with farmers in the field. Our simulations show that a farmer using seasonal forecasts has more diversified crop selections, which drive increases in average agricultural income. Increases in income are particularly notable under a drier climate scenario, when a farmer using seasonal forecasts is more likely to plant onions, a crop with higher possible returns. Our results indicate that, when water resources are scarce (i.e. drier climate scenario), farmer incomes could become stratified, potentially compounding existing disparities in farmers’ financial and technical abilities to use forecasts to inform their crop selections. This analysis highlights that while programs that promote production of certain crops may ensure food security in the short-term, the long-term implications of these dynamics need careful evaluation.

Seasonal Forecast Skill And Teleconnections Over East Africa

Science.gov (United States)

MacLeod, D.; Palmer, T.

2017-12-01

Many people living in East Africa are significantly exposed to risks arising from climate variability. The region experiences two rainy seasons and poor performance of either or both of these (such as seen recently in 2016/17) reduces agricultural productivity and threatens food security. In combination with other factors this can lead to famine. By utilizing seasonal climate forecasts, preparatory actions can be taken in order to mitigate the risks arising from such climate variability. As part of the project ForPAc: "Towards forecast-based preparedness action", we are working with humanitarian agencies in Kenya to build such early warning systems on subseasonal-to-seasonal timescales. Here, the seasonal predictability and forecast skill of the two East African rainy seasons will be presented. Results from the new ECMWF operational forecasting system SEAS5 will be shown and compared to the previous System 4. Analysis of a new 110 year long atmosphere-only simulation will also be discussed, demonstrating impacts of atmosphere-ocean coupling as well as putting operational forecast skill in a long-term context. Particular focus will be given to the model representation of teleconnections of seasonal climate with global sea surface temperatures; highlighting sources of forecast error and informing future model development.

Impact of Healthcare Information Technology on Nursing Practice.

Science.gov (United States)

Piscotty, Ronald J; Kalisch, Beatrice; Gracey-Thomas, Angel

2015-07-01

To report additional mediation findings from a descriptive cross sectional study to examine if nurses' perceptions of the impact of healthcare information technology on their practice mediates the relationship between electronic nursing care reminder use and missed nursing care. The study used a descriptive design. The sample (N = 165) was composed of registered nurses working on acute care hospital units. The sample was obtained from a large teaching hospital in Southeast Michigan in the fall of 2012. All eligible nursing units (n = 19) were included. The MISSCARE Survey, Nursing Care Reminders Usage Survey, and the Impact of Healthcare Information Technology Scale were used to collect data to test for mediation. Mediation was tested using the method described by Baron and Kenny. Multiple regression equations were used to analyze the data to determine if mediation occurred between the variables. Missed nursing care, the outcome variable, was regressed on the predictor variable, reminder usage, and the mediator variable impact of technology on nursing practice. The impact of healthcare information technology (IHIT) on nursing practice negatively affected missed nursing care (t = -4.12, p information technology mediates the relationship between nursing care reminder use and missed nursing care. The findings are beneficial to the advancement of healthcare technology in that designers of healthcare information technology systems need to keep in mind that perceptions regarding impacts of the technology will influence usage. Many times, information technology systems are not designed to match the workflow of nurses. Systems built with redundant or impertinent reminders may be ignored. System designers must study which reminders nurses find most useful and which reminders result in the best quality outcomes. © 2015 Sigma Theta Tau International.

Wind power forecasting accuracy and uncertainty in Finland

Energy Technology Data Exchange (ETDEWEB)

Holttinen, H.; Miettinen, J.; Sillanpaeae, S.

2013-04-15

Wind power cannot be dispatched so the production levels need to be forecasted for electricity market trading. Lower prediction errors mean lower regulation balancing costs, since relatively less energy needs to go through balance settlement. From the power system operator point of view, wind power forecast errors will impact the system net imbalances when the share of wind power increases, and more accurate forecasts mean less regulating capacity will be activated from the real time Regulating Power Market. In this publication short term forecasting of wind power is studied mainly from a wind power producer point of view. The forecast errors and imbalance costs from the day-ahead Nordic electricity markets are calculated based on real data from distributed wind power plants. Improvements to forecasting accuracy are presented using several wind forecast providers, and measures for uncertainty of the forecast are presented. Aggregation of sites lowers relative share of prediction errors considerably, up to 60%. The balancing costs were also reduced up to 60%, from 3 euro/MWh for one site to 1-1.4 euro/MWh to aggregate 24 sites. Pooling wind power production for balance settlement will be very beneficial, and larger producers who can have sites from larger geographical area will benefit in lower imbalance costs. The aggregation benefits were already significant for smaller areas, resulting in 30-40% decrease in forecast errors and 13-36% decrease in unit balancing costs, depending on the year. The resulting costs are strongly dependent on Regulating Market prices that determine the prices for the imbalances. Similar level of forecast errors resulted in 40% higher imbalance costs for 2012 compared with 2011. Combining wind forecasts from different Numerical Weather Prediction providers was studied with different combination methods for 6 sites. Averaging different providers' forecasts will lower the forecast errors by 6% for day-ahead purposes. When combining

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

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

CSIR Technology Impact 2001

CSIR Research Space (South Africa)

CSIR

2001-01-01

Full Text Available This issue of Technology Impact offers a brief snapshot of CSIR activities during the year under review (1 March 2000 - 28 February 2001) by highlighting a number of innovative projects and initiatives in these areas. It present a rich canvas...

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.

Adapting a Markov Monte Carlo simulation model for forecasting the number of Coronary Artery Revascularisation Procedures in an era of rapidly changing technology and policy

Directory of Open Access Journals (Sweden)

Knuiman Matthew

2008-06-01

Full Text Available Abstract Background Treatments for coronary heart disease (CHD have evolved rapidly over the last 15 years with considerable change in the number and effectiveness of both medical and surgical treatments. This period has seen the rapid development and uptake of statin drugs and coronary artery revascularization procedures (CARPs that include Coronary Artery Bypass Graft procedures (CABGs and Percutaneous Coronary Interventions (PCIs. It is difficult in an era of such rapid change to accurately forecast requirements for treatment services such as CARPs. In a previous paper we have described and outlined the use of a Markov Monte Carlo simulation model for analyzing and predicting the requirements for CARPs for the population of Western Australia (Mannan et al, 2007. In this paper, we expand on the use of this model for forecasting CARPs in Western Australia with a focus on the lack of adequate performance of the (standard model for forecasting CARPs in a period during the mid 1990s when there were considerable changes to CARP technology and implementation policy and an exploration and demonstration of how the standard model may be adapted to achieve better performance. Methods Selected key CARP event model probabilities are modified based on information relating to changes in the effectiveness of CARPs from clinical trial evidence and an awareness of trends in policy and practice of CARPs. These modified model probabilities and the ones obtained by standard methods are used as inputs in our Markov simulation model. Results The projected numbers of CARPs in the population of Western Australia over 1995–99 only improve marginally when modifications to model probabilities are made to incorporate an increase in effectiveness of PCI procedures. However, the projected numbers improve substantially when, in addition, further modifications are incorporated that relate to the increased probability of a PCI procedure and the reduced probability of a CABG

Adapting a Markov Monte Carlo simulation model for forecasting the number of coronary artery revascularisation procedures in an era of rapidly changing technology and policy.

Science.gov (United States)

Mannan, Haider R; Knuiman, Matthew; Hobbs, Michael

2008-06-25

Treatments for coronary heart disease (CHD) have evolved rapidly over the last 15 years with considerable change in the number and effectiveness of both medical and surgical treatments. This period has seen the rapid development and uptake of statin drugs and coronary artery revascularization procedures (CARPs) that include Coronary Artery Bypass Graft procedures (CABGs) and Percutaneous Coronary Interventions (PCIs). It is difficult in an era of such rapid change to accurately forecast requirements for treatment services such as CARPs. In a previous paper we have described and outlined the use of a Markov Monte Carlo simulation model for analyzing and predicting the requirements for CARPs for the population of Western Australia (Mannan et al, 2007). In this paper, we expand on the use of this model for forecasting CARPs in Western Australia with a focus on the lack of adequate performance of the (standard) model for forecasting CARPs in a period during the mid 1990s when there were considerable changes to CARP technology and implementation policy and an exploration and demonstration of how the standard model may be adapted to achieve better performance. Selected key CARP event model probabilities are modified based on information relating to changes in the effectiveness of CARPs from clinical trial evidence and an awareness of trends in policy and practice of CARPs. These modified model probabilities and the ones obtained by standard methods are used as inputs in our Markov simulation model. The projected numbers of CARPs in the population of Western Australia over 1995-99 only improve marginally when modifications to model probabilities are made to incorporate an increase in effectiveness of PCI procedures. However, the projected numbers improve substantially when, in addition, further modifications are incorporated that relate to the increased probability of a PCI procedure and the reduced probability of a CABG procedure stemming from changed CARP preference

A hierarchical bayesian model to quantify uncertainty of stream water temperature forecasts.

Directory of Open Access Journals (Sweden)

Guillaume Bal

Full Text Available Providing generic and cost effective modelling approaches to reconstruct and forecast freshwater temperature using predictors as air temperature and water discharge is a prerequisite to understanding ecological processes underlying the impact of water temperature and of global warming on continental aquatic ecosystems. Using air temperature as a simple linear predictor of water temperature can lead to significant bias in forecasts as it does not disentangle seasonality and long term trends in the signal. Here, we develop an alternative approach based on hierarchical Bayesian statistical time series modelling of water temperature, air temperature and water discharge using seasonal sinusoidal periodic signals and time varying means and amplitudes. Fitting and forecasting performances of this approach are compared with that of simple linear regression between water and air temperatures using i an emotive simulated example, ii application to three French coastal streams with contrasting bio-geographical conditions and sizes. The time series modelling approach better fit data and does not exhibit forecasting bias in long term trends contrary to the linear regression. This new model also allows for more accurate forecasts of water temperature than linear regression together with a fair assessment of the uncertainty around forecasting. Warming of water temperature forecast by our hierarchical Bayesian model was slower and more uncertain than that expected with the classical regression approach. These new forecasts are in a form that is readily usable in further ecological analyses and will allow weighting of outcomes from different scenarios to manage climate change impacts on freshwater wildlife.

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)

Market penetration of new energy technologies

Energy Technology Data Exchange (ETDEWEB)

Packey, D.J.

1993-02-01

This report examines the characteristics, advantages, disadvantages, and, for some, the mathematical formulas of forecasting methods that can be used to forecast the market penetration of renewable energy technologies. Among the methods studied are subjective estimation, market surveys, historical analogy models, cost models, diffusion models, time-series models, and econometric models. Some of these forecasting methods are more effective than others at different developmental stages of new technologies.

Increasing the temporal resolution of direct normal solar irradiance forecasted series

Science.gov (United States)

Fernández-Peruchena, Carlos M.; Gastón, Martin; Schroedter-Homscheidt, Marion; Marco, Isabel Martínez; Casado-Rubio, José L.; García-Moya, José Antonio

2017-06-01

A detailed knowledge of the solar resource is a critical point in the design and control of Concentrating Solar Power (CSP) plants. In particular, accurate forecasting of solar irradiance is essential for the efficient operation of solar thermal power plants, the management of energy markets, and the widespread implementation of this technology. Numerical weather prediction (NWP) models are commonly used for solar radiation forecasting. In the ECMWF deterministic forecasting system, all forecast parameters are commercially available worldwide at 3-hourly intervals. Unfortunately, as Direct Normal solar Irradiance (DNI) exhibits a great variability due to the dynamic effects of passing clouds, 3-h time resolution is insufficient for accurate simulations of CSP plants due to their nonlinear response to DNI, governed by various thermal inertias due to their complex response characteristics. DNI series of hourly or sub-hourly frequency resolution are normally used for an accurate modeling and analysis of transient processes in CSP technologies. In this context, the objective of this study is to propose a methodology for generating synthetic DNI time series at 1-h (or higher) temporal resolution from 3-h DNI series. The methodology is based upon patterns as being defined with help of the clear-sky envelope approach together with a forecast of maximum DNI value, and it has been validated with high quality measured DNI data.

Assessing flood forecast uncertainty with fuzzy arithmetic

Directory of Open Access Journals (Sweden)

de Bruyn Bertrand

2016-01-01

Full Text Available Providing forecasts for flow rates and water levels during floods have to be associated with uncertainty estimates. The forecast sources of uncertainty are plural. For hydrological forecasts (rainfall-runoff performed using a deterministic hydrological model with basic physics, two main sources can be identified. The first obvious source is the forcing data: rainfall forecast data are supplied in real time by meteorological forecasting services to the Flood Forecasting Service within a range between a lowest and a highest predicted discharge. These two values define an uncertainty interval for the rainfall variable provided on a given watershed. The second source of uncertainty is related to the complexity of the modeled system (the catchment impacted by the hydro-meteorological phenomenon, the number of variables that may describe the problem and their spatial and time variability. The model simplifies the system by reducing the number of variables to a few parameters. Thus it contains an intrinsic uncertainty. This model uncertainty is assessed by comparing simulated and observed rates for a large number of hydro-meteorological events. We propose a method based on fuzzy arithmetic to estimate the possible range of flow rates (and levels of water making a forecast based on possible rainfalls provided by forcing and uncertainty model. The model uncertainty is here expressed as a range of possible values. Both rainfall and model uncertainties are combined with fuzzy arithmetic. This method allows to evaluate the prediction uncertainty range. The Flood Forecasting Service of Oise and Aisne rivers, in particular, monitors the upstream watershed of the Oise at Hirson. This watershed’s area is 310 km2. Its response time is about 10 hours. Several hydrological models are calibrated for flood forecasting in this watershed and use the rainfall forecast. This method presents the advantage to be easily implemented. Moreover, it permits to be carried out

A GM (1, 1 Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature

Directory of Open Access Journals (Sweden)

Ning-bo Zhao

2014-01-01

Full Text Available Performance degradation forecast technology for quantitatively assessing degradation states of aeroengine using exhaust gas temperature is an important technology in the aeroengine health management. In this paper, a GM (1, 1 Markov chain-based approach is introduced to forecast exhaust gas temperature by taking the advantages of GM (1, 1 model in time series and the advantages of Markov chain model in dealing with highly nonlinear and stochastic data caused by uncertain factors. In this approach, firstly, the GM (1, 1 model is used to forecast the trend by using limited data samples. Then, Markov chain model is integrated into GM (1, 1 model in order to enhance the forecast performance, which can solve the influence of random fluctuation data on forecasting accuracy and achieving an accurate estimate of the nonlinear forecast. As an example, the historical monitoring data of exhaust gas temperature from CFM56 aeroengine of China Southern is used to verify the forecast performance of the GM (1, 1 Markov chain model. The results show that the GM (1, 1 Markov chain model is able to forecast exhaust gas temperature accurately, which can effectively reflect the random fluctuation characteristics of exhaust gas temperature changes over time.

