Development and application of a statistical methodology to evaluate the predictive accuracy of building energy baseline models

Energy Technology Data Exchange (ETDEWEB)

Granderson, Jessica [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Energy Technologies Area Div.; Price, Phillip N. [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Energy Technologies Area Div.

2014-03-01

This paper documents the development and application of a general statistical methodology to assess the accuracy of baseline energy models, focusing on its application to Measurement and Verification (M&V) of whole-­building energy savings. The methodology complements the principles addressed in resources such as ASHRAE Guideline 14 and the International Performance Measurement and Verification Protocol. It requires fitting a baseline model to data from a ``training period’’ and using the model to predict total electricity consumption during a subsequent ``prediction period.’’ We illustrate the methodology by evaluating five baseline models using data from 29 buildings. The training period and prediction period were varied, and model predictions of daily, weekly, and monthly energy consumption were compared to meter data to determine model accuracy. Several metrics were used to characterize the accuracy of the predictions, and in some cases the best-­performing model as judged by one metric was not the best performer when judged by another metric.

A model to predict radon exhalation from walls to indoor air based on the exhalation from building material samples

International Nuclear Information System (INIS)

Sahoo, B.K.; Sapra, B.K.; Gaware, J.J.; Kanse, S.D.; Mayya, Y.S.

2011-01-01

In recognition of the fact that building materials are an important source of indoor radon, second only to soil, surface radon exhalation fluxes have been extensively measured from the samples of these materials. Based on this flux data, several researchers have attempted to predict the inhalation dose attributable to radon emitted from walls and ceilings made up of these materials. However, an important aspect not considered in this methodology is the enhancement of the radon flux from the wall or the ceiling constructed using the same building material. This enhancement occurs mainly because of the change in the radon diffusion process from the former to the latter configuration. To predict the true radon flux from the wall based on the flux data of building material samples, we now propose a semi-empirical model involving radon diffusion length and the physical dimensions of the samples as well as wall thickness as other input parameters. This model has been established by statistically fitting the ratio of the solution to radon diffusion equations for the cases of three-dimensional cuboidal shaped building materials (such as brick, concrete block) and one dimensional wall system to a simple mathematical function. The model predictions have been validated against the measurements made at a new construction site. This model provides an alternative tool (substitute to conventional 1-D model) to estimate radon flux from a wall without relying on 226 Ra content, radon emanation factor and bulk density of the samples. Moreover, it may be very useful in the context of developing building codes for radon regulation in new buildings. - Research highlights: → A model is proposed to predict radon flux from wall using flux of building material. → It is established based on the diffusion mechanism in building material and wall. → Study showed a large difference in radon flux from building material and wall. → Model has been validated against the measurements made at

Comparative analysis of modified PMV models and SET models to predict human thermal sensation in naturally ventilated buildings

DEFF Research Database (Denmark)

Gao, Jie; Wang, Yi; Wargocki, Pawel

2015-01-01

In this paper, a comparative analysis was performed on the human thermal sensation estimated by modified predicted mean vote (PMV) models and modified standard effective temperature (SET) models in naturally ventilated buildings; the data were collected in field study. These prediction models were....../s, the expectancy factors for the extended PMV model and the extended SET model were from 0.770 to 0.974 and from 1.330 to 1.363, and the adaptive coefficients for the adaptive PMV model and the adaptive SET model were from 0.029 to 0.167 and from-0.213 to-0.195. In addition, the difference in thermal sensation...... between the measured and predicted values using the modified PMV models exceeded 25%, while the difference between the measured thermal sensation and the predicted thermal sensation using modified SET models was approximately less than 25%. It is concluded that the modified SET models can predict human...

Actual building energy use patterns and their implications for predictive modeling

International Nuclear Information System (INIS)

Heidarinejad, Mohammad; Cedeño-Laurent, Jose G.; Wentz, Joshua R.; Rekstad, Nicholas M.; Spengler, John D.; Srebric, Jelena

2017-01-01

Highlights: • Developed three building categories based on energy use patterns of campus buildings. • Evaluated implication of temporal energy data granularity on predictive modeling. • Demonstrated importance of monitoring daily chilled water consumption. • Identified interval electricity data as an indicator of building operation schedules. • Demonstrated a calibration process for energy modeling of a campus building. - Abstract: The main goal of this study is to understand the patterns in which commercial buildings consume energy, rather than evaluating building energy use based on aggregate utility bills typically linked to building principal tenant activity or occupancy type. The energy consumption patterns define buildings as externally-load, internally-load, or mixed-load dominated buildings. Penn State and Harvard campuses serve as case studies for this particular research project. The buildings in these two campuses use steam, chilled water, and electricity as energy commodities and maintain databases of different resolutions to include minute, hourly, daily, and monthly data instances depending on the commodity and available data acquisition system. The results of this study show monthly steam consumption directly correlates to outdoor environmental conditions for 88% of the studied buildings, while chilled water consumption has negligible correlation to the outdoor environmental conditions. Thus, in terms of monthly chilled water consumption, 86% of buildings are internally-load and mixed-load dominated, respectively. Chilled water consumption is better suited for the daily analyses compared to the monthly and hourly analyses. While the influence of building operation schedules affects the analyses at the hourly level, the monthly chilled water consumptions are not good indicators of the building energy consumption patterns. Electricity consumption at the monthly (or seasonal) level can support the building energy simulation tools for the

Predicting energy performance of a net-zero energy building: A statistical approach

International Nuclear Information System (INIS)

Kneifel, Joshua; Webb, David

2016-01-01

Highlights: • A regression model is applied to actual energy data from a net-zero energy building. • The model is validated through a rigorous statistical analysis. • Comparisons are made between model predictions and those of a physics-based model. • The model is a viable baseline for evaluating future models from the energy data. - Abstract: Performance-based building requirements have become more prevalent because it gives freedom in building design while still maintaining or exceeding the energy performance required by prescriptive-based requirements. In order to determine if building designs reach target energy efficiency improvements, it is necessary to estimate the energy performance of a building using predictive models and different weather conditions. Physics-based whole building energy simulation modeling is the most common approach. However, these physics-based models include underlying assumptions and require significant amounts of information in order to specify the input parameter values. An alternative approach to test the performance of a building is to develop a statistically derived predictive regression model using post-occupancy data that can accurately predict energy consumption and production based on a few common weather-based factors, thus requiring less information than simulation models. A regression model based on measured data should be able to predict energy performance of a building for a given day as long as the weather conditions are similar to those during the data collection time frame. This article uses data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility (NZERTF) to develop and validate a regression model to predict the energy performance of the NZERTF using two weather variables aggregated to the daily level, applies the model to estimate the energy performance of hypothetical NZERTFs located in different cities in the Mixed-Humid Climate Zone, and compares these

Influence of precision of emission characteristic parameters on model prediction error of VOCs/formaldehyde from dry building material.

Directory of Open Access Journals (Sweden)

Wenjuan Wei

Full Text Available Mass transfer models are useful in predicting the emissions of volatile organic compounds (VOCs and formaldehyde from building materials in indoor environments. They are also useful for human exposure evaluation and in sustainable building design. The measurement errors in the emission characteristic parameters in these mass transfer models, i.e., the initial emittable concentration (C 0, the diffusion coefficient (D, and the partition coefficient (K, can result in errors in predicting indoor VOC and formaldehyde concentrations. These errors have not yet been quantitatively well analyzed in the literature. This paper addresses this by using modelling to assess these errors for some typical building conditions. The error in C 0, as measured in environmental chambers and applied to a reference living room in Beijing, has the largest influence on the model prediction error in indoor VOC and formaldehyde concentration, while the error in K has the least effect. A correlation between the errors in D, K, and C 0 and the error in the indoor VOC and formaldehyde concentration prediction is then derived for engineering applications. In addition, the influence of temperature on the model prediction of emissions is investigated. It shows the impact of temperature fluctuations on the prediction errors in indoor VOC and formaldehyde concentrations to be less than 7% at 23±0.5°C and less than 30% at 23±2°C.

A diffusivity model for predicting VOC diffusion in porous building materials based on fractal theory.

Science.gov (United States)

Liu, Yanfeng; Zhou, Xiaojun; Wang, Dengjia; Song, Cong; Liu, Jiaping

2015-12-15

Most building materials are porous media, and the internal diffusion coefficients of such materials have an important influences on the emission characteristics of volatile organic compounds (VOCs). The pore structure of porous building materials has a significant impact on the diffusion coefficient. However, the complex structural characteristics bring great difficulties to the model development. The existing prediction models of the diffusion coefficient are flawed and need to be improved. Using scanning electron microscope (SEM) observations and mercury intrusion porosimetry (MIP) tests of typical porous building materials, this study developed a new diffusivity model: the multistage series-connection fractal capillary-bundle (MSFC) model. The model considers the variable-diameter capillaries formed by macropores connected in series as the main mass transfer paths, and the diameter distribution of the capillary bundles obeys a fractal power law in the cross section. In addition, the tortuosity of the macrocapillary segments with different diameters is obtained by the fractal theory. Mesopores serve as the connections between the macrocapillary segments rather than as the main mass transfer paths. The theoretical results obtained using the MSFC model yielded a highly accurate prediction of the diffusion coefficients and were in a good agreement with the VOC concentration measurements in the environmental test chamber. Copyright © 2015 Elsevier B.V. All rights reserved.

Economic Model Predictive Control for Building Climate Control in a Smart Grid

DEFF Research Database (Denmark)

Halvgaard, Rasmus; Poulsen, Niels Kjølstad; Madsen, Henrik

2012-01-01

Model Predictive Control (MPC) can be used to control a system of energy producers and consumers in a Smart Grid. In this paper, we use heat pumps for heating residential buildings with a floor heating system. We use the thermal capacity of the building to shift the electricity consumptions...... to periods with low energy prices. In this way the heating system of the house becomes a flexible power consumer in the Smart Grid. This scenario is relevant for systems with a significant share of stochastic energy producers, e.g. wind turbines, where the ability to shift power consumption according...... and electricity price. Simulation studies demonstrate the capabilities of the proposed model and algorithm. Compared to traditional operation of heat pumps with constant electricity prices, the optimized operating strategy saves 25-33% of the electricity cost....

Analytical Model of Underground Train Induced Vibrations on Nearby Building Structures in Cameroon: Assessment and Prediction

Directory of Open Access Journals (Sweden)

Lezin Seba MINSILI

2013-11-01

Full Text Available The purpose of this research paper was to assess and predict the effect of vibrations induced by an underground railway on nearby-existing buildings prior to the construction of projected new railway lines of the National Railway Master Plan of Cameroon and after upgrading of the railway conceded to CAMRAIL linking the two most densely populated cities of Cameroon: Douala and Yaoundé. With the source-transmitter-receiver mathematical model as the train-soil-structure interaction model, taking into account sub-model parameters such as type of the train-railway system, typical geotechnical conditions of the ground and the sensitivity of the nearby buildings, the analysis is carried out over the entire system using the dynamic finite element method in the time domain. This subdivision of the model is a powerful tool that allows to consider different alternatives of sub-models with different characteristics, and thus to determine any critical excessive vibration impact. Based on semi-empirical analytical results obtained from presented models, the present work assesses and predicts characteristics of traffic-induced vibrations as a function of time duration, intensity and vehicle speed, as well as their influence on buildings at different levels.

A neuro-fuzzy model for prediction of the indoor temperature in typical Australian residential buildings

Energy Technology Data Exchange (ETDEWEB)

Alasha' ary, Haitham; Moghtaderi, Behdad; Page, Adrian; Sugo, Heber [Priority Research Centre for Energy, Chemical Engineering, School of Engineering, Faculty of Engineering and Built Environment, the University of Newcastle, Callaghan, Newcastle, NSW 2308 (Australia)

2009-07-15

The Masonry Research Group at The University of Newcastle, Australia has embarked on an extensive research program to study the thermal performance of common walling systems in Australian residential buildings by studying the thermal behaviour of four representative purpose-built thermal test buildings (referred to as 'test modules' or simply 'modules' hereafter). The modules are situated on the university campus and are constructed from brick veneer (BV), cavity brick (CB) and lightweight (LW) constructions. The program of study has both experimental and analytical strands, including the use of a neuro-fuzzy approach to predict the thermal behaviour. The latter approach employs an experimental adaptive neuro-fuzzy inference system (ANFIS) which is used in this study to predict the room (indoor) temperatures of the modules under a range of climatic conditions pertinent to Newcastle (NSW, Australia). The study shows that this neuro-fuzzy model is capable of accurately predicting the room temperature of such buildings; thus providing a potential computationally efficient and inexpensive predictive tool for the more effective thermal design of housing. (author)

Solar energy in buildings solved by building information modeling

Science.gov (United States)

Chudikova, B.; Faltejsek, M.

2018-03-01

Building lead us to use renewable energy sources for all types of buildings. The use of solar energy is the alternatives that can be applied in a good ratio of space, price, and resultant benefits. Building Information Modelling is a modern and effective way of dealing with buildings with regard to all aspects of the life cycle. The basis is careful planning and simulation in the pre-investment phase, where it is possible to determine the effective result and influence the lifetime of the building and the cost of its operation. By simulating, analysing and insert a building model into its future environment where climate conditions and surrounding buildings play a role, it is possible to predict the usability of the solar energy and establish an ideal model. Solar systems also very affect the internal layout of buildings. Pre-investment phase analysis, with a view to future aspects, will ensure that the resulting building will be both low-energy and environmentally friendly.

Towards building a neural network model for predicting pile static load test curves

Directory of Open Access Journals (Sweden)

Alzo’ubi A. K.

2018-01-01

Full Text Available In the United Arab Emirates, Continuous Flight Auger piles are the most widely used type of deep foundation. To test the pile behaviour, the Static Load Test is routinely conducted in the field by increasing the dead load while monitoring the displacement. Although the test is reliable, it is expensive to conduct. This test is usually conducted in the UAE to verify the pile capacity and displacement as the load increase and decreases in two cycles. In this paper we will utilize the Artificial Neural Network approach to build a model that can predict a complete Static Load Pile test. We will show that by integrating the pile configuration, soil properties, and ground water table in one artificial neural network model, the Static Load Test can be predicted with confidence. We believe that based on this approach, the model is able to predict the entire pile load test from start to end. The suggested approach is an excellent tool to reduce the cost associated with such expensive tests or to predict pile’s performance ahead of the actual test.

Tailored high-resolution numerical weather forecasts for energy efficient predictive building control

Science.gov (United States)

Stauch, V. J.; Gwerder, M.; Gyalistras, D.; Oldewurtel, F.; Schubiger, F.; Steiner, P.

2010-09-01

The high proportion of the total primary energy consumption by buildings has increased the public interest in the optimisation of buildings' operation and is also driving the development of novel control approaches for the indoor climate. In this context, the use of weather forecasts presents an interesting and - thanks to advances in information and predictive control technologies and the continuous improvement of numerical weather prediction (NWP) models - an increasingly attractive option for improved building control. Within the research project OptiControl (www.opticontrol.ethz.ch) predictive control strategies for a wide range of buildings, heating, ventilation and air conditioning (HVAC) systems, and representative locations in Europe are being investigated with the aid of newly developed modelling and simulation tools. Grid point predictions for radiation, temperature and humidity of the high-resolution limited area NWP model COSMO-7 (see www.cosmo-model.org) and local measurements are used as disturbances and inputs into the building system. The control task considered consists in minimizing energy consumption whilst maintaining occupant comfort. In this presentation, we use the simulation-based OptiControl methodology to investigate the impact of COSMO-7 forecasts on the performance of predictive building control and the resulting energy savings. For this, we have selected building cases that were shown to benefit from a prediction horizon of up to 3 days and therefore, are particularly suitable for the use of numerical weather forecasts. We show that the controller performance is sensitive to the quality of the weather predictions, most importantly of the incident radiation on differently oriented façades. However, radiation is characterised by a high temporal and spatial variability in part caused by small scale and fast changing cloud formation and dissolution processes being only partially represented in the COSMO-7 grid point predictions. On the

A study of the importance of occupancy to building cooling load in prediction by intelligent approach

Energy Technology Data Exchange (ETDEWEB)

Kwok, Simon S.K. [Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong (Hong Kong); Lee, Eric W.M., E-mail: ericlee@cityu.edu.h [Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong (Hong Kong)

2011-07-15

Research highlights: {yields} The building occupancy affecting the cooling load prediction is studied. {yields} PENN model is adopted in this study for predicting the building cooling load. {yields} Statistical approach is adopted to result a less prejudice prediction performance. {yields} Results show that occupancy data can significantly improve the prediction. -- Abstract: Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry today have been developed from either forward or inverse modeling approaches. However, most of these models require extensive computer resources and involve lengthy computation. This paper discusses the use of data-driven intelligent approaches, a probabilistic entropy-based neural (PENN) model to predict the cooling load of a building. Although it is common knowledge that the presence and activity of building occupants have a significant impact on the required cooling load of buildings, practices currently adopted in modeling the presence and activity of people in buildings do not reflect the complexity of the impact occupants have on building cooling load. In contrast to previous artificial neural network (ANN) models, most of which employ a fixed schedule or historic load data to represent building occupancy in simulating building cooling load, this paper introduces two input parameters, dynamic occupancy area and rate and uses it to mimic building cooling load. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing grade A mega office buildings in Hong Kong with tenants including many multi-national financial companies that require 24-h air conditioning seven days a week. The dynamic changes that occur in the occupancy of these buildings therefore make it very difficult to forecast building cooling load by means of a fixed time schedule. The performance of

A study of the importance of occupancy to building cooling load in prediction by intelligent approach

International Nuclear Information System (INIS)

Kwok, Simon S.K.; Lee, Eric W.M.

2011-01-01

Research highlights: → The building occupancy affecting the cooling load prediction is studied. → PENN model is adopted in this study for predicting the building cooling load. → Statistical approach is adopted to result a less prejudice prediction performance. → Results show that occupancy data can significantly improve the prediction. -- Abstract: Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry today have been developed from either forward or inverse modeling approaches. However, most of these models require extensive computer resources and involve lengthy computation. This paper discusses the use of data-driven intelligent approaches, a probabilistic entropy-based neural (PENN) model to predict the cooling load of a building. Although it is common knowledge that the presence and activity of building occupants have a significant impact on the required cooling load of buildings, practices currently adopted in modeling the presence and activity of people in buildings do not reflect the complexity of the impact occupants have on building cooling load. In contrast to previous artificial neural network (ANN) models, most of which employ a fixed schedule or historic load data to represent building occupancy in simulating building cooling load, this paper introduces two input parameters, dynamic occupancy area and rate and uses it to mimic building cooling load. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing grade A mega office buildings in Hong Kong with tenants including many multi-national financial companies that require 24-h air conditioning seven days a week. The dynamic changes that occur in the occupancy of these buildings therefore make it very difficult to forecast building cooling load by means of a fixed time schedule. The performance of simulation results

Prediction of failure modes for concrete nuclear-containment buildings

International Nuclear Information System (INIS)

Butler, T.A.

1982-01-01

The failure modes and associated failure pressures for two common generic types of PWR containments are predicted. One building type is a lightly reinforced, posttensioned structure represented by the Zion nuclear reactor containment. The other is the normally reinforced Indian Point containment. Two-dimensional models of the buildings developed using the finite element method are used to predict the failure modes and failure pressures. Predicted failure modes for both containments involve loss of structural integrity at the intersection of the cylindrical sidewall with the base slab

Prediction of Indoor Air Exposure from Outdoor Air Quality Using an Artificial Neural Network Model for Inner City Commercial Buildings.

Science.gov (United States)

Challoner, Avril; Pilla, Francesco; Gill, Laurence

2015-12-01

NO₂ and particulate matter are the air pollutants of most concern in Ireland, with possible links to the higher respiratory and cardiovascular mortality and morbidity rates found in the country compared to the rest of Europe. Currently, air quality limits in Europe only cover outdoor environments yet the quality of indoor air is an essential determinant of a person's well-being, especially since the average person spends more than 90% of their time indoors. The modelling conducted in this research aims to provide a framework for epidemiological studies by the use of publically available data from fixed outdoor monitoring stations to predict indoor air quality more accurately. Predictions are made using two modelling techniques, the Personal-exposure Activity Location Model (PALM), to predict outdoor air quality at a particular building, and Artificial Neural Networks, to model the indoor/outdoor relationship of the building. This joint approach has been used to predict indoor air concentrations for three inner city commercial buildings in Dublin, where parallel indoor and outdoor diurnal monitoring had been carried out on site. This modelling methodology has been shown to provide reasonable predictions of average NO₂ indoor air quality compared to the monitored data, but did not perform well in the prediction of indoor PM2.5 concentrations. Hence, this approach could be used to determine NO₂ exposures more rigorously of those who work and/or live in the city centre, which can then be linked to potential health impacts.

Development of Building Thermal Load and Discomfort Degree Hour Prediction Models Using Data Mining Approaches

Directory of Open Access Journals (Sweden)

Yaolin Lin

2018-06-01

Full Text Available Thermal load and indoor comfort level are two important building performance indicators, rapid predictions of which can help significantly reduce the computation time during design optimization. In this paper, a three-step approach is used to develop and evaluate prediction models. Firstly, the Latin Hypercube Sampling Method (LHSM is used to generate a representative 19-dimensional design database and DesignBuilder is then used to obtain the thermal load and discomfort degree hours through simulation. Secondly, samples from the database are used to develop and validate seven prediction models, using data mining approaches including multilinear regression (MLR, chi-square automatic interaction detector (CHAID, exhaustive CHAID (ECHAID, back-propagation neural network (BPNN, radial basis function network (RBFN, classification and regression trees (CART, and support vector machines (SVM. It is found that the MLR and BPNN models outperform the others in the prediction of thermal load with average absolute error of less than 1.19%, and the BPNN model is the best at predicting discomfort degree hour with 0.62% average absolute error. Finally, two hybrid models—MLR (MLR + BPNN and MLR-BPNN—are developed. The MLR-BPNN models are found to be the best prediction models, with average absolute error of 0.82% in thermal load and 0.59% in discomfort degree hour.

USE OF ROUGH SETS AND SPECTRAL DATA FOR BUILDING PREDICTIVE MODELS OF REACTION RATE CONSTANTS

Science.gov (United States)

A model for predicting the log of the rate constants for alkaline hydrolysis of organic esters has been developed with the use of gas-phase min-infrared library spectra and a rule-building software system based on the mathematical theory of rough sets. A diverse set of 41 esters ...

User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response: Preprint

Energy Technology Data Exchange (ETDEWEB)

Jin, Xin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Baker, Kyri A. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Christensen, Dane T. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Isley, Steven C. [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-08-21

This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility and reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.

RCrane: semi-automated RNA model building

International Nuclear Information System (INIS)

Keating, Kevin S.; Pyle, Anna Marie

2012-01-01

RCrane is a new tool for the partially automated building of RNA crystallographic models into electron-density maps of low or intermediate resolution. This tool helps crystallographers to place phosphates and bases into electron density and then automatically predicts and builds the detailed all-atom structure of the traced nucleotides. RNA crystals typically diffract to much lower resolutions than protein crystals. This low-resolution diffraction results in unclear density maps, which cause considerable difficulties during the model-building process. These difficulties are exacerbated by the lack of computational tools for RNA modeling. Here, RCrane, a tool for the partially automated building of RNA into electron-density maps of low or intermediate resolution, is presented. This tool works within Coot, a common program for macromolecular model building. RCrane helps crystallographers to place phosphates and bases into electron density and then automatically predicts and builds the detailed all-atom structure of the traced nucleotides. RCrane then allows the crystallographer to review the newly built structure and select alternative backbone conformations where desired. This tool can also be used to automatically correct the backbone structure of previously built nucleotides. These automated corrections can fix incorrect sugar puckers, steric clashes and other structural problems

Prediction of Indoor Air Exposure from Outdoor Air Quality Using an Artificial Neural Network Model for Inner City Commercial Buildings

Directory of Open Access Journals (Sweden)

Avril Challoner

2015-12-01

Full Text Available NO2 and particulate matter are the air pollutants of most concern in Ireland, with possible links to the higher respiratory and cardiovascular mortality and morbidity rates found in the country compared to the rest of Europe. Currently, air quality limits in Europe only cover outdoor environments yet the quality of indoor air is an essential determinant of a person’s well-being, especially since the average person spends more than 90% of their time indoors. The modelling conducted in this research aims to provide a framework for epidemiological studies by the use of publically available data from fixed outdoor monitoring stations to predict indoor air quality more accurately. Predictions are made using two modelling techniques, the Personal-exposure Activity Location Model (PALM, to predict outdoor air quality at a particular building, and Artificial Neural Networks, to model the indoor/outdoor relationship of the building. This joint approach has been used to predict indoor air concentrations for three inner city commercial buildings in Dublin, where parallel indoor and outdoor diurnal monitoring had been carried out on site. This modelling methodology has been shown to provide reasonable predictions of average NO2 indoor air quality compared to the monitored data, but did not perform well in the prediction of indoor PM2.5 concentrations. Hence, this approach could be used to determine NO2 exposures more rigorously of those who work and/or live in the city centre, which can then be linked to potential health impacts.

Whole-building Hygrothermal Simulation Model

DEFF Research Database (Denmark)

Rode, Carsten; Grau, Karl

2003-01-01

An existing integrated simulation tool for dynamic thermal simulation of building was extended with a transient model for moisture release and uptake in building materials. Validation of the new model was begun with comparison against measurements in an outdoor test cell furnished with single...... materials. Almost quasi-steady, cyclic experiments were used to compare the indoor humidity variation and the numerical results of the integrated simulation tool with the new moisture model. Except for the case with chipboard as furnishing, the predictions of indoor humidity with the detailed model were...