Assimilation of extrapolated radar reflectivity into a NWP model and its impact on a precipitation forecast at high resolution

Czech Academy of Sciences Publication Activity Database

Sokol, Zbyněk

2011-01-01

Roč. 100, 2-3 (2011), s. 201-212 ISSN 0169-8095 R&D Projects: GA ČR GA205/07/0905; GA MŠk ME09033 Institutional research plan: CEZ:AV0Z30420517 Keywords : Precipitation forecast * Nowcasting * Assimilation of radar reflectivity * Numerical weather prediction * Convective storms Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.911, year: 2011 http://www.sciencedirect.com/science/article/pii/S0169809510002462

The technological singularity and exponential medicine

OpenAIRE

Iraj Nabipour; Majid Assadi

2016-01-01

The "technological singularity" is forecasted to occur in 2045. It is a point when non-biological intelligence becomes more intelligent than humans and each generation of intelligent machines re-designs itself smarter. Beyond this point, there is a symbiosis between machines and humans. This co-existence will produce incredible impacts on medicine that its sparkles could be seen in healthcare industry and the future medicine since 2025. Ray Kurzweil, the great futurist, suggested th...

Non-seismic tsunamis: filling the forecast gap

Science.gov (United States)

Moore, C. W.; Titov, V. V.; Spillane, M. C.

2015-12-01

Earthquakes are the generation mechanism in over 85% of tsunamis. However, non-seismic tsunamis, including those generated by meteorological events, landslides, volcanoes, and asteroid impacts, can inundate significant area and have a large far-field effect. The current National Oceanographic and Atmospheric Administration (NOAA) tsunami forecast system falls short in detecting these phenomena. This study attempts to classify the range of effects possible from these non-seismic threats, and to investigate detection methods appropriate for use in a forecast system. Typical observation platforms are assessed, including DART bottom pressure recorders and tide gauges. Other detection paths include atmospheric pressure anomaly algorithms for detecting meteotsunamis and the early identification of asteroids large enough to produce a regional hazard. Real-time assessment of observations for forecast use can provide guidance to mitigate the effects of a non-seismic tsunami.

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

Life cycle assessment of forecasting scenarios for urban water management: A first implementation of the WaLA model on Paris suburban area.

Science.gov (United States)

Loubet, Philippe; Roux, Philippe; Guérin-Schneider, Laetitia; Bellon-Maurel, Véronique

2016-03-01

A framework and an associated modeling tool to perform life cycle assessment (LCA) of urban water system, namely the WaLA model, has been recently developed. In this paper, the WaLA model is applied to a real case study: the urban water system of the Paris suburban area, in France. It aims to verify the capacity of the model to provide environmental insights to stakeholder's issues related to future trends influencing the system (e.g., evolution of water demand, increasing water scarcity) or policy responses (e.g., choices of water resources and technologies). This is achieved by evaluating a baseline scenario for 2012 and several forecasting scenarios for 2022 and 2050. The scenarios are designed through the modeling tool WaLA, which is implemented in Simulink/Matlab: it combines components representing the different technologies, users and resources of the UWS. The life cycle inventories of the technologies and users components include water quantity and quality changes, specific operation (electricity, chemicals) and infrastructures data (construction materials). The methods selected for the LCIA are midpoint ILCD, midpoint water deprivation impacts at the sub-river basin scale, and endpoint Impact 2002+. The results of the baseline scenario show that wastewater treatment plants have the highest impacts compared to drinking water production and distribution, as traditionally encountered in LCA of UWS. The results of the forecasting scenarios show important changes in water deprivation impacts due to water management choices or effects of climate change. They also enable to identify tradeoffs with other impact categories and to compare several scenarios. It suggests the capacity of the model to deliver information for decision making about future policies. Copyright © 2015 Elsevier Ltd. All rights reserved.

Benefits of seasonal forecasts of crop yields

Science.gov (United States)

Sakurai, G.; Okada, M.; Nishimori, M.; Yokozawa, M.

2017-12-01

Major factors behind recent fluctuations in food prices include increased biofuel production and oil price fluctuations. In addition, several extreme climate events that reduced worldwide food production coincided with upward spikes in food prices. The stabilization of crop yields is one of the most important tasks to stabilize food prices and thereby enhance food security. Recent development of technologies related to crop modeling and seasonal weather forecasting has made it possible to forecast future crop yields for maize and soybean. However, the effective use of these technologies remains limited. Here we present the potential benefits of seasonal crop-yield forecasts on a global scale for choice of planting day. For this purpose, we used a model (PRYSBI-2) that can well replicate past crop yields both for maize and soybean. This model system uses a Bayesian statistical approach to estimate the parameters of a basic process-based model of crop growth. The spatial variability of model parameters was considered by estimating the posterior distribution of the parameters from historical yield data by using the Markov-chain Monte Carlo (MCMC) method with a resolution of 1.125° × 1.125°. The posterior distributions of model parameters were estimated for each spatial grid with 30 000 MCMC steps of 10 chains each. By using this model and the estimated parameter distributions, we were able to estimate not only crop yield but also levels of associated uncertainty. We found that the global average crop yield increased about 30% as the result of the optimal selection of planting day and that the seasonal forecast of crop yield had a large benefit in and near the eastern part of Brazil and India for maize and the northern area of China for soybean. In these countries, the effects of El Niño and Indian Ocean dipole are large. The results highlight the importance of developing a system to forecast global crop yields.

Improving wave forecasting by integrating ensemble modelling and machine learning

Science.gov (United States)

O'Donncha, F.; Zhang, Y.; James, S. C.

2017-12-01

Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

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

Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model

Directory of Open Access Journals (Sweden)

José R. Andrade

2017-10-01

Full Text Available Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 €/MWh for day-ahead market and a maximum value of 2.53 €/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.

Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast

OpenAIRE

Chen, Junfei; Li, Ming; Wang, Weiguang

2012-01-01

Drought is part of natural climate variability and ranks the first natural disaster in the world. Drought forecasting plays an important role in mitigating impacts on agriculture and water resources. In this study, a drought forecast model based on the random forest method is proposed to predict the time series of monthly standardized precipitation index (SPI). We demonstrate model application by four stations in the Haihe river basin, China. The random-forest- (RF-) based forecast model has ...

Impact of data assimilation on ocean current forecasts in the Angola Basin

Science.gov (United States)

Phillipson, Luke; Toumi, Ralf

2017-06-01

The ocean current predictability in the data limited Angola Basin was investigated using the Regional Ocean Modelling System (ROMS) with four-dimensional variational data assimilation. Six experiments were undertaken comprising a baseline case of the assimilation of salinity/temperature profiles and satellite sea surface temperature, with the subsequent addition of altimetry, OSCAR (satellite-derived sea surface currents), drifters, altimetry and drifters combined, and OSCAR and drifters combined. The addition of drifters significantly improves Lagrangian predictability in comparison to the baseline case as well as the addition of either altimetry or OSCAR. OSCAR assimilation only improves Lagrangian predictability as much as altimetry assimilation. On average the assimilation of either altimetry or OSCAR with drifter velocities does not significantly improve Lagrangian predictability compared to the drifter assimilation alone, even degrading predictability in some cases. When the forecast current speed is large, it is more likely that the combination improves trajectory forecasts. Conversely, when the currents are weaker, it is more likely that the combination degrades the trajectory forecast.

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.

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.

High Resolution Trajectory-Based Smoke Forecasts Using VIIRS Aerosol Optical Depth and NUCAPS Carbon Monoxide Retrievals

Science.gov (United States)

Pierce, R. B.; Smith, N.; Barnet, C.; Barnet, C. D.; Kondragunta, S.; Davies, J. E.; Strabala, K.

2016-12-01

We use Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Aerosol Optical Depth (AOD) and combined Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) NOAA-Unique CrIS-ATMS Processing System (NUCAPS) carbon monoxide (CO) retrievals to initialize trajectory-based, high spatial resolution North American smoke dispersion forecasts during the May 2016 Fort McMurray wildfire in northern Alberta and the July 2016 Soberanes Fire in Northern California. These two case studies illustrate how long range transport of wild fire smoke can adversely impact surface air quality thousands of kilometers downwind and how local topographic flow can lead to complex transport patterns near the wildfire source region. The NUCAPS CO retrievals are shown to complement the high resolution VIIRS AOD retrievals by providing retrievals in partially cloudy scenes and also providing information on the vertical distribution of the wildfire smoke. This work addresses the need for low latency, web-based, high resolution forecasts of smoke dispersion for use by NWS Incident Meteorologists (IMET) to support on-site decision support services for fire incident management teams. The primary user community for the IDEA-I smoke forecasts is the Western regions of the NWS and US EPA due to the significant impacts of wildfires in these regions. Secondary users include Alaskan NWS offices and Western State and Local air quality management agencies such as the Western Regional Air Partnership (WRAP).

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.

Scheduled Operation of PV Power Station Considering Solar Radiation Forecast Error

Science.gov (United States)

Takayama, Satoshi; Hara, Ryoichi; Kita, Hiroyuki; Ito, Takamitsu; Ueda, Yoshinobu; Saito, Yutaka; Takitani, Katsuyuki; Yamaguchi, Koji

Massive penetration of photovoltaic generation (PV) power stations may cause some serious impacts on a power system operation due to their volatile and unpredictable output. Growth of uncertainty may require larger operating reserve capacity and regulating capacity. Therefore, in order to utilize a PV power station as an alternative for an existing power plant, improvement in controllability and adjustability of station output become very important factor. Purpose of this paper is to develop the scheduled operation technique using a battery system (NAS battery) and the meteorological forecast. The performance of scheduled operation strongly depends on the accuracy of solar radiation forecast. However, the solar radiation forecast contains error. This paper proposes scheduling method and rescheduling method considering the trend of forecast error. More specifically, the forecast error scenario is modeled by means of the clustering analysis of the past actual forecast error. Validity and effectiveness of the proposed method is ascertained through computational simulations using the actual PV generation data monitored at the Wakkanai PV power station and solar radiation forecast data provided by the Japan Weather Association.

Biometeorological Forecasts as Adaptation Measures in the Contest of Climate Change

Directory of Open Access Journals (Sweden)

Ieva Nariūnaitė

2011-04-01

Full Text Available The basic aim of this paper is to review biometeorological forecasts in Lithuania as well as their necessities and adjustments toward climate change. Weather alterations and climate anomalies make a substantial impact on human health (physical and psychological, and therefore biometeorological forecasts are useful to foresee and elude some negative climate influence. Article in Lithuanian

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

Short-term load forecasting of power system

Science.gov (United States)

Xu, Xiaobin

2017-05-01

In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model.

Forecasting and management of technology

National Research Council Canada - National Science Library

Roper, A. T

2011-01-01

... what the authors see as the innovations to technology management in the last 17 years: the Internet; the greater focus on group decision-making including process management and mechanism design; and desktop software that has transformed the analytical capabilities of technology managers"--Provided by publisher.

Student pharmacists' use and perceived impact of educational technologies.

Science.gov (United States)

Stolte, Scott K; Richard, Craig; Rahman, Ateequr; Kidd, Robert S

2011-06-10

To assess the frequency of use by and perceived impact of various educational technologies on student pharmacists. Data were obtained using a validated, Web-based survey instrument designed to evaluate the frequency of use and impact on learning of various technologies used in educating first-, second-, and third-year student pharmacists. Basic demographic data also were collected and analyzed. The majority (89.4%) of the 179 respondents were comfortable with the technology used in the academic program. The most frequently used technologies for educational purposes were in class electronic presentations, course materials posted on the school Web site, and e-mail. The technologies cited as having the most beneficial impact on learning were course materials posted on the Web site and in-class electronic presentations, and those cited as most detrimental were video-teleconferencing and online testing. Compared to the course textbook, students reported more frequent use of technologies such as electronic course materials, presentations, digital lecture recordings, e-mail, and hand-held devices. Because students' opinions of educational technologies varied, colleges and schools should incorporate educational technologies that students frequently use and that positively impact learning.

Waste Information Management System with Integrated Transportation Forecast Data

International Nuclear Information System (INIS)

Upadhyay, H.; Quintero, W.; Shoffner, P.; Lagos, L.

2009-01-01

The Waste Information Management System with Integrated Transportation Forecast Data was developed to support the Department of Energy (DOE) mandated accelerated cleanup program. The schedule compression required close coordination and a comprehensive review and prioritization of the barriers that impeded treatment and disposition of the waste streams at each site. Many issues related to site waste treatment and disposal were potential critical path issues under the accelerated schedules. In order to facilitate accelerated cleanup initiatives, waste managers at DOE field sites and at DOE Headquarters in Washington, D.C., needed timely waste forecast and transportation information regarding the volumes and types of waste that would be generated by the DOE sites over the next 40 years. Each local DOE site has historically collected, organized, and displayed site waste forecast information in separate and unique systems. However, waste and shipment information from all sites needed a common application to allow interested parties to understand and view the complete complex-wide picture. The Waste Information Management System with Integrated Transportation Forecast Data allows identification of total forecasted waste volumes, material classes, disposition sites, choke points, technological or regulatory barriers to treatment and disposal, along with forecasted waste transportation information by rail, truck and inter-modal shipments. The Applied Research Center (ARC) at Florida International University (FIU) in Miami, Florida, has deployed the web-based forecast and transportation system and is responsible for updating the waste forecast and transportation data on a regular basis to ensure the long-term viability and value of this system. (authors)

Forecasting the combined effects of urbanization and climate change on stream ecosystems: from impacts to management options

Science.gov (United States)

Nelson, Kären C.; Palmer, Margaret A.; Pizzuto, James E.; Moglen, Glenn E.; Angermeier, Paul L.; Hilderbrand, Robert H.; Dettinger, Mike; Hayhoe, Katharine

2015-01-01

Streams collect runoff, heat, and sediment from their watersheds, making them highly vulnerable to anthropogenic disturbances such as urbanization and climate change. Forecasting the effects of these disturbances using process-based models is critical to identifying the form and magnitude of likely impacts. Here, we integrate a new biotic model with four previously developed physical models (downscaled climate projections, stream hydrology, geomorphology, and water temperature) to predict how stream fish growth and reproduction will most probably respond to shifts in climate and urbanization over the next several decades.

Ecological forecasting under climate change: the case of Baltic cod

DEFF Research Database (Denmark)

Lindegren, Martin; Möllmann, Christian; Nielsen, Anders

2010-01-01

Good decision making for fisheries and marine ecosystems requires a capacity to anticipate the consequences of management under different scenarios of climate change. The necessary ecological forecasting calls for ecosystem-based models capable of integrating multiple drivers across trophic levels...... and properly including uncertainty. The methodology presented here assesses the combined impacts of climate and fishing on marine food-web dynamics and provides estimates of the confidence envelope of the forecasts. It is applied to cod (Gadus morhua) in the Baltic Sea, which is vulnerable to climate......-related decline in salinity owing to both direct and indirect effects (i.e. through species interactions) on early-life survival. A stochastic food web-model driven by regional climate scenarios is used to produce quantitative forecasts of cod dynamics in the twenty-first century. The forecasts show how...

Loss aversion is an affective forecasting error.

Science.gov (United States)

Kermer, Deborah A; Driver-Linn, Erin; Wilson, Timothy D; Gilbert, Daniel T

2006-08-01

Loss aversion occurs because people expect losses to have greater hedonic impact than gains of equal magnitude. In two studies, people predicted that losses in a gambling task would have greater hedonic impact than would gains of equal magnitude, but when people actually gambled, losses did not have as much of an emotional impact as they predicted. People overestimated the hedonic impact of losses because they underestimated their tendency to rationalize losses and overestimated their tendency to dwell on losses. The asymmetrical impact of losses and gains was thus more a property of affective forecasts than a property of affective experience.

Characterizing Agricultural Impacts of Recent Large-Scale US Droughts and Changing Technology and Management

Science.gov (United States)

Elliott, Joshua; Glotter, Michael; Ruane, Alex C.; Boote, Kenneth J.; Hatfield, Jerry L.; Jones, James W.; Rosenzweig, Cynthia; Smith, Leonard A.; Foster, Ian

2017-01-01

Process-based agricultural models, applied in novel ways, can reproduce historical crop yield anomalies in the US, with median absolute deviation from observations of 6.7% at national-level and 11% at state-level. In seasons for which drought is the overriding factor, performance is further improved. Historical counterfactual scenarios for the 1988 and 2012 droughts show that changes in agricultural technologies and management have reduced system-level drought sensitivity in US maize production by about 25% in the intervening years. Finally, we estimate the economic costs of the two droughts in terms of insured and uninsured crop losses in each US county (for a total, adjusted for inflation, of $9 billion in 1988 and $21.6 billion in 2012). We compare these with cost estimates from the counterfactual scenarios and with crop indemnity data where available. Model based measures are capable of accurately reproducing the direct agro-economic losses associated with extreme drought and can be used to characterize and compare events that occurred under very different conditions. This work suggests new approaches to modeling, monitoring, forecasting, and evaluating drought impacts on agriculture, as well as evaluating technological changes to inform adaptation strategies for future climate change and extreme events.

Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management

Energy Technology Data Exchange (ETDEWEB)

Elliott, Joshua [Univ. of Chicago, IL (United States). Computation Inst.; Argonne National Lab. (ANL), Lemont, IL (United States); Glotter, Michael [Univ. of Chicago, IL (United States). Dept. of the Geophysical Sciences; Ruane, Alex C. [NASA Goddard Inst. for Space Studies (GISS), New York, NY (United States); Boote, Kenneth J. [Univ. of Florida, Gainesville, FL (United States). Agricultural and Biological Engineering Dept.; Hatfield, Jerry L. [US Dept. of Agriculture (USDA)., Ames, IA (United States). National Lab. for Agriculture and the Environment; Jones, James W. [Univ. of Florida, Gainesville, FL (United States). Agricultural and Biological Engineering Dept.; Rosenzweig, Cynthia [NASA Goddard Inst. for Space Studies (GISS), New York, NY (United States); Smith, Leonard A. [London School of Economics, London (United Kingdom). Center for Analysis of Time Series; Foster, Ian [Univ. of Chicago, IL (United States). Computation Inst.; Computation Inst.; Argonne National Lab. (ANL), Lemont, IL (United States)

2018-01-01

Process-based agricultural models, applied in novel ways, can reproduce historical crop yield anomalies in the US, with median absolute deviation from observations of 6.7% at national-level and 11% at state-level. In seasons for which drought is the overriding factor, performance is further improved. Historical counterfactual scenarios for the 1988 and 2012 droughts show that changes in agricultural technologies and management have reduced system-level drought sensitivity in US maize production by about 25% in the intervening years. Finally, we estimate the economic costs of the two droughts in terms of insured and uninsured crop losses in each US county (for a total, adjusted for inflation, of $9 billion in 1988 and $21.6 billion in 2012). We compare these with cost estimates from the counterfactual scenarios and with crop indemnity data where available. Model-based measures are capable of accurately reproducing the direct agro-economic losses associated with extreme drought and can be used to characterize and compare events that occurred under very different conditions. This work suggests new approaches to modeling, monitoring, forecasting, and evaluating drought impacts on agriculture, as well as evaluating technological changes to inform adaptation strategies for future climate change and extreme events.

Multiple-Decrement Compositional Forecasting with the Lee-Carter Model

OpenAIRE

Guan, Tianyu

2014-01-01

Changes in cause of death patterns have a great impact on health and social care costs paid by government and insurance companies. Unfortunately an overwhelming majority of methods for mortality projections is based on overall mortality with only very few studies focusing on forecasting cause-specific mortality. In this project, our aim is to forecast cause-specific death density with a coherent model. Since cause-specific death density obeys a unit sum constraint, it can be considered as com...

The Impact of Experience and Technology Change on Task-Technology Fit of a Collaborative Technology

Science.gov (United States)

Iversen, Jakob H.; Eierman, Michael A.

2018-01-01

This study continues a long running effort to examine collaborative writing and editing tools and the factors that impact Task-Technology Fit and Technology Acceptance. Previous studies found that MS Word/email performed better than technologies such as Twiki, Google Docs, and Office Live. The current study seeks to examine specifically the impact…

Modeling and forecasting crude oil markets using ARCH-type models

International Nuclear Information System (INIS)

Cheong, Chin Wen

2009-01-01

This study investigates the time-varying volatility of two major crude oil markets, the West Texas Intermediate (WTI) and Europe Brent. A flexible autoregressive conditional heteroskedasticity (ARCH) model is used to take into account the stylized volatility facts such as clustering volatility, asymmetric news impact and long memory volatility among others. The empirical results indicate that the intensity of long-persistence volatility in the WTI is greater than in the Brent. It is also found that for the WTI, the appreciation and depreciation shocks of the WTI have similar impact on the resulting volatility. However, a leverage effect is found in Brent. Although both the estimation and diagnostic evaluations are in favor of an asymmetric long memory ARCH model, only the WTI models provide superior in the out-of-sample forecasts. On the other hand, from the empirical out-of-sample forecasts, it appears that the simplest parsimonious generalized ARCH provides the best forecasted evaluations for the Brent crude oil data.

Modeling and forecasting crude oil markets using ARCH-type models

Energy Technology Data Exchange (ETDEWEB)

Cheong, Chin Wen [Research Centre of Mathematical Sciences, Faculty of Information Technology, Multimedia University, 63100 Cyberjaya, Selangor (Malaysia)

2009-06-15

This study investigates the time-varying volatility of two major crude oil markets, the West Texas Intermediate (WTI) and Europe Brent. A flexible autoregressive conditional heteroskedasticity (ARCH) model is used to take into account the stylized volatility facts such as clustering volatility, asymmetric news impact and long memory volatility among others. The empirical results indicate that the intensity of long-persistence volatility in the WTI is greater than in the Brent. It is also found that for the WTI, the appreciation and depreciation shocks of the WTI have similar impact on the resulting volatility. However, a leverage effect is found in Brent. Although both the estimation and diagnostic evaluations are in favor of an asymmetric long memory ARCH model, only the WTI models provide superior in the out-of-sample forecasts. On the other hand, from the empirical out-of-sample forecasts, it appears that the simplest parsimonious generalized ARCH provides the best forecasted evaluations for the Brent crude oil data. (author)

Evaluating Forecasting Models for Unemployment Rates by Gender in Selected European Countries

Directory of Open Access Journals (Sweden)

Ksenija Dumičić

2017-03-01

Full Text Available The unemployment can be considered as one of the main economic problems. The aim of this article is to examine the differences in male and female unemployment rates in selected European countries and to predict their future trends by using different statistical forecasting models. Furthermore, the impact of adding a new data point on the selection of the most appropriate statistical forecasting model and on the overall forecasting errors values is also evaluated. Male and female unemployment rates are observed for twelve European countries in the period from 1991 to 2014. Four statistical forecasting models have been selected and applied and the most appropriate model is considered to be the one with the lowest overall forecasting errors values. The analysis has shown that in the period from 1991 to 2014 the decreasing trend of unemployment rates in the short-run is forecasted for more Eastern Balkan than the EU-28 countries. An additional data point for male and female unemployment rates in 2014 led to somewhat smaller forecasting errors in more than half of the observed countries. However, the additional data point does not necessarily improve forecasting performances of the used statistical forecasting models.

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint

Energy Technology Data Exchange (ETDEWEB)

Wang, Qin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cui, Mingjian [Univ. of Texas-Dallas, Richardson, TX (United States); Feng, Cong [Univ. of Texas-Dallas, Richardson, TX (United States); Wang, Zhenke [Univ. of Texas-Dallas, Richardson, TX (United States); Zhang, Jie [Univ. of Texas-Dallas, Richardson, TX (United States)

2017-08-31

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.

Testing an innovative framework for flood forecasting, monitoring and mapping in Europe

Science.gov (United States)

Dottori, Francesco; Kalas, Milan; Lorini, Valerio; Wania, Annett; Pappenberger, Florian; Salamon, Peter; Ramos, Maria Helena; Cloke, Hannah; Castillo, Carlos

2017-04-01

Between May and June 2016, France was hit by severe floods, particularly in the Loire and Seine river basins. In this work, we use this case study to test an innovative framework for flood forecasting, mapping and monitoring. More in detail, the system integrates in real-time two components of the Copernicus Emergency mapping services, namely the European Flood Awareness System and the satellite-based Rapid Mapping, with new procedures for rapid risk assessment and social media and news monitoring. We explore in detail the performance of each component of the system, demonstrating the improvements in respect to stand-alone flood forecasting and monitoring systems. We show how the performances of the forecasting component can be refined using the real-time feedback from social media monitoring to identify which areas were flooded, to evaluate the flood intensity, and therefore to correct impact estimations. Moreover, we show how the integration with impact forecast and social media monitoring can improve the timeliness and efficiency of satellite based emergency mapping, and reduce the chances of missing areas where flooding is already happening. These results illustrate how the new integrated approach leads to a better and earlier decision making and a timely evaluation of impacts.

A Condition Based Maintenance Approach to Forecasting B-1 Aircraft Parts

Science.gov (United States)

2017-03-23

Air Force Institute of Technology AFIT Scholar Theses and Dissertations 3-23-2017 A Condition Based Maintenance Approach to Forecasting B-1 Aircraft...component’s life history where reliability forecasts could be stipulated based on a component’s current condition . One of the major issues their report noted...Engine Condition Monitoring System Specification. Contract Number DOT-CG-80513-A. Grand Prairie, TX. Air Force Materiel Command. (2011) Requirements For

Technology data characterizing water heating in commercial buildings: Application to end-use forecasting

Energy Technology Data Exchange (ETDEWEB)

Sezgen, O.; Koomey, J.G.

1995-12-01

Commercial-sector conservation analyses have traditionally focused on lighting and space conditioning because of their relatively-large shares of electricity and fuel consumption in commercial buildings. In this report we focus on water heating, which is one of the neglected end uses in the commercial sector. The share of the water-heating end use in commercial-sector electricity consumption is 3%, which corresponds to 0.3 quadrillion Btu (quads) of primary energy consumption. Water heating accounts for 15% of commercial-sector fuel use, which corresponds to 1.6 quads of primary energy consumption. Although smaller in absolute size than the savings associated with lighting and space conditioning, the potential cost-effective energy savings from water heaters are large enough in percentage terms to warrant closer attention. In addition, water heating is much more important in particular building types than in the commercial sector as a whole. Fuel consumption for water heating is highest in lodging establishments, hospitals, and restaurants (0.27, 0.22, and 0.19 quads, respectively); water heating`s share of fuel consumption for these building types is 35%, 18% and 32%, respectively. At the Lawrence Berkeley National Laboratory, we have developed and refined a base-year data set characterizing water heating technologies in commercial buildings as well as a modeling framework. We present the data and modeling framework in this report. The present commercial floorstock is characterized in terms of water heating requirements and technology saturations. Cost-efficiency data for water heating technologies are also developed. These data are intended to support models used for forecasting energy use of water heating in the commercial sector.

Forecasting Container Throughput at the Doraleh Port in Djibouti through Time Series Analysis

Science.gov (United States)

Mohamed Ismael, Hawa; Vandyck, George Kobina

The Doraleh Container Terminal (DCT) located in Djibouti has been noted as the most technologically advanced container terminal on the African continent. DCT's strategic location at the crossroads of the main shipping lanes connecting Asia, Africa and Europe put it in a unique position to provide important shipping services to vessels plying that route. This paper aims to forecast container throughput through the Doraleh Container Port in Djibouti by Time Series Analysis. A selection of univariate forecasting models has been used, namely Triple Exponential Smoothing Model, Grey Model and Linear Regression Model. By utilizing the above three models and their combination, the forecast of container throughput through the Doraleh port was realized. A comparison of the different forecasting results of the three models, in addition to the combination forecast is then undertaken, based on commonly used evaluation criteria Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The study found that the Linear Regression forecasting Model was the best prediction method for forecasting the container throughput, since its forecast error was the least. Based on the regression model, a ten (10) year forecast for container throughput at DCT has been made.