Confidence scores for prediction models

DEFF Research Database (Denmark)

Gerds, Thomas Alexander; van de Wiel, MA

2011-01-01

In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation,...

Building Chaotic Model From Incomplete Time Series

Science.gov (United States)

Siek, Michael; Solomatine, Dimitri

2010-05-01

This paper presents a number of novel techniques for building a predictive chaotic model from incomplete time series. A predictive chaotic model is built by reconstructing the time-delayed phase space from observed time series and the prediction is made by a global model or adaptive local models based on the dynamical neighbors found in the reconstructed phase space. In general, the building of any data-driven models depends on the completeness and quality of the data itself. However, the completeness of the data availability can not always be guaranteed since the measurement or data transmission is intermittently not working properly due to some reasons. We propose two main solutions dealing with incomplete time series: using imputing and non-imputing methods. For imputing methods, we utilized the interpolation methods (weighted sum of linear interpolations, Bayesian principle component analysis and cubic spline interpolation) and predictive models (neural network, kernel machine, chaotic model) for estimating the missing values. After imputing the missing values, the phase space reconstruction and chaotic model prediction are executed as a standard procedure. For non-imputing methods, we reconstructed the time-delayed phase space from observed time series with missing values. This reconstruction results in non-continuous trajectories. However, the local model prediction can still be made from the other dynamical neighbors reconstructed from non-missing values. We implemented and tested these methods to construct a chaotic model for predicting storm surges at Hoek van Holland as the entrance of Rotterdam Port. The hourly surge time series is available for duration of 1990-1996. For measuring the performance of the proposed methods, a synthetic time series with missing values generated by a particular random variable to the original (complete) time series is utilized. There exist two main performance measures used in this work: (1) error measures between the actual

Model building and new particles

International Nuclear Information System (INIS)

Frampton, P.H.

1992-01-01

After an outline of the Standard Model, indications of new physics beyond it are discussed. The nature of model building is illustrated by three examples which predict, respectively, new particles called the axigluon, sarks and the aspon. (author). 11 refs

Mould growth prediction by computational simulation on historic buildings

OpenAIRE

Krus, M.; Kilian, R.; Sedlbauer, K.

2007-01-01

Historical buildings are often renovated with a high expenditure of time and money without investigating and considering the causes of the damages. In many cases historic buildings can only be maintained by changing their usage. This change of use may influence the interior climate enormously. To assess the effect on the risk of mould growth on building parts or historic monuments a predictive model has been developed recently, describing the hygrothermal behaviour of the spore. It allows for...

Development of a prediction model for the cost saving potentials in implementing the building energy efficiency rating certification

International Nuclear Information System (INIS)

Jeong, Jaewook; Hong, Taehoon; Ji, Changyoon; Kim, Jimin; Lee, Minhyun; Jeong, Kwangbok; Koo, Choongwan

2017-01-01

Highlights: • This study evaluates the building energy efficiency rating (BEER) certification. • Prediction model was developed for cost saving potentials by the BEER certification. • Prediction model was developed using LCC analysis, ROV, and Monte Carlo simulation. • Cost saving potential was predicted to be 2.78–3.77% of the construction cost. • Cost saving potential can be used for estimating the investment value of BEER. - Abstract: Building energy efficiency rating (BEER) certification is an energy performance certificates (EPCs) in South Korea. It is critical to examine the cost saving potentials of the BEER-certification in advance. This study aimed to develop a prediction model for the cost saving potentials in implementing the BEER-certification, in which the cost saving potentials included the energy cost savings of the BEER-certification and the relevant CO_2 emissions reduction as well as the additional construction cost for the BEER-certification. The prediction model was developed by using data mining, life cycle cost analysis, real option valuation, and Monte Carlo simulation. The database were established with 437 multi-family housing complexes (MFHCs), including 116 BEER-certified MFHCs and 321 non-certified MFHCs. The case study was conducted to validate the developed prediction model using 321 non-certified MFHCs, which considered 20-year life cycle. As a result, compared to the additional construction cost, the average cost saving potentials of the 1st-BEER-certified MFHCs in Groups 1, 2, and 3 were predicted to be 3.77%, 2.78%, and 2.87%, respectively. The cost saving potentials can be used as a guideline for the additional construction cost of the BEER-certification in the early design phase.

A Heat Dynamic Model for Intelligent Heating of Buildings

DEFF Research Database (Denmark)

Thavlov, Anders; Bindner, Henrik W.

2015-01-01

This article presents a heat dynamic model for prediction of the indoor temperature in an office building. The model has been used in several flexible load applications, where the indoor temperature is allowed to vary around a given reference to provide power system services by shifting the heating...... of the building in time. This way the thermal mass of the building can be used to absorb energy from renewable energy source when available and postpone heating in periods with lack of renewable energy generation. The model is used in a model predictive controller to ensure the residential comfort over a given...

Power Admission Control with Predictive Thermal Management in Smart Buildings

DEFF Research Database (Denmark)

Yao, Jianguo; Costanzo, Giuseppe Tommaso; Zhu, Guchuan

2015-01-01

This paper presents a control scheme for thermal management in smart buildings based on predictive power admission control. This approach combines model predictive control with budget-schedulability analysis in order to reduce peak power consumption as well as ensure thermal comfort. First...

Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression.

Science.gov (United States)

Jovanovic, Milos; Radovanovic, Sandro; Vukicevic, Milan; Van Poucke, Sven; Delibasic, Boris

2016-09-01

similar performances reaching AUC values 0.783 and 0.779 for traditional Lasso and Tree-Lasso, respectfully. However, information loss of Lasso models is 0.35 bits higher compared to Tree-Lasso model. We propose a method for building predictive models applicable for the detection of readmission risk based on Electronic Health records. Integration of domain knowledge (in the form of ICD-9-CM taxonomy) and a data-driven, sparse predictive algorithm (Tree-Lasso Logistic Regression) resulted in an increase of interpretability of the resulting model. The models are interpreted for the readmission prediction problem in general pediatric population in California, as well as several important subpopulations, and the interpretations of models comply with existing medical understanding of pediatric readmission. Finally, quantitative assessment of the interpretability of the models is given, that is beyond simple counts of selected low-level features. Copyright © 2016 Elsevier B.V. All rights reserved.

Analysis of a Residential Building Energy Consumption Demand Model

Directory of Open Access Journals (Sweden)

Meng Liu

2011-03-01

Full Text Available In order to estimate the energy consumption demand of residential buildings, this paper first discusses the status and shortcomings of current domestic energy consumption models. Then it proposes and develops a residential building energy consumption demand model based on a back propagation (BP neural network model. After that, taking residential buildings in Chongqing (P.R. China as an example, 16 energy consumption indicators are introduced as characteristics of the residential buildings in Chongqing. The index system of the BP neutral network prediction model is established and the multi-factorial BP neural network prediction model of Chongqing residential building energy consumption is developed using the Cshap language, based on the SQL server 2005 platform. The results obtained by applying the model in Chongqing are in good agreement with actual ones. In addition, the model provides corresponding approximate data by taking into account the potential energy structure adjustments and relevant energy policy regulations.

Effect of length of measurement period on accuracy of predicted annual heating energy consumption of buildings

International Nuclear Information System (INIS)

Cho, Sung-Hwan; Kim, Won-Tae; Tae, Choon-Soeb; Zaheeruddin, M.

2004-01-01

This study examined the temperature dependent regression models of energy consumption as a function of the length of the measurement period. The methodology applied was to construct linear regression models of daily energy consumption from 1 day to 3 months data sets and compare the annual heating energy consumption predicted by these models with actual annual heating energy consumption. A commercial building in Daejon was selected, and the energy consumption was measured over a heating season. The results from the investigation show that the predicted energy consumption based on 1 day of measurements to build the regression model could lead to errors of 100% or more. The prediction error decreased to 30% when 1 week of data was used to build the regression model. Likewise, the regression model based on 3 months of measured data predicted the annual energy consumption within 6% of the measured energy consumption. These analyses show that the length of the measurement period has a significant impact on the accuracy of the predicted annual energy consumption of buildings

Model Predictive Controller for Active Demand Side Management with PV Self-consumption in an Intelligent Building

DEFF Research Database (Denmark)

Zong, Yi; Mihet-Popa, Lucian; Kullmann, Daniel

2012-01-01

This paper presents a Model Predictive Controller (MPC) for electrical heaters’ predictive power consumption including maximizing the use of local generation (e.g. solar power) in an intelligent building. The MPC is based on dynamic power price and weather forecast, considering users’ comfort...... settings to meet an optimization objective such as minimum cost and minimum reference temperature error. It demonstrates that this MPC strategy can realize load shifting, and maximize the PV self-consumption in the residential sector. With this demand side control study, it is expected that MPC strategy...

RANDOM FUNCTIONS AND INTERVAL METHOD FOR PREDICTING THE RESIDUAL RESOURCE OF BUILDING STRUCTURES

Directory of Open Access Journals (Sweden)

Shmelev Gennadiy Dmitrievich

2017-11-01

Full Text Available Subject: possibility of using random functions and interval prediction method for estimating the residual life of building structures in the currently used buildings. Research objectives: coordination of ranges of values to develop predictions and random functions that characterize the processes being predicted. Materials and methods: when performing this research, the method of random functions and the method of interval prediction were used. Results: in the course of this work, the basic properties of random functions, including the properties of families of random functions, are studied. The coordination of time-varying impacts and loads on building structures is considered from the viewpoint of their influence on structures and representation of the structures’ behavior in the form of random functions. Several models of random functions are proposed for predicting individual parameters of structures. For each of the proposed models, its scope of application is defined. The article notes that the considered approach of forecasting has been used many times at various sites. In addition, the available results allowed the authors to develop a methodology for assessing the technical condition and residual life of building structures for the currently used facilities. Conclusions: we studied the possibility of using random functions and processes for the purposes of forecasting the residual service lives of structures in buildings and engineering constructions. We considered the possibility of using an interval forecasting approach to estimate changes in defining parameters of building structures and their technical condition. A comprehensive technique for forecasting the residual life of building structures using the interval approach is proposed.

Predictive control techniques for energy and indoor environmental quality management in buildings

Energy Technology Data Exchange (ETDEWEB)

Kolokotsa, D. [Technological Educational Institute of Crete, Department of Natural Resources and Environment, 3, Romanou str., 73133, Hania, Crete (Greece); Pouliezos, A. [Technical University of Crete, Department of Production Engineering and Management, University Campus, Kounoupidiana, 73100 Hania (Greece); Stavrakakis, G.; Lazos, C. [Technical University of Crete, Department of Electronics and Computer Engineering, University Campus, Kounoupidiana, 73100 Hania (Greece)

2009-09-15

The aim of the present paper is to present a model-based predictive controller, combined with a Building Energy Management System (BEMS). The overall system predicts the indoor environmental conditions of a specific building and selects the most appropriate actions so as to reach the set points and contribute to the indoor environmental quality by minimizing energy costs. The controller is tested using a BEMS installation in Hania, Crete, Greece. (author)

Quantification of Uncertainty in Predicting Building Energy Consumption

DEFF Research Database (Denmark)

Brohus, Henrik; Frier, Christian; Heiselberg, Per

2012-01-01

Traditional building energy consumption calculation methods are characterised by rough approaches providing approximate figures with high and unknown levels of uncertainty. Lack of reliable energy resources and increasing concerns about climate change call for improved predictive tools. A new...... approach for the prediction of building energy consumption is presented. The approach quantifies the uncertainty of building energy consumption by means of stochastic differential equations. The approach is applied to a general heat balance for an arbitrary number of loads and zones in a building...... for the dynamic thermal behaviour of buildings. However, for air flow and energy consumption it is found to be much more significant due to less “damping”. Probabilistic methods establish a new approach to the prediction of building energy consumption, enabling designers to include stochastic parameters like...

Optimizing Energy Consumption in Building Designs Using Building Information Model (BIM

Directory of Open Access Journals (Sweden)

Egwunatum Samuel

2016-09-01

Full Text Available Given the ability of a Building Information Model (BIM to serve as a multi-disciplinary data repository, this paper seeks to explore and exploit the sustainability value of Building Information Modelling/models in delivering buildings that require less energy for their operation, emit less CO2 and at the same time provide a comfortable living environment for their occupants. This objective was achieved by a critical and extensive review of the literature covering: (1 building energy consumption, (2 building energy performance and analysis, and (3 building information modeling and energy assessment. The literature cited in this paper showed that linking an energy analysis tool with a BIM model helped project design teams to predict and create optimized energy consumption. To validate this finding, an in-depth analysis was carried out on a completed BIM integrated construction project using the Arboleda Project in the Dominican Republic. The findings showed that the BIM-based energy analysis helped the design team achieve the world’s first 103% positive energy building. From the research findings, the paper concludes that linking an energy analysis tool with a BIM model helps to expedite the energy analysis process, provide more detailed and accurate results as well as deliver energy-efficient buildings. The study further recommends that the adoption of a level 2 BIM and the integration of BIM in energy optimization analyse should be made compulsory for all projects irrespective of the method of procurement (government-funded or otherwise or its size.

Potential Energy Flexibility for a Hot-Water Based Heating System in Smart Buildings Via Economic Model Predictive Control

DEFF Research Database (Denmark)

Ahmed, Awadelrahman M. A.; Zong, Yi; Mihet-Popa, Lucian

2017-01-01

This paper studies the potential of shifting the heating energy consumption in a residential building to low price periods based on varying electricity price signals suing Economic Model Predictive Control strategy. The investigated heating system consists of a heat pump incorporated with a hot...... water tank as active thermal energy storage, where two optimization problems are integrated together to optimize both the heat pump electricity consumption and the building heating consumption. A sensitivity analysis for the system flexibility is examined. The results revealed that the proposed...

Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory

Energy Technology Data Exchange (ETDEWEB)

Gregor P. Henze; Moncef Krarti

2005-09-30

Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid. Time-of-use electricity rates encourage shifting of electrical loads to off-peak periods at night and weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building's massive structure or the use of active thermal energy storage systems such as ice storage. While these two thermal batteries have been engaged separately in the past, this project investigated the merits of harnessing both storage media concurrently in the context of predictive optimal control. To pursue the analysis, modeling, and simulation research of Phase 1, two separate simulation environments were developed. Based on the new dynamic building simulation program EnergyPlus, a utility rate module, two thermal energy storage models were added. Also, a sequential optimization approach to the cost minimization problem using direct search, gradient-based, and dynamic programming methods was incorporated. The objective function was the total utility bill including the cost of reheat and a time-of-use electricity rate either with or without demand charges. An alternative simulation environment based on TRNSYS and Matlab was developed to allow for comparison and cross-validation with EnergyPlus. The initial evaluation of the theoretical potential of the combined optimal control assumed perfect weather prediction and match between the building model and the actual building counterpart. The analysis showed that the combined utilization leads to cost savings that is significantly greater than either storage but less than the sum of the individual savings. The findings reveal that the cooling-related on-peak electrical demand of commercial buildings can be considerably reduced. A subsequent analysis of the impact of forecasting uncertainty in the required short-term weather forecasts determined that it takes only very

Prediction of thermal sensation in non-air-conditioned buildings in warm climates

DEFF Research Database (Denmark)

Fanger, Povl Ole; Toftum, Jørn

2002-01-01

The PMV model agrees well with high-quality field studies in buildings with HVAC systems, situated in cold, temperate and warm climates, studied during both summer and winter. In non-air-conditioned buildings in warm climates, occupants may sense the warmth as being less severe than the PMV...... predicts. The main reason is low expectations, but a metabolic rate that is estimated too high can also contribute to explaining the difference. An extension of the PMV model that includes an expectancy factor is introduced for use in non-air-conditioned buildings in warm climates. The extended PMV model...... agrees well with quality field studies in non-air-conditioned buildings of three continents....

RCrane: semi-automated RNA model building.

Science.gov (United States)

Keating, Kevin S; Pyle, Anna Marie

2012-08-01

RNA crystals typically diffract to much lower resolutions than protein crystals. This low-resolution diffraction results in unclear density maps, which cause considerable difficulties during the model-building process. These difficulties are exacerbated by the lack of computational tools for RNA modeling. Here, RCrane, a tool for the partially automated building of RNA into electron-density maps of low or intermediate resolution, is presented. This tool works within Coot, a common program for macromolecular model building. RCrane helps crystallographers to place phosphates and bases into electron density and then automatically predicts and builds the detailed all-atom structure of the traced nucleotides. RCrane then allows the crystallographer to review the newly built structure and select alternative backbone conformations where desired. This tool can also be used to automatically correct the backbone structure of previously built nucleotides. These automated corrections can fix incorrect sugar puckers, steric clashes and other structural problems.

A new, accurate predictive model for incident hypertension

DEFF Research Database (Denmark)

Völzke, Henry; Fung, Glenn; Ittermann, Till

2013-01-01

Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures.......Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures....

Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks

International Nuclear Information System (INIS)

Li Qiong; Meng Qinglin; Cai Jiejin; Yoshino, Hiroshi; Mochida, Akashi

2009-01-01

This study presents four modeling techniques for the prediction of hourly cooling load in the building. In addition to the traditional back propagation neural network (BPNN), the radial basis function neural network (RBFNN), general regression neural network (GRNN) and support vector machine (SVM) are considered. All the prediction models have been applied to an office building in Guangzhou, China. Evaluation of the prediction accuracy of the four models is based on the root mean square error (RMSE) and mean relative error (MRE). The simulation results demonstrate that the four discussed models can be effective for building cooling load prediction. The SVM and GRNN methods can achieve better accuracy and generalization than the BPNN and RBFNN methods

Method for simulating predictive control of building systems operation in the early stages of building design

DEFF Research Database (Denmark)

Petersen, Steffen; Svendsen, Svend

2011-01-01

A method for simulating predictive control of building systems operation in the early stages of building design is presented. The method uses building simulation based on weather forecasts to predict whether there is a future heating or cooling requirement. This information enables the thermal...... control systems of the building to respond proactively to keep the operational temperature within the thermal comfort range with the minimum use of energy. The method is implemented in an existing building simulation tool designed to inform decisions in the early stages of building design through...... parametric analysis. This enables building designers to predict the performance of the method and include it as a part of the solution space. The method furthermore facilitates the task of configuring appropriate building systems control schemes in the tool, and it eliminates time consuming manual...

Modeling the building blocks of biodiversity.

Directory of Open Access Journals (Sweden)

Lucas N Joppa

Full Text Available BACKGROUND: Networks of single interaction types, such as plant-pollinator mutualisms, are biodiversity's "building blocks". Yet, the structure of mutualistic and antagonistic networks differs, leaving no unified modeling framework across biodiversity's component pieces. METHODS/PRINCIPAL FINDINGS: We use a one-dimensional "niche model" to predict antagonistic and mutualistic species interactions, finding that accuracy decreases with the size of the network. We show that properties of the modeled network structure closely approximate empirical properties even where individual interactions are poorly predicted. Further, some aspects of the structure of the niche space were consistently different between network classes. CONCLUSIONS/SIGNIFICANCE: These novel results reveal fundamental differences between the ability to predict ecologically important features of the overall structure of a network and the ability to predict pair-wise species interactions.

Damage Level Prediction of Reinforced Concrete Building Based on Earthquake Time History Using Artificial Neural Network

Directory of Open Access Journals (Sweden)

Suryanita Reni

2017-01-01

Full Text Available The strong motion earthquake could cause the building damage in case of the building not considered in the earthquake design of the building. The study aims to predict the damage-level of building due to earthquake using Artificial Neural Networks method. The building model is a reinforced concrete building with ten floors and height between floors is 3.6 m. The model building received a load of the earthquake based on nine earthquake time history records. Each time history scaled to 0,5g, 0,75g, and 1,0g. The Artificial Neural Networks are designed in 4 architectural models using the MATLAB program. Model 1 used the displacement, velocity, and acceleration as input and Model 2 used the displacement only as the input. Model 3 used the velocity as input, and Model 4 used the acceleration just as input. The output of the Neural Networks is the damage level of the building with the category of Safe (1, Immediate Occupancy (2, Life Safety (3 or in a condition of Collapse Prevention (4. According to the results, Neural Network models have the prediction rate of the damage level between 85%-95%. Therefore, one of the solutions for analyzing the structural responses and the damage level promptly and efficiently when the earthquake occurred is by using Artificial Neural Network

Key Questions in Building Defect Prediction Models in Practice

Science.gov (United States)

Ramler, Rudolf; Wolfmaier, Klaus; Stauder, Erwin; Kossak, Felix; Natschläger, Thomas

The information about which modules of a future version of a software system are defect-prone is a valuable planning aid for quality managers and testers. Defect prediction promises to indicate these defect-prone modules. However, constructing effective defect prediction models in an industrial setting involves a number of key questions. In this paper we discuss ten key questions identified in context of establishing defect prediction in a large software development project. Seven consecutive versions of the software system have been used to construct and validate defect prediction models for system test planning. Furthermore, the paper presents initial empirical results from the studied project and, by this means, contributes answers to the identified questions.

Thermal Models for Intelligent Heating of Buildings

DEFF Research Database (Denmark)

Thavlov, Anders; Bindner, Henrik W.

2012-01-01

the comfort of residents, proper prediction models for indoor temperature have to be developed. This paper presents a model for prediction of indoor temperature and power consumption from electrical space heating in an office building, using stochastic differential equations. The heat dynamic model is build......The Danish government has set the ambitious goal that the share of the total Danish electricity consumption, covered by wind energy, should be increased to 50% by year 2020. This asks for radical changes in how we utilize and transmit electricity in the future power grid. To fully utilize the high...... share of renewable power generation, which is in general intermittent and non-controllable, the consumption side has to be much more flexible than today. To achieve such flexibility, methods for moving power consumption in time, within the hourly timescale, have to be developed. One approach currently...

Challenges of implementing economic model predictive control strategy for buildings interacting with smart energy systems

DEFF Research Database (Denmark)

Zong, Yi; Böning, Georg Martin; Santos, Rui Mirra

2016-01-01

) strategy for energy management in smart buildings, which can act as active users interacting with smart energy systems. The challenges encountered during the implementation of EMPC for active demand side management are investigated in detail in this paper. A pilot testing study shows energy savings......When there is a high penetration of renewables in the energy system, it requires proactive control of large numbers of distributed demand response resources to maintain the system’s reliability and improve its operational economics. This paper presents the Economic Model Predictive Control (EMPC...

Inverse and Predictive Modeling

Energy Technology Data Exchange (ETDEWEB)

Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-09-27

The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

Prediction of hydrogen distribution in the reactor building in CANDU6 plant

International Nuclear Information System (INIS)

Jin, Y.; Song, Y.

2008-01-01

The CANDU plants have a lot of zircaloy. The fuel cladding, calandria tubes and pressure tubes are made of zircaloy. The zircaloy can be oxidized and hydrogen is generated during severe accident progression. The detonation or deflagration to detonation transition (DDT) due to hydrogen combustion may occur if the local hydrogen concentration or global hydrogen concentration exceeds certain value. The detonation may result in the rupture of the reactor building. The inside of the reactor building of CANDU plants is complex. So prediction of hydrogen distribution in the reactor building is important. This prediction is made using ISAAC code and GOTHIC code. ISAAC code partitioned the reactor building in to 7 compartments. GOTHIC code modeled the CANDU6 reactor building using 12 nodes. The hydrogen concentrations in the various compartments in the reactor building are compared. GOTHIC code slightly underpredicts hydrogen concentration in the F/M rooms than ISAAC code, but trend is same. The hydrogen concentration in the boiler room and the moderator room shows almost same as for both codes. (author)

A numerical simulation strategy on occupant evacuation behaviors and casualty prediction in a building during earthquakes

Science.gov (United States)

Li, Shuang; Yu, Xiaohui; Zhang, Yanjuan; Zhai, Changhai

2018-01-01

Casualty prediction in a building during earthquakes benefits to implement the economic loss estimation in the performance-based earthquake engineering methodology. Although after-earthquake observations reveal that the evacuation has effects on the quantity of occupant casualties during earthquakes, few current studies consider occupant movements in the building in casualty prediction procedures. To bridge this knowledge gap, a numerical simulation method using refined cellular automata model is presented, which can describe various occupant dynamic behaviors and building dimensions. The simulation on the occupant evacuation is verified by a recorded evacuation process from a school classroom in real-life 2013 Ya'an earthquake in China. The occupant casualties in the building under earthquakes are evaluated by coupling the building collapse process simulation by finite element method, the occupant evacuation simulation, and the casualty occurrence criteria with time and space synchronization. A case study of casualty prediction in a building during an earthquake is provided to demonstrate the effect of occupant movements on casualty prediction.

Economic Model Predictive Control for Smart Energy Systems

DEFF Research Database (Denmark)

Halvgaard, Rasmus

Model Predictive Control (MPC) can be used to control the energy distribution in a Smart Grid with a high share of stochastic energy production from renewable energy sources like wind. Heat pumps for heating residential buildings can exploit the slow heat dynamics of a building to store heat and ...... and hereby shift the heat pump power consumption to periods with both low electricity prices and a high fraction of green energy in the grid.......Model Predictive Control (MPC) can be used to control the energy distribution in a Smart Grid with a high share of stochastic energy production from renewable energy sources like wind. Heat pumps for heating residential buildings can exploit the slow heat dynamics of a building to store heat...