Multi-parametric variational data assimilation for hydrological forecasting

Science.gov (United States)

Alvarado-Montero, R.; Schwanenberg, D.; Krahe, P.; Helmke, P.; Klein, B.

2017-12-01

Ensemble forecasting is increasingly applied in flow forecasting systems to provide users with a better understanding of forecast uncertainty and consequently to take better-informed decisions. A common practice in probabilistic streamflow forecasting is to force deterministic hydrological model with an ensemble of numerical weather predictions. This approach aims at the representation of meteorological uncertainty but neglects uncertainty of the hydrological model as well as its initial conditions. Complementary approaches use probabilistic data assimilation techniques to receive a variety of initial states or represent model uncertainty by model pools instead of single deterministic models. This paper introduces a novel approach that extends a variational data assimilation based on Moving Horizon Estimation to enable the assimilation of observations into multi-parametric model pools. It results in a probabilistic estimate of initial model states that takes into account the parametric model uncertainty in the data assimilation. The assimilation technique is applied to the uppermost area of River Main in Germany. We use different parametric pools, each of them with five parameter sets, to assimilate streamflow data, as well as remotely sensed data from the H-SAF project. We assess the impact of the assimilation in the lead time performance of perfect forecasts (i.e. observed data as forcing variables) as well as deterministic and probabilistic forecasts from ECMWF. The multi-parametric assimilation shows an improvement of up to 23% for CRPS performance and approximately 20% in Brier Skill Scores with respect to the deterministic approach. It also improves the skill of the forecast in terms of rank histogram and produces a narrower ensemble spread.

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.

Forecasting electric demand of distribution system planing in rural and sparsely populated regions

Energy Technology Data Exchange (ETDEWEB)

Willis, H.L.; Buri, M.J. [ABB Automated Distribution Div., Raleigh, NC (United States); Finley, L.A. [Snohomish County PUD, Everett, WA (United States)

1995-11-01

Modern computerized distribution load forecasting methods, although accurate when applied to urban areas, give somewhat less satisfactory results when forecasting load growth in sparsely populated rural areas. This paper examines the differences between rural and urban load growth histories, identifying a major difference in the observed behavior of load growth. This difference is exploited in a new simulation forecasting algorithm. Tests show the new method is as accurate in forecasting rural load growth and as useful for analyzing DSM impacts than past methods, while requiring considerably lower computer resources and data than other simulation methods of comparable accuracy.

Macroeconomic impact of the Solar Thermal Electricity Industry in Spain

Energy Technology Data Exchange (ETDEWEB)

NONE

2011-10-15

In the last three years, Solar Thermal Electricity (STE) in Spain has grown significantly. Its weight within the renewables mix is becoming relevant, and even more so, its impact on economics, society, the environment, and reducing energy dependence. This report was carried out by Deloitte for Protermosolar to quantitatively and qualitatively evaluate the main macroeconomic variables derived from the development of this technology in Spain from 2008 to 2010, and forecast its possible future impact.

Verification of surface minimum, mean, and maximum temperature forecasts in Calabria for summer 2008

Directory of Open Access Journals (Sweden)

S. Federico

2011-02-01

Full Text Available Since 2005, one-hour temperature forecasts for the Calabria region (southern Italy, modelled by the Regional Atmospheric Modeling System (RAMS, have been issued by CRATI/ISAC-CNR (Consortium for Research and Application of Innovative Technologies/Institute for Atmospheric and Climate Sciences of the National Research Council and are available online at http://meteo.crati.it/previsioni.html (every six hours. Beginning in June 2008, the horizontal resolution was enhanced to 2.5 km. In the present paper, forecast skill and accuracy are evaluated out to four days for the 2008 summer season (from 6 June to 30 September, 112 runs. For this purpose, gridded high horizontal resolution forecasts of minimum, mean, and maximum temperatures are evaluated against gridded analyses at the same horizontal resolution (2.5 km.

Gridded analysis is based on Optimal Interpolation (OI and uses the RAMS first-day temperature forecast as the background field. Observations from 87 thermometers are used in the analysis system. The analysis error is introduced to quantify the effect of using the RAMS first-day forecast as the background field in the OI analyses and to define the forecast error unambiguously, while spatial interpolation (SI analysis is considered to quantify the statistics' sensitivity to the verifying analysis and to show the quality of the OI analyses for different background fields.

Two case studies, the first one with a low (less than the 10th percentile root mean square error (RMSE in the OI analysis, the second with the largest RMSE of the whole period in the OI analysis, are discussed to show the forecast performance under two different conditions. Cumulative statistics are used to quantify forecast errors out to four days. Results show that maximum temperature has the largest RMSE, while minimum and mean temperature errors are similar. For the period considered

Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting

Science.gov (United States)

Badrzadeh, Honey; Sarukkalige, Ranjan; Jayawardena, A. W.

2013-12-01

Discrete wavelet transform was applied to decomposed ANN and ANFIS inputs.Novel approach of WNF with subtractive clustering applied for flow forecasting.Forecasting was performed in 1-5 step ahead, using multi-variate inputs.Forecasting accuracy of peak values and longer lead-time significantly improved.

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...

Enhancing COSMO-DE ensemble forecasts by inexpensive techniques

Directory of Open Access Journals (Sweden)

Zied Ben Bouallègue

2013-02-01

Full Text Available COSMO-DE-EPS, a convection-permitting ensemble prediction system based on the high-resolution numerical weather prediction model COSMO-DE, is pre-operational since December 2010, providing probabilistic forecasts which cover Germany. This ensemble system comprises 20 members based on variations of the lateral boundary conditions, the physics parameterizations and the initial conditions. In order to increase the sample size in a computationally inexpensive way, COSMO-DE-EPS is combined with alternative ensemble techniques: the neighborhood method and the time-lagged approach. Their impact on the quality of the resulting probabilistic forecasts is assessed. Objective verification is performed over a six months period, scores based on the Brier score and its decomposition are shown for June 2011. The combination of the ensemble system with the alternative approaches improves probabilistic forecasts of precipitation in particular for high precipitation thresholds. Moreover, combining COSMO-DE-EPS with only the time-lagged approach improves the skill of area probabilities for precipitation and does not deteriorate the skill of 2 m-temperature and wind gusts forecasts.

Forecasting short-term wind farm production in complex terrain. Volume 1

International Nuclear Information System (INIS)

LeBlanc, M.

2005-01-01

Wind energy forecasting adds financial value to wind farms and may soon become a regulatory requirement. A robust information technology system is essential for addressing industry demands. Various forecasting methodologies for short-term wind production in complex terrain were presented. Numerical weather predictions were discussed with reference to supervisory control and data acquisition (SCADA) system site measurements. Forecasting methods using wind speed, direction, temperature and pressure, as well as issues concerning statistical modelling were presented. Model output statistics and neural networks were reviewed, as well as significant components of error. Results from a Garrad Hassan forecaster with a European wind farm were presented, including wind speed evaluation, and forecast horizon for T + 1 hours, T + 12 hours, and T + 36 hours. It was suggested that buy prices often reflect the cost of under-prediction, and that forecasting has more potential where the spread is greatest. Accurate T + 19 hours to T + 31 hours could enable participation in the day-ahead market, which is less volatile and prices are usually better. Estimates of possible profits per annum through the use of GH forecaster power predictions were presented, calculated over and above spilling power to the grid. It was concluded that accurate forecasts combined with certainty evaluation enables the optimization of wind energy in the market, and is applicable to a wide range of weather regimes and terrain types. It was suggested that site feedback is essential for good forecasts at short horizons, and that the value of forecasting is dependent on the market. refs., tabs., figs

Extended analysis on impact of nuclear technology

International Nuclear Information System (INIS)

Ainul Hayati Daud; Hazmimi Kasim

2010-01-01

This chapter discusses a number of economic, social and knowledge impacts of the applications of nuclear technology in Malaysia as well as benchmarking with Japan and the Republic of Korea. Under economic impacts, index of gross value of products and services, index of gross value of exports, index of gross value of training expenditures, and index of total number of human resource trained are developed. In addition, the contribution of the application of nuclear technology to both Gross Domestic Products (GDP) and GDP per capita are also highlighted. The impact of the application of nuclear technology to Total Factor Productivity (TFP) is also covered in this chapter. Much of the discussions on economic impacts are based on findings in private companies. That is because many of their operations can be expressed in monetary terms by virtue of them operating in commercial environment. Public agencies, however, play crucial role in enabling the private companies attain the level of development reported in this study. Towards that end, public agencies invested in Research and development activities, human capital development, as well as in the setting-up, operation and maintenance of both technical and administrative infrastructures. The impact of such activities is discussed in the later part of this chapter. (author)

Remote Sensing and River Discharge Forecasting for Major Rivers in South Asia (Invited)

Science.gov (United States)

Webster, P. J.; Hopson, T. M.; Hirpa, F. A.; Brakenridge, G. R.; De-Groeve, T.; Shrestha, K.; Gebremichael, M.; Restrepo, P. J.

2013-12-01

The South Asia is a flashpoint for natural disasters particularly flooding of the Indus, Ganges, and Brahmaputra has profound societal impacts for the region and globally. The 2007 Brahmaputra floods affecting India and Bangladesh, the 2008 avulsion of the Kosi River in India, the 2010 flooding of the Indus River in Pakistan and the 2013 Uttarakhand exemplify disasters on scales almost inconceivable elsewhere. Their frequent occurrence of floods combined with large and rapidly growing populations, high levels of poverty and low resilience, exacerbate the impact of the hazards. Mitigation of these devastating hazards are compounded by limited flood forecast capability, lack of rain/gauge measuring stations and forecast use within and outside the country, and transboundary data sharing on natural hazards. Here, we demonstrate the utility of remotely-derived hydrologic and weather products in producing skillful flood forecasting information without reliance on vulnerable in situ data sources. Over the last decade a forecast system has been providing operational probabilistic forecasts of severe flooding of the Brahmaputra and Ganges Rivers in Bangldesh was developed (Hopson and Webster 2010). The system utilizes ECMWF weather forecast uncertainty information and ensemble weather forecasts, rain gauge and satellite-derived precipitation estimates, together with the limited near-real-time river stage observations from Bangladesh. This system has been expanded to Pakistan and has successfully forecast the 2010-2012 flooding (Shrestha and Webster 2013). To overcome the in situ hydrological data problem, recent efforts in parallel with the numerical modeling have utilized microwave satellite remote sensing of river widths to generate operational discharge advective-based forecasts for the Ganges and Brahmaputra. More than twenty remotely locations upstream of Bangldesh were used to produce stand-alone river flow nowcasts and forecasts at 1-15 days lead time. showing that

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.

Project in fiscal 1988 for research and development of basic technologies in next generation industries. Research and development of superconducting materials and superconducting elements (Achievement report on forecast and research of superconducting element technologies); 1988 nendo chodendo soshi gijutsu yosoku kenkyu seika hokokusho

Energy Technology Data Exchange (ETDEWEB)

NONE

1989-03-01

With an objective to perform survey and forecast on the future of superconducting elements, collection of latest technological information and analyses of technological trends were carried out by members of the Technology Forecast and Research Committee. This paper summarizes the achievements therein. It was discovered that the Josephson element using an Al{sub 2}O{sub 3} barrier and an Nb electrode shows excellent characteristics with very good reproducibility. Trial fabrication of a four-bit micro processor was recently executed successfully by the SQUID gate using the above element. On the other hand, application of devices using high-temperature superconductors has not come out with an achievement. Although a large number of achievements have been released on mono-crystalline thin films that show good characteristics, development of substrates and barrier materials is still needed for device configuration. The method for manufacturing metal-based superconducting films has been established nearly completely as an elementary technology to develop the superconducting elements. However, making thinner the high-temperature superconducting films having been discovered recently is encountering a number of inherent problems, whereas the present stage is such that experimental discussions are being made. The process technologies, simulation, and evaluation technologies are basically the same as those for the metallic systems even for the oxide superconduction. (NEDO)

Parameter estimation using the genetic algorithm and its impact on quantitative precipitation forecast

Directory of Open Access Journals (Sweden)

Y. H. Lee

2006-12-01

Full Text Available In this study, optimal parameter estimations are performed for both physical and computational parameters in a mesoscale meteorological model, and their impacts on the quantitative precipitation forecasting (QPF are assessed for a heavy rainfall case occurred at the Korean Peninsula in June 2005. Experiments are carried out using the PSU/NCAR MM5 model and the genetic algorithm (GA for two parameters: the reduction rate of the convective available potential energy in the Kain-Fritsch (KF scheme for cumulus parameterization, and the Asselin filter parameter for numerical stability. The fitness function is defined based on a QPF skill score. It turns out that each optimized parameter significantly improves the QPF skill. Such improvement is maximized when the two optimized parameters are used simultaneously. Our results indicate that optimizations of computational parameters as well as physical parameters and their adequate applications are essential in improving model performance.

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.

Innovation Value of Information Technology: Impact of Information Technology--Intensity on Innovation Capability and Firm Performance

Science.gov (United States)

Ramamani, Mahesh Kumar

2010-01-01

Though information technology adoptions have been always referred to as innovations in firms, much of the business value literature has concentrated on the tangible and immediately measurable impacts of information technology (IT) adoptions. This study aims to explore the impact of information technology investments on the innovativeness of a…

Development of a WRF-RTFDDA-based high-resolution hybrid data-assimilation and forecasting system toward to operation in the Middle East

Science.gov (United States)

Liu, Y.; Wu, W.; Zhang, Y.; Kucera, P. A.; Liu, Y.; Pan, L.