Simplified Building Thermal Model Used for Optimal Control of Radiant Cooling System

Directory of Open Access Journals (Sweden)

Lei He

2016-01-01

Full Text Available MPC has the ability to optimize the system operation parameters for energy conservation. Recently, it has been used in HVAC systems for saving energy, but there are very few applications in radiant cooling systems. To implement MPC in buildings with radiant terminals, the predictions of cooling load and thermal environment are indispensable. In this paper, a simplified thermal model is proposed for predicting cooling load and thermal environment in buildings with radiant floor. In this thermal model, the black-box model is introduced to derive the incident solar radiation, while the genetic algorithm is utilized to identify the parameters of the thermal model. In order to further validate this simplified thermal model, simulated results from TRNSYS are compared with those from this model and the deviation is evaluated based on coefficient of variation of root mean square (CV. The results show that the simplified model can predict the operative temperature with a CV lower than 1% and predict cooling loads with a CV lower than 10%. For the purpose of supervisory control in HVAC systems, this simplified RC thermal model has an acceptable accuracy and can be used for further MPC in buildings with radiation terminals.

Nonlinear chaotic model for predicting storm surges

Directory of Open Access Journals (Sweden)

M. Siek

2010-09-01

Full Text Available This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.

Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

Directory of Open Access Journals (Sweden)

Anand Krishnan Prakash

2018-04-01

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

Impacts of building information modeling on facility maintenance management

Energy Technology Data Exchange (ETDEWEB)

Ahamed, Shafee; Neelamkavil, Joseph; Canas, Roberto [Centre for Computer-assisted Construction Technologies, National Research Council of Canada, London, Ontario (Canada)

2010-07-01

Building information modeling (BIM) is a digital representation of the physical and functional properties of a building; it has been used by construction professionals for a long time and stakeholders are now using it in different aspects of the building lifecycle. This paper intends to present how BIM impacts the construction industry and how it can be used for facility maintenance management. The maintenance and operations of buildings are in most cases still managed through the use of drawings and spreadsheets although life cycle costs of a building are significantly higher than initial investment costs; thus, the use of BIM could help in achieving a higher efficiency and so important benefits. This study is part of an ongoing research project, the nD modeling project, which aims at predicting building energy consumption with better accuracy.

Predictive Models and Computational Embryology

Science.gov (United States)

EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

Translating building information modeling to building energy modeling using model view definition.

Science.gov (United States)

Jeong, WoonSeong; Kim, Jong Bum; Clayton, Mark J; Haberl, Jeff S; Yan, Wei

2014-01-01

This paper presents a new approach to translate between Building Information Modeling (BIM) and Building Energy Modeling (BEM) that uses Modelica, an object-oriented declarative, equation-based simulation environment. The approach (BIM2BEM) has been developed using a data modeling method to enable seamless model translations of building geometry, materials, and topology. Using data modeling, we created a Model View Definition (MVD) consisting of a process model and a class diagram. The process model demonstrates object-mapping between BIM and Modelica-based BEM (ModelicaBEM) and facilitates the definition of required information during model translations. The class diagram represents the information and object relationships to produce a class package intermediate between the BIM and BEM. The implementation of the intermediate class package enables system interface (Revit2Modelica) development for automatic BIM data translation into ModelicaBEM. In order to demonstrate and validate our approach, simulation result comparisons have been conducted via three test cases using (1) the BIM-based Modelica models generated from Revit2Modelica and (2) BEM models manually created using LBNL Modelica Buildings library. Our implementation shows that BIM2BEM (1) enables BIM models to be translated into ModelicaBEM models, (2) enables system interface development based on the MVD for thermal simulation, and (3) facilitates the reuse of original BIM data into building energy simulation without an import/export process.

Translating Building Information Modeling to Building Energy Modeling Using Model View Definition

Directory of Open Access Journals (Sweden)

WoonSeong Jeong

2014-01-01

Full Text Available This paper presents a new approach to translate between Building Information Modeling (BIM and Building Energy Modeling (BEM that uses Modelica, an object-oriented declarative, equation-based simulation environment. The approach (BIM2BEM has been developed using a data modeling method to enable seamless model translations of building geometry, materials, and topology. Using data modeling, we created a Model View Definition (MVD consisting of a process model and a class diagram. The process model demonstrates object-mapping between BIM and Modelica-based BEM (ModelicaBEM and facilitates the definition of required information during model translations. The class diagram represents the information and object relationships to produce a class package intermediate between the BIM and BEM. The implementation of the intermediate class package enables system interface (Revit2Modelica development for automatic BIM data translation into ModelicaBEM. In order to demonstrate and validate our approach, simulation result comparisons have been conducted via three test cases using (1 the BIM-based Modelica models generated from Revit2Modelica and (2 BEM models manually created using LBNL Modelica Buildings library. Our implementation shows that BIM2BEM (1 enables BIM models to be translated into ModelicaBEM models, (2 enables system interface development based on the MVD for thermal simulation, and (3 facilitates the reuse of original BIM data into building energy simulation without an import/export process.

Application of artificial neural network to predict the optimal start time for heating system in building

International Nuclear Information System (INIS)

Yang, In-Ho; Yeo, Myoung-Souk; Kim, Kwang-Woo

2003-01-01

The artificial neural network (ANN) approach is a generic technique for mapping non-linear relationships between inputs and outputs without knowing the details of these relationships. This paper presents an application of the ANN in a building control system. The objective of this study is to develop an optimized ANN model to determine the optimal start time for a heating system in a building. For this, programs for predicting the room air temperature and the learning of the ANN model based on back propagation learning were developed, and learning data for various building conditions were collected through program simulation for predicting the room air temperature using systems of experimental design. Then, the optimized ANN model was presented through learning of the ANN, and its performance to determine the optimal start time was evaluated

A Learning Framework for Control-Oriented Modeling of Buildings

Energy Technology Data Exchange (ETDEWEB)

Rubio-Herrero, Javier; Chandan, Vikas; Siegel, Charles M.; Vishnu, Abhinav; Vrabie, Draguna L.

2018-01-18

Buildings consume a significant amount of energy worldwide. Several building optimization and control use cases require models of energy consumption which are control oriented, have high predictive capability, imposes minimal data pre-processing requirements, and have the ability to be adapted continuously to account for changing conditions as new data becomes available. Data driven modeling techniques, that have been investigated so far, while promising in the context of buildings, have been unable to simultaneously satisfy all the requirements mentioned above. In this context, deep learning techniques such as Recurrent Neural Networks (RNNs) hold promise, empowered by advanced computational capabilities and big data opportunities. In this paper, we propose a deep learning based methodology for the development of control oriented models for building energy management and test in on data from a real building. Results show that the proposed methodology outperforms other data driven modeling techniques significantly. We perform a detailed analysis of the proposed methodology along dimensions such as topology, sensitivity, and downsampling. Lastly, we conclude by envisioning a building analytics suite empowered by the proposed deep framework, that can drive several use cases related to building energy management.

Prediction of residential radon exposure of the whole Swiss population: comparison of model-based predictions with measurement-based predictions.

Science.gov (United States)

Hauri, D D; Huss, A; Zimmermann, F; Kuehni, C E; Röösli, M

2013-10-01

Radon plays an important role for human exposure to natural sources of ionizing radiation. The aim of this article is to compare two approaches to estimate mean radon exposure in the Swiss population: model-based predictions at individual level and measurement-based predictions based on measurements aggregated at municipality level. A nationwide model was used to predict radon levels in each household and for each individual based on the corresponding tectonic unit, building age, building type, soil texture, degree of urbanization, and floor. Measurement-based predictions were carried out within a health impact assessment on residential radon and lung cancer. Mean measured radon levels were corrected for the average floor distribution and weighted with population size of each municipality. Model-based predictions yielded a mean radon exposure of the Swiss population of 84.1 Bq/m(3) . Measurement-based predictions yielded an average exposure of 78 Bq/m(3) . This study demonstrates that the model- and the measurement-based predictions provided similar results. The advantage of the measurement-based approach is its simplicity, which is sufficient for assessing exposure distribution in a population. The model-based approach allows predicting radon levels at specific sites, which is needed in an epidemiological study, and the results do not depend on how the measurement sites have been selected. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Computer Prediction of Air Quality in Livestock Buildings

DEFF Research Database (Denmark)

Svidt, Kjeld; Bjerg, Bjarne

In modem livestock buildings the design of ventilation systems is important in order to obtain good air quality. The use of Computational Fluid Dynamics for predicting the air distribution makes it possible to include the effect of room geometry and heat sources in the design process. This paper...... presents numerical prediction of air flow in a livestock building compared with laboratory measurements. An example of the calculation of contaminant distribution is given, and the future possibilities of the method are discussed....

Improving the accuracy of energy baseline models for commercial buildings with occupancy data

International Nuclear Information System (INIS)

Liang, Xin; Hong, Tianzhen; Shen, Geoffrey Qiping

2016-01-01

Highlights: • We evaluated several baseline models predicting energy use in buildings. • Including occupancy data improved accuracy of baseline model prediction. • Occupancy is highly correlated with energy use in buildings. • This simple approach can be used in decision makings of energy retrofit projects. - Abstract: More than 80% of energy is consumed during operation phase of a building’s life cycle, so energy efficiency retrofit for existing buildings is considered a promising way to reduce energy use in buildings. The investment strategies of retrofit depend on the ability to quantify energy savings by “measurement and verification” (M&V), which compares actual energy consumption to how much energy would have been used without retrofit (called the “baseline” of energy use). Although numerous models exist for predicting baseline of energy use, a critical limitation is that occupancy has not been included as a variable. However, occupancy rate is essential for energy consumption and was emphasized by previous studies. This study develops a new baseline model which is built upon the Lawrence Berkeley National Laboratory (LBNL) model but includes the use of building occupancy data. The study also proposes metrics to quantify the accuracy of prediction and the impacts of variables. However, the results show that including occupancy data does not significantly improve the accuracy of the baseline model, especially for HVAC load. The reasons are discussed further. In addition, sensitivity analysis is conducted to show the influence of parameters in baseline models. The results from this study can help us understand the influence of occupancy on energy use, improve energy baseline prediction by including the occupancy factor, reduce risks of M&V and facilitate investment strategies of energy efficiency retrofit.

Predictive Solar-Integrated Commercial Building Load Control

Energy Technology Data Exchange (ETDEWEB)

Glasgow, Nathan [EdgePower Inc., Aspen, CO (United States)

2017-01-31

This report is the final technical report for the Department of Energy SunShot award number EE0007180 to EdgePower Inc., for the project entitled “Predictive Solar-Integrated Commercial Building Load Control.” The goal of this project was to successfully prove that the integration of solar forecasting and building load control can reduce demand charge costs for commercial building owners with solar PV. This proof of concept Tier 0 project demonstrated its value through a pilot project at a commercial building. This final report contains a summary of the work completed through he duration of the project. Clean Power Research was a sub-recipient on the award.

BIM. Building Information Model. Special issue; BIM. Building Information Model. Themanummer

Energy Technology Data Exchange (ETDEWEB)

Van Gelder, A.L.A. [Arta and Consultancy, Lage Zwaluwe (Netherlands); Van den Eijnden, P.A.A. [Stichting Marktwerking Installatietechniek, Zoetermeer (Netherlands); Veerman, J.; Mackaij, J.; Borst, E. [Royal Haskoning DHV, Nijmegen (Netherlands); Kruijsse, P.M.D. [Wolter en Dros, Amersfoort (Netherlands); Buma, W. [Merlijn Media, Waddinxveen (Netherlands); Bomhof, F.; Willems, P.H.; Boehms, M. [TNO, Delft (Netherlands); Hofman, M.; Verkerk, M. [ISSO, Rotterdam (Netherlands); Bodeving, M. [VIAC Installatie Adviseurs, Houten (Netherlands); Van Ravenswaaij, J.; Van Hoven, H. [BAM Techniek, Bunnik (Netherlands); Boeije, I.; Schalk, E. [Stabiplan, Bodegraven (Netherlands)

2012-11-15

A series of 14 articles illustrates the various aspects of the Building Information Model (BIM). The essence of BIM is to capture information about the building process and the building product. [Dutch] In 14 artikelen worden diverse aspecten m.b.t. het Building Information Model (BIM) belicht. De essentie van BIM is het vastleggen van informatie over het bouwproces en het bouwproduct.

Prediction of residential building energy consumption: A neural network approach

International Nuclear Information System (INIS)

Biswas, M.A. Rafe; Robinson, Melvin D.; Fumo, Nelson

2016-01-01

Some of the challenges to predict energy utilization has gained recognition in the residential sector due to the significant energy consumption in recent decades. However, the modeling of residential building energy consumption is still underdeveloped for optimal and robust solutions while this research area has become of greater relevance with significant advances in computation and simulation. Such advances include the advent of artificial intelligence research in statistical model development. Artificial neural network has emerged as a key method to address the issue of nonlinearity of building energy data and the robust calculation of large and dynamic data. The development and validation of such models on one of the TxAIRE Research houses has been demonstrated in this paper. The TxAIRE houses have been designed to serve as realistic test facilities for demonstrating new technologies. The input variables used from the house data include number of days, outdoor temperature and solar radiation while the output variables are house and heat pump energy consumption. The models based on Levenberg-Marquardt and OWO-Newton algorithms had promising results of coefficients of determination within 0.87–0.91, which is comparable to prior literature. Further work will be explored to develop a robust model for residential building application. - Highlights: • A TxAIRE research house energy consumption data was collected in model development. • Neural network models developed using Levenberg–Marquardt or OWO-Newton algorithms. • Model results match well with data and statistically consistent with literature.

Consensus models to predict endocrine disruption for all ...

Science.gov (United States)

Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte

Building and verifying a severity prediction model of acute pancreatitis (AP) based on BISAP, MEWS and routine test indexes.

Science.gov (United States)

Ye, Jiang-Feng; Zhao, Yu-Xin; Ju, Jian; Wang, Wei

2017-10-01

To discuss the value of the Bedside Index for Severity in Acute Pancreatitis (BISAP), Modified Early Warning Score (MEWS), serum Ca2+, similarly hereinafter, and red cell distribution width (RDW) for predicting the severity grade of acute pancreatitis and to develop and verify a more accurate scoring system to predict the severity of AP. In 302 patients with AP, we calculated BISAP and MEWS scores and conducted regression analyses on the relationships of BISAP scoring, RDW, MEWS, and serum Ca2+ with the severity of AP using single-factor logistics. The variables with statistical significance in the single-factor logistic regression were used in a multi-factor logistic regression model; forward stepwise regression was used to screen variables and build a multi-factor prediction model. A receiver operating characteristic curve (ROC curve) was constructed, and the significance of multi- and single-factor prediction models in predicting the severity of AP using the area under the ROC curve (AUC) was evaluated. The internal validity of the model was verified through bootstrapping. Among 302 patients with AP, 209 had mild acute pancreatitis (MAP) and 93 had severe acute pancreatitis (SAP). According to single-factor logistic regression analysis, we found that BISAP, MEWS and serum Ca2+ are prediction indexes of the severity of AP (P-value0.05). The multi-factor logistic regression analysis showed that BISAP and serum Ca2+ are independent prediction indexes of AP severity (P-value0.05); BISAP is negatively related to serum Ca2+ (r=-0.330, P-valuemodel is as follows: ln()=7.306+1.151*BISAP-4.516*serum Ca2+. The predictive ability of each model for SAP follows the order of the combined BISAP and serum Ca2+ prediction model>Ca2+>BISAP. There is no statistical significance for the predictive ability of BISAP and serum Ca2+ (P-value>0.05); however, there is remarkable statistical significance for the predictive ability using the newly built prediction model as well as BISAP

Weather Correlations to Calculate Infiltration Rates for U. S. Commercial Building Energy Models.

Science.gov (United States)

Ng, Lisa C; Quiles, Nelson Ojeda; Dols, W Stuart; Emmerich, Steven J

2018-01-01

As building envelope performance improves, a greater percentage of building energy loss will occur through envelope leakage. Although the energy impacts of infiltration on building energy use can be significant, current energy simulation software have limited ability to accurately account for envelope infiltration and the impacts of improved airtightness. This paper extends previous work by the National Institute of Standards and Technology that developed a set of EnergyPlus inputs for modeling infiltration in several commercial reference buildings using Chicago weather. The current work includes cities in seven additional climate zones and uses the updated versions of the prototype commercial building types developed by the Pacific Northwest National Laboratory for the U. S. Department of Energy. Comparisons were made between the predicted infiltration rates using three representations of the commercial building types: PNNL EnergyPlus models, CONTAM models, and EnergyPlus models using the infiltration inputs developed in this paper. The newly developed infiltration inputs in EnergyPlus yielded average annual increases of 3 % and 8 % in the HVAC electrical and gas use, respectively, over the original infiltration inputs in the PNNL EnergyPlus models. When analyzing the benefits of building envelope airtightening, greater HVAC energy savings were predicted using the newly developed infiltration inputs in EnergyPlus compared with using the original infiltration inputs. These results indicate that the effects of infiltration on HVAC energy use can be significant and that infiltration can and should be better accounted for in whole-building energy models.

Models for map building and navigation

International Nuclear Information System (INIS)

Penna, M.A.; Jian Wu

1993-01-01

In this paper the authors present several models for solving map building and navigation problems. These models are motivated by biological processes, and presented in the context of artificial neural networks. Since the nodes, weights, and threshold functions of the models all have physical meanings, they can easily predict network topologies and avoid traditional trial-and-error training. On one hand, this makes their models useful in constructing solutions to engineering problems (problems such as those that occur in robotics, for example). On the other hand, this might also contribute to the ability of their models to explain some biological processes, few of which are completely understood at this time

Prediction Models for Dynamic Demand Response

Energy Technology Data Exchange (ETDEWEB)

Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

2015-11-02

As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D²R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D²R, which we address in this paper. Our first contribution is the formal definition of D²R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D²R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D²R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D²R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D²R. Also, prediction models require just few days’ worth of data indicating that small amounts of

Issues of Application of Machine Learning Models for Virtual and Real-Life Buildings

Directory of Open Access Journals (Sweden)

Young Min Kim

2016-06-01

Full Text Available The current Building Energy Performance Simulation (BEPS tools are based on first principles. For the correct use of BEPS tools, simulationists should have an in-depth understanding of building physics, numerical methods, control logics of building systems, etc. However, it takes significant time and effort to develop a first principles-based simulation model for existing buildings—mainly due to the laborious process of data gathering, uncertain inputs, model calibration, etc. Rather than resorting to an expert’s effort, a data-driven approach (so-called “inverse” approach has received growing attention for the simulation of existing buildings. This paper reports a cross-comparison of three popular machine learning models (Artificial Neural Network (ANN, Support Vector Machine (SVM, and Gaussian Process (GP for predicting a chiller’s energy consumption in a virtual and a real-life building. The predictions based on the three models are sufficiently accurate compared to the virtual and real measurements. This paper addresses the following issues for the successful development of machine learning models: reproducibility, selection of inputs, training period, outlying data obtained from the building energy management system (BEMS, and validation of the models. From the result of this comparative study, it was found that SVM has a disadvantage in computation time compared to ANN and GP. GP is the most sensitive to a training period among the three models.

A kind of prediction from superstring model building

CERN Document Server

Muñoz, C

2001-01-01

Assuming that the Standard Model of Particle Physics arises from the $E_8\\times E_8$ Heterotic String Theory, we try to solve the discrepancy between the unification scale predicted by this theory ($\\approx g_{GUT}\\times 5.27\\cdot 10^{17}$ GeV) and the value deduced from LEP experiments ($\\approx 2\\cdot 10^{16}$ GeV). A crucial ingredient in our solution is the presence at low energies of three generations of supersymmetric Higgses and vector-like colour triplets. As a by-product our analysis gives rise to a strategy which might be useful in order to construct realistic models.

Modelling diversity in building occupant behaviour: a novel statistical approach

DEFF Research Database (Denmark)

Haldi, Frédéric; Calì, Davide; Andersen, Rune Korsholm

2016-01-01

We propose an advanced modelling framework to predict the scope and effects of behavioural diversity regarding building occupant actions on window openings, shading devices and lighting. We develop a statistical approach based on generalised linear mixed models to account for the longitudinal nat...

Predicted carbonation of existing concrete building based on the Indonesian tropical micro-climate

Science.gov (United States)

Hilmy, M.; Prabowo, H.

2018-03-01

This paper is aimed to predict the carbonation progress based on the previous mathematical model. It shortly explains the nature of carbonation including the processes and effects. Environmental humidity and temperature of the existing concrete building are measured and compared to data from local Meteorological, Climatological, and Geophysical Agency. The data gained are expressed in the form of annual hygrothermal values which will use as the input parameter in carbonation model. The physical properties of the observed building such as its location, dimensions, and structural material used are quantified. These data then utilized as an important input parameter for carbonation coefficients. The relationships between relative humidity and the rate of carbonation established. The results can provide a basis for repair and maintenance of existing concrete buildings and the sake of service life analysis of them.

Systematic model building with flavor symmetries

Energy Technology Data Exchange (ETDEWEB)

Plentinger, Florian

2009-12-19

The observation of neutrino masses and lepton mixing has highlighted the incompleteness of the Standard Model of particle physics. In conjunction with this discovery, new questions arise: why are the neutrino masses so small, which form has their mass hierarchy, why is the mixing in the quark and lepton sectors so different or what is the structure of the Higgs sector. In order to address these issues and to predict future experimental results, different approaches are considered. One particularly interesting possibility, are Grand Unified Theories such as SU(5) or SO(10). GUTs are vertical symmetries since they unify the SM particles into multiplets and usually predict new particles which can naturally explain the smallness of the neutrino masses via the seesaw mechanism. On the other hand, also horizontal symmetries, i.e., flavor symmetries, acting on the generation space of the SM particles, are promising. They can serve as an explanation for the quark and lepton mass hierarchies as well as for the different mixings in the quark and lepton sectors. In addition, flavor symmetries are significantly involved in the Higgs sector and predict certain forms of mass matrices. This high predictivity makes GUTs and flavor symmetries interesting for both, theorists and experimentalists. These extensions of the SM can be also combined with theories such as supersymmetry or extra dimensions. In addition, they usually have implications on the observed matter-antimatter asymmetry of the universe or can provide a dark matter candidate. In general, they also predict the lepton flavor violating rare decays {mu} {yields} e{gamma}, {tau} {yields} {mu}{gamma}, and {tau} {yields} e{gamma} which are strongly bounded by experiments but might be observed in the future. In this thesis, we combine all of these approaches, i.e., GUTs, the seesaw mechanism and flavor symmetries. Moreover, our request is to develop and perform a systematic model building approach with flavor symmetries and

Systematic model building with flavor symmetries

International Nuclear Information System (INIS)

Plentinger, Florian

2009-01-01

The observation of neutrino masses and lepton mixing has highlighted the incompleteness of the Standard Model of particle physics. In conjunction with this discovery, new questions arise: why are the neutrino masses so small, which form has their mass hierarchy, why is the mixing in the quark and lepton sectors so different or what is the structure of the Higgs sector. In order to address these issues and to predict future experimental results, different approaches are considered. One particularly interesting possibility, are Grand Unified Theories such as SU(5) or SO(10). GUTs are vertical symmetries since they unify the SM particles into multiplets and usually predict new particles which can naturally explain the smallness of the neutrino masses via the seesaw mechanism. On the other hand, also horizontal symmetries, i.e., flavor symmetries, acting on the generation space of the SM particles, are promising. They can serve as an explanation for the quark and lepton mass hierarchies as well as for the different mixings in the quark and lepton sectors. In addition, flavor symmetries are significantly involved in the Higgs sector and predict certain forms of mass matrices. This high predictivity makes GUTs and flavor symmetries interesting for both, theorists and experimentalists. These extensions of the SM can be also combined with theories such as supersymmetry or extra dimensions. In addition, they usually have implications on the observed matter-antimatter asymmetry of the universe or can provide a dark matter candidate. In general, they also predict the lepton flavor violating rare decays μ → eγ, τ → μγ, and τ → eγ which are strongly bounded by experiments but might be observed in the future. In this thesis, we combine all of these approaches, i.e., GUTs, the seesaw mechanism and flavor symmetries. Moreover, our request is to develop and perform a systematic model building approach with flavor symmetries and to search for phenomenological

Building Energy Modeling and Control Methods for Optimization and Renewables Integration

Science.gov (United States)

Burger, Eric M.

This dissertation presents techniques for the numerical modeling and control of building systems, with an emphasis on thermostatically controlled loads. The primary objective of this work is to address technical challenges related to the management of energy use in commercial and residential buildings. This work is motivated by the need to enhance the performance of building systems and by the potential for aggregated loads to perform load following and regulation ancillary services, thereby enabling the further adoption of intermittent renewable energy generation technologies. To increase the generalizability of the techniques, an emphasis is placed on recursive and adaptive methods which minimize the need for customization to specific buildings and applications. The techniques presented in this dissertation can be divided into two general categories: modeling and control. Modeling techniques encompass the processing of data streams from sensors and the training of numerical models. These models enable us to predict the energy use of a building and of sub-systems, such as a heating, ventilation, and air conditioning (HVAC) unit. Specifically, we first present an ensemble learning method for the short-term forecasting of total electricity demand in buildings. As the deployment of intermittent renewable energy resources continues to rise, the generation of accurate building-level electricity demand forecasts will be valuable to both grid operators and building energy management systems. Second, we present a recursive parameter estimation technique for identifying a thermostatically controlled load (TCL) model that is non-linear in the parameters. For TCLs to perform demand response services in real-time markets, online methods for parameter estimation are needed. Third, we develop a piecewise linear thermal model of a residential building and train the model using data collected from a custom-built thermostat. This model is capable of approximating unmodeled

Numerical prediction of energy consumption in buildings with controlled interior temperature

Energy Technology Data Exchange (ETDEWEB)

Jarošová, P.; Št’astník, S. [Brno University of Technology, Faculty of Civil Engineering, 602 00 Brno, Veveří 95, Czech Republic, e-mail jarosova.p@fce.vutbr.cz, stastnik.s@fce.vutbr.cz (Czech Republic)

2015-03-10

New European directives bring strong requirement to the energy consumption of building objects, supporting the renewable energy sources. Whereas in the case of family and similar houses this can lead up to absurd consequences, for building objects with controlled interior temperature the optimization of energy demand is really needed. The paper demonstrates the system approach to the modelling of thermal insulation and accumulation abilities of such objetcs, incorporating the significant influence of additional physical processes, as surface heat radiation and moisture-driven deterioration of insulation layers. An illustrative example shows the numerical prediction of energy consumption of a freezing plant in one Central European climatic year.

A model predictive control strategy for the space heating of a smart building including cogeneration of a fuel cell-electrolyzer system

DEFF Research Database (Denmark)

Sossan, Fabrizio; Bindner, Henrik W.; Madsen, Henrik

2014-01-01

The objective of this paper is to analyze the value of energy replacement in the context of demand response. Energy replacement is dened as the possibility of the consumer to choose the most convenient source for providing space heating to a smart building according to a dynamic electricity price....... In the proposed setup, heat is provided by conventional electric radiators and a combined heat and power generation system, composed by a fuel cell and an electrolyzer. The energy replacement strategy is formulated using model predictive control and mathematical models of the components involved. Simulations show...... that the predictive energy replacement strategy reduces the operating costs of the system and is able to provide a larger amount of regulating power to the grid. In the paper, we also develop a novel dynamic model of a PEM fuel cell suitable for micro-grid applications. The model is realized applying a grey...

Prediction error, ketamine and psychosis: An updated model.

Science.gov (United States)

Corlett, Philip R; Honey, Garry D; Fletcher, Paul C

2016-11-01

In 2007, we proposed an explanation of delusion formation as aberrant prediction error-driven associative learning. Further, we argued that the NMDA receptor antagonist ketamine provided a good model for this process. Subsequently, we validated the model in patients with psychosis, relating aberrant prediction error signals to delusion severity. During the ensuing period, we have developed these ideas, drawing on the simple principle that brains build a model of the world and refine it by minimising prediction errors, as well as using it to guide perceptual inferences. While previously we focused on the prediction error signal per se, an updated view takes into account its precision, as well as the precision of prior expectations. With this expanded perspective, we see several possible routes to psychotic symptoms - which may explain the heterogeneity of psychotic illness, as well as the fact that other drugs, with different pharmacological actions, can produce psychotomimetic effects. In this article, we review the basic principles of this model and highlight specific ways in which prediction errors can be perturbed, in particular considering the reliability and uncertainty of predictions. The expanded model explains hallucinations as perturbations of the uncertainty mediated balance between expectation and prediction error. Here, expectations dominate and create perceptions by suppressing or ignoring actual inputs. Negative symptoms may arise due to poor reliability of predictions in service of action. By mapping from biology to belief and perception, the account proffers new explanations of psychosis. However, challenges remain. We attempt to address some of these concerns and suggest future directions, incorporating other symptoms into the model, building towards better understanding of psychosis. © The Author(s) 2016.

Modeling of heat and mass transfer in lateritic building envelopes

International Nuclear Information System (INIS)

Meukam, Pierre

2004-10-01

The aim of the present work is to investigate the behavior of building envelopes made of local lateritic soil bricks subjected to different climatic conditions. The analysis is developed for the prediction of the temperature, relative humidity and water content behavior within the walls. The building envelopes studied in this work consist of lateritic soil bricks with incorporation of natural pozzolan or sawdust in order to obtain small thermal conductivity and low-density materials, and limit the heat transfer between the atmospheric climate and the inside environment. In order to describe coupled heat and moisture transfer in wet porous materials, the coupled equations were solved by the introduction of diffusion coefficients. A numerical model HMtrans, developed for prediction of beat and moisture transfer in multi-layered building components, was used to simulate the temperature, water content and relative humidity profiles within the building envelopes. The results allow the prediction of the duration of the exposed building walls to the local weather conditions. They show that for any of three climatic conditions considered, relative humidity and water content do not exceed 87% and 5% respectively. There is therefore minimum possibility of water condensation in the materials studied. The durability of building envelopes made of lateritic soil bricks with incorporation of natural pozzolan or sawdust is not strongly affected by the climatic conditions in tropical and equatorial regions. (author)

Scaling predictive modeling in drug development with cloud computing.

Science.gov (United States)

Moghadam, Behrooz Torabi; Alvarsson, Jonathan; Holm, Marcus; Eklund, Martin; Carlsson, Lars; Spjuth, Ola

2015-01-26

Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

A model for the build-up of disordered material in ion bombarded Si

International Nuclear Information System (INIS)

Nelson, R.S.

1977-01-01

A new model based on experimental observation is developed for the build-up of disordered material in ion bombarded silicon. The model assumes that disordered zones are created in a background of migrating point defects, these zones then act as neutral sinks for such defects which interact with the zones and cause recrystallization. A simple steady state rate theory is developed to describe the build-up of disordered material with ion dose as a function of temperature. In general the theory predicts two distinct behaviour patterns depending on the temperature and the ion mass, namely a linear build-up with dose to complete disorder for heavy bombarding ions and a build-up to saturation at a relatively low level for light ions such as protons. However, in some special circumstances a transition region is predicted where the build-up of disorder approximately follows a (dose)sup(1/2) relationship before reverting to a linear behaviour at high dose. (author)

Number of Clusters and the Quality of Hybrid Predictive Models in Analytical CRM

Directory of Open Access Journals (Sweden)

Łapczyński Mariusz

2014-08-01

Full Text Available Making more accurate marketing decisions by managers requires building effective predictive models. Typically, these models specify the probability of customer belonging to a particular category, group or segment. The analytical CRM categories refer to customers interested in starting cooperation with the company (acquisition models, customers who purchase additional products (cross- and up-sell models or customers intending to resign from the cooperation (churn models. During building predictive models researchers use analytical tools from various disciplines with an emphasis on their best performance. This article attempts to build a hybrid predictive model combining decision trees (C&RT algorithm and cluster analysis (k-means. During experiments five different cluster validity indices and eight datasets were used. The performance of models was evaluated by using popular measures such as: accuracy, precision, recall, G-mean, F-measure and lift in the first and in the second decile. The authors tried to find a connection between the number of clusters and models' quality.

eTOXlab, an open source modeling framework for implementing predictive models in production environments.

Science.gov (United States)

Carrió, Pau; López, Oriol; Sanz, Ferran; Pastor, Manuel

2015-01-01

Computational models based in Quantitative-Structure Activity Relationship (QSAR) methodologies are widely used tools for predicting the biological properties of new compounds. In many instances, such models are used as a routine in the industry (e.g. food, cosmetic or pharmaceutical industry) for the early assessment of the biological properties of new compounds. However, most of the tools currently available for developing QSAR models are not well suited for supporting the whole QSAR model life cycle in production environments. We have developed eTOXlab; an open source modeling framework designed to be used at the core of a self-contained virtual machine that can be easily deployed in production environments, providing predictions as web services. eTOXlab consists on a collection of object-oriented Python modules with methods mapping common tasks of standard modeling workflows. This framework allows building and validating QSAR models as well as predicting the properties of new compounds using either a command line interface or a graphic user interface (GUI). Simple models can be easily generated by setting a few parameters, while more complex models can be implemented by overriding pieces of the original source code. eTOXlab benefits from the object-oriented capabilities of Python for providing high flexibility: any model implemented using eTOXlab inherits the features implemented in the parent model, like common tools and services or the automatic exposure of the models as prediction web services. The particular eTOXlab architecture as a self-contained, portable prediction engine allows building models with confidential information within corporate facilities, which can be safely exported and used for prediction without disclosing the structures of the training series. The software presented here provides full support to the specific needs of users that want to develop, use and maintain predictive models in corporate environments. The technologies used by e

Automated model building

CERN Document Server

Caferra, Ricardo; Peltier, Nicholas

2004-01-01

This is the first book on automated model building, a discipline of automated deduction that is of growing importance Although models and their construction are important per se, automated model building has appeared as a natural enrichment of automated deduction, especially in the attempt to capture the human way of reasoning The book provides an historical overview of the field of automated deduction, and presents the foundations of different existing approaches to model construction, in particular those developed by the authors Finite and infinite model building techniques are presented The main emphasis is on calculi-based methods, and relevant practical results are provided The book is of interest to researchers and graduate students in computer science, computational logic and artificial intelligence It can also be used as a textbook in advanced undergraduate courses

The Use of Modelling for Theory Building in Qualitative Analysis

Science.gov (United States)

Briggs, Ann R. J.

2007-01-01

The purpose of this article is to exemplify and enhance the place of modelling as a qualitative process in educational research. Modelling is widely used in quantitative research as a tool for analysis, theory building and prediction. Statistical data lend themselves to graphical representation of values, interrelationships and operational…

Predictive Models and Computational Toxicology (II IBAMTOX)

Science.gov (United States)

EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

Numerical model for stack gas diffusion in terrain with buildings. Variations in air flow and gas concentration with additional building near stack

International Nuclear Information System (INIS)

Sada, Koichi; Michioka, Takenobu; Ichikawa, Yoichi; Komiyama, Sumito; Numata, Kunio

2009-01-01

A numerical simulation method for predicting atmospheric flow and stack gas diffusion using a calculation domain of several km around a stack under complex terrain conditions containing buildings has been developed. The turbulence closure technique using a modified k-ε-type model without a hydrostatic approximation was used for flow calculation, and some of the calculation grids near the ground were treated as buildings using a terrain-following coordinate system. Stack gas diffusion was predicted using the Lagrangian particle model, that is, the stack gas was represented by trajectories of released particles. The developed numerical model was applied to a virtual terrain and building conditions in this study prior to the applications of a numerical model for real terrain and building conditions. The height of the additional building (H a ), located about 200 m leeward from the stack, was varied (i.e., H a =0, 20, 30 and 50 m), and its effects on airflow and the concentration of stack gas at a released height of 75 m were calculated. Furthermore, effective stack height, which was used in the safety analysis of atmospheric diffusion for nuclear facilities in Japan, was evaluated from the calculated ground-level concentration of stack gas. The cavity region behind the additional building was calculated, and turbulence near the cavity was observed to decrease when the additional building was present. According to these flow variations with the additional building, tracer gas tended to diffuse to the ground surface rapidly with the additional building at the leeward position of the cavity, and the ground-level stack gas concentration along the plume axis also increased with the height of the additional building. However, the variations in effective stack height with the height of the additional building were relatively small and ranged within several m in this study. (author)

Model calibration for building energy efficiency simulation

International Nuclear Information System (INIS)

Mustafaraj, Giorgio; Marini, Dashamir; Costa, Andrea; Keane, Marcus

2014-01-01

Highlights: • Developing a 3D model relating to building architecture, occupancy and HVAC operation. • Two calibration stages developed, final model providing accurate results. • Using an onsite weather station for generating the weather data file in EnergyPlus. • Predicting thermal behaviour of underfloor heating, heat pump and natural ventilation. • Monthly energy saving opportunities related to heat pump of 20–27% was identified. - Abstract: This research work deals with an Environmental Research Institute (ERI) building where an underfloor heating system and natural ventilation are the main systems used to maintain comfort condition throughout 80% of the building areas. Firstly, this work involved developing a 3D model relating to building architecture, occupancy and HVAC operation. Secondly, the calibration methodology, which consists of two levels, was then applied in order to insure accuracy and reduce the likelihood of errors. To further improve the accuracy of calibration a historical weather data file related to year 2011, was created from the on-site local weather station of ERI building. After applying the second level of calibration process, the values of Mean bias Error (MBE) and Cumulative Variation of Root Mean Squared Error (CV(RMSE)) on hourly based analysis for heat pump electricity consumption varied within the following ranges: (MBE) hourly from −5.6% to 7.5% and CV(RMSE) hourly from 7.3% to 25.1%. Finally, the building was simulated with EnergyPlus to identify further possibilities of energy savings supplied by a water to water heat pump to underfloor heating system. It found that electricity consumption savings from the heat pump can vary between 20% and 27% on monthly bases

Support vector machine in prediction of building energy demand using pseudo dynamic approach

NARCIS (Netherlands)

Paudel, S.; Nguyen, H.P.; Kling, W.L.; Elmitri, Mohamed; Lacarriere, B.; Corre, le O.

2015-01-01

Building’s energy consumption prediction is a major concern in the recent years and many efforts have been achieved in order to improve the energy management of buildings. In particular, the prediction of energy consumption in building is essential for the energy operator to build an optimal

Lost opportunities: Modeling commercial building energy code adoption in the United States

International Nuclear Information System (INIS)

Nelson, Hal T.

2012-01-01

This paper models the adoption of commercial building energy codes in the US between 1977 and 2006. Energy code adoption typically results in an increase in aggregate social welfare by cost effectively reducing energy expenditures. Using a Cox proportional hazards model, I test if relative state funding, a new, objective, multivariate regression-derived measure of government capacity, as well as a vector of control variables commonly used in comparative state research, predict commercial building energy code adoption. The research shows little political influence over historical commercial building energy code adoption in the sample. Colder climates and higher electricity prices also do not predict more frequent code adoptions. I do find evidence of high government capacity states being 60 percent more likely than low capacity states to adopt commercial building energy codes in the following year. Wealthier states are also more likely to adopt commercial codes. Policy recommendations to increase building code adoption include increasing access to low cost capital for the private sector and providing noncompetitive block grants to the states from the federal government. - Highlights: ► Model the adoption of commercial building energy codes from 1977–2006 in the US. ► Little political influence over historical building energy code adoption. ► High capacity states are over 60 percent more likely than low capacity states to adopt codes. ► Wealthier states are more likely to adopt commercial codes. ► Access to capital and technical assistance is critical to increase code adoption.

Building energy modeling for green architecture and intelligent dashboard applications

Science.gov (United States)

DeBlois, Justin

Buildings are responsible for 40% of the carbon emissions in the United States. Energy efficiency in this sector is key to reducing overall greenhouse gas emissions. This work studied the passive technique called the roof solar chimney for reducing the cooling load in homes architecturally. Three models of the chimney were created: a zonal building energy model, computational fluid dynamics model, and numerical analytic model. The study estimated the error introduced to the building energy model (BEM) through key assumptions, and then used a sensitivity analysis to examine the impact on the model outputs. The conclusion was that the error in the building energy model is small enough to use it for building simulation reliably. Further studies simulated the roof solar chimney in a whole building, integrated into one side of the roof. Comparisons were made between high and low efficiency constructions, and three ventilation strategies. The results showed that in four US climates, the roof solar chimney results in significant cooling load energy savings of up to 90%. After developing this new method for the small scale representation of a passive architecture technique in BEM, the study expanded the scope to address a fundamental issue in modeling - the implementation of the uncertainty from and improvement of occupant behavior. This is believed to be one of the weakest links in both accurate modeling and proper, energy efficient building operation. A calibrated model of the Mascaro Center for Sustainable Innovation's LEED Gold, 3,400 m2 building was created. Then algorithms were developed for integration to the building's dashboard application that show the occupant the energy savings for a variety of behaviors in real time. An approach using neural networks to act on real-time building automation system data was found to be the most accurate and efficient way to predict the current energy savings for each scenario. A stochastic study examined the impact of the

Structural observability analysis and EKF based parameter estimation of building heating models

Directory of Open Access Journals (Sweden)

D.W.U. Perera

2016-07-01

Full Text Available Research for enhanced energy-efficient buildings has been given much recognition in the recent years owing to their high energy consumptions. Increasing energy needs can be precisely controlled by practicing advanced controllers for building Heating, Ventilation, and Air-Conditioning (HVAC systems. Advanced controllers require a mathematical building heating model to operate, and these models need to be accurate and computationally efficient. One main concern associated with such models is the accurate estimation of the unknown model parameters. This paper presents the feasibility of implementing a simplified building heating model and the computation of physical parameters using an off-line approach. Structural observability analysis is conducted using graph-theoretic techniques to analyze the observability of the developed system model. Then Extended Kalman Filter (EKF algorithm is utilized for parameter estimates using the real measurements of a single-zone building. The simulation-based results confirm that even with a simple model, the EKF follows the state variables accurately. The predicted parameters vary depending on the inputs and disturbances.

BUILDING DETECTION USING AERIAL IMAGES AND DIGITAL SURFACE MODELS

Directory of Open Access Journals (Sweden)

J. Mu

2017-05-01

Full Text Available In this paper a method for building detection in aerial images based on variational inference of logistic regression is proposed. It consists of three steps. In order to characterize the appearances of buildings in aerial images, an effective bag-of-Words (BoW method is applied for feature extraction in the first step. In the second step, a classifier of logistic regression is learned using these local features. The logistic regression can be trained using different methods. In this paper we adopt a fully Bayesian treatment for learning the classifier, which has a number of obvious advantages over other learning methods. Due to the presence of hyper prior in the probabilistic model of logistic regression, approximate inference methods have to be applied for prediction. In order to speed up the inference, a variational inference method based on mean field instead of stochastic approximation such as Markov Chain Monte Carlo is applied. After the prediction, a probabilistic map is obtained. In the third step, a fully connected conditional random field model is formulated and the probabilistic map is used as the data term in the model. A mean field inference is utilized in order to obtain a binary building mask. A benchmark data set consisting of aerial images and digital surfaced model (DSM released by ISPRS for 2D semantic labeling is used for performance evaluation. The results demonstrate the effectiveness of the proposed method.

Comparison of Demand Response Performance with an EnergyPlus Model in a Low Energy Campus Building

Energy Technology Data Exchange (ETDEWEB)

Dudley, Junqiao Han; Black, Doug; Apte, Mike; Piette, Mary Ann; Berkeley, Pam

2010-05-14

We have studied a low energy building on a campus of the University of California. It has efficient heating, ventilation, and air conditioning (HVAC) systems, consisting of a dual-fan/dual-duct variable air volume (VAV) system. As a major building on the campus, it was included in two demand response (DR) events in the summers of 2008 and 2009. With chilled water supplied by thermal energy storage in the central plant, cooling fans played a critical role during DR events. In this paper, an EnergyPlus model of the building was developed and calibrated. We compared both whole-building and HVAC fan energy consumption with model predictions to understand why demand savings in 2009 were much lower than in 2008. We also used model simulations of the study building to assess pre-cooling, a strategy that has been shown to improve demand saving and thermal comfort in many types of building. This study indicates a properly calibrated EnergyPlus model can reasonably predict demand savings from DR events and can be useful for designing or optimizing DR strategies.

Massive Predictive Modeling using Oracle R Enterprise

CERN Multimedia

CERN. Geneva

2014-01-01

R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...

Extension of the PMV model to non-air-conditioned building in warm climates

DEFF Research Database (Denmark)

Fanger, Povl Ole; Toftum, Jørn

2002-01-01

The PMV model agrees well with high-quality field studies in buildings with HVAC systems, situated in cold, temperate and warm climates, studied during both summer and winter. In non-air-conditioned buildings in warm climates, occupants may sense the warmth as being less severe than the PMV...... predicts. The main reason is low expectations, but a metabolic rate that is estimated too high can also contribute to explaining the difference. An extension of the PMV model that includes an expectancy factor is introduced for use in non-air-conditioned buildings in warm climates. The extended PMV model...... agrees well with quality field studies in non-air-conditioned buildings of three continents....

Preliminary Empirical Models for Predicting Shrinkage, Part Geometry and Metallurgical Aspects of Ti-6Al-4V Shaped Metal Deposition Builds

Science.gov (United States)

Escobar-Palafox, Gustavo; Gault, Rosemary; Ridgway, Keith

2011-12-01

Shaped Metal Deposition (SMD) is an additive manufacturing process which creates parts layer by layer by weld depositions. In this work, empirical models that predict part geometry (wall thickness and outer diameter) and some metallurgical aspects (i.e. surface texture, portion of finer Widmanstätten microstructure) for the SMD process were developed. The models are based on an orthogonal fractional factorial design of experiments with four factors at two levels. The factors considered were energy level (a relationship between heat source power and the rate of raw material input.), step size, programmed diameter and travel speed. The models were validated using previous builds; the prediction error for part geometry was under 11%. Several relationships between the factors and responses were identified. Current had a significant effect on wall thickness; thickness increases with increasing current. Programmed diameter had a significant effect on percentage of shrinkage; this decreased with increasing component size. Surface finish decreased with decreasing step size and current.

Preliminary Empirical Models for Predicting Shrinkage, Part Geometry and Metallurgical Aspects of Ti-6Al-4V Shaped Metal Deposition Builds

International Nuclear Information System (INIS)

Escobar-Palafox, Gustavo; Gault, Rosemary; Ridgway, Keith

2011-01-01

Shaped Metal Deposition (SMD) is an additive manufacturing process which creates parts layer by layer by weld depositions. In this work, empirical models that predict part geometry (wall thickness and outer diameter) and some metallurgical aspects (i.e. surface texture, portion of finer Widmanstätten microstructure) for the SMD process were developed. The models are based on an orthogonal fractional factorial design of experiments with four factors at two levels. The factors considered were energy level (a relationship between heat source power and the rate of raw material input.), step size, programmed diameter and travel speed. The models were validated using previous builds; the prediction error for part geometry was under 11%. Several relationships between the factors and responses were identified. Current had a significant effect on wall thickness; thickness increases with increasing current. Programmed diameter had a significant effect on percentage of shrinkage; this decreased with increasing component size. Surface finish decreased with decreasing step size and current.

Modeling of HVAC operational faults in building performance simulation

International Nuclear Information System (INIS)

Zhang, Rongpeng; Hong, Tianzhen

2017-01-01

Highlights: •Discuss significance of capturing operational faults in existing buildings. •Develop a novel feature in EnergyPlus to model operational faults of HVAC systems. •Compare three approaches to faults modeling using EnergyPlus. •A case study demonstrates the use of the fault-modeling feature. •Future developments of new faults are discussed. -- Abstract: Operational faults are common in the heating, ventilating, and air conditioning (HVAC) systems of existing buildings, leading to a decrease in energy efficiency and occupant comfort. Various fault detection and diagnostic methods have been developed to identify and analyze HVAC operational faults at the component or subsystem level. However, current methods lack a holistic approach to predicting the overall impacts of faults at the building level—an approach that adequately addresses the coupling between various operational components, the synchronized effect between simultaneous faults, and the dynamic nature of fault severity. This study introduces the novel development of a fault-modeling feature in EnergyPlus which fills in the knowledge gap left by previous studies. This paper presents the design and implementation of the new feature in EnergyPlus and discusses in detail the fault-modeling challenges faced. The new fault-modeling feature enables EnergyPlus to quantify the impacts of faults on building energy use and occupant comfort, thus supporting the decision making of timely fault corrections. Including actual building operational faults in energy models also improves the accuracy of the baseline model, which is critical in the measurement and verification of retrofit or commissioning projects. As an example, EnergyPlus version 8.6 was used to investigate the impacts of a number of typical operational faults in an office building across several U.S. climate zones. The results demonstrate that the faults have significant impacts on building energy performance as well as on occupant

Selecting the group method of data handling as one of the most perspective algorithmes for building a predictive model of petroleum consumption in the system of energy balance of Ukraine

Directory of Open Access Journals (Sweden)

Trachuk A.R.

2017-06-01

Full Text Available This paper deals with issues of petroleum consumption in Ukraine. The dynamics of consumption of petroleum is analysed and proposed guidelines for the efficient production, consumption and import of petroleum in Ukraine. Constructed and developed predictive models of petroleum consumption in Ukraine through the use of modern software and using the group method of data handling, which allowed building adequate predictive models of petroleum consumption in the system of Ukraine’s energy balance. Researched and forecasted scenarios of petroleum consumption in the Ukraine. The problem of efficient use of energy resources is critical for sustainable economic development against the backdrop of energy saving national economy depends on energy imports, on the one hand, and rising prices for these resources. The basic foundation of the formation energy system of Ukraine is to build forecasting scenarios for different types of energy and different criteria for effective use of energy resources. Solving this problem is not only with ensuring energy security, but also with the level of development of regions of Ukraine and ensuring quality of life. Prediction of petroleum consumption in Ukraine today is an extremely important issue of strategic importance since conducted through analysis and building predictive models will be possible to develop guidelines for the efficient production and consumption of petroleum across Ukraine as a whole.

Impact of whole-building hygrothermal modelling on the assessment of indoor climate in a library building

Energy Technology Data Exchange (ETDEWEB)

Steeman, M.; Janssens, A. [Ghent University, Department of Architecture and Urban Planning, Jozef Plateaustraat 22, B-9000 Gent (Belgium); De Paepe, M. [Ghent University, Department of Flow, Heat and Combustion Mechanics, Sint-Pietersnieuwstraat 41, B-9000 Gent (Belgium)

2010-07-15

This paper focuses on the importance of accurately modelling the hygrothermal interaction between the building and its hygroscopic content for the assessment of the indoor climate. Libraries contain a large amount of stored books which require a stable relative humidity to guarantee their preservation. On the other hand, visitors and staff must be comfortable with the indoor climate. The indoor climate of a new library building is evaluated by means of measurements and simulations. Complaints of the staff are confirmed by measured data during the winter and summer of 2007-2008. For the evaluation of the indoor climate, a building simulation model is used in which the porous books are either described by a HAM model or by a simplified isothermal model. Calculations demonstrate that the HAM model predicts a more stable indoor climate regarding both temperature and relative humidity variations in comparison to the estimations by the simplified model. This is attributed to the ability of the HAM model to account for the effect of temperature variations on moisture storage. Moreover, by applying the HAM model, a good agreement with the measured indoor climate is found. As expected, a larger exposed book surface ameliorates the indoor climate because a more stable indoor relative humidity is obtained. Finally, the building simulation model is used to improve the indoor climate with respect to the preservation of valuable books. Results demonstrate that more stringent interventions on the air handling unit are expected when a simplified approach is used to model the hygroscopic books. (author)

The prediction of engineering cost for green buildings based on information entropy

Science.gov (United States)

Liang, Guoqiang; Huang, Jinglian

2018-03-01

Green building is the developing trend in the world building industry. Additionally, construction costs are an essential consideration in building constructions. Therefore, it is necessary to investigate the problems of cost prediction in green building. On the basis of analyzing the cost of green building, this paper proposes the forecasting method of actual cost in green building based on information entropy and provides the forecasting working procedure. Using the probability density obtained from statistical data, such as labor costs, material costs, machinery costs, administration costs, profits, risk costs a unit project quotation and etc., situations can be predicted which lead to cost variations between budgeted cost and actual cost in constructions, through estimating the information entropy of budgeted cost and actual cost. The research results of this article have a practical significance in cost control of green building. Additionally, the method proposed in this article can be generalized and applied to a variety of other aspects in building management.