2012-12-01

Weather forecasting in the Middle East is challenging because of its complicated geographical nature including massive coastal area and heterogeneous land, and regional spare observational network. Strong air-land-sea interactions form multi-scale weather regimes in the area, which require a numerical weather prediction model capable of properly representing multi-scale atmospheric flow with appropriate initial conditions. The WRF-based Real-Time Four Dimensional Data Assimilation (RTFDDA) system is one of advanced multi-scale weather analysis and forecasting facilities developed at the Research Applications Laboratory (RAL) of NCAR. The forecasting system is applied for the Middle East with careful configuration. To overcome the limitation of the very sparsely available conventional observations in the region, we develop a hybrid data assimilation algorithm combining RTFDDA and WRF-3DVAR, which ingests remote sensing data from satellites and radar. This hybrid data assimilation blends Newtonian nudging FDDA and 3DVAR technology to effectively assimilate both conventional observations and remote sensing measurements and provide improved initial conditions for the forecasting system. For brevity, the forecasting system is called RTF3H (RTFDDA-3DVAR Hybrid). In this presentation, we will discuss the hybrid data assimilation algorithm, and its implementation, and the applications for high-impact weather events in the area. Sensitivity studies are conducted to understand the strength and limitations of this hybrid data assimilation algorithm.

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

An Assessment of Data from the Advanced Technology Microwave Sounder at the Met Office

Directory of Open Access Journals (Sweden)

Amy Doherty

2015-01-01

Full Text Available An appraisal of the Advanced Technology Microwave Sounder (ATMS for use in numerical weather prediction (NWP is presented, including an assessment of the data quality, the impact on Met Office global forecasts in preoperational trials, and a summary of performance over a period of 17 months operational use. After remapping, the noise performance (NEΔT of the tropospheric temperature sounding channels is evaluated to be approximately 0.1 K, comparing favourably with AMSU-A. However, the noise is not random, differences between observations and simulations based on short-range forecast fields show a spurious striping effect, due to 1/f noise in the receiver. The amplitude of this signal is several tenths of a Kelvin, potentially a concern for NWP applications. In preoperational tests, adding ATMS data to a full Met Office system already exploiting data from four microwave sounders improves southern hemisphere mean sea level pressure forecasts in the 2- to 5-day range by 1-2%. In operational use, where data from five other microwave sounders is assimilated, forecast impact is typically between −0.05 and −0.1 J/kg (3.4% of total mean impact per day over the period 1 April to 31 July 2013. This suggests benefits beyond redundancy, associated with reducing already small analysis errors.

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...

Convective Mode and Mesoscale Heavy Rainfall Forecast Challenges during a High-Impact Weather Period along the Gulf Coast and Florida from 17-20 May 2016

Science.gov (United States)

Bosart, L. F.; Wallace, B. C.

2017-12-01

Two high-impact convective storm forecast challenges occurred between 17-20 May 2016 during NOAA's Hazardous Weather Testbed Spring Forecast Experiment (SFE) at the Storm Prediction Center. The first forecast challenge was 286 mm of unexpected record-breaking rain that fell on Vero Beach (VRB), Florida, between 1500 UTC 17 May and 0600 UTC 18 May, more than doubling the previous May daily rainfall record. The record rains in VRB occurred subsequent to the formation of a massive MCS over the central Gulf of Mexico between 0900-1000 UTC 17 May. This MCS, linked to the earlier convection associated with an anomalously strong subtropical jet (STJ) over the Gulf of Mexico, moved east-northeastward toward Florida. The second forecast challenge was a large MCS that formed over the Mexican mountains near the Texas-Mexican border, moved eastward and grew upscale prior to 1200 UTC 19 May. This MCS further strengthened offshore after 1800 UTC 19 May beneath the STJ. SPC SFE participants expected this MCS to move east-northeastward and bring heavy rain due to training echoes along the Gulf coast as far eastward as the Florida panhandle. Instead, this MCS transitioned into a bowing MCS that resembled a low-end derecho and produced a 4-6 hPa cold pool with widespread surface wind gusts between 35-50 kt. Both MCS events occurred in a large-scale baroclinic environment along the northern Gulf coast. Both MCS events responded to antecedent convection within this favorable large-scale environment. Rainfall amounts with the first heavy rain-producing MCS were severely underestimated by models and forecasters alike. The second MCS produced the greatest forecaster angst because rainfall totals were forecast too high (MCS propagated too fast) and severe wind reports were much more widespread than anticipated (because of cold pool formation). This presentation will attempt to untangle what happened and why it happened.

Perspectives on model forecasts of the 2014-2015 Ebola epidemic in West Africa

DEFF Research Database (Denmark)

Chowell, Gerardo; Viboud, Cécile; Simonsen, Lone

2017-01-01

The unprecedented impact and modeling efforts associated with the 2014–2015 Ebola epidemic in West Africa provides a unique opportunity to document the performances and caveats of forecasting approaches used in near-real time for generating evidence and to guide policy. A number of international...... academic groups have developed and parameterized mathematical models of disease spread to forecast the trajectory of the outbreak. These modeling efforts often relied on limited epidemiological data to derive key transmission and severity parameters, which are needed to calibrate mechanistic models. Here...... changes and case clustering; (3) challenges in forecasting the long-term epidemic impact very early in the outbreak; and (4) ways to move forward. We conclude that rapid availability of aggregated population-level data and detailed information on a subset of transmission chains is crucial to characterize...

Pharmaceutical expenditure forecast model to support health policy decision making.

Science.gov (United States)

Rémuzat, Cécile; Urbinati, Duccio; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

2014-01-01

With constant incentives for healthcare payers to contain their pharmaceutical budgets, modelling policy decision impact became critical. The objective of this project was to test the impact of various policy decisions on pharmaceutical budget (developed for the European Commission for the project 'European Union (EU) Pharmaceutical expenditure forecast' - http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). A model was built to assess policy scenarios' impact on the pharmaceutical budgets of seven member states of the EU, namely France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. The following scenarios were tested: expanding the UK policies to EU, changing time to market access, modifying generic price and penetration, shifting the distribution chain of biosimilars (retail/hospital). Applying the UK policy resulted in dramatic savings for Germany (10 times the base case forecast) and substantial additional savings for France and Portugal (2 and 4 times the base case forecast, respectively). Delaying time to market was found be to a very powerful tool to reduce pharmaceutical expenditure. Applying the EU transparency directive (6-month process for pricing and reimbursement) increased pharmaceutical expenditure for all countries (from 1.1 to 4 times the base case forecast), except in Germany (additional savings). Decreasing the price of generics and boosting the penetration rate, as well as shifting distribution of biosimilars through hospital chain were also key methods to reduce pharmaceutical expenditure. Change in the level of reimbursement rate to 100% in all countries led to an important increase in the pharmaceutical budget. Forecasting pharmaceutical expenditure is a critical exercise to inform policy decision makers. The most important leverages identified by the model on pharmaceutical budget were driven by generic and biosimilar prices, penetration rate, and distribution. Reducing, even slightly, the prices of

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,

Use of MLCM3 Software for Flash Flood Modeling and Forecasting

OpenAIRE

Inna Pivovarova; Daria Sokolova; Artur Batyrov; Vadim Kuzmin; Ngoc Anh Tran; DinhKha Dang; Kirill V. Shemanaev

2018-01-01

Accurate and timely flash floods forecasting, especially, in ungauged and poorly gauged basins, is one of the most important and challenging problems to be solved by the international hydrological community. In changing climate and variable anthropogenic impact on river basins, as well as due to low density of surface hydrometeorological network, flash flood forecasting based on “traditional” physically based, or conceptual, or statistical hydrological models often becomes inefficient. Unfort...

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.

The Rise of Complexity in Flood Forecasting: Opportunities, Challenges and Tradeoffs

Science.gov (United States)

Wood, A. W.; Clark, M. P.; Nijssen, B.

2017-12-01

Operational flood forecasting is currently undergoing a major transformation. Most national flood forecasting services have relied for decades on lumped, highly calibrated conceptual hydrological models running on local office computing resources, providing deterministic streamflow predictions at gauged river locations that are important to stakeholders and emergency managers. A variety of recent technological advances now make it possible to run complex, high-to-hyper-resolution models for operational hydrologic prediction over large domains, and the US National Weather Service is now attempting to use hyper-resolution models to create new forecast services and products. Yet other `increased-complexity' forecasting strategies also exist that pursue different tradeoffs between model complexity (i.e., spatial resolution, physics) and streamflow forecast system objectives. There is currently a pressing need for a greater understanding in the hydrology community of the opportunities, challenges and tradeoffs associated with these different forecasting approaches, and for a greater participation by the hydrology community in evaluating, guiding and implementing these approaches. Intermediate-resolution forecast systems, for instance, use distributed land surface model (LSM) physics but retain the agility to deploy ensemble methods (including hydrologic data assimilation and hindcast-based post-processing). Fully coupled numerical weather prediction (NWP) systems, another example, use still coarser LSMs to produce ensemble streamflow predictions either at the model scale or after sub-grid scale runoff routing. Based on the direct experience of the authors and colleagues in research and operational forecasting, this presentation describes examples of different streamflow forecast paradigms, from the traditional to the recent hyper-resolution, to illustrate the range of choices facing forecast system developers. We also discuss the degree to which the strengths and

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

Evaluation of the Plant-Craig stochastic convection scheme in an ensemble forecasting system

Science.gov (United States)

Keane, R. J.; Plant, R. S.; Tennant, W. J.

2015-12-01

The Plant-Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic element only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant-Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant-Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature

OpenAIRE

Zhao, Ning-bo; Yang, Jia-long; Li, Shu-ying; Sun, Yue-wu

2014-01-01

Performance degradation forecast technology for quantitatively assessing degradation states of aeroengine using exhaust gas temperature is an important technology in the aeroengine health management. In this paper, a GM (1, 1) Markov chain-based approach is introduced to forecast exhaust gas temperature by taking the advantages of GM (1, 1) model in time series and the advantages of Markov chain model in dealing with highly nonlinear and stochastic data caused by uncertain factors. In this ap...

Large-scale analysis and forecast experiments with wind data from the Seasat A scatterometer

Science.gov (United States)

Baker, W. E.; Atlas, R.; Kalnay, E.; Halem, M.; Woiceshyn, P. M.; Peteherych, S.; Edelmann, D.

1984-01-01

A series of data assimilation experiments is performed to assess the impact of Seasat A satellite scatterometer (SASS) wind data on Goddard Laboratory for Atmospheric Sciences (GLAS) model forecasts. The SASS data are dealiased as part of an objective analysis system utilizing a three-pass procedure. The impact of the SASS data is evaluated with and without temperature soundings from the NOAA 4 Vertical Temperature Profile Radiometer (VTPR) instrument in order to study possible redundancy between surface wind data and upper air temperature data. In the northern hemisphere the SASS data are generally found to have a negligible effect on the forecasts. In the southern hemisphere the forecast impact from SASS data is somewhat larger and primarily beneficial in the absence of VTPR data. However, the inclusion of VTPR data effectively eliminates the positive impact over Australia and South America. This indicates that SASS data can be beneficial for numerical weather prediction in regions with large data gaps, but in the presence of satellite soundings the usefulness of SASS data is significantly reduced.

Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast

Directory of Open Access Journals (Sweden)

Junfei Chen

2012-01-01

Full Text Available Drought is part of natural climate variability and ranks the first natural disaster in the world. Drought forecasting plays an important role in mitigating impacts on agriculture and water resources. In this study, a drought forecast model based on the random forest method is proposed to predict the time series of monthly standardized precipitation index (SPI. We demonstrate model application by four stations in the Haihe river basin, China. The random-forest- (RF- based forecast model has consistently shown better predictive skills than the ARIMA model for both long and short drought forecasting. The confidence intervals derived from the proposed model generally have good coverage, but still tend to be conservative to predict some extreme drought events.

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

OpenAIRE

Jun-He Yang; Ching-Hsue Cheng; Chia-Pan Chan

2017-01-01

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting m...

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

Impact of new technologies on diabetes care.

Science.gov (United States)

Giani, Elisa; Scaramuzza, Andrea Enzo; Zuccotti, Gian Vincenzo

2015-07-25

Technologies for diabetes management, such as continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) systems, have improved remarkably over the last decades. These developments are impacting the capacity to achieve recommended hemoglobin A1c levels and assisting in preventing the development and progression of micro- and macro vascular complications. While improvements in metabolic control and decreases in risk of severe and moderate hypoglycemia have been described with use of these technologies, large epidemiological international studies show that many patients are still unable to meet their glycemic goals, even when these technologies are used. This editorial will review the impact of technology on glycemic control, hypoglycemia and quality of life in children and youth with type 1 diabetes. Technologies reviewed include CSII, CGM systems and sensor-augmented insulin pumps. In addition, the usefulness of advanced functions such as bolus profiles, bolus calculators and threshold-suspend features will be also discussed. Moreover, the current editorial will explore the challenges of using these technologies. Indeed, despite the evidence currently available of the potential benefits of using advanced technologies in diabetes management, many patients still report barriers to using them. Finally this article will highlight the importance of future studies tailored toward overcome these barriers to optimizing glycemic control and avoiding severe hypoglycemia.

Evolutionary Forecast Engines for Solar Meteorology

Science.gov (United States)

Coimbra, C. F.

2012-12-01

A detailed comparison of non-stationary regression and stochastic learning methods based on k-Nearest Neighbor (kNN), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) approaches is carried out in order to develop high-fidelity solar forecast engines for several time horizons of interest. A hybrid GA/ANN method emerges as the most robust stochastic learning candidate. The GA/ANN approach In general the following decisions need to be made when creating an ANN-based solar forecast model: the ANN architecture: number of layers, numbers of neurons per layer; the preprocessing scheme; the fraction and distribution between training and testing data, and the meteorological and radiometric inputs. ANNs are very well suited to handle multivariate forecasting models due to their overall flexibility and nonlinear pattern recognition abilities. However, the forecasting skill of ANNs depends on a new set of parameters to be optimized within the context of the forecast model, which is the selection of input variables that most directly impact the fidelity of the forecasts. In a data rich scenario where irradiation, meteorological, and cloud cover data are available, it is not always evident which variables to include in the model a priori. New variables can also arise from data preprocessing such as smoothing or spectral decomposition. One way to avoid time-consuming trial-and-error approaches that have limited chance to result in optimal ANN topology and input selection is to couple the ANN with some optimization algorithm that scans the solution space and "evolves" the ANN structure. Genetic Algorithms (GAs) are well suited for this task. Results and Discussion The models built upon the historical data of 2009 and 2010 are applied to the 2011 data without modifications or retraining. We consider 3 solar variability seasons or periods, which are subsets of the total error evaluation data set. The 3 periods are defined based on the solar variability study as: - a high

Impact of a high density GPS network on the operational forecast

Directory of Open Access Journals (Sweden)

C. Faccani

2005-01-01

Full Text Available Global Positioning System Zenith Total Delay (GPS ZTD can provide information about the water vapour in atmosphere. Its assimilation into the analysis used to initialize a model can then improve the weather forecast, giving the right amount of moisture and reducing the model spinup. In the last year, an high density GPS network has been created on the Basilicata region (south of Italy by the Italian Space Agency in the framework of a national project named MAGIC2. MAGIC2 is the Italian follow on of the EC project MAGIC has. Daily operational data assimilation experiments are performed since December 2003. The results show that the assimilation of GPS ZTD improves the forecast especially during the transition from winter to spring even if a no very high model resolution (9km is used.