Validating computational predictions of night-time ventilation in Stanford's Y2E2 building

Science.gov (United States)

Chen, Chen; Lamberti, Giacomo; Gorle, Catherine

2017-11-01

Natural ventilation can significantly reduce building energy consumption, but robust design is a challenging task. We previously presented predictions of natural ventilation performance in Stanford's Y2E2 building using two models with different levels of fidelity, embedded in an uncertainty quantification framework to identify the dominant uncertain parameters and predict quantified confidence intervals. The results showed a slightly high cooling rate for the volume-averaged temperature, and the initial thermal mass temperature and window discharge coefficients were found to have an important influence on the results. To further investigate the potential role of these parameters on the observed discrepancies, the current study is performing additional measurements in the Y2E2 building. Wall temperatures are recorded throughout the nightflush using thermocouples; flow rates through windows are measured using hotwires; and spatial variability in the air temperature is explored. The measured wall temperatures are found the be within the range of our model assumptions, and the measured velocities agree reasonably well with our CFD predications. Considerable local variations in the indoor air temperature have been recorded, largely explaining the discrepancies in our earlier validation study. Future work will therefore focus on a local validation of the CFD results with the measurements. Center for Integrated Facility Engineering (CIFE).

A deep auto-encoder model for gene expression prediction.

Science.gov (United States)

Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

2017-11-17

Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.

Irregular Shaped Building Design Optimization with Building Information Modelling

Directory of Open Access Journals (Sweden)

Lee Xia Sheng

2016-01-01

Full Text Available This research is to recognise the function of Building Information Modelling (BIM in design optimization for irregular shaped buildings. The study focuses on a conceptual irregular shaped “twisted” building design similar to some existing sculpture-like architectures. Form and function are the two most important aspects of new buildings, which are becoming more sophisticated as parts of equally sophisticated “systems” that we are living in. Nowadays, it is common to have irregular shaped or sculpture-like buildings which are very different when compared to regular buildings. Construction industry stakeholders are facing stiff challenges in many aspects such as buildability, cost effectiveness, delivery time and facility management when dealing with irregular shaped building projects. Building Information Modelling (BIM is being utilized to enable architects, engineers and constructors to gain improved visualization for irregular shaped buildings; this has a purpose of identifying critical issues before initiating physical construction work. In this study, three variations of design options differing in rotating angle: 30 degrees, 60 degrees and 90 degrees are created to conduct quantifiable comparisons. Discussions are focused on three major aspects including structural planning, usable building space, and structural constructability. This research concludes that Building Information Modelling is instrumental in facilitating design optimization for irregular shaped building. In the process of comparing different design variations, instead of just giving “yes or no” type of response, stakeholders can now easily visualize, evaluate and decide to achieve the right balance based on their own criteria. Therefore, construction project stakeholders are empowered with superior evaluation and decision making capability.

MJO prediction skill of the subseasonal-to-seasonal (S2S) prediction models

Science.gov (United States)

Son, S. W.; Lim, Y.; Kim, D.

2017-12-01

The Madden-Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, provides the primary source of tropical and extratropical predictability on subseasonal to seasonal timescales. To better understand its predictability, this study conducts quantitative evaluation of MJO prediction skill in the state-of-the-art operational models participating in the subseasonal-to-seasonal (S2S) prediction project. Based on bivariate correlation coefficient of 0.5, the S2S models exhibit MJO prediction skill ranging from 12 to 36 days. These prediction skills are affected by both the MJO amplitude and phase errors, the latter becoming more important with forecast lead times. Consistent with previous studies, the MJO events with stronger initial amplitude are typically better predicted. However, essentially no sensitivity to the initial MJO phase is observed. Overall MJO prediction skill and its inter-model spread are further related with the model mean biases in moisture fields and longwave cloud-radiation feedbacks. In most models, a dry bias quickly builds up in the deep tropics, especially across the Maritime Continent, weakening horizontal moisture gradient. This likely dampens the organization and propagation of MJO. Most S2S models also underestimate the longwave cloud-radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelop. In general, the models with a smaller bias in horizontal moisture gradient and longwave cloud-radiation feedbacks show a higher MJO prediction skill, suggesting that improving those processes would enhance MJO prediction skill.

models for predicting compressive strength and water absorption

African Journals Online (AJOL)

user

presents a mathematical model for predicting the compressive strength and water absorption of laterite-quarry dust cement block using ... building and construction of new infrastructure and .... In (6), R is a vector containing the real ratios of the.

Fundamental mass transfer modeling of emission of volatile organic compounds from building materials

Science.gov (United States)

Bodalal, Awad Saad

In this study, a mass transfer theory based model is presented for characterizing the VOC emissions from building materials. A 3-D diffusion model is developed to describe the emissions of volatile organic compounds (VOCs) from individual sources. Then the formulation is extended to include the emissions from composite sources (system comprising an assemblage of individual sources). The key parameters for the model (The diffusion coefficient of the VOC in the source material D, and the equilibrium partition coefficient k e) were determined independently (model parameters are determined without the use of chamber emission data). This procedure eliminated to a large extent the need for emission testing using environmental chambers, which is costly, time consuming, and may be subject to confounding sink effects. An experimental method is developed and implemented to measure directly the internal diffusion (D) and partition coefficients ( ke). The use of the method is illustrated for three types of VOC's: (i) Aliphatic Hydrocarbons, (ii) Aromatic Hydrocarbons and ( iii) Aldehydes, through typical dry building materials (carpet, plywood, particleboard, vinyl floor tile, gypsum board, sub-floor tile and OSB). Then correlations for predicting D and ke based solely on commonly available properties such as molecular weight and vapour pressure were proposed for each product and type of VOC. These correlations can be used to estimate the D and ke when direct measurement data are not available, and thus facilitate the prediction of VOC emissions from the building materials using mass transfer theory. The VOC emissions from a sub-floor material (made of the recycled automobile tires), and a particleboard are measured and predicted. Finally, a mathematical model to predict the diffusion coefficient through complex sources (floor adhesive) as a function of time was developed. Then this model (for diffusion coefficient in complex sources) was used to predict the emission rate from

SUSY GUT Model Building

International Nuclear Information System (INIS)

Raby, Stuart

2008-01-01

In this talk I discuss the evolution of SUSY GUT model building as I see it. Starting with 4 dimensional model building, I then consider orbifold GUTs in 5 dimensions and finally orbifold GUTs embedded into the E 8 xE 8 heterotic string.

Seismic Response Prediction of Buildings with Base Isolation Using Advanced Soft Computing Approaches

Directory of Open Access Journals (Sweden)

Mosbeh R. Kaloop

2017-01-01

Full Text Available Modeling response of structures under seismic loads is an important factor in Civil Engineering as it crucially affects the design and management of structures, especially for the high-risk areas. In this study, novel applications of advanced soft computing techniques are utilized for predicting the behavior of centrically braced frame (CBF buildings with lead-rubber bearing (LRB isolation system under ground motion effects. These techniques include least square support vector machine (LSSVM, wavelet neural networks (WNN, and adaptive neurofuzzy inference system (ANFIS along with wavelet denoising. The simulation of a 2D frame model and eight ground motions are considered in this study to evaluate the prediction models. The comparison results indicate that the least square support vector machine is superior to other techniques in estimating the behavior of smart structures.

Towards evaluation and prediction of building sustainability using life cycle behaviour simulation

Directory of Open Access Journals (Sweden)

Marzouk Mohamed

2017-01-01

Full Text Available Nowadays researchers and practitioners are oriented towards questioning how effective are the different building life cycle activities contribution to preserving the environment and fulfilling the need for equilibrium. Terminologies such as Building sustainability and Green Buildings have long been adopted yet the evaluation of such has been driven through the use of rating systems. LEED of the United States, BREEAM of the United Kingdom, and Pearl of the United Arab Emirates are namely good examples of these rating systems. This paper introduces a new approach for evaluation of building life cycle sustainability through simulation of activities interaction and studying its behaviour. The effort focuses on comprehending impact and effect of suitability related activities over the whole building life cycle or period of time. The methodology includes gathering a pool of parameters through benchmarking of five selected rating systems, analytical factorization for the gathered parameters is used to elect the most influencing parameters. Followed by simulation modelling using System dynamics to capture the interaction of the considered parameters. The resulting behaviour obtained from simulation is studied and used in designing a tool for prediction of sustainability.

Functional Testing Protocols for Commercial Building Efficiency Baseline Modeling Software

Energy Technology Data Exchange (ETDEWEB)

Jump, David; Price, Phillip N.; Granderson, Jessica; Sohn, Michael

2013-09-06

This document describes procedures for testing and validating proprietary baseline energy modeling software accuracy in predicting energy use over the period of interest, such as a month or a year. The procedures are designed according to the methodology used for public domain baselining software in another LBNL report that was (like the present report) prepared for Pacific Gas and Electric Company: ?Commercial Building Energy Baseline Modeling Software: Performance Metrics and Method Testing with Open Source Models and Implications for Proprietary Software Testing Protocols? (referred to here as the ?Model Analysis Report?). The test procedure focuses on the quality of the software?s predictions rather than on the specific algorithms used to predict energy use. In this way the software vendor is not required to divulge or share proprietary information about how their software works, while enabling stakeholders to assess its performance.

Building information modelling (BIM)

CSIR Research Space (South Africa)

Conradie, Dirk CU

2009-02-01

Full Text Available The concept of a Building Information Model (BIM) also known as a Building Product Model (BPM) is nothing new. A short article on BIM will never cover the entire filed, because it is a particularly complex filed that is recently beginning to receive...

A prediction model for assessing residential radon concentration in Switzerland

International Nuclear Information System (INIS)

Hauri, Dimitri D.; Huss, Anke; Zimmermann, Frank; Kuehni, Claudia E.; Röösli, Martin

2012-01-01

Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th–90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40–111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69–215 Bq/m³) in the medium category, and 219 Bq/m³ (108–427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be

Challenges in microbial ecology: Building predictive understanding of community function and dynamics

DEFF Research Database (Denmark)

Widder, Stefanie; Allen, Rosalind J.; Pfeiffer, Thomas

2016-01-01

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly...... complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development...... is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved....

Neuro-fuzzy modeling in bankruptcy prediction

Directory of Open Access Journals (Sweden)

Vlachos D.

2003-01-01

Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection.

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2017-11-01

Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.

Modeling the Temperature Effect of Orientations in Residential Buildings

Directory of Open Access Journals (Sweden)

Sabahat Arif

2012-07-01

Full Text Available Indoor thermal comfort in a building has been an important issue for the environmental sustainability. It is an accepted fact that their designs and planning consume a lot of energy in the modern architecture of 20th and 21st centuries. An appropriate orientation of a building can provide thermally comfortable indoor temperatures which otherwise can consume extra energy to condition these spaces through all the seasons. This experimental study investigates the potential effect of this solar passive design strategy on indoor temperatures and a simple model is presented for predicting indoor temperatures based upon the ambient temperatures.

Improving stability of prediction models based on correlated omics data by using network approaches.

Directory of Open Access Journals (Sweden)

Renaud Tissier

Full Text Available Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1 network construction, 2 clustering to empirically derive modules or pathways, and 3 building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset.

Building Bridges between Neuroscience, Cognition and Education with Predictive Modeling

Science.gov (United States)

Stringer, Steve; Tommerdahl, Jodi

2015-01-01

As the field of Mind, Brain, and Education seeks new ways to credibly bridge the gap between neuroscience, the cognitive sciences, and education, various connections are being developed and tested. This article presents a framework and offers examples of one approach, predictive modeling within a virtual educational system that can include…

Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network

NARCIS (Netherlands)

Paudel, S.; Elmtiri, M.; Kling, W.L.; Corre, le O.; Lacarriere, B.

2014-01-01

This paper presents the building heating demand prediction model with occupancy profile and operational heating power level characteristics in short time horizon (a couple of days) using artificial neural network. In addition, novel pseudo dynamic transitional model is introduced, which consider

Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings

International Nuclear Information System (INIS)

Wang, Qinpeng; Augenbroe, Godfried; Kim, Ji-Hyun; Gu, Li

2016-01-01

Highlights: • A meta-analysis framework for a stochastic characterization of occupancy variables. • Sensitivity ranking of occupancy variability against all other sources of uncertainty. • Sensitivity of occupant presence for building energy consumption is low. • Accurate mean knowledge is sufficient for predicting building energy consumption. • Prediction of peak demand behavior requires stochastic occupancy modeling. - Abstract: Occupants interact with buildings in various ways via their presence (passive effects) and control actions (active effects). Therefore, understanding the influence of occupants is essential if we are to evaluate the performance of a building. In this paper, we model the mean profiles and variability of occupancy variables (presence and actions) separately. We will use a multi-variate Gaussian distribution to generate mean profiles of occupancy variables, while the variability will be represented by a multi-dimensional time series model, within a framework for a meta-analysis that synthesizes occupancy data gathered from a pool of buildings. We then discuss variants of occupancy models with respect to various outcomes of interest such as HVAC energy consumption and peak demand behavior via a sensitivity analysis. Results show that our approach is able to generate stochastic occupancy profiles, requiring minimum additional input from the energy modeler other than standard diversity profiles. Along with the meta-analysis, we enable the generalization of previous research results and statistical inferences to choose occupancy variables for future buildings. The sensitivity analysis shows that for aggregated building energy consumption, occupant presence has a smaller impact compared to lighting and appliance usage. Specifically, being accumulatively 55% wrong with regard to presence, only translates to 2% error in aggregated cooling energy in July and 3.6% error in heating energy in January. Such a finding redirects focus to the

Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes.

Science.gov (United States)

Zhao, Lue Ping; Carlsson, Annelie; Larsson, Helena Elding; Forsander, Gun; Ivarsson, Sten A; Kockum, Ingrid; Ludvigsson, Johnny; Marcus, Claude; Persson, Martina; Samuelsson, Ulf; Örtqvist, Eva; Pyo, Chul-Woo; Bolouri, Hamid; Zhao, Michael; Nelson, Wyatt C; Geraghty, Daniel E; Lernmark, Åke

2017-11-01

It is of interest to predict possible lifetime risk of type 1 diabetes (T1D) in young children for recruiting high-risk subjects into longitudinal studies of effective prevention strategies. Utilizing a case-control study in Sweden, we applied a recently developed next generation targeted sequencing technology to genotype class II genes and applied an object-oriented regression to build and validate a prediction model for T1D. In the training set, estimated risk scores were significantly different between patients and controls (P = 8.12 × 10 -92 ), and the area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was 0.917. Using the validation data set, we validated the result with AUC of 0.886. Combining both training and validation data resulted in a predictive model with AUC of 0.903. Further, we performed a "biological validation" by correlating risk scores with 6 islet autoantibodies, and found that the risk score was significantly correlated with IA-2A (Z-score = 3.628, P < 0.001). When applying this prediction model to the Swedish population, where the lifetime T1D risk ranges from 0.5% to 2%, we anticipate identifying approximately 20 000 high-risk subjects after testing all newborns, and this calculation would identify approximately 80% of all patients expected to develop T1D in their lifetime. Through both empirical and biological validation, we have established a prediction model for estimating lifetime T1D risk, using class II HLA. This prediction model should prove useful for future investigations to identify high-risk subjects for prevention research in high-risk populations. Copyright © 2017 John Wiley & Sons, Ltd.

On the prediction of building damage from ground motion

Energy Technology Data Exchange (ETDEWEB)

Blume, John A [John A. Blume and Associates Research Division, San Francisco, CA (United States)

1970-05-15

In the planning of a nuclear event it is essential to consider the effects of the expected ground motion on all exposed buildings and other structures. There are various steps and procedures in this process which generally increase in scope and refinement as the preparations advance. Initial, rough estimates, based upon rules-of-thumb and preliminary predictions of ground motion and structural response, may be adequate to show general feasibility of the project. Subsequent work is done in both the field and analysis phases, to estimate the total structure exposure, to isolate special hazards, and to make damage cost estimates. Finally, specific analyses are made of special buildings or structures to identify safety problems and to make recommendations for safety measures during the proposed event. Because the ground motion and the structural response both involve many random variables and therefore some uncertainties in prediction, the probabilistic aspects must be considered, both on a broad statistical basis and for specific safety considerations. Decisions must be made as to the acceptability or non-acceptability of the risks and any indicated procedures before and during the event to reduce or to eliminate the risks. The paper discusses various techniques involved in these operations including the Spectral Matrix Method of damage prediction, the Threshold Evaluation Scale for specific building analysis, and the inelastic and probabilistic aspects of the problem. (author)

On the prediction of building damage from ground motion

International Nuclear Information System (INIS)

Blume, John A.

1970-01-01

In the planning of a nuclear event it is essential to consider the effects of the expected ground motion on all exposed buildings and other structures. There are various steps and procedures in this process which generally increase in scope and refinement as the preparations advance. Initial, rough estimates, based upon rules-of-thumb and preliminary predictions of ground motion and structural response, may be adequate to show general feasibility of the project. Subsequent work is done in both the field and analysis phases, to estimate the total structure exposure, to isolate special hazards, and to make damage cost estimates. Finally, specific analyses are made of special buildings or structures to identify safety problems and to make recommendations for safety measures during the proposed event. Because the ground motion and the structural response both involve many random variables and therefore some uncertainties in prediction, the probabilistic aspects must be considered, both on a broad statistical basis and for specific safety considerations. Decisions must be made as to the acceptability or non-acceptability of the risks and any indicated procedures before and during the event to reduce or to eliminate the risks. The paper discusses various techniques involved in these operations including the Spectral Matrix Method of damage prediction, the Threshold Evaluation Scale for specific building analysis, and the inelastic and probabilistic aspects of the problem. (author)

A human capital predictive model for agent performance in contact centres

Directory of Open Access Journals (Sweden)

Chris Jacobs

2011-10-01

Research purpose: The primary focus of this article was to develop a theoretically derived human capital predictive model for agent performance in contact centres and Business Process Outsourcing (BPO based on a review of current empirical research literature. Motivation for the study: The study was motivated by the need for a human capital predictive model that can predict agent and overall business performance. Research design: A nonempirical (theoretical research paradigm was adopted for this study and more specifically a theory or model-building approach was followed. A systematic review of published empirical research articles (for the period 2000–2009 in scholarly search portals was performed. Main findings: Eight building blocks of the human capital predictive model for agent performance in contact centres were identified. Forty-two of the human capital contact centre related articles are detailed in this study. Key empirical findings suggest that person– environment fit, job demands-resources, human resources management practices, engagement, agent well-being, agent competence; turnover intention; and agent performance are related to contact centre performance. Practical/managerial implications: The human capital predictive model serves as an operational management model that has performance implications for agents and ultimately influences the contact centre’s overall business performance. Contribution/value-add: This research can contribute to the fields of human resource management (HRM, human capital and performance management within the contact centre and BPO environment.

Large-scale ligand-based predictive modelling using support vector machines.

Science.gov (United States)

Alvarsson, Jonathan; Lampa, Samuel; Schaal, Wesley; Andersson, Claes; Wikberg, Jarl E S; Spjuth, Ola

2016-01-01

The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.

An Empirical Model for Build-Up of Sodium and Calcium Ions in Small Scale Reverse Osmosis

Directory of Open Access Journals (Sweden)

Subriyer Nasir

2011-05-01

Full Text Available A simple models for predicting build-up of solute on membrane surface were formulated in this paper. The experiments were conducted with secondary effluent, groundwater and simulated feed water in small-scale of RO with capacity of 2000 L/d. Feed water used in the experiments contained varying concentrations of sodium, calcium, combined sodium and calcium. In order to study the effect of sodium and calcium ions on membrane performance, experiments with ground water and secondary effluent wastewater were also performed. Build-up of salts on the membrane surface was calculated by measuring concentrations of sodium and calcium ions in feed water permeate and reject streams using Atomic Absorption Spectrophotometer (AAS. Multiple linear regression of natural logarithmic transformation was used to develop the model based on four main parameters that affect the build-up of solute in a small scale of RO namely applied pressure, permeate flux, membrane resistance, and feed concentration. Experimental data obtained in a small scale RO unit were used to develop the empirical model. The predicted values of theoretical build-up of sodium and calcium on membrane surface were found in agreement with experimental data. The deviation in the prediction of build-up of sodium and calcium were found to be 1.4 to 10.47 % and 1.12 to 4.46%, respectively.

Fuzzy model predictive control algorithm applied in nuclear power plant

International Nuclear Information System (INIS)

Zuheir, Ahmad

2006-01-01

The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)

PV (photovoltaics) performance evaluation and simulation-based energy yield prediction for tropical buildings

International Nuclear Information System (INIS)

Saber, Esmail M.; Lee, Siew Eang; Manthapuri, Sumanth; Yi, Wang; Deb, Chirag

2014-01-01

Air pollution and climate change increased the importance of renewable energy resources like solar energy in the last decades. Rack-mounted PhotoVoltaics (PV) and Building Integrated PhotoVoltaics (BIPV) are the most common photovoltaic systems which convert incident solar radiation on façade or surrounding area to electricity. In this paper the performance of different solar cell types is evaluated for the tropical weather of Singapore. As a case study, on-site measured data of PV systems implemented in a zero energy building in Singapore, is analyzed. Different types of PV systems (silicon wafer and thin film) have been installed on rooftop, façade, car park shelter, railing and etc. The impact of different solar cell generations, arrays environmental conditions (no shading, dappled shading, full shading), orientation (South, North, East or West facing) and inclination (between PV module and horizontal direction) is investigated on performance of modules. In the second stage of research, the whole PV systems in the case study are simulated in EnergyPlus energy simulation software with several PV performance models including Simple, Equivalent one-diode and Sandia. The predicted results by different models are compared with measured data and the validated model is used to provide simulation-based energy yield predictions for wide ranges of scenarios. It has been concluded that orientation of low-slope rooftop PV has negligible impact on annual energy yield but in case of PV external sunshade, east façade and panel slope of 30–40° are the most suitable location and inclination. - Highlights: • Characteristics of PV systems in tropics are analyzed in depth. • The ambiguity toward amorphous panel energy yield in tropics is discussed. • Equivalent-one diode and Sandia models can fairly predict the energy yield. • A general guideline is provided to estimate the energy yield of PV systems in tropics

Thermal comfort in residential buildings - Failure to predict by Standard model

Energy Technology Data Exchange (ETDEWEB)

Becker, R. [Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Rabin Building, Technion City, Haifa 32000 (Israel); Paciuk, M. [National Building Research Institute, Technion - IIT, Haifa 32000 (Israel)

2009-05-15

A field study, conducted in 189 dwellings in winter and 205 dwellings in summer, included measurement of hygro-thermal conditions and documentation of occupant responses and behavior patterns. Both samples included both passive and actively space-conditioned dwellings. Predicted mean votes (PMV) computed using Fanger's model yielded significantly lower-than-reported thermal sensation (TS) values, especially for the winter heated and summer air-conditioned groups. The basic model assumption of a proportional relationship between thermal response and thermal load proved to be inadequate, with actual thermal comfort achieved at substantially lower loads than predicted. Survey results also refuted the model's second assumption that symmetrical responses in the negative and positive directions of the scale represent similar comfort levels. Results showed that the model's curve of predicted percentage of dissatisfied (PPD) substantially overestimated the actual percentage of dissatisfied within the partial group of respondents who voted TS > 0 in winter as well as within the partial group of respondents who voted TS < 0 in summer. Analyses of sensitivity to possible survey-related inaccuracy factors (metabolic rate, clothing thermal resistance) did not explain the systematic discrepancies. These discrepancies highlight the role of contextual variables (local climate, expectations, available control) in thermal adaptation in actual settings. Collected data was analyzed statistically to establish baseline data for local standardized thermal and energy calculations. A 90% satisfaction criterion yielded 19.5 C and 26 C as limit values for passive winter and summer design conditions, respectively, while during active conditioning periods, set-point temperatures of 21.5 C and 23 C should be assumed for winter and summer, respectively. (author)

Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach

International Nuclear Information System (INIS)

Lü, Xiaoshu; Lu, Tao; Kibert, Charles J.; Viljanen, Martti

2015-01-01

Highlights: • This paper presents a new modeling method to forecast energy demands. • The model is based on physical–statistical approach to improving forecast accuracy. • A new method is proposed to address the heterogeneity challenge. • Comparison with measurements shows accurate forecasts of the model. • The first physical–statistical/heterogeneous building energy modeling approach is proposed and validated. - Abstract: Energy consumption forecasting is a critical and necessary input to planning and controlling energy usage in the building sector which accounts for 40% of the world’s energy use and the world’s greatest fraction of greenhouse gas emissions. However, due to the diversity and complexity of buildings as well as the random nature of weather conditions, energy consumption and loads are stochastic and difficult to predict. This paper presents a new methodology for energy demand forecasting that addresses the heterogeneity challenges in energy modeling of buildings. The new method is based on a physical–statistical approach designed to account for building heterogeneity to improve forecast accuracy. The physical model provides a theoretical input to characterize the underlying physical mechanism of energy flows. Then stochastic parameters are introduced into the physical model and the statistical time series model is formulated to reflect model uncertainties and individual heterogeneity in buildings. A new method of model generalization based on a convex hull technique is further derived to parameterize the individual-level model parameters for consistent model coefficients while maintaining satisfactory modeling accuracy for heterogeneous buildings. The proposed method and its validation are presented in detail for four different sports buildings with field measurements. The results show that the proposed methodology and model can provide a considerable improvement in forecasting accuracy

Guidelines for developing efficient thermal conduction and storage models within building energy simulations

International Nuclear Information System (INIS)

Hillary, Jason; Walsh, Ed; Shah, Amip; Zhou, Rongliang; Walsh, Pat

2017-01-01

Improving building energy efficiency is of paramount importance due to the large proportion of energy consumed by thermal operations. Consequently, simulating a building's environment has gained popularity for assessing thermal comfort and design. The extended timeframes and large physical scales involved necessitate compact modelling approaches. The accuracy of such simulations is of chief concern, yet there is little guidance offered on achieving accurate solutions whilst mitigating prohibitive computational costs. Therefore, the present study addresses this deficit by providing clear guidance on discretisation levels required for achieving accurate but computationally inexpensive models. This is achieved by comparing numerical models of varying discretisation levels to benchmark analytical solutions with prediction accuracy assessed and reported in terms of governing dimensionless parameters, Biot and Fourier numbers, to ensure generality of findings. Furthermore, spatial and temporal discretisation errors are separated and assessed independently. Contour plots are presented to intuitively determine the optimal discretisation levels and time-steps required to achieve accurate thermal response predictions. Simulations derived from these contour plots were tested against various building conditions with excellent agreement observed throughout. Additionally, various scenarios are highlighted where the classical single lumped capacitance model can be applied for Biot numbers much greater than 0.1 without reducing accuracy. - Highlights: • Addressing the problems of inadequate discretisation within building energy models. • Accuracy of numerical models assessed against analytical solutions. • Fourier and Biot numbers used to provide generality of results for any material. • Contour plots offer intuitive way to interpret results for manual discretisation. • Results show proposed technique promising for automation of discretisation process.

Actual service life prediction of building components

DEFF Research Database (Denmark)

Aagaard, Niels-Jørgen; Brandt, Erik; Hansen, Ernst Jan de Place

2014-01-01

In recent years, sustainability and life cycle cost in the construction industry have been given great attention in many countries due to the heavy climatic and environmental impact from this sector. In Denmark, a sustainability certification scheme for buildings has been developed including....... Finally, it is discussed how to adjust the model for practical purposes, and a scheme for actual service life for selected building components important for analysis of sustainability is linked. The schemes are now being implemented as basis for sustainability certification of new buildings in Denmark....

Model building

International Nuclear Information System (INIS)

Frampton, Paul H.

1998-01-01

In this talk I begin with some general discussion of model building in particle theory, emphasizing the need for motivation and testability. Three illustrative examples are then described. The first is the Left-Right model which provides an explanation for the chirality of quarks and leptons. The second is the 331-model which offers a first step to understanding the three generations of quarks and leptons. Third and last is the SU(15) model which can accommodate the light leptoquarks possibly seen at HERA

Predicting the hurricane damage ratio of commercial buildings by claim payout from Hurricane Ike

OpenAIRE

J. M. Kim; P. K. Woods; Y. J. Park; T. H. Kim; J. S. Choi; K. Son

2013-01-01

The increasing occurrence of natural disaster events and related damages have led to a growing demand for models that predict financial loss. Although considerable research has studied the financial losses related to natural disaster events, and has found significant predictors, there has not yet been a comprehensive study that addresses the relationship among the vulnerabilities, natural disasters, and economic losses of the individual buildings. This study...

Rain water runoff from porous building facades : implementation and application of a first-order runoff model coupled to a HAM model

NARCIS (Netherlands)

Brande, T. van den; Blocken, B.J.E.; Roels, S.

2013-01-01

Wind-driven rain (WDR) is one of the most important moisture sources for a building facade. Therefore, a reliable prediction of WDR loads is a prerequisite to assess the durability of building facade components. However, current state of the art Heat-Air-Moisture (HAM) models that are used to assess

Predictive modeling in e-mental health: A common language framework

Directory of Open Access Journals (Sweden)

Dennis Becker

2018-06-01

Full Text Available Recent developments in mobile technology, sensor devices, and artificial intelligence have created new opportunities for mental health care research. Enabled by large datasets collected in e-mental health research and practice, clinical researchers and members of the data mining community increasingly join forces to build predictive models for health monitoring, treatment selection, and treatment personalization. This paper aims to bridge the historical and conceptual gaps between the distant research domains involved in this new collaborative research by providing a conceptual model of common research goals. We first provide a brief overview of the data mining field and methods used for predictive modeling. Next, we propose to characterize predictive modeling research in mental health care on three dimensions: 1 time, relative to treatment (i.e., from screening to post-treatment relapse monitoring, 2 types of available data (e.g., questionnaire data, ecological momentary assessments, smartphone sensor data, and 3 type of clinical decision (i.e., whether data are used for screening purposes, treatment selection or treatment personalization. Building on these three dimensions, we introduce a framework that identifies four model types that can be used to classify existing and future research and applications. To illustrate this, we use the framework to classify and discuss published predictive modeling mental health research. Finally, in the discussion, we reflect on the next steps that are required to drive forward this promising new interdisciplinary field.

Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature

Directory of Open Access Journals (Sweden)

Jin Woo Moon

2016-12-01

Full Text Available The aim of this study was to develop an artificial neural network (ANN prediction model for controlling building heating systems. This model was used to calculate the ascent time of indoor temperature from the setback period (when a building was not occupied to a target setpoint temperature (when a building was occupied. The calculated ascent time was applied to determine the proper moment to start increasing the temperature from the setback temperature to reach the target temperature at an appropriate time. Three major steps were conducted: (1 model development; (2 model optimization; and (3 performance evaluation. Two software programs—Matrix Laboratory (MATLAB and Transient Systems Simulation (TRNSYS—were used for model development, performance tests, and numerical simulation methods. Correlation analysis between input variables and the output variable of the ANN model revealed that two input variables (current indoor air temperature and temperature difference from the target setpoint temperature, presented relatively strong relationships with the ascent time to the target setpoint temperature. These two variables were used as input neurons. Analyzing the difference between the simulated and predicted values from the ANN model provided the optimal number of hidden neurons (9, hidden layers (3, moment (0.9, and learning rate (0.9. At the study’s conclusion, the optimized model proved its prediction accuracy with acceptable errors.

Atmospheric dispersion estimates in the vicinity of buildings

International Nuclear Information System (INIS)

Ramsdell, J.V. Jr.; Fosmire, C.J.

1995-01-01

A model describing atmospheric dispersion in the vicinity of buildings was developed for the U.S. Nuclear Regulatory Commission (NRC) in the late 1980s. That model has recently undergone additional peer review. The reviewers identified four areas of concern related to the model and its application. This report describes revisions to the model in response to the reviewers concerns. Model revision involved incorporation of explicit treatment of enhanced dispersion at low wind speeds in addition to explicit treatment of enhanced dispersion at high speeds resulting from building wakes. Model parameters are evaluated from turbulence data. Experimental diffusion data from seven reactor sites are used for model evaluation. Compared with models recommended in current NRC guidance to licensees, the revised model is less biased and shows more predictive skill. The revised model is also compared with two non-Gaussian models developed to estimate maximum concentrations in building wakes. The revised model concentration predictions are nearly the same as the predictions of the non-Gaussian models. On the basis of these comparisons of the revised model concentration predictions with experimental data and the predictions of other models, the revised model is found to be an appropriate model for estimating concentrations in the vicinity of buildings

Model building

International Nuclear Information System (INIS)

Frampton, P.H.

1998-01-01

In this talk I begin with some general discussion of model building in particle theory, emphasizing the need for motivation and testability. Three illustrative examples are then described. The first is the Left-Right model which provides an explanation for the chirality of quarks and leptons. The second is the 331-model which offers a first step to understanding the three generations of quarks and leptons. Third and last is the SU(15) model which can accommodate the light leptoquarks possibly seen at HERA. copyright 1998 American Institute of Physics

Building Customer Churn Prediction Models in Fitness Industry with Machine Learning Methods

OpenAIRE

Shan, Min

2017-01-01

With the rapid growth of digital systems, churn management has become a major focus within customer relationship management in many industries. Ample research has been conducted for churn prediction in different industries with various machine learning methods. This thesis aims to combine feature selection and supervised machine learning methods for defining models of churn prediction and apply them on fitness industry. Forward selection is chosen as feature selection methods. Support Vector ...

Integrating Building Information Modeling and Green Building Certification: The BIM-LEED Application Model Development

Science.gov (United States)

Wu, Wei

2010-01-01

Building information modeling (BIM) and green building are currently two major trends in the architecture, engineering and construction (AEC) industry. This research recognizes the market demand for better solutions to achieve green building certification such as LEED in the United States. It proposes a new strategy based on the integration of BIM…

Validation of Occupants’ Behaviour Models for Indoor Quality Parameter and Energy Consumption Prediction

DEFF Research Database (Denmark)

Fabi, Valentina; Sugliano, Martina; Andersen, Rune Korsholm

2015-01-01

Occupants’ behaviour related to building control system plays a significant role to achieve thermal comfort and air quality in naturally-ventilated buildings. Generally, the published models of occupant's behavior are not validated, meaning that the predictive power has not yet been tested. For t...

Multiple regression models for energy use in air-conditioned office buildings in different climates

International Nuclear Information System (INIS)

Lam, Joseph C.; Wan, Kevin K.W.; Liu Dalong; Tsang, C.L.

2010-01-01

An attempt was made to develop multiple regression models for office buildings in the five major climates in China - severe cold, cold, hot summer and cold winter, mild, and hot summer and warm winter. A total of 12 key building design variables were identified through parametric and sensitivity analysis, and considered as inputs in the regression models. The coefficient of determination R 2 varies from 0.89 in Harbin to 0.97 in Kunming, indicating that 89-97% of the variations in annual building energy use can be explained by the changes in the 12 parameters. A pseudo-random number generator based on three simple multiplicative congruential generators was employed to generate random designs for evaluation of the regression models. The difference between regression-predicted and DOE-simulated annual building energy use are largely within 10%. It is envisaged that the regression models developed can be used to estimate the likely energy savings/penalty during the initial design stage when different building schemes and design concepts are being considered.

Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures

Directory of Open Access Journals (Sweden)

Liu Yufeng

2011-01-01

Full Text Available Abstract Background Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes. Methods Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR to neoadjuvant chemotherapy were also built using this approach. Results We identified statistically significant prognostic models for relapse-free survival (RFS at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR predictions for the entire population. Conclusions Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA

A Probabilistic Model for Exteriors of Residential Buildings

KAUST Repository

Fan, Lubin

2016-07-29

We propose a new framework to model the exterior of residential buildings. The main goal of our work is to design a model that can be learned from data that is observable from the outside of a building and that can be trained with widely available data such as aerial images and street-view images. First, we propose a parametric model to describe the exterior of a building (with a varying number of parameters) and propose a set of attributes as a building representation with fixed dimensionality. Second, we propose a hierarchical graphical model with hidden variables to encode the relationships between building attributes and learn both the structure and parameters of the model from the database. Third, we propose optimization algorithms to generate three-dimensional models based on building attributes sampled from the graphical model. Finally, we demonstrate our framework by synthesizing new building models and completing partially observed building models from photographs.

Iterative model building, structure refinement and density modification with the PHENIX AutoBuild wizard

International Nuclear Information System (INIS)

Terwilliger, Thomas C.; Grosse-Kunstleve, Ralf W.; Afonine, Pavel V.; Moriarty, Nigel W.; Zwart, Peter H.; Hung, Li-Wei; Read, Randy J.; Adams, Paul D.

2008-01-01

The highly automated PHENIX AutoBuild wizard is described. The procedure can be applied equally well to phases derived from isomorphous/anomalous and molecular-replacement methods. The PHENIX AutoBuild wizard is a highly automated tool for iterative model building, structure refinement and density modification using RESOLVE model building, RESOLVE statistical density modification and phenix.refine structure refinement. Recent advances in the AutoBuild wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model-completion algorithms and automated solvent-molecule picking. Model-completion algorithms in the AutoBuild wizard include loop building, crossovers between chains in different models of a structure and side-chain optimization. The AutoBuild wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 to 3.2 Å, resulting in a mean R factor of 0.24 and a mean free R factor of 0.29. The R factor of the final model is dependent on the quality of the starting electron density and is relatively independent of resolution

Occupant behavior in building energy simulation: towards a fit-for-purpose modeling strategy

NARCIS (Netherlands)

Gaetani, I.; Hoes, P.; Hensen, J.L.M.

2016-01-01

Occupant behavior is nowadays acknowledged as a main source of discrepancy between predicted and actual building performance; therefore, researchers attempt to model occupants' presence and adaptive actions more realistically. Literature shows a proliferation of increasingly complex, data-based

Iterative model-building, structure refinement, and density modification with the PHENIX AutoBuild Wizard

Energy Technology Data Exchange (ETDEWEB)

Los Alamos National Laboratory, Mailstop M888, Los Alamos, NM 87545, USA; Lawrence Berkeley National Laboratory, One Cyclotron Road, Building 64R0121, Berkeley, CA 94720, USA; Department of Haematology, University of Cambridge, Cambridge CB2 0XY, England; Terwilliger, Thomas; Terwilliger, T.C.; Grosse-Kunstleve, Ralf Wilhelm; Afonine, P.V.; Moriarty, N.W.; Zwart, P.H.; Hung, L.-W.; Read, R.J.; Adams, P.D.

2007-04-29

The PHENIX AutoBuild Wizard is a highly automated tool for iterative model-building, structure refinement and density modification using RESOLVE or TEXTAL model-building, RESOLVE statistical density modification, and phenix.refine structure refinement. Recent advances in the AutoBuild Wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model completion algorithms, and automated solvent molecule picking. Model completion algorithms in the AutoBuild Wizard include loop-building, crossovers between chains in different models of a structure, and side-chain optimization. The AutoBuild Wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 {angstrom} to 3.2 {angstrom}, resulting in a mean R-factor of 0.24 and a mean free R factor of 0.29. The R-factor of the final model is dependent on the quality of the starting electron density, and relatively independent of resolution.

Influence of radiation on predictive accuracy in numerical simulations of the thermal environment in industrial buildings with buoyancy-driven natural ventilation

International Nuclear Information System (INIS)

Meng, Xiaojing; Wang, Yi; Liu, Tiening; Xing, Xiao; Cao, Yingxue; Zhao, Jiangping

2016-01-01

Highlights: • The effects of radiation on predictive accuracy in numerical simulations were studied. • A scaled experimental model with a high-temperature heat source was set up. • Simulation results were discussed considering with and without radiation model. • The buoyancy force and the ventilation rate were investigated. - Abstract: This paper investigates the effects of radiation on predictive accuracy in the numerical simulations of industrial buildings. A scaled experimental model with a high-temperature heat source is set up and the buoyancy-driven natural ventilation performance is presented. Besides predicting ventilation performance in an industrial building, the scaled model in this paper is also used to generate data to validate the numerical simulations. The simulation results show good agreement with the experiment data. The effects of radiation on predictive accuracy in the numerical simulations are studied for both pure convection model and combined convection and radiation model. Detailed results are discussed regarding the temperature and velocity distribution, the buoyancy force and the ventilation rate. The temperature and velocity distributions through the middle plane are presented for the pure convection model and the combined convection and radiation model. It is observed that the overall temperature and velocity magnitude predicted by the simulations for pure convection were significantly greater than those for the combined convection and radiation model. In addition, the Grashof number and the ventilation rate are investigated. The results show that the Grashof number and the ventilation rate are greater for the pure convection model than for the combined convection and radiation model.

Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

Science.gov (United States)

Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

2016-03-01

In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of Neural Network Optimized by Mind Evolutionary Computation in Building Energy Prediction

Science.gov (United States)

Song, Chen; Zhong-Cheng, Wu; Hong, Lv

2018-03-01

Building Energy forecasting plays an important role in energy management and plan. Using mind evolutionary algorithm to find the optimal network weights and threshold, to optimize the BP neural network, can overcome the problem of the BP neural network into a local minimum point. The optimized network is used for time series prediction, and the same month forecast, to get two predictive values. Then two kinds of predictive values are put into neural network, to get the final forecast value. The effectiveness of the method was verified by experiment with the energy value of three buildings in Hefei.

Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU.

Science.gov (United States)

Kennedy, Curtis E; Turley, James P

2011-10-24

Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9

Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity.

Directory of Open Access Journals (Sweden)

Lian Leng Low

Full Text Available To reduce readmissions, it may be cost-effective to consider risk stratification, with targeting intervention programs to patients at high risk of readmissions. In this study, we aimed to derive and validate a prediction model including several novel markers of hospitalization severity, and compare the model with the LACE index (Length of stay, Acuity of admission, Charlson comorbidity index, Emergency department visits in past 6 months, an established risk stratification tool.This was a retrospective cohort study of all patients ≥ 21 years of age, who were admitted to a tertiary hospital in Singapore from January 1, 2013 through May 31, 2015. Data were extracted from the hospital's electronic health records. The outcome was defined as unplanned readmissions within 30 days of discharge from the index hospitalization. Candidate predictive variables were broadly grouped into five categories: Patient demographics, social determinants of health, past healthcare utilization, medical comorbidities, and markers of hospitalization severity. Multivariable logistic regression was used to predict the outcome, and receiver operating characteristic analysis was performed to compare our model with the LACE index.74,102 cases were enrolled for analysis. Of these, 11,492 patient cases (15.5% were readmitted within 30 days of discharge. A total of fifteen predictive variables were strongly associated with the risk of 30-day readmissions, including number of emergency department visits in the past 6 months, Charlson Comorbidity Index, markers of hospitalization severity such as 'requiring inpatient dialysis during index admission, and 'treatment with intravenous furosemide 40 milligrams or more' during index admission. Our predictive model outperformed the LACE index by achieving larger area under the curve values: 0.78 (95% confidence interval [CI]: 0.77-0.79 versus 0.70 (95% CI: 0.69-0.71.Several factors are important for the risk of 30-day readmissions

Virtual building environments (VBE) - Applying information modeling to buildings

Energy Technology Data Exchange (ETDEWEB)

Bazjanac, Vladimir

2004-06-21

A Virtual Building Environment (VBE) is a ''place'' where building industry project staffs can get help in creating Building Information Models (BIM) and in the use of virtual buildings. It consists of a group of industry software that is operated by industry experts who are also experts in the use of that software. The purpose of a VBE is to facilitate expert use of appropriate software applications in conjunction with each other to efficiently support multidisciplinary work. This paper defines BIM and virtual buildings, and describes VBE objectives, set-up and characteristics of operation. It informs about the VBE Initiative and the benefits from a couple of early VBE projects.

Bridging the gap between the linear and nonlinear predictive control: Adaptations fo refficient building climate control

Czech Academy of Sciences Publication Activity Database

Pčolka, M.; Žáčeková, E.; Robinett, R.; Čelikovský, Sergej; Šebek, M.

2016-01-01

Roč. 53, č. 1 (2016), s. 124-138 ISSN 0967-0661 R&D Projects: GA ČR GA13-20433S Institutional support: RVO:67985556 Keywords : Model predictive control * Identification for control * Building climatecontrol Subject RIV: BC - Control Systems Theory Impact factor: 2.602, year: 2016 http://library.utia.cas.cz/separaty/2016/TR/celikovsky-0460306.pdf

Field Measurement-Based System Identification and Dynamic Response Prediction of a Unique MIT Building.

Science.gov (United States)

Cha, Young-Jin; Trocha, Peter; Büyüköztürk, Oral

2016-07-01

Tall buildings are ubiquitous in major cities and house the homes and workplaces of many individuals. However, relatively few studies have been carried out to study the dynamic characteristics of tall buildings based on field measurements. In this paper, the dynamic behavior of the Green Building, a unique 21-story tall structure located on the campus of the Massachusetts Institute of Technology (MIT, Cambridge, MA, USA), was characterized and modeled as a simplified lumped-mass beam model (SLMM), using data from a network of accelerometers. The accelerometer network was used to record structural responses due to ambient vibrations, blast loading, and the October 16th 2012 earthquake near Hollis Center (ME, USA). Spectral and signal coherence analysis of the collected data was used to identify natural frequencies, modes, foundation rocking behavior, and structural asymmetries. A relation between foundation rocking and structural natural frequencies was also found. Natural frequencies and structural acceleration from the field measurements were compared with those predicted by the SLMM which was updated by inverse solving based on advanced multiobjective optimization methods using the measured structural responses and found to have good agreement.

Field Measurement-Based System Identification and Dynamic Response Prediction of a Unique MIT Building

Directory of Open Access Journals (Sweden)

Young-Jin Cha

2016-07-01

Full Text Available Tall buildings are ubiquitous in major cities and house the homes and workplaces of many individuals. However, relatively few studies have been carried out to study the dynamic characteristics of tall buildings based on field measurements. In this paper, the dynamic behavior of the Green Building, a unique 21-story tall structure located on the campus of the Massachusetts Institute of Technology (MIT, Cambridge, MA, USA, was characterized and modeled as a simplified lumped-mass beam model (SLMM, using data from a network of accelerometers. The accelerometer network was used to record structural responses due to ambient vibrations, blast loading, and the October 16th 2012 earthquake near Hollis Center (ME, USA. Spectral and signal coherence analysis of the collected data was used to identify natural frequencies, modes, foundation rocking behavior, and structural asymmetries. A relation between foundation rocking and structural natural frequencies was also found. Natural frequencies and structural acceleration from the field measurements were compared with those predicted by the SLMM which was updated by inverse solving based on advanced multiobjective optimization methods using the measured structural responses and found to have good agreement.

Field Measurement-Based System Identification and Dynamic Response Prediction of a Unique MIT Building

Science.gov (United States)

Cha, Young-Jin; Trocha, Peter; Büyüköztürk, Oral

2016-01-01

Tall buildings are ubiquitous in major cities and house the homes and workplaces of many individuals. However, relatively few studies have been carried out to study the dynamic characteristics of tall buildings based on field measurements. In this paper, the dynamic behavior of the Green Building, a unique 21-story tall structure located on the campus of the Massachusetts Institute of Technology (MIT, Cambridge, MA, USA), was characterized and modeled as a simplified lumped-mass beam model (SLMM), using data from a network of accelerometers. The accelerometer network was used to record structural responses due to ambient vibrations, blast loading, and the October 16th 2012 earthquake near Hollis Center (ME, USA). Spectral and signal coherence analysis of the collected data was used to identify natural frequencies, modes, foundation rocking behavior, and structural asymmetries. A relation between foundation rocking and structural natural frequencies was also found. Natural frequencies and structural acceleration from the field measurements were compared with those predicted by the SLMM which was updated by inverse solving based on advanced multiobjective optimization methods using the measured structural responses and found to have good agreement. PMID:27376303

Energy Prediction versus Energy Performance of Green Buildings in Malaysia. Comparison of Predicted and Operational Measurement of GBI Certified Green Office in Kuala Lumpur

Directory of Open Access Journals (Sweden)

Zaid Suzaini M

2016-01-01

Full Text Available Forward from the sustainability agenda of Brundtland in 1987 and the increasing demand for energy efficient buildings, the building industry has taken steps in meeting the challenge of reducing its environmental impact. Initiatives such as ‘green’ or ‘sustainable’ design have been at the forefront of architecture, while green assessment tools have been used to predict the energy performance of building during its operational phase. However, there is still a significant hap between predicted or simulated energy measurements compared to actual operational energy consumption, or is more commonly referred as the ‘performance gap’. This paper tries to bridge this gap by comparing measured operational energy consumption of a Green Building Index (GBI certified office building in Kuala Lumpur, with its predicted energy rating qualification.

Diffusion in building wakes

International Nuclear Information System (INIS)

Ramsdell, J.V.

1988-03-01

Straight-line Gaussian models adequately describe atmospheric diffusion for many applications. They have been modified for use in estimating diffusion in building wakes by adding terms that include projected building area and by redefining the diffusion coefficients so that the coefficients have minimum values that are related to building dimensions. In a recent study, Ramsdell reviewed the building-wake dispersion models used by the Nuclear Regulatory Commission (NRC) in its control room habitability assessments. The review included comparison of model estimates of centerline concentrations with concentrations observed in experiments at seven nuclear reactors. In general, the models are conservative in that they tend to predict concentrations that are greater than those actually observed. However, the models show little skill in accounting for variations in the observed concentrations. Subsequently, the experimental data and multiples linear regression techniques have been used to develop a new building wake diffusion model. This paper describes the new building wake model and compares it with other models. 8 refs., 2 figs

Verification and improvement of a predictive model for radionuclide migration

International Nuclear Information System (INIS)

Miller, C.W.; Benson, L.V.; Carnahan, C.L.

1982-01-01

Prediction of the rates of migration of contaminant chemical species in groundwater flowing through toxic waste repositories is essential to the assessment of a repository's capability of meeting standards for release rates. A large number of chemical transport models, of varying degrees of complexity, have been devised for the purpose of providing this predictive capability. In general, the transport of dissolved chemical species through a water-saturated porous medium is influenced by convection, diffusion/dispersion, sorption, formation of complexes in the aqueous phase, and chemical precipitation. The reliability of predictions made with the models which omit certain of these processes is difficult to assess. A numerical model, CHEMTRN, has been developed to determine which chemical processes govern radionuclide migration. CHEMTRN builds on a model called MCCTM developed previously by Lichtner and Benson

Uncertainty analysis of pollutant build-up modelling based on a Bayesian weighted least squares approach

International Nuclear Information System (INIS)

Haddad, Khaled; Egodawatta, Prasanna; Rahman, Ataur; Goonetilleke, Ashantha

2013-01-01

Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality datasets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares regression and Bayesian weighted least squares regression for the estimation of uncertainty associated with pollutant build-up prediction using limited datasets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling. - Highlights: ► Water quality data spans short time scales leading to significant model uncertainty. ► Assessment of uncertainty essential for informed decision making in water

Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model.