The impact of convection in the West African monsoon region on global weather forecasts - explicit vs. parameterised convection simulations using the ICON model

Science.gov (United States)

Pante, Gregor; Knippertz, Peter

2017-04-01

The West African monsoon is the driving element of weather and climate during summer in the Sahel region. It interacts with mesoscale convective systems (MCSs) and the African easterly jet and African easterly waves. Poor representation of convection in numerical models, particularly its organisation on the mesoscale, can result in unrealistic forecasts of the monsoon dynamics. Arguably, the parameterisation of convection is one of the main deficiencies in models over this region. Overall, this has negative impacts on forecasts over West Africa itself but may also affect remote regions, as waves originating from convective heating are badly represented. Here we investigate those remote forecast impacts based on daily initialised 10-day forecasts for July 2016 using the ICON model. One set of simulations employs the default setup of the global model with a horizontal grid spacing of 13 km. It is compared with simulations using the 2-way nesting capability of ICON. A second model domain over West Africa (the nest) with 6.5 km grid spacing is sufficient to explicitly resolve MCSs in this region. In the 2-way nested simulations, the prognostic variables of the global model are influenced by the results of the nest through relaxation. The nest with explicit convection is able to reproduce single MCSs much more realistically compared to the stand-alone global simulation with parameterised convection. Explicit convection leads to cooler temperatures in the lower troposphere (below 500 hPa) over the northern Sahel due to stronger evaporational cooling. Overall, the feedback of dynamic variables from the nest to the global model shows clear positive effects when evaluating the output of the global domain of the 2-way nesting simulation and the output of the stand-alone global model with ERA-Interim re-analyses. Averaged over the 2-way nested region, bias and root mean squared error (RMSE) of temperature, geopotential, wind and relative humidity are significantly reduced in

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.

The Technological Innovations and Their Impact on the Economy

Directory of Open Access Journals (Sweden)

Mikhail Ivanovich Maslennikov

2017-12-01

Full Text Available Drastic and inclusive technology progress is changing the economy of Russia and foreign countries. This progress significantly impacts production forces. . This paper analyzes theoretical and applied approaches to the investigation of production forces development. I reveal the role and importance of innovations in science and technology as well as their impact on various segments of the economy. I analyze the conceptual framework used for technological shifts. The paper describes the indicators showing the level of scientific and technological potential development in various countries and regions including the Urals. I define growth areas in technology and assess their contribution to gross domestic product (GDP growth. The administrations and authorities at federal and regional levels impact the development of science, innovations, technologies and breakthrough areas. The development of these spheres leads to the increase in productivity of social activities, production and business activity. The paper reveals the impact of the technological breakthrough in such latest fields as the alternative energy sources, drones, electric car industry, storage and delivery of energy. This fields transform the economy and society. Furthermore, they change theoretical concepts of functional and institutional social structure. The development paradigm is to be modified from fuel and raw model to the innovative and technological one. The economic development and scientific and technological potential are interrelated. I discuss the reason of close attention to the development of science, technologies, innovations in the developed countries. as well as the measures to stimulate their development. The article studies the mechanism and tools of science and education funding in various regions of the world. The results of the research may be used for updating the strategy of scientific and technological development of Russia and its regions in the current

Forecast of useful energy for the TIMES-Norway model

International Nuclear Information System (INIS)

Rosenberg, Eva

2012-01-01

A regional forecast of useful energy demand in seven Norwegian regions is calculated based on an earlier work with a national forecast. This forecast will be input to the energy system model TIMES-Norway and analyses will result in forecasts of energy use of different energy carriers with varying external conditions (not included in this report). The forecast presented here describes the methodology used and the resulting forecast of useful energy. lt is based on information of the long-term development of the economy by the Ministry of Finance, projections of population growths from Statistics Norway and several other studies. The definition of a forecast of useful energy demand is not absolute, but depends on the purpose. One has to be careful not to include parts that are a part of the energy system model, such as energy efficiency measures. In the forecast presented here the influence of new building regulations and the prohibition of production of incandescent light bulbs in EU etc. are included. Other energy efficiency measures such as energy management, heat pumps, tightening of leaks etc. are modelled as technologies to invest in and are included in the TIMES-Norway model. The elasticity between different energy carriers are handled by the TIMES-Norway model and some elasticity is also included as the possibility to invest in energy efficiency measures. The forecast results in an increase of the total useful energy from 2006 to 2050 by 18 o/o. The growth is expected to be highest in the regions South and East. The industry remains at a constant level in the base case and increased or reduced energy demand is analysed as different scenarios with the TIMES-Norway model. The most important driver is the population growth. Together with the assumptions made it results in increased useful energy demand in the household and service sectors of 25 o/o and 57 % respectively.(au)

Forecast of useful energy for the TIMES-Norway model

Energy Technology Data Exchange (ETDEWEB)

Rosenberg, Eva

2012-07-25

A regional forecast of useful energy demand in seven Norwegian regions is calculated based on an earlier work with a national forecast. This forecast will be input to the energy system model TIMES-Norway and analyses will result in forecasts of energy use of different energy carriers with varying external conditions (not included in this report). The forecast presented here describes the methodology used and the resulting forecast of useful energy. lt is based on information of the long-term development of the economy by the Ministry of Finance, projections of population growths from Statistics Norway and several other studies. The definition of a forecast of useful energy demand is not absolute, but depends on the purpose. One has to be careful not to include parts that are a part of the energy system model, such as energy efficiency measures. In the forecast presented here the influence of new building regulations and the prohibition of production of incandescent light bulbs in EU etc. are included. Other energy efficiency measures such as energy management, heat pumps, tightening of leaks etc. are modelled as technologies to invest in and are included in the TIMES-Norway model. The elasticity between different energy carriers are handled by the TIMES-Norway model and some elasticity is also included as the possibility to invest in energy efficiency measures. The forecast results in an increase of the total useful energy from 2006 to 2050 by 18 o/o. The growth is expected to be highest in the regions South and East. The industry remains at a constant level in the base case and increased or reduced energy demand is analysed as different scenarios with the TIMES-Norway model. The most important driver is the population growth. Together with the assumptions made it results in increased useful energy demand in the household and service sectors of 25 o/o and 57 % respectively.(au)

The state of the art of flood forecasting - Hydrological Ensemble Prediction Systems

Science.gov (United States)

Thielen-Del Pozo, J.; Pappenberger, F.; Salamon, P.; Bogner, K.; Burek, P.; de Roo, A.

2010-09-01

Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Because of the specific characteristics of each catchment, varying data availability and end-user demands, the design of the best flood forecasting system may differ from catchment to catchment. However, despite the differences in concept and data needs, there is one underlying issue that spans across all systems. There has been an growing awareness and acceptance that uncertainty is a fundamental issue of flood forecasting and needs to be dealt with at the different spatial and temporal scales as well as the different stages of the flood generating processes. Today, operational flood forecasting centres change increasingly from single deterministic forecasts to probabilistic forecasts with various representations of the different contributions of uncertainty. The move towards these so-called Hydrological Ensemble Prediction Systems (HEPS) in flood forecasting represents the state of the art in forecasting science, following on the success of the use of ensembles for weather forecasting (Buizza et al., 2005) and paralleling the move towards ensemble forecasting in other related disciplines such as climate change predictions. The use of HEPS has been internationally fostered by initiatives such as "The Hydrologic Ensemble Prediction Experiment" (HEPEX), created with the aim to investigate how best to produce, communicate and use hydrologic ensemble forecasts in hydrological short-, medium- und long term prediction of hydrological processes. The advantages of quantifying the different contributions of uncertainty as well as the overall uncertainty to obtain reliable and useful flood forecasts also for extreme events

Recent Progress of Solar Weather Forecasting at Naoc

Science.gov (United States)

He, Han; Wang, Huaning; Du, Zhanle; Zhang, Liyun; Huang, Xin; Yan, Yan; Fan, Yuliang; Zhu, Xiaoshuai; Guo, Xiaobo; Dai, Xinghua

The history of solar weather forecasting services at National Astronomical Observatories, Chinese Academy of Sciences (NAOC) can be traced back to 1960s. Nowadays, NAOC is the headquarters of the Regional Warning Center of China (RWC-China), which is one of the members of the International Space Environment Service (ISES). NAOC is responsible for exchanging data, information and space weather forecasts of RWC-China with other RWCs. The solar weather forecasting services at NAOC cover short-term prediction (within two or three days), medium-term prediction (within several weeks), and long-term prediction (in time scale of solar cycle) of solar activities. Most efforts of the short-term prediction research are concentrated on the solar eruptive phenomena, such as flares, coronal mass ejections (CMEs) and solar proton events, which are the key driving sources of strong space weather disturbances. Based on the high quality observation data of the latest space-based and ground-based solar telescopes and with the help of artificial intelligence techniques, new numerical models with quantitative analyses and physical consideration are being developed for the predictions of solar eruptive events. The 3-D computer simulation technology is being introduced for the operational solar weather service platform to visualize the monitoring of solar activities, the running of the prediction models, as well as the presenting of the forecasting results. A new generation operational solar weather monitoring and forecasting system is expected to be constructed in the near future at NAOC.

Using soil moisture forecasts for sub-seasonal summer temperature predictions in Europe

Science.gov (United States)

Orth, René; Seneviratne, Sonia I.

2014-12-01

Soil moisture exhibits outstanding memory characteristics and plays a key role within the climate system. Especially through its impacts on the evapotranspiration of soils and plants, it may influence the land energy balance and therefore surface temperature. These attributes make soil moisture an important variable in the context of weather and climate forecasting. In this study we investigate the value of (initial) soil moisture information for sub-seasonal temperature forecasts. For this purpose we employ a simple water balance model to infer soil moisture from streamflow observations in 400 catchments across Europe. Running this model with forecasted atmospheric forcing, we derive soil moisture forecasts, which we then translate into temperature forecasts using simple linear relationships. The resulting temperature forecasts show skill beyond climatology up to 2 weeks in most of the considered catchments. Even if forecasting skills are rather small at longer lead times with significant skill only in some catchments at lead times of 3 and 4 weeks, this soil moisture-based approach shows local improvements compared to the monthly European Centre for Medium Range Weather Forecasting (ECMWF) temperature forecasts at these lead times. For both products (soil moisture-only forecast and ECMWF forecast), we find comparable or better forecast performance in the case of extreme events, especially at long lead times. Even though a product based on soil moisture information alone is not of practical relevance, our results indicate that soil moisture (memory) is a potentially valuable contributor to temperature forecast skill. Investigating the underlying soil moisture of the ECMWF forecasts we find good agreement with the simple model forecasts, especially at longer lead times. Analyzing the drivers of the temperature forecast skills we find that they are mainly controlled by the strengths of (1) the soil moisture-temperature coupling and (2) the soil moisture memory. We

Seasonal forecasts of the summer 2016 Yangtze River basin rainfall

OpenAIRE

Bett, Philip E.; Scaife, Adam A.; Li, Chaofan; Hewitt, Chris; Golding, Nicola; Zhang, Peiqun; Dunstone, Nick; Smith, Doug M.; Thornton, Hazel E.; Lu, Riyu; Ren, Hong-Li

2017-01-01

The Yangtze River has been subject to heavy flooding throughout history, and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods. Dams along the river help to manage flood waters, and are important sources of electricity for the region. Being able to forecast high-impact events at long lead times therefore has enormous potential benefit. Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used direc...

Long-term fashion forecast based on the sociological model of cyclic changes

Directory of Open Access Journals (Sweden)

А V Lebsak-Kleimans

2010-09-01

Full Text Available The concepts of social changes coined by classical sociology may be incorporated as the basis for the elaboration of social prognostication models which, in turn, may suitable for fashion forecast applied technologies development. In the framework of the given paper fashion is described as the phenomenon of collective behaviour. The principles of long-term fashion trends forecast are shown to be in line with the concepts of cyclic development.

The impact of health information technology on patient safety

Directory of Open Access Journals (Sweden)

Yasser K. Alotaibi

2017-12-01

Full Text Available Since the original Institute of Medicine (IOM report was published there has been an accelerated development and adoption of health information technology with varying degrees of evidence about the impact of health information technology on patient safety. This article is intended to review the current available scientific evidence on the impact of different health information technologies on improving patient safety outcomes. We conclude that health information technology improves patient’s safety by reducing medication errors, reducing adverse drug reactions, and improving compliance to practice guidelines. There should be no doubt that health information technology is an important tool for improving healthcare quality and safety. Healthcare organizations need to be selective in which technology to invest in, as literature shows that some technologies have limited evidence in improving patient safety outcomes.

The impact of health information technology on patient safety.

Science.gov (United States)

Alotaibi, Yasser K; Federico, Frank

2017-12-01

Since the original Institute of Medicine (IOM) report was published there has been an accelerated development and adoption of health information technology with varying degrees of evidence about the impact of health information technology on patient safety.  This article is intended to review the current available scientific evidence on the impact of different health information technologies on improving patient safety outcomes. We conclude that health information technology improves patient's safety by reducing medication errors, reducing adverse drug reactions, and improving compliance to practice guidelines. There should be no doubt that health information technology is an important tool for improving healthcare quality and safety. Healthcare organizations need to be selective in which technology to invest in, as literature shows that some technologies have limited evidence in improving patient safety outcomes.