Science.gov (United States)

Huang, Yanqi; He, Lan; Dong, Di; Yang, Caiyun; Liang, Cuishan; Chen, Xin; Ma, Zelan; Huang, Xiaomei; Yao, Su; Liang, Changhong; Tian, Jie; Liu, Zaiyi

2018-02-01

To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

The theoretical modelling of aerosol behaviour within containment buildings

International Nuclear Information System (INIS)

Dunbar, I.H.

1988-01-01

The modelling of the deposition of aerosol particles within the containment building plays an important part in determining the effectiveness of the building in reducing releases of activity following accidents. This paper describes attempts to ensure the accuracy of computer codes which model aerosol behaviour, with special reference to the code AEROSIM-M. Code intercomparisons have been used to test the reliability of the coding and the accuracy of the numerical methods. Those codes which assume that the particle size distribution is always lognormal give significantly different results from those which do not make this assumption but instead discretise the range of particle sizes. When the same physical assumptions are made, the predictions of different discrete codes are in reasonable agreement. In comparisons between an earlier version of AEROSIM and sodium fire experiments, the code achieved good agreement on the overall time-scale of deposition. An extensive set of tests of AEROSIM-M against experiments relevant to LWR conditions is underway. (author)

Comparing artificial neural networks, general linear models and support vector machines in building predictive models for small interfering RNAs.

Directory of Open Access Journals (Sweden)

Kyle A McQuisten

2009-10-01

Full Text Available Exogenous short interfering RNAs (siRNAs induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs, General Linear Models (GLMs and Support Vector Machines (SVMs. Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are

Predicting wind-induced vibrations of high-rise buildings using unsteady CFD and modal analysis

KAUST Repository

Zhang, Yue

2015-01-01

This paper investigates the wind-induced vibration of the CAARC standard tall building model, via unsteady Computational Fluid Dynamics (CFD) and a structural modal analysis. In this numerical procedure, the natural unsteady wind in the atmospheric boundary layer is modeled with an artificial inflow turbulence generation method. Then, the turbulent flow is simulated by the second mode of a Zonal Detached-Eddy Simulation, and a conservative quadrature-projection scheme is adopted to transfer unsteady loads from fluid to structural nodes. The aerodynamic damping that represents the fluid-structure interaction mechanism is determined by empirical functions extracted from wind tunnel experiments. Eventually, the flow solutions and the structural responses in terms of mean and root mean square quantities are compared with experimental measurements, over a wide range of reduced velocities. The significance of turbulent inflow conditions and aeroelastic effects is highlighted. The current methodology provides predictions of good accuracy and can be considered as a preliminary design tool to evaluate the unsteady wind effects on tall buildings.

Predicting the microbial exposure risks in urban floods using GIS, building simulation, and microbial models.

Science.gov (United States)

Taylor, Jonathon; Biddulph, Phillip; Davies, Michael; Lai, Ka man

2013-01-01

London is expected to experience more frequent periods of intense rainfall and tidal surges, leading to an increase in the risk of flooding. Damp and flooded dwellings can support microbial growth, including mould, bacteria, and protozoa, as well as persistence of flood-borne microorganisms. The amount of time flooded dwellings remain damp will depend on the duration and height of the flood, the contents of the flood water, the drying conditions, and the building construction, leading to particular properties and property types being prone to lingering damp and human pathogen growth or persistence. The impact of flooding on buildings can be simulated using Heat Air and Moisture (HAM) models of varying complexity in order to understand how water can be absorbed and dry out of the building structure. This paper describes the simulation of the drying of building archetypes representative of the English building stock using the EnergyPlus based tool 'UCL-HAMT' in order to determine the drying rates of different abandoned structures flooded to different heights and during different seasons. The results are mapped out using GIS in order to estimate the spatial risk across London in terms of comparative flood vulnerability, as well as for specific flood events. Areas of South and East London were found to be particularly vulnerable to long-term microbial exposure following major flood events. Copyright © 2012 Elsevier Ltd. All rights reserved.

Influence of covariate distribution on the predictive performance of pharmacokinetic models in paediatric research

Science.gov (United States)

Piana, Chiara; Danhof, Meindert; Della Pasqua, Oscar

2014-01-01

Aims The accuracy of model-based predictions often reported in paediatric research has not been thoroughly characterized. The aim of this exercise is therefore to evaluate the role of covariate distributions when a pharmacokinetic model is used for simulation purposes. Methods Plasma concentrations of a hypothetical drug were simulated in a paediatric population using a pharmacokinetic model in which body weight was correlated with clearance and volume of distribution. Two subgroups of children were then selected from the overall population according to a typical study design, in which pre-specified body weight ranges (10–15 kg and 30–40 kg) were used as inclusion criteria. The simulated data sets were then analyzed using non-linear mixed effects modelling. Model performance was assessed by comparing the accuracy of AUC predictions obtained for each subgroup, based on the model derived from the overall population and by extrapolation of the model parameters across subgroups. Results Our findings show that systemic exposure as well as pharmacokinetic parameters cannot be accurately predicted from the pharmacokinetic model obtained from a population with a different covariate range from the one explored during model building. Predictions were accurate only when a model was used for prediction in a subgroup of the initial population. Conclusions In contrast to current practice, the use of pharmacokinetic modelling in children should be limited to interpolations within the range of values observed during model building. Furthermore, the covariate point estimate must be kept in the model even when predictions refer to a subset different from the original population. PMID:24433411

Development of Interpretable Predictive Models for BPH and Prostate Cancer.

Science.gov (United States)

Bermejo, Pablo; Vivo, Alicia; Tárraga, Pedro J; Rodríguez-Montes, J A

2015-01-01

Traditional methods for deciding whether to recommend a patient for a prostate biopsy are based on cut-off levels of stand-alone markers such as prostate-specific antigen (PSA) or any of its derivatives. However, in the last decade we have seen the increasing use of predictive models that combine, in a non-linear manner, several predictives that are better able to predict prostate cancer (PC), but these fail to help the clinician to distinguish between PC and benign prostate hyperplasia (BPH) patients. We construct two new models that are capable of predicting both PC and BPH. An observational study was performed on 150 patients with PSA ≥3 ng/mL and age >50 years. We built a decision tree and a logistic regression model, validated with the leave-one-out methodology, in order to predict PC or BPH, or reject both. Statistical dependence with PC and BPH was found for prostate volume (P-value BPH prediction. PSA and volume together help to build predictive models that accurately distinguish among PC, BPH, and patients without any of these pathologies. Our decision tree and logistic regression models outperform the AUC obtained in the compared studies. Using these models as decision support, the number of unnecessary biopsies might be significantly reduced.

Characterizing polycyclic aromatic hydrocarbon build-up processes on urban road surfaces

International Nuclear Information System (INIS)

Liu, Liang; Liu, An; Li, Dunzhu; Zhang, Lixun; Guan, Yuntao

2016-01-01

Reliable prediction models are essential for modeling pollutant build-up processes on urban road surfaces. Based on successive samplings of road deposited sediments (RDS), this study presents empirical models for mathematical replication of the polycyclic aromatic hydrocarbon (PAH) build-up processes on urban road surfaces. The contaminant build-up behavior was modeled using saturation functions, which are commonly applied in US EPA's Stormwater Management Model (SWMM). Accurate fitting results were achieved in three typical urban land use types, and the applicability of the models was confirmed based on their acceptable relative prediction errors. The fitting results showed high variability in PAH saturation value and build-up rate among different land use types. Results of multivariate data and temporal-based analyses suggested that the quantity and property of RDS significantly influenced PAH build-up. Furthermore, pollution sources, traffic parameters, road surface conditions, and sweeping frequency could synthetically impact the RDS build-up and RDS property change processes. Thus, changes in these parameters could be the main reason for variations in PAH build-up in different urban land use types. - Highlights: • Sufficient robust prediction models were established for analysis of PAH build-up on urban road surfaces. • PAH build-up processes showed high variability among different land use types. • Pollution sources as well as the quantity and property of RDS mainly influenced PAH build-up. - Sufficient robust prediction models were established for analysis of PAH build-up on urban road surfaces. Pollution sources as well as the quantity and property of RDS mainly influenced PAH build-up.

Flexible building stock modelling with array-programming

DEFF Research Database (Denmark)

Brøgger, Morten; Wittchen, Kim Bjarne

2017-01-01

Many building stock models employ archetype-buildings in order to capture the essential characteristics of a diverse building stock. However, these models often require multiple archetypes, which make them inflexible. This paper proposes an array-programming based model, which calculates the heat...... tend to overestimate potential energy-savings, if we do not consider these discrepancies. The proposed model makes it possible to compute and visualize potential energy-savings in a flexible and transparent way....

A comprehensive model for the prediction of vibrations due to underground railway traffic: formulation and validation

International Nuclear Information System (INIS)

Costa, Pedro Alvares; Cardoso Silva, Antonio; Calçada, Rui; Lopes, Patricia; Fernandez, Jesus

2016-01-01

n this communication, a numerical approach for the prediction of vibrations induced in buildings due to railway traffic in tunnels is presented. The numerical model is based on the concept of dynamic sub structuring, being composed by three autonomous models to simulate the following main parts of the problem: i) generation of vibrations (train-track interaction); ii) propagation of vibrations (track - tunnel-ground system); iii) reception of vibrations (building coupled to the ground). The methodology proposed allows dealing with the three-dimensional characteristics of the problem with a reasonable computational effort [ 1 , 2 ] . After the brief description of the model, its experimental validation is performed. For that, a case study about vibrations inside of a building close to a shallow railway tunnel in Madrid are simulated and the experimental data [ 3 ] is compared with the predicted results [ 4 ]. Finally, the communication finishes with some insights about the potentialities and challenges of this numerical modelling approach on the prediction of the behavior of ancient structures subjected to vibrations induced by human sources (railway and road traffic, pile driving, etc)

Lipid Processing Technology: Building a Multilevel Modeling Network

DEFF Research Database (Denmark)

Diaz Tovar, Carlos Axel; Mustaffa, Azizul Azri; Hukkerikar, Amol

2011-01-01

of a computer aided multilevel modeling network consisting a collection of new and adopted models, methods and tools for the systematic design and analysis of processes employing lipid technology. This is achieved by decomposing the problem into four levels of modeling: 1. pure component properties; 2. mixtures...... and phase behavior; 3. unit operations; and 4. process synthesis and design. The methods and tools in each level include: For the first level, a lipid‐database of collected experimental data from the open literature, confidential data from industry and generated data from validated predictive property...... of these unit operations with respect to performance parameters such as minimum total cost, product yield improvement, operability etc., and process intensification for the retrofit of existing biofuel plants. In the fourth level the information and models developed are used as building blocks...

A Method for Driving Route Predictions Based on Hidden Markov Model

Directory of Open Access Journals (Sweden)

Ning Ye

2015-01-01

Full Text Available We present a driving route prediction method that is based on Hidden Markov Model (HMM. This method can accurately predict a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we propose the route recommendation system architecture, where route predictions play important role in the system. Secondly, we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to make preparation for route predictions using a method of training set extension based on K-means++ and the add-one (Laplace smoothing technique. Thirdly, we present the route prediction algorithm. Finally, the experimental results of the effectiveness of the route predictions that is based on HMM are shown.

Time dependent patient no-show predictive modelling development.

Science.gov (United States)

Huang, Yu-Li; Hanauer, David A

2016-05-09

Purpose - The purpose of this paper is to develop evident-based predictive no-show models considering patients' each past appointment status, a time-dependent component, as an independent predictor to improve predictability. Design/methodology/approach - A ten-year retrospective data set was extracted from a pediatric clinic. It consisted of 7,291 distinct patients who had at least two visits along with their appointment characteristics, patient demographics, and insurance information. Logistic regression was adopted to develop no-show models using two-thirds of the data for training and the remaining data for validation. The no-show threshold was then determined based on minimizing the misclassification of show/no-show assignments. There were a total of 26 predictive model developed based on the number of available past appointments. Simulation was employed to test the effective of each model on costs of patient wait time, physician idle time, and overtime. Findings - The results demonstrated the misclassification rate and the area under the curve of the receiver operating characteristic gradually improved as more appointment history was included until around the 20th predictive model. The overbooking method with no-show predictive models suggested incorporating up to the 16th model and outperformed other overbooking methods by as much as 9.4 per cent in the cost per patient while allowing two additional patients in a clinic day. Research limitations/implications - The challenge now is to actually implement the no-show predictive model systematically to further demonstrate its robustness and simplicity in various scheduling systems. Originality/value - This paper provides examples of how to build the no-show predictive models with time-dependent components to improve the overbooking policy. Accurately identifying scheduled patients' show/no-show status allows clinics to proactively schedule patients to reduce the negative impact of patient no-shows.

U.S. Department of Energy Commercial Reference Building Models of the National Building Stock

Energy Technology Data Exchange (ETDEWEB)

Deru, M.; Field, K.; Studer, D.; Benne, K.; Griffith, B.; Torcellini, P.; Liu, B.; Halverson, M.; Winiarski, D.; Rosenberg, M.; Yazdanian, M.; Huang, J.; Crawley, D.

2011-02-01

The U.S. Department of Energy (DOE) Building Technologies Program has set the aggressive goal of producing marketable net-zero energy buildings by 2025. This goal will require collaboration between the DOE laboratories and the building industry. We developed standard or reference energy models for the most common commercial buildings to serve as starting points for energy efficiency research. These models represent fairly realistic buildings and typical construction practices. Fifteen commercial building types and one multifamily residential building were determined by consensus between DOE, the National Renewable Energy Laboratory, Pacific Northwest National Laboratory, and Lawrence Berkeley National Laboratory, and represent approximately two-thirds of the commercial building stock.

Development of a thermal control algorithm using artificial neural network models for improved thermal comfort and energy efficiency in accommodation buildings

International Nuclear Information System (INIS)

Moon, Jin Woo; Jung, Sung Kwon

2016-01-01

Highlights: • An ANN model for predicting optimal start moment of the cooling system was developed. • An ANN model for predicting the amount of cooling energy consumption was developed. • An optimal control algorithm was developed employing two ANN models. • The algorithm showed the advanced thermal comfort and energy efficiency. - Abstract: The aim of this study was to develop a control algorithm to demonstrate the improved thermal comfort and building energy efficiency of accommodation buildings in the cooling season. For this, two artificial neural network (ANN)-based predictive and adaptive models were developed and employed in the algorithm. One model predicted the cooling energy consumption during the unoccupied period for different setback temperatures and the other predicted the time required for restoring current indoor temperature to the normal set-point temperature. Using numerical simulation methods, the prediction accuracy of the two ANN models and the performance of the algorithm were tested. Through the test result analysis, the two ANN models showed their prediction accuracy with an acceptable error rate when applied in the control algorithm. In addition, the two ANN models based algorithm can be used to provide a more comfortable and energy efficient indoor thermal environment than the two conventional control methods, which respectively employed a fixed set-point temperature for the entire day and a setback temperature during the unoccupied period. Therefore, the operating range was 23–26 °C during the occupied period and 25–28 °C during the unoccupied period. Based on the analysis, it can be concluded that the optimal algorithm with two predictive and adaptive ANN models can be used to design a more comfortable and energy efficient indoor thermal environment for accommodation buildings in a comprehensive manner.

Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

Directory of Open Access Journals (Sweden)

Peek Andrew S

2007-06-01

Full Text Available Abstract Background RNA interference (RNAi is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM approach was used to quantitatively model RNA interference activities. Results Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative. Conclusion The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid

Impacts of Model Building Energy Codes

Energy Technology Data Exchange (ETDEWEB)

Athalye, Rahul A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Sivaraman, Deepak [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Elliott, Douglas B. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Liu, Bing [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Bartlett, Rosemarie [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2016-10-31

The U.S. Department of Energy (DOE) Building Energy Codes Program (BECP) periodically evaluates national and state-level impacts associated with energy codes in residential and commercial buildings. Pacific Northwest National Laboratory (PNNL), funded by DOE, conducted an assessment of the prospective impacts of national model building energy codes from 2010 through 2040. A previous PNNL study evaluated the impact of the Building Energy Codes Program; this study looked more broadly at overall code impacts. This report describes the methodology used for the assessment and presents the impacts in terms of energy savings, consumer cost savings, and reduced CO₂ emissions at the state level and at aggregated levels. This analysis does not represent all potential savings from energy codes in the U.S. because it excludes several states which have codes which are fundamentally different from the national model energy codes or which do not have state-wide codes. Energy codes follow a three-phase cycle that starts with the development of a new model code, proceeds with the adoption of the new code by states and local jurisdictions, and finishes when buildings comply with the code. The development of new model code editions creates the potential for increased energy savings. After a new model code is adopted, potential savings are realized in the field when new buildings (or additions and alterations) are constructed to comply with the new code. Delayed adoption of a model code and incomplete compliance with the code’s requirements erode potential savings. The contributions of all three phases are crucial to the overall impact of codes, and are considered in this assessment.

A simplified model of dynamic interior cooling load evaluation for office buildings

International Nuclear Information System (INIS)

Ding, Yan; Zhang, Qiang; Wang, Zhaoxia; Liu, Min; He, Qing

2016-01-01

Highlights: • The core interior disturbance was determined by principle component analysis. • Influences of occupants on cooling load should be described using time series. • A simplified model was built to evaluate dynamic interior building cooling load. - Abstract: Predicted cooling load is a valuable tool for assessing the operation of air-conditioning systems. Compared with exterior cooling load, interior cooling load is more unpredictable. According to principle components analysis, occupancy was proved to be a typical factor influencing interior cooling loads in buildings. By exploring the regularity of interior disturbances in an office building, a simplified evaluation model for interior cooling load was established in this paper. The stochastic occupancy rate was represented by a Markov transition model. Equipment power, lighting power and fresh air were all related to occupancy rate based on time sequence. The superposition of different types of interior cooling loads was also considered in the evaluation model. The error between the evaluation results and measurement results was found to be lower than 10%. In reference to the cooling loads calculated by the traditional design method and area-based method in case study office rooms, the evaluated cooling loads were suitable for operation regulation.

Building Predictive Human Performance Models of Skill Acquisition in a Data Entry Task

National Research Council Canada - National Science Library

Fu, Wai-Tat; Gonzalez, Cleotilde; Healy, Alice F; Kole, James A; Bourne, Jr., Lyle E

2006-01-01

.... Since data entry is a central component in most human-machine interaction, a predictive model of performance will provide useful information that informs interface design and effectiveness of training...

Prediction models for successful external cephalic version: a systematic review.

Science.gov (United States)

Velzel, Joost; de Hundt, Marcella; Mulder, Frederique M; Molkenboer, Jan F M; Van der Post, Joris A M; Mol, Ben W; Kok, Marjolein

2015-12-01

To provide an overview of existing prediction models for successful ECV, and to assess their quality, development and performance. We searched MEDLINE, EMBASE and the Cochrane Library to identify all articles reporting on prediction models for successful ECV published from inception to January 2015. We extracted information on study design, sample size, model-building strategies and validation. We evaluated the phases of model development and summarized their performance in terms of discrimination, calibration and clinical usefulness. We collected different predictor variables together with their defined significance, in order to identify important predictor variables for successful ECV. We identified eight articles reporting on seven prediction models. All models were subjected to internal validation. Only one model was also validated in an external cohort. Two prediction models had a low overall risk of bias, of which only one showed promising predictive performance at internal validation. This model also completed the phase of external validation. For none of the models their impact on clinical practice was evaluated. The most important predictor variables for successful ECV described in the selected articles were parity, placental location, breech engagement and the fetal head being palpable. One model was assessed using discrimination and calibration using internal (AUC 0.71) and external validation (AUC 0.64), while two other models were assessed with discrimination and calibration, respectively. We found one prediction model for breech presentation that was validated in an external cohort and had acceptable predictive performance. This model should be used to council women considering ECV. Copyright © 2015. Published by Elsevier Ireland Ltd.

Comparison of Building Energy Modeling Programs: Building Loads

Energy Technology Data Exchange (ETDEWEB)

Zhu, Dandan [Tsinghua Univ., Beijing (China); Hong, Tianzhen [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Yan, Da [Tsinghua Univ., Beijing (China); Wang, Chuang [Tsinghua Univ., Beijing (China)

2012-06-01

This technical report presented the methodologies, processes, and results of comparing three Building Energy Modeling Programs (BEMPs) for load calculations: EnergyPlus, DeST and DOE-2.1E. This joint effort, between Lawrence Berkeley National Laboratory, USA and Tsinghua University, China, was part of research projects under the US-China Clean Energy Research Center on Building Energy Efficiency (CERC-BEE). Energy Foundation, an industrial partner of CERC-BEE, was the co-sponsor of this study work. It is widely known that large discrepancies in simulation results can exist between different BEMPs. The result is a lack of confidence in building simulation amongst many users and stakeholders. In the fields of building energy code development and energy labeling programs where building simulation plays a key role, there are also confusing and misleading claims that some BEMPs are better than others. In order to address these problems, it is essential to identify and understand differences between widely-used BEMPs, and the impact of these differences on load simulation results, by detailed comparisons of these BEMPs from source code to results. The primary goal of this work was to research methods and processes that would allow a thorough scientific comparison of the BEMPs. The secondary goal was to provide a list of strengths and weaknesses for each BEMP, based on in-depth understandings of their modeling capabilities, mathematical algorithms, advantages and limitations. This is to guide the use of BEMPs in the design and retrofit of buildings, especially to support China’s building energy standard development and energy labeling program. The research findings could also serve as a good reference to improve the modeling capabilities and applications of the three BEMPs. The methodologies, processes, and analyses employed in the comparison work could also be used to compare other programs. The load calculation method of each program was analyzed and compared to

Integration of design applications with building models

DEFF Research Database (Denmark)

Eastman, C. M.; Jeng, T. S.; Chowdbury, R.

1997-01-01

This paper reviews various issues in the integration of applications with a building model... (Truncated.)......This paper reviews various issues in the integration of applications with a building model... (Truncated.)...

Airflow and radon transport modeling in four large buildings

International Nuclear Information System (INIS)

Fan, J.B.; Persily, A.K.

1995-01-01

Computer simulations of multizone airflow and contaminant transport were performed in four large buildings using the program CONTAM88. This paper describes the physical characteristics of the buildings and their idealizations as multizone building airflow systems. These buildings include a twelve-story multifamily residential building, a five-story mechanically ventilated office building with an atrium, a seven-story mechanically ventilated office building with an underground parking garage, and a one-story school building. The air change rates and interzonal airflows of these buildings are predicted for a range of wind speeds, indoor-outdoor temperature differences, and percentages of outdoor air intake in the supply air Simulations of radon transport were also performed in the buildings to investigate the effects of indoor-outdoor temperature difference and wind speed on indoor radon concentrations

A new, accurate predictive model for incident hypertension.

Science.gov (United States)

Völzke, Henry; Fung, Glenn; Ittermann, Till; Yu, Shipeng; Baumeister, Sebastian E; Dörr, Marcus; Lieb, Wolfgang; Völker, Uwe; Linneberg, Allan; Jørgensen, Torben; Felix, Stephan B; Rettig, Rainer; Rao, Bharat; Kroemer, Heyo K

2013-11-01

Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures. The primary study population consisted of 1605 normotensive individuals aged 20-79 years with 5-year follow-up from the population-based study, that is the Study of Health in Pomerania (SHIP). The initial set was randomly split into a training and a testing set. We used a probabilistic graphical model applying a Bayesian network to create a predictive model for incident hypertension and compared the predictive performance with the established Framingham risk score for hypertension. Finally, the model was validated in 2887 participants from INTER99, a Danish community-based intervention study. In the training set of SHIP data, the Bayesian network used a small subset of relevant baseline features including age, mean arterial pressure, rs16998073, serum glucose and urinary albumin concentrations. Furthermore, we detected relevant interactions between age and serum glucose as well as between rs16998073 and urinary albumin concentrations [area under the receiver operating characteristic (AUC 0.76)]. The model was confirmed in the SHIP validation set (AUC 0.78) and externally replicated in INTER99 (AUC 0.77). Compared to the established Framingham risk score for hypertension, the predictive performance of the new model was similar in the SHIP validation set and moderately better in INTER99. Data mining procedures identified a predictive model for incident hypertension, which included innovative and easy-to-measure variables. The findings promise great applicability in screening settings and clinical practice.

Development of an atmospheric diffusion numerical model for a nuclear facility. Numerical calculation method incorporating building effects

International Nuclear Information System (INIS)

Sada, Koichi; Michioka, Takenobu; Ichikawa, Yoichi

2002-01-01

Because effluent gas is sometimes released from low positions, viz., near the ground surface and around buildings, the effects caused by buildings within the site area are not negligible for gas diffusion predictions. For these reasons, the effects caused by buildings for gas diffusion are considered under the terrain following calculation coordinate system in this report. Numerical calculation meshes on the ground surface are treated as the building with the adaptation of wall function techniques of turbulent quantities in the flow calculations using a turbulence closure model. The reflection conditions of released particles on building surfaces are taken into consideration in the diffusion calculation using the Lagrangian particle model. Obtained flow and diffusion calculation results are compared with those of wind tunnel experiments around the building. It was apparent that features observed in a wind tunnel, viz., the formation of cavity regions behind the building and the gas diffusion to the ground surface behind the building, are also obtained by numerical calculation. (author)

Armagh Observatory - Historic Building Information Modelling for Virtual Learning in Building Conservation

Science.gov (United States)

Murphy, M.; Chenaux, A.; Keenaghan, G.; GIbson, V..; Butler, J.; Pybusr, C.

2017-08-01

In this paper the recording and design for a Virtual Reality Immersive Model of Armagh Observatory is presented, which will replicate the historic buildings and landscape with distant meridian markers and position of its principal historic instruments within a model of the night sky showing the position of bright stars. The virtual reality model can be used for educational purposes allowing the instruments within the historic building model to be manipulated within 3D space to demonstrate how the position measurements of stars were made in the 18th century. A description is given of current student and researchers activities concerning on-site recording and surveying and the virtual modelling of the buildings and landscape. This is followed by a design for a Virtual Reality Immersive Model of Armagh Observatory use game engine and virtual learning platforms and concepts.

[A prediction model for internet game addiction in adolescents: using a decision tree analysis].

Science.gov (United States)

Kim, Ki Sook; Kim, Kyung Hee

2010-06-01

This study was designed to build a theoretical frame to provide practical help to prevent and manage adolescent internet game addiction by developing a prediction model through a comprehensive analysis of related factors. The participants were 1,318 students studying in elementary, middle, and high schools in Seoul and Gyeonggi Province, Korea. Collected data were analyzed using the SPSS program. Decision Tree Analysis using the Clementine program was applied to build an optimum and significant prediction model to predict internet game addiction related to various factors, especially parent related factors. From the data analyses, the prediction model for factors related to internet game addiction presented with 5 pathways. Causative factors included gender, type of school, siblings, economic status, religion, time spent alone, gaming place, payment to Internet café, frequency, duration, parent's ability to use internet, occupation (mother), trust (father), expectations regarding adolescent's study (mother), supervising (both parents), rearing attitude (both parents). The results suggest preventive and managerial nursing programs for specific groups by path. Use of this predictive model can expand the role of school nurses, not only in counseling addicted adolescents but also, in developing and carrying out programs with parents and approaching adolescents individually through databases and computer programming.

Semi-Automatic Modelling of Building FAÇADES with Shape Grammars Using Historic Building Information Modelling

Science.gov (United States)

Dore, C.; Murphy, M.

2013-02-01

This paper outlines a new approach for generating digital heritage models from laser scan or photogrammetric data using Historic Building Information Modelling (HBIM). HBIM is a plug-in for Building Information Modelling (BIM) software that uses parametric library objects and procedural modelling techniques to automate the modelling stage. The HBIM process involves a reverse engineering solution whereby parametric interactive objects representing architectural elements are mapped onto laser scan or photogrammetric survey data. A library of parametric architectural objects has been designed from historic manuscripts and architectural pattern books. These parametric objects were built using an embedded programming language within the ArchiCAD BIM software called Geometric Description Language (GDL). Procedural modelling techniques have been implemented with the same language to create a parametric building façade which automatically combines library objects based on architectural rules and proportions. Different configurations of the façade are controlled by user parameter adjustment. The automatically positioned elements of the façade can be subsequently refined using graphical editing while overlaying the model with orthographic imagery. Along with this semi-automatic method for generating façade models, manual plotting of library objects can also be used to generate a BIM model from survey data. After the 3D model has been completed conservation documents such as plans, sections, elevations and 3D views can be automatically generated for conservation projects.

Procedure to predict the storey where plastic drift dominates in two-storey building under strong ground motion

DEFF Research Database (Denmark)

Hibino, Y.; Ichinose, T.; Costa, J.L.D.

2009-01-01

A procedure is presented to predict the storey where plastic drift dominates in two-storey buildings under strong ground motion. The procedure utilizes the yield strength and the mass of each storey as well as the peak ground acceleration. The procedure is based on two different assumptions: (1....... The efficiency of the procedure is verified by dynamic response analyses using elasto-plastic model....

Artificial intelligence support for scientific model-building

Science.gov (United States)

Keller, Richard M.

1992-01-01

Scientific model-building can be a time-intensive and painstaking process, often involving the development of large and complex computer programs. Despite the effort involved, scientific models cannot easily be distributed and shared with other scientists. In general, implemented scientific models are complex, idiosyncratic, and difficult for anyone but the original scientific development team to understand. We believe that artificial intelligence techniques can facilitate both the model-building and model-sharing process. In this paper, we overview our effort to build a scientific modeling software tool that aids the scientist in developing and using models. This tool includes an interactive intelligent graphical interface, a high-level domain specific modeling language, a library of physics equations and experimental datasets, and a suite of data display facilities.

Developing Verification Systems for Building Information Models of Heritage Buildings with Heterogeneous Datasets

Science.gov (United States)

Chow, L.; Fai, S.

2017-08-01

The digitization and abstraction of existing buildings into building information models requires the translation of heterogeneous datasets that may include CAD, technical reports, historic texts, archival drawings, terrestrial laser scanning, and photogrammetry into model elements. In this paper, we discuss a project undertaken by the Carleton Immersive Media Studio (CIMS) that explored the synthesis of heterogeneous datasets for the development of a building information model (BIM) for one of Canada's most significant heritage assets - the Centre Block of the Parliament Hill National Historic Site. The scope of the project included the development of an as-found model of the century-old, six-story building in anticipation of specific model uses for an extensive rehabilitation program. The as-found Centre Block model was developed in Revit using primarily point cloud data from terrestrial laser scanning. The data was captured by CIMS in partnership with Heritage Conservation Services (HCS), Public Services and Procurement Canada (PSPC), using a Leica C10 and P40 (exterior and large interior spaces) and a Faro Focus (small to mid-sized interior spaces). Secondary sources such as archival drawings, photographs, and technical reports were referenced in cases where point cloud data was not available. As a result of working with heterogeneous data sets, a verification system was introduced in order to communicate to model users/viewers the source of information for each building element within the model.

Atmospheric dispersion from releases in the vicinity of buildings

International Nuclear Information System (INIS)

Walsh, C.; Jones, J.A.

2002-01-01

The objective of this study is to advise FSA on the extent to which its current models for calculating air concentration and deposition for continuous releases close to sites with many buildings are adequate, whether there are circumstances for which the explicit modelling of building wake effects is required, and, if so, to recommend an appropriate model for this. The study considered the predictions of simple Gaussian models and the ADMS model. Results from the models are presented and compared, for a range of on-site building configurations and release locations. In addition, the extent to which details of the buildings on the site are required in ADMS is considered. The results indicate that buildings only affect the predicted concentration in a relatively small area around the site (less than 1 km from the site even for tall buildings). For dose calculations beyond 1 km, no allowance is required for modelling building effects. The results suggest that modelling the effects of buildings can be sensitive to a number of parameters and care should be used in interpreting results for locations within the region affected by buildings. However, because ADMS explicitly treats these factors, it is considered a better model for use than those based on a simple Gaussian approach. (author)

Buildings Lean Maintenance Implementation Model

Science.gov (United States)

Abreu, Antonio; Calado, João; Requeijo, José

2016-11-01

Nowadays, companies in global markets have to achieve high levels of performance and competitiveness to stay "alive".Within this assumption, the building maintenance cannot be done in a casual and improvised way due to the costs related. Starting with some discussion about lean management and building maintenance, this paper introduces a model to support the Lean Building Maintenance (LBM) approach. Finally based on a real case study from a Portuguese company, the benefits, challenges and difficulties are presented and discussed.

Modelling the probability of building fires

Directory of Open Access Journals (Sweden)

Vojtěch Barták

2014-12-01

Full Text Available Systematic spatial risk analysis plays a crucial role in preventing emergencies.In the Czech Republic, risk mapping is currently based on the risk accumulationprinciple, area vulnerability, and preparedness levels of Integrated Rescue Systemcomponents. Expert estimates are used to determine risk levels for individualhazard types, while statistical modelling based on data from actual incidents andtheir possible causes is not used. Our model study, conducted in cooperation withthe Fire Rescue Service of the Czech Republic as a model within the Liberec andHradec Králové regions, presents an analytical procedure leading to the creation ofbuilding fire probability maps based on recent incidents in the studied areas andon building parameters. In order to estimate the probability of building fires, aprediction model based on logistic regression was used. Probability of fire calculatedby means of model parameters and attributes of specific buildings can subsequentlybe visualized in probability maps.

Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint

Energy Technology Data Exchange (ETDEWEB)

Raszmann, Emma; Baker, Kyri; Shi, Ying; Christensen, Dane

2017-02-22

Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modeling approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.

Guidelines for Using Building Information Modeling for Energy Analysis of Buildings

Directory of Open Access Journals (Sweden)

Thomas Reeves

2015-12-01

Full Text Available Building energy modeling (BEM, a subset of building information modeling (BIM, integrates energy analysis into the design, construction, and operation and maintenance of buildings. As there are various existing BEM tools available, there is a need to evaluate the utility of these tools in various phases of the building lifecycle. The goal of this research was to develop guidelines for evaluation and selection of BEM tools to be used in particular building lifecycle phases. The objectives of this research were to: (1 Evaluate existing BEM tools; (2 Illustrate the application of the three BEM tools; (3 Re-evaluate the three BEM tools; and (4 Develop guidelines for evaluation, selection and application of BEM tools in the design, construction and operation/maintenance phases of buildings. Twelve BEM tools were initially evaluated using four criteria: interoperability, usability, available inputs, and available outputs. Each of the top three BEM tools selected based on this initial evaluation was used in a case study to simulate and evaluate energy usage, daylighting performance, and natural ventilation for two academic buildings (LEED-certified and non-LEED-certified. The results of the case study were used to re-evaluate the three BEM tools using the initial criteria with addition of the two new criteria (speed and accuracy, and to develop guidelines for evaluating and selecting BEM tools to analyze building energy performance. The major contribution of this research is the development of these guidelines that can help potential BEM users to identify the most appropriate BEM tool for application in particular building lifecycle phases.

Predicting the Texas Windstorm Insurance Association claim payout of commercial buildings from Hurricane Ike

Science.gov (United States)

Kim, J. M.; Woods, P. K.; Park, Y. J.; Son, K.

2013-08-01

Following growing public awareness of the danger from hurricanes and tremendous demands for analysis of loss, many researchers have conducted studies to develop hurricane damage analysis methods. Although researchers have identified the significant indicators, there currently is no comprehensive research for identifying the relationship among the vulnerabilities, natural disasters, and economic losses associated with individual buildings. To address this lack of research, this study will identify vulnerabilities and hurricane indicators, develop metrics to measure the influence of economic losses from hurricanes, and visualize the spatial distribution of vulnerability to evaluate overall hurricane damage. This paper has utilized the Geographic Information System to facilitate collecting and managing data, and has combined vulnerability factors to assess the financial losses suffered by Texas coastal counties. A multiple linear regression method has been applied to develop hurricane economic damage predicting models. To reflect the pecuniary loss, insured loss payment was used as the dependent variable to predict the actual financial damage. Geographical vulnerability indicators, built environment vulnerability indicators, and hurricane indicators were all used as independent variables. Accordingly, the models and findings may possibly provide vital references for government agencies, emergency planners, and insurance companies hoping to predict hurricane damage.

Statistical models describing the energy signature of buildings

DEFF Research Database (Denmark)

Bacher, Peder; Madsen, Henrik; Thavlov, Anders

2010-01-01

Approximately one third of the primary energy production in Denmark is used for heating in buildings. Therefore efforts to accurately describe and improve energy performance of the building mass are very important. For this purpose statistical models describing the energy signature of a building, i...... or varying energy prices. The paper will give an overview of statistical methods and applied models based on experiments carried out in FlexHouse, which is an experimental building in SYSLAB, Risø DTU. The models are of different complexity and can provide estimates of physical quantities such as UA......-values, time constants of the building, and other parameters related to the heat dynamics. A method for selecting the most appropriate model for a given building is outlined and finally a perspective of the applications is given. Aknowledgements to the Danish Energy Saving Trust and the Interreg IV ``Vind i...

DEVELOPING VERIFICATION SYSTEMS FOR BUILDING INFORMATION MODELS OF HERITAGE BUILDINGS WITH HETEROGENEOUS DATASETS

Directory of Open Access Journals (Sweden)

L. Chow

2017-08-01

Full Text Available The digitization and abstraction of existing buildings into building information models requires the translation of heterogeneous datasets that may include CAD, technical reports, historic texts, archival drawings, terrestrial laser scanning, and photogrammetry into model elements. In this paper, we discuss a project undertaken by the Carleton Immersive Media Studio (CIMS that explored the synthesis of heterogeneous datasets for the development of a building information model (BIM for one of Canada’s most significant heritage assets – the Centre Block of the Parliament Hill National Historic Site. The scope of the project included the development of an as-found model of the century-old, six-story building in anticipation of specific model uses for an extensive rehabilitation program. The as-found Centre Block model was developed in Revit using primarily point cloud data from terrestrial laser scanning. The data was captured by CIMS in partnership with Heritage Conservation Services (HCS, Public Services and Procurement Canada (PSPC, using a Leica C10 and P40 (exterior and large interior spaces and a Faro Focus (small to mid-sized interior spaces. Secondary sources such as archival drawings, photographs, and technical reports were referenced in cases where point cloud data was not available. As a result of working with heterogeneous data sets, a verification system was introduced in order to communicate to model users/viewers the source of information for each building element within the model.

Test cell data-based predictive modelling to determine HVAC energy consumption for three façade solutions in Madrid

Directory of Open Access Journals (Sweden)

J. Guerrero-Rubio

2018-01-01

Full Text Available This study aims to narrow the gap between predicted and actual energy performance in buildings. Predictive models were established that relate the electric consumption by HVAC systems to maintain certain indoor environmental conditions in variable weather to the type of façade. The models were developed using data gathered from test cells with adiabatic envelopes on all but the façade to be tested. Three façade types were studied. The first, the standard solution, consisted in a double wythe brick wall with an intermediate air space, the configuration most commonly deployed in multi-family dwellings built in Spain between 1940 and 1980 (prior to the enactment of the first building codes that limited overall energy demand in buildings. The other two were retrofits frequently found in such buildings: ventilated façades and ETICS (external thermal insulation composite systems. Two predictive models were designed for each type of façade, one for summer and the other for winter. The linear regression equations and the main statistical parameters are reported.

Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools.

Directory of Open Access Journals (Sweden)

Lei Jia

Full Text Available Thermostability issue of protein point mutations is a common occurrence in protein engineering. An application which predicts the thermostability of mutants can be helpful for guiding decision making process in protein design via mutagenesis. An in silico point mutation scanning method is frequently used to find "hot spots" in proteins for focused mutagenesis. ProTherm (http://gibk26.bio.kyutech.ac.jp/jouhou/Protherm/protherm.html is a public database that consists of thousands of protein mutants' experimentally measured thermostability. Two data sets based on two differently measured thermostability properties of protein single point mutations, namely the unfolding free energy change (ddG and melting temperature change (dTm were obtained from this database. Folding free energy change calculation from Rosetta, structural information of the point mutations as well as amino acid physical properties were obtained for building thermostability prediction models with informatics modeling tools. Five supervised machine learning methods (support vector machine, random forests, artificial neural network, naïve Bayes classifier, K nearest neighbor and partial least squares regression are used for building the prediction models. Binary and ternary classifications as well as regression models were built and evaluated. Data set redundancy and balancing, the reverse mutations technique, feature selection, and comparison to other published methods were discussed. Rosetta calculated folding free energy change ranked as the most influential features in all prediction models. Other descriptors also made significant contributions to increasing the accuracy of the prediction models.

EVALUATION OF A FAST-RESPONSE URBAN WIND MODEL - COMPARISON TO SINGLE-BUILDING WIND TUNNEL DATA

International Nuclear Information System (INIS)

E.R. PARDYJAK; M.J. BROWN

2001-01-01

Prediction of the 3-dimensional flow field around buildings and other obstacles is important for a number of applications, including urban air quality studies, the tracking of plumes from accidental releases of toxic air contaminants, indoor/outdoor air pollution problems, and thermal comfort assessments. Various types of computational fluid dynamics (CFD) models have been used for determining the flow fields around buildings (e.g., Reisner et al., 1998; Eichhorn et al., 1988). Comparisons to measurements show that these models work reasonably well for the most part (e.g., Ehrhard et al., 2 ; Johnson and Hunter, 1998; Murakami, 1997). However, CFD models are computationally intensive and for some applications turn-around time is of the essence. For example, planning and assessment studies in which hundreds of cases must be analyzed or emergency response scenarios in which plume transport must be computed quickly. Several fast-response dispersion models of varying levels of fidelity have been developed to explicitly account for the effects of a single building or groups of buildings (e.g., UDM - Hall et al. (2000), NRC-Ramsdell and Fosmire (1995), CBP-3 - Yamartino and Wiegand (1986), APRAC - Daerdt et al. (1973)). Although a few of these models include the Hotchkiss and Harlow (1973) analytical solution for potential flow in a notch to describe the velocity field within an urban canyon, in general, these models do not explicitly compute the velocity field around groups of buildings. The EPA PRIME model (Schulman et al., 2000) has been empirically derived to provide streamlines around a single isolated building

Prediction Model of Interval Grey Numbers with a Real Parameter and Its Application

Directory of Open Access Journals (Sweden)

Bo Zeng

2014-01-01

Full Text Available Grey prediction models have become common methods which are widely employed to solve the problems with “small examples and poor information.” However, modeling objects of existing grey prediction models are limited to the homogenous data sequences which only contain the same data type. This paper studies the methodology of building prediction models of interval grey numbers that are grey heterogeneous data sequence, with a real parameter. Firstly, the position of the real parameter in an interval grey number sequence is discussed, and the real number is expanded into an interval grey number by adopting the method of grey generation. On this basis, a prediction model of interval grey number with a real parameter is deduced and built. Finally, this novel model is successfully applied to forecast the concentration of organic pollutant DDT in the atmosphere. The analysis and research results in this paper extend the object of grey prediction from homogenous data sequence to grey heterogeneous data sequence. Those research findings are of positive significance in terms of enriching and improving the theory system of grey prediction models.

Prediction of moisture migration and pore pressure build-up in concrete at high temperatures

International Nuclear Information System (INIS)

Ichikawa, Y.; England, G.L.

2004-01-01

Prediction of moisture migration and pore pressure build-up in non-uniformly heated concrete is important for safe operation of concrete containment vessels in nuclear power reactors and for assessing the behaviour of fire-exposed concrete structures. (1) Changes in moisture content distribution in a concrete containment vessel during long-term operation should be investigated, since the durability and radiation shielding ability of concrete are strongly influenced by its moisture content. (2) The pressure build-up in a concrete containment vessel in a postulated accident should be evaluated in order to determine whether a venting system is necessary between liner and concrete to relieve the pore pressure. (3) When concrete is subjected to rapid heating during a fire, the concrete can suffer from spalling due to pressure build-up in the concrete pores. This paper presents a mathematical and computational model for predicting changes in temperature, moisture content and pore pressure in concrete at elevated temperatures. A pair of differential equations for one-dimensional heat and moisture transfer in concrete are derived from the conservation of energy and mass, and take into account the temperature-dependent release of gel water and chemically bound water due to dehydration. These equations are numerically solved by the finite difference method. In the numerical analysis, the pressure, density and dynamic viscosity of water in the concrete pores are calculated explicitly from a set of formulated equations. The numerical analysis results are compared with two different sets of experimental data: (a) long-term (531 days) moisture migration test under a steady-state temperature of 200 deg. C, and (b) short-term (114 min) pressure build-up test under transient heating. These experiments were performed to investigate the moisture migration and pressure build-up in the concrete wall of a reactor containment vessel at high temperatures. The former experiment simulated

Iterative-build OMIT maps: map improvement by iterative model building and refinement without model bias

International Nuclear Information System (INIS)

Terwilliger, Thomas C.; Grosse-Kunstleve, Ralf W.; Afonine, Pavel V.; Moriarty, Nigel W.; Adams, Paul D.; Read, Randy J.; Zwart, Peter H.; Hung, Li-Wei

2008-01-01

An OMIT procedure is presented that has the benefits of iterative model building density modification and refinement yet is essentially unbiased by the atomic model that is built. A procedure for carrying out iterative model building, density modification and refinement is presented in which the density in an OMIT region is essentially unbiased by an atomic model. Density from a set of overlapping OMIT regions can be combined to create a composite ‘iterative-build’ OMIT map that is everywhere unbiased by an atomic model but also everywhere benefiting from the model-based information present elsewhere in the unit cell. The procedure may have applications in the validation of specific features in atomic models as well as in overall model validation. The procedure is demonstrated with a molecular-replacement structure and with an experimentally phased structure and a variation on the method is demonstrated by removing model bias from a structure from the Protein Data Bank

Some aspects to improve sound insulation prediction models for lightweight elements

NARCIS (Netherlands)

Gerretsen, E.

2007-01-01

The best approach to include lightweight building elements in prediction models for airborne and impact sound insulation between rooms, as in EN 12354, is not yet completely clear. Two aspects are at least of importance, i.e. to derive the sound reduction index R for lightweight elements for

QSAR Modeling and Prediction of Drug-Drug Interactions.

Science.gov (United States)

Zakharov, Alexey V; Varlamova, Ekaterina V; Lagunin, Alexey A; Dmitriev, Alexander V; Muratov, Eugene N; Fourches, Denis; Kuz'min, Victor E; Poroikov, Vladimir V; Tropsha, Alexander; Nicklaus, Marc C

2016-02-01

Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

Model-based and model-free “plug-and-play” building energy efficient control

International Nuclear Information System (INIS)

Baldi, Simone; Michailidis, Iakovos; Ravanis, Christos; Kosmatopoulos, Elias B.

2015-01-01

Highlights: • “Plug-and-play” Building Optimization and Control (BOC) driven by building data. • Ability to handle the large-scale and complex nature of the BOC problem. • Adaptation to learn the optimal BOC policy when no building model is available. • Comparisons with rule-based and advanced BOC strategies. • Simulation and real-life experiments in a ten-office building. - Abstract: Considerable research efforts in Building Optimization and Control (BOC) have been directed toward the development of “plug-and-play” BOC systems that can achieve energy efficiency without compromising thermal comfort and without the need of qualified personnel engaged in a tedious and time-consuming manual fine-tuning phase. In this paper, we report on how a recently introduced Parametrized Cognitive Adaptive Optimization – abbreviated as PCAO – can be used toward the design of both model-based and model-free “plug-and-play” BOC systems, with minimum human effort required to accomplish the design. In the model-based case, PCAO assesses the performance of its control strategy via a simulation model of the building dynamics; in the model-free case, PCAO optimizes its control strategy without relying on any model of the building dynamics. Extensive simulation and real-life experiments performed on a 10-office building demonstrate the effectiveness of the PCAO–BOC system in providing significant energy efficiency and improved thermal comfort. The mechanisms embedded within PCAO render it capable of automatically and quickly learning an efficient BOC strategy either in the presence of complex nonlinear simulation models of the building dynamics (model-based) or when no model for the building dynamics is available (model-free). Comparative studies with alternative state-of-the-art BOC systems show the effectiveness of the PCAO–BOC solution

Multidisciplinary Energy Assessment of Tertiary Buildings: Automated Geomatic Inspection, Building Information Modeling Reconstruction and Building Performance Simulation

Directory of Open Access Journals (Sweden)

Faustino Patiño-Cambeiro

2017-07-01

Full Text Available There is an urgent need for energy efficiency in buildings within the European framework, considering its environmental implications, and Europe’s energy dependence. Furthermore, the need for enhancing and increasing productivity in the building industry turns new technologies and building energy performance simulation environments into extremely interesting solutions towards rigorous analysis and decision making in renovation within acceptable risk levels. The present work describes a multidisciplinary approach for the estimation of the energy performance of an educational building. The research involved data acquisition with advanced geomatic tools, the development of an optimized building information model, and energy assessment in Building Performance Simulation (BPS software. Interoperability issues were observed in the different steps of the process. The inspection and diagnostic phases were conducted in a timely, accurate manner thanks to automated data acquisition and subsequent analysis using Building Information Modeling based tools (BIM-based tools. Energy simulation was performed using Design Builder, and the results obtained were compared with those yielded by the official software tool established by Spanish regulations for energy certification. The discrepancies between the results of both programs have proven that the official software program is conservative in this sense. This may cause the depreciation of the assessed buildings.

Software Tools For Building Decision-support Models For Flood Emergency Situations

Science.gov (United States)

Garrote, L.; Molina, M.; Ruiz, J. M.; Mosquera, J. C.

The SAIDA decision-support system was developed by the Spanish Ministry of the Environment to provide assistance to decision-makers during flood situations. SAIDA has been tentatively implemented in two test basins: Jucar and Guadalhorce, and the Ministry is currently planning to have it implemented in all major Spanish basins in a few years' time. During the development cycle of SAIDA, the need for providing as- sistance to end-users in model definition and calibration was clearly identified. System developers usually emphasise abstraction and generality with the goal of providing a versatile software environment. End users, on the other hand, require concretion and specificity to adapt the general model to their local basins. As decision-support models become more complex, the gap between model developers and users gets wider: Who takes care of model definition, calibration and validation?. Initially, model developers perform these tasks, but the scope is usually limited to a few small test basins. Before the model enters operational stage, end users must get involved in model construction and calibration, in order to gain confidence in the model recommendations. However, getting the users involved in these activities is a difficult task. The goal of this re- search is to develop representation techniques for simulation and management models in order to define, develop and validate a mechanism, supported by a software envi- ronment, oriented to provide assistance to the end-user in building decision models for the prediction and management of river floods in real time. The system is based on three main building blocks: A library of simulators of the physical system, an editor to assist the user in building simulation models, and a machine learning method to calibrate decision models based on the simulation models provided by the user.

Comparison of Predictive Modeling