Seasonal forecasting of discharge for the Raccoon River, Iowa

Science.gov (United States)

Slater, Louise; Villarini, Gabriele; Bradley, Allen; Vecchi, Gabriel

2016-04-01

The state of Iowa (central United States) is regularly afflicted by severe natural hazards such as the 2008/2013 floods and the 2012 drought. To improve preparedness for these catastrophic events and allow Iowans to make more informed decisions about the most suitable water management strategies, we have developed a framework for medium to long range probabilistic seasonal streamflow forecasting for the Raccoon River at Van Meter, a 8900-km2 catchment located in central-western Iowa. Our flow forecasts use statistical models to predict seasonal discharge for low to high flows, with lead forecasting times ranging from one to ten months. Historical measurements of daily discharge are obtained from the U.S. Geological Survey (USGS) at the Van Meter stream gage, and used to compute quantile time series from minimum to maximum seasonal flow. The model is forced with basin-averaged total seasonal precipitation records from the PRISM Climate Group and annual row crop production acreage from the U.S. Department of Agriculture's National Agricultural Statistics Services database. For the forecasts, we use corn and soybean production from the previous year (persistence forecast) as a proxy for the impacts of agricultural practices on streamflow. The monthly precipitation forecasts are provided by eight Global Climate Models (GCMs) from the North American Multi-Model Ensemble (NMME), with lead times ranging from 0.5 to 11.5 months, and a resolution of 1 decimal degree. Additionally, precipitation from the month preceding each season is used to characterize antecedent soil moisture conditions. The accuracy of our modelled (1927-2015) and forecasted (2001-2015) discharge values is assessed by comparison with the observed USGS data. We explore the sensitivity of forecast skill over the full range of lead times, flow quantiles, forecast seasons, and with each GCM. Forecast skill is also examined using different formulations of the statistical models, as well as NMME forecast

Application of Regional Drought and Crop Yield Information System to enhance drought monitoring and forecasting in Lower Mekong region

Science.gov (United States)

Jayasinghe, S.; Dutta, R.; Basnayake, S. B.; Granger, S. L.; Andreadis, K. M.; Das, N.; Markert, K. N.; Cutter, P. G.; Towashiraporn, P.; Anderson, E.

2017-12-01

The Lower Mekong Region has been experiencing frequent and prolonged droughts resulting in severe damage to agricultural production leading to food insecurity and impacts on livelihoods of the farming communities. Climate variability further complicates the situation by making drought harder to forecast. The Regional Drought and Crop Yield Information System (RDCYIS), developed by SERVIR-Mekong, helps decision makers to take effective measures through monitoring, analyzing and forecasting of drought conditions and providing early warnings to farmers to make adjustments to cropping calendars. The RDCYIS is built on regionally calibrated Regional Hydrologic Extreme Assessment System (RHEAS) framework that integrates the Variable Infiltration Capacity (VIC) and Decision Support System for Agro-technology Transfer (DSSAT) models, allowing both nowcast and forecast of drought. The RHEAS allows ingestion of numerus freely available earth observation and ground observation data to generate and customize drought related indices, variables and crop yield information for better decision making. The Lower Mekong region has experienced severe drought in 2016 encompassing the region's worst drought in 90 years. This paper presents the simulation of the 2016 drought event using RDCYIS based on its hindcast and forecast capabilities. The regionally calibrated RDCYIS can help capture salient features of drought through a variety of drought indices, soil variables, energy balance variables and water balance variables. The RDCYIS is capable of assimilating soil moisture data from different satellite products and perform ensemble runs to further reduce the uncertainty of it outputs. The calibrated results have correlation coefficient around 0.73 and NSE between 0.4-0.5. Based on the acceptable results of the retrospective runs, the system has the potential to generate reliable drought monitoring and forecasting information to improve decision-makings at operational, technological and

Winter wheat quality monitoring and forecasting system based on remote sensing and environmental factors

International Nuclear Information System (INIS)

Haiyang, Yu; Yanmei, Liu; Guijun, Yang; Xiaodong, Yang; Chenwei, Nie; Dong, Ren

2014-01-01

To achieve dynamic winter wheat quality monitoring and forecasting in larger scale regions, the objective of this study was to design and develop a winter wheat quality monitoring and forecasting system by using a remote sensing index and environmental factors. The winter wheat quality trend was forecasted before the harvest and quality was monitored after the harvest, respectively. The traditional quality-vegetation index from remote sensing monitoring and forecasting models were improved. Combining with latitude information, the vegetation index was used to estimate agronomy parameters which were related with winter wheat quality in the early stages for forecasting the quality trend. A combination of rainfall in May, temperature in May, illumination at later May, the soil available nitrogen content and other environmental factors established the quality monitoring model. Compared with a simple quality-vegetation index, the remote sensing monitoring and forecasting model used in this system get greatly improved accuracy. Winter wheat quality was monitored and forecasted based on the above models, and this system was completed based on WebGIS technology. Finally, in 2010 the operation process of winter wheat quality monitoring system was presented in Beijing, the monitoring and forecasting results was outputted as thematic maps

Dynamic neural network modeling of HF radar current maps for forecasting oil spill trajectories

International Nuclear Information System (INIS)

Tissot, P.; Perez, J.; Kelly, F.J.; Bonner, J.; Michaud, P.

2001-01-01

This paper examined the concept of dynamic neural network (NN) modeling for short-term forecasts of coastal high-frequency (HF) radar current maps offshore of Galveston Texas. HF radar technology is emerging as a viable and affordable way to measure surface currents in real time and the number of users applying the technology is increasing. A 25 megahertz, two site, Seasonde HF radar system was used to map ocean and bay surface currents along the coast of Texas where wind and river discharge create complex and rapidly changing current patters that override the weaker tidal flow component. The HF radar system is particularly useful in this type of setting because its mobility makes it a good marine spill response tool that could provide hourly current maps. This capability helps improve deployment of response resources. In addition, the NN model recently developed by the Conrad Blucher Institute can be used to forecast water levels during storm events. Forecasted currents are based on time series of current vectors from HF radar plus wind speed, wind direction, and water levels, as well as tidal forecasts. The dynamic NN model was tested to evaluate its performance and the results were compared with a baseline model which assumes the currents do not change from the time of the forecast up to the forecasted time. The NN model showed improvements over the baseline model for forecasting time equal or greater than 3 hours, but the difference was relatively small. The test demonstrated the ability of the dynamic NN model to link meteorological forcing functions with HF radar current maps. Development of the dynamic NN modeling is still ongoing. 18 refs., 1 tab., 5 figs

Forecast Informed Reservoir Operations: Bringing Science and Decision-Makers Together to Explore Use of Hydrometeorological Forecasts to Support Future Reservoir Operations

Science.gov (United States)

Ralph, F. M.; Jasperse, J.

2017-12-01

Forecast Informed Reservoir Operations (FIRO) is a proposed strategy that is exploring inorporation of improved hydrometeorological forecasts of land-falling atmospheric rivers on the U.S. West Coast into reservoir operations. The first testbed for this strategy is Lake Mendocino, which is located in the East Fork of the 1485 mi2 Russian River Watershed in northern California. This project is guided by the Lake Mendocino FIRO Steering Committee (SC). The SC is an ad hoc committee that consists of water managers and scientists from several federal, state, and local agencies, and universities who have teamed to evaluate whether current or improved technology and scientific understanding can be utilized to improve water supply reliability, enhance flood mitigation and support recovery of listed salmon for the Russian River of northern California. In 2015, the SC created a detailed work plan, which included a Preliminary Viability Assessment, which has now been completed. The SC developed a vision that operational efficiency would be improved by using forecasts to inform decisions about releasing or storing water. FIRO would use available reservoir storage in an efficient manner by (1) better forecasting inflow (or lack of inflow) with enhanced technology, and (2) adapting operation in real time to meet the need for storage, rather than making storage available just in case it is needed. The envisioned FIRO strategy has the potential to simultaneously improve water supply reliability, flood protection, and ecosystem outcomes through a more efficient use of existing infrastructure while requiring minimal capital improvements in the physical structure of the dam. This presentation will provide an overview of the creation of the FIRO SC and how it operates, and describes the lessons learned through this partnership. Results in the FIRO Preliminary Viability Assessment will be summarized and next steps described.

Hydrodynamics and Water Quality forecasting over a Cloud Computing environment: INDIGO-DataCloud

Science.gov (United States)

Aguilar Gómez, Fernando; de Lucas, Jesús Marco; García, Daniel; Monteoliva, Agustín

2017-04-01

Algae Bloom due to eutrophication is an extended problem for water reservoirs and lakes that impacts directly in water quality. It can create a dead zone that lacks enough oxygen to support life and it can also be human harmful, so it must be controlled in water masses for supplying, bathing or other uses. Hydrodynamic and Water Quality modelling can contribute to forecast the status of the water system in order to alert authorities before an algae bloom event occurs. It can be used to predict scenarios and find solutions to reduce the harmful impact of the blooms. High resolution models need to process a big amount of data using a robust enough computing infrastructure. INDIGO-DataCloud (https://www.indigo-datacloud.eu/) is an European Commission funded project that aims at developing a data and computing platform targeting scientific communities, deployable on multiple hardware and provisioned over hybrid (private or public) e-infrastructures. The project addresses the development of solutions for different Case Studies using different Cloud-based alternatives. In the first INDIGO software release, a set of components are ready to manage the deployment of services to perform N number of Delft3D simulations (for calibrating or scenario definition) over a Cloud Computing environment, using the Docker technology: TOSCA requirement description, Docker repository, Orchestrator, AAI (Authorization, Authentication) and OneData (Distributed Storage System). Moreover, the Future Gateway portal based on Liferay, provides an user-friendly interface where the user can configure the simulations. Due to the data approach of INDIGO, the developed solutions can contribute to manage the full data life cycle of a project, thanks to different tools to manage datasets or even metadata. Furthermore, the cloud environment contributes to provide a dynamic, scalable and easy-to-use framework for non-IT experts users. This framework is potentially capable to automatize the processing of

Forecasting land cover change impacts on drinking water treatment costs in Minneapolis, Minnesota

Science.gov (United States)

Woznicki, S. A.; Wickham, J.

2017-12-01

Source protection is a critical aspect of drinking water treatment. The benefits of protecting source water quality in reducing drinking water treatment costs are clear. However, forecasting the impacts of environmental change on source water quality and its potential to influence future treatment processes is lacking. The drinking water treatment plant in Minneapolis, MN has recognized that land cover change threatens water quality in their source watershed, the Upper Mississippi River Basin (UMRB). Over 1,000 km2 of forests, wetlands, and grasslands in the UMRB were lost to agriculture from 2008-2013. This trend, coupled with a projected population increase of one million people in Minnesota by 2030, concerns drinking water treatment plant operators in Minneapolis with respect to meeting future demand for clean water in the UMRB. The objective of this study is to relate land cover change (forest and wetland loss, agricultural expansion, urbanization) to changes in treatment costs for the Minneapolis, MN drinking water utility. To do this, we first developed a framework to determine the relationship between land cover change and water quality in the context of recent historical changes and projected future changes in land cover. Next we coupled a watershed model, the Soil and Water Assessment Tool (SWAT) to projections of land cover change from the FOREcasting SCEnarios of Land-use Change (FORE-SCE) model for the mid-21st century. Using historical Minneapolis drinking water treatment data (chemical usage and costs), source water quality in the UMRB was linked to changes in treatment requirements as a function of projected future land cover change. These analyses will quantify the value of natural landscapes in protecting drinking water quality and future treatment processes requirements. In addition, our study provides the Minneapolis drinking water utility with information critical to their planning and capital improvement process.

Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain

International Nuclear Information System (INIS)

González-Aparicio, I.; Zucker, A.

2015-01-01

Highlights: • Reduction wind power forecasting uncertainty for day ahead and intraday markets. • Statistical relationship between total load and wind power generation. • Accurately forecast expected revenues from wind producer’s perspective. - Abstract: The growing share of electricity production from variable renewable energy sources increases the stochastic nature of the power system. This has repercussions on the markets for electricity. Deviations from forecasted production schedules require balancing of a generator’s position within a day. Short term products that are traded on power and/or reserve markets have been developed for this purpose, providing opportunities to actors who can offer flexibility in the short term. The value of flexibility is typically modelled using stochastic scenario extensions of dispatch models which requires, as a first step, understanding the nature of forecast uncertainties. This study provides a new approach for determining the forecast errors of wind power generation in the time period between the closure of the day ahead and the opening of the first intraday session using Spain as an example. The methodology has been developed using time series analysis for the years 2010–2013 to find the explanatory variables of the wind error variability by applying clustering techniques to reduce the range of uncertainty, and regressive techniques to forecast the probability density functions of the intra-day price. This methodology has been tested considering different system actions showing its suitability for developing intra-day bidding strategies and also for the generation of electricity generated from Renewable Energy Sources scenarios. This methodology could help a wind power producer to optimally bid into the intraday market based on more accurate scenarios, increasing their revenues and the system value of wind.

Strategic Forecasting

DEFF Research Database (Denmark)

Duus, Henrik Johannsen

2016-01-01

Purpose: The purpose of this article is to present an overview of the area of strategic forecasting and its research directions and to put forward some ideas for improving management decisions. Design/methodology/approach: This article is conceptual but also informed by the author’s long contact...... and collaboration with various business firms. It starts by presenting an overview of the area and argues that the area is as much a way of thinking as a toolbox of theories and methodologies. It then spells out a number of research directions and ideas for management. Findings: Strategic forecasting is seen...... as a rebirth of long range planning, albeit with new methods and theories. Firms should make the building of strategic forecasting capability a priority. Research limitations/implications: The article subdivides strategic forecasting into three research avenues and suggests avenues for further research efforts...

Regional hydrological models for distributed flash-floods forecasting: towards an estimation of potential impacts and damages

Science.gov (United States)

Le Bihan, Guillaume; Payrastre, Olivier; Gaume, Eric; Pons, Frederic; Moncoulon, David

2016-04-01

Hydrometeorological forecasting is an essential component of real-time flood management. The information it provides is of great help for crisis managers to anticipate the inundations and the associated risks. In the particular case of flash-floods, which may affect a large amount of small watersheds spread over the territory (up to 300 000 km of waterways considering a drained area of 5 km² minimum in France), appropriate flood forecasting systems are still under development. In France, highly distributed hydrological models have been implemented, enabling a real-time assessment of the potential intensity of flash-floods from the records of weather radars: AIGA-hydro system (Lavabre et al., 2005; Javelle et al., 2014), PreDiFlood project (Naulin et al., 2013). The approach presented here aims to go one step further by offering a direct assessment of the potential impacts of the simulated floods on inhabited areas. This approach is based on an a priori analysis of the study area in order (1) to evaluate with a simplified hydraulic approach (DTM treatment) the potentially flooded areas for different discharge levels, and (2) to identify the associated buildings and/or population at risk from geographic databases. This preliminary analysis enables to build an impact model (discharge-impact curve) on each river reach, which is then used to directly estimate the potentially affected assets based on a distributed rainfall runoff model. The overall principle of this approach was already presented at the 8th Hymex workshop. Therefore, the presentation will be here focused on the first validation results in terms of (1) accuracy of flooded areas simulated from DTM treatments, and (2) relevance of estimated impacts. The inundated areas simulated were compared to the European Directive cartography results (where available), showing an overall good correspondence in a large majority of cases, but also very significant errors for approximatively 10% of the river reaches

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.

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.

Science.gov (United States)

Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan

2017-01-01

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

Effect of heterogeneity in a horizontal well with multiple fractures on the long term forecast in shale gas reservoirs

Energy Technology Data Exchange (ETDEWEB)

Nobakht, M.; Ambrose, R.; Clarkson, C.R. [Society of Petroleum Engineers (Canada)

2011-07-01

Multiple fracture horizontal wells (MFHWs) are the most popular type of method used for exploiting shale gas reservoirs. When analyzing MFHW's a homogeneous completion model is often used, but this rarely occurs in the field. This paper develops a hybrid method for forecasting MFHWs based on a heterogeneous completion and investigates the effect of completion heterogeneity on production forecasts. First, a current forecasting method for homogeneous completions was modified for heterogeneous completions. The new forecasting method was then validated using a numerical simulation. A relationship between Arps' hyperbolic decline exponent and the heterogeneity of a completion for a particular case was then developed. Lastly, a field case was analyzed to compare the impact of forecasting with and without taking a heterogeneous completion into consideration. Through analysis and simulations this paper found that the long-term forecast of MFHWs can be greatly impacted should heterogeneity of the completion be ignored.

Information Technology Diffusion: Impact on Student Achievement

Science.gov (United States)

Lee, Gregory M.; Lind, Mary L.

2011-01-01

For student achievement, the diffusion and adoption of information technology (IT) infrastructure enabled by special funding was posited to have a positive impact on student achievement. Four urban school districts provided the context for this study to assess the impact of IT adoption on standardized test scores.

Multigenerational organisations: a challenge for technology and social change

NARCIS (Netherlands)

Millar-Schijf, Carla C.J.M.; Lockett, Martin

2014-01-01

This paper analyses demographic and organisational trends associated with an ageing workforce and introduces the articles in the special issue of Technological Forecasting and Social Change on Ageing2Agility: Multi-stakeholder Technological Forecasting for the Multi-generational Challenges in the

Air Quality Forecasting through Different Statistical and Artificial Intelligence Techniques

Science.gov (United States)

Mishra, D.; Goyal, P.

2014-12-01

Urban air pollution forecasting has emerged as an acute problem in recent years because there are sever environmental degradation due to increase in harmful air pollutants in the ambient atmosphere. In this study, there are different types of statistical as well as artificial intelligence techniques are used for forecasting and analysis of air pollution over Delhi urban area. These techniques are principle component analysis (PCA), multiple linear regression (MLR) and artificial neural network (ANN) and the forecasting are observed in good agreement with the observed concentrations through Central Pollution Control Board (CPCB) at different locations in Delhi. But such methods suffers from disadvantages like they provide limited accuracy as they are unable to predict the extreme points i.e. the pollution maximum and minimum cut-offs cannot be determined using such approach. Also, such methods are inefficient approach for better output forecasting. But with the advancement in technology and research, an alternative to the above traditional methods has been proposed i.e. the coupling of statistical techniques with artificial Intelligence (AI) can be used for forecasting purposes. The coupling of PCA, ANN and fuzzy logic is used for forecasting of air pollutant over Delhi urban area. The statistical measures e.g., correlation coefficient (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA) of the proposed model are observed in better agreement with the all other models. Hence, the coupling of statistical and artificial intelligence can be use for the forecasting of air pollutant over urban area.

Understanding the impact of technology on firms’ business models

DEFF Research Database (Denmark)

Cavalcante, Sergio Andre

2013-01-01

of a new business model for the partner companies in the consortium. Practical implications – This paper is important in that it will help companies understand technological impact from a business model perspective, thereby enabling them to manage innovation better by distinguishing between the creation......, extension, revision or termination of business models. Originality/value – The main contribution of this study is its use of the business model perspective to analyse the impact of an emergent technology on companies’ innovation activities. This perspective makes it easier to develop strategic initiatives......Purpose – The purpose of this paper is to identify the impact of a new global positioning technology on firms’ business models. Design/methodology/approach – The empirical setting was a consortium of Danish organizations, established to develop a positioning-based technology platform as a basis...

Prevention and forecasting of rock burst hazards in coal mines

Energy Technology Data Exchange (ETDEWEB)

Lin-ming Dou; Cai-ping Lu; Zong-long Mu; Ming-shi Gao [China University of Mining & Technology, Xuzhou (China). State Key Laboratory for Coal Resource and Mine Safety

2009-09-15

Rock bursts signify extreme behavior in coal mine strata and severely threaten the safety of the lives of miners, as well as the effectiveness and productivity of miners. In our study, an elastic-plastic-brittle model for the deformation and failure of coal/rock was established through theoretical analyses, laboratory experiments and field testing, simulation and other means, which perfectly predict sudden and delayed rock bursts. Based on electromagnetic emission (EME), acoustic emission (AE) and microseismic (MS) effects in the process from deformation until impact rupture of coal-rock combination samples, a multi-parameter identification of premonitory technology was formed, largely depending on these three forms of emission. Thus a system of classification for forecasting rock bursts in space and time was established. We have presented the intensity weakening theory for rock bursts and a strong-soft-strong (3S) structural model for controlling the impact on rock surrounding roadways, with the objective of laying a theoretical foundation and establishing references for parameters for the weakening control of rock bursts. For the purpose of prevention, key technical parameters of directional hydraulic fracturing are revealed. Based on these results, as well as those from deep-hole controlled blasting in coal seams and rock, integrated control techniques were established and anti-impact hydraulic props, suitable for roadways subject to hazards from rockbursts have also been developed. These technologies have been widely used in most coal mines in China, subject to these hazards and have achieved remarkable economic and social benefits. 28 refs., 9 figs., 1 tab.

Forecasting wind-driven wildfires using an inverse modelling approach

Directory of Open Access Journals (Sweden)

O. Rios

2014-06-01

Full Text Available A technology able to rapidly forecast wildfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the ongoing fire. This paper presents and explores a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real-time data assimilation and inverse modelling. The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimisation problem with a tangent linear approach and forward automatic differentiation. Its potential is investigated using synthetic data and evaluated in different wildfire scenarios. The results show the capacity of the method to quickly predict the location of the fire front with a positive lead time (ahead of the event in the order of 10 min for a spatial scale of 100 m. The greatest strengths of our method are lightness, speed and flexibility. We specifically tailor the forecast to be efficient and computationally cheap so it can be used in mobile systems for field deployment and operativeness. Thus, we put emphasis on producing a positive lead time and the means to maximise it.

Technology trends in econometric energy models: Ignorance or information?

International Nuclear Information System (INIS)

Boyd, G.; Kokkelenberg, E.; State Univ., of New York, Binghamton, NY; Ross, M.; Michigan Univ., Ann Arbor, MI

1991-01-01

Simple time trend variables in factor demand models can be statistically powerful variables, but may tell the researcher very little. Even more complex specification of technical change, e.g. factor biased, are still the economentrician's ''measure of ignorance'' about the shifts that occur in the underlying production process. Furthermore, in periods of rapid technology change the parameters based on time trends may be too large for long run forecasting. When there is clearly identifiable engineering information about new technology adoption that changes the factor input mix, data for the technology adoption may be included in the traditional factor demand model to economically model specific factor biased technical change and econometrically test their contribution. The adoption of thermomechanical pulping (TMP) and electric are furnaces (EAF) are two electricity intensive technology trends in the Paper and Steel industries, respectively. This paper presents the results of including these variables in a tradition econometric factor demand model, which is based on the Generalized Leontief. The coefficients obtained for this ''engineering based'' technical change compares quite favorably to engineering estimates of the impact of TMP and EAF on electricity intensities, improves the estimates of the other price coefficients, and yields a more believable long run electricity forecast. 6 refs., 1 fig

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

Using Science Data and Models for Space Weather Forecasting - Challenges and Opportunities

Science.gov (United States)

Hesse, Michael; Pulkkinen, Antti; Zheng, Yihua; Maddox, Marlo; Berrios, David; Taktakishvili, Sandro; Kuznetsova, Masha; Chulaki, Anna; Lee, Hyesook; Mullinix, Rick;

A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm

International Nuclear Information System (INIS)

Guo, Zhenhai; Chi, Dezhong; Wu, Jie; Zhang, Wenyu

2014-01-01

Highlights: • Impact of meteorological factors on wind speed forecasting is taken into account. • Forecasted wind speed results are corrected by the associated rules. • Forecasting accuracy is improved by the new wind speed forecasting strategy. • Robust of the proposed model is validated by data sampled from different sites. - Abstract: Wind energy has been the fastest growing renewable energy resource in recent years. Because of the intermittent nature of wind, wind power is a fluctuating source of electrical energy. Therefore, to minimize the impact of wind power on the electrical grid, accurate and reliable wind power forecasting is mandatory. In this paper, a new wind speed forecasting approach based on based on the chaotic time series modelling technique and the Apriori algorithm has been developed. The new approach consists of four procedures: (I) Clustering by using the k-means clustering approach; (II) Employing the Apriori algorithm to discover the association rules; (III) Forecasting the wind speed according to the chaotic time series forecasting model; and (IV) Correcting the forecasted wind speed data using the associated rules discovered previously. This procedure has been verified by 31-day-ahead daily average wind speed forecasting case studies, which employed the wind speed and other meteorological data collected from four meteorological stations located in the Hexi Corridor area of China. The results of these case studies reveal that the chaotic forecasting model can efficiently improve the accuracy of the wind speed forecasting, and the Apriori algorithm can effectively discover the association rules between the wind speed and other meteorological factors. In addition, the correction results demonstrate that the association rules discovered by the Apriori algorithm have powerful capacities in handling the forecasted wind speed values correction when the forecasted values do not match the classification discovered by the association rules

Development of a forecast model for global air traffic emissions

Energy Technology Data Exchange (ETDEWEB)

Schaefer, Martin

2012-07-01

The thesis describes the methodology and results of a simulation model that quantifies fuel consumption and emissions of civil air traffic. Besides covering historical emissions, the model aims at forecasting emissions in the medium-term future. For this purpose, simulation models of aircraft and engine types are used in combination with a database of global flight movements and assumptions about traffic growth, fleet rollover and operational aspects. Results from an application of the model include emissions of scheduled air traffic for the years 2000 to 2010 as well as forecasted emissions until the year 2030. In a baseline scenario of the forecast, input assumptions (e.g. traffic growth rates) are in line with predictions by the aircraft industry. Considering the effects of advanced technologies of the short-term and medium-term future, the forecast focusses on fuel consumption and emissions of nitric oxides. Calculations for historical air traffic additionally cover emissions of carbon monoxide, unburned hydrocarbons and soot. Results are validated against reference data including studies by the International Civil Aviation Organization (ICAO) and simulation results from international research projects. (orig.)

Air Pollution Forecasts: An Overview.

Science.gov (United States)

Bai, Lu; Wang, Jianzhou; Ma, Xuejiao; Lu, Haiyan

2018-04-17

Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

Air Pollution Forecasts: An Overview

Directory of Open Access Journals (Sweden)

Lu Bai

2018-04-01

Full Text Available Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

Air Pollution Forecasts: An Overview

Science.gov (United States)

Bai, Lu; Wang, Jianzhou; Lu, Haiyan

2018-01-01

Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies. PMID:29673227

Short-Term Solar Forecasting Performance of Popular Machine Learning Algorithms: Preprint

Energy Technology Data Exchange (ETDEWEB)

Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Elgindy, Tarek [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dobbs, Alex [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-10-03

A framework for assessing the performance of short-term solar forecasting is presented in conjunction with a range of numerical results using global horizontal irradiation (GHI) from the open-source Surface Radiation Budget (SURFRAD) data network. A suite of popular machine learning algorithms is compared according to a set of statistically distinct metrics and benchmarked against the persistence-of-cloudiness forecast and a cloud motion forecast. Results show significant improvement compared to the benchmarks with trade-offs among the machine learning algorithms depending on the desired error metric. Training inputs include time series observations of GHI for a history of years, historical weather and atmospheric measurements, and corresponding date and time stamps such that training sensitivities might be inferred. Prediction outputs are GHI forecasts for 1, 2, 3, and 4 hours ahead of the issue time, and they are made for every month of the year for 7 locations. Photovoltaic power and energy outputs can then be made using the solar forecasts to better understand power system impacts.

The History of Technology in Education. A Comparative Study and Forecast

OpenAIRE

del Campo, Jose María; Negro, Vicente; Núñez, Miguel

2012-01-01

Area, launched in 1999 with the Bologna Declaration, has bestowed such a magnitude and unprecedented agility to the transformation process undertaken by European universities. However, the change has been more profound and drastic with regards to the use of new technologies both inside and outside the classroom. This article focuses on the study and analysis of the technology’s history within the university education and its impact on teachers, students and teaching methods. A...

Satellite fixed communications service: A forecast of potential domestic demand through the year 2000. Volume 3: Appendices

Science.gov (United States)

Kratochvil, D.; Bowyer, J.; Bhushan, C.; Steinnagel, K.; Kaushal, D.; Al-Kinani, G.

1983-09-01

Voice applications, data applications, video applications, impacted baseline forecasts, market distribution model, net long haul forecasts, trunking earth station definition and costs, trunking space segment cost, trunking entrance/exit links, trunking network costs and crossover distances with terrestrial tariffs, net addressable forecasts, capacity requirements, improving spectrum utilization, satellite system market development, and the 30/20 net accessible market are considered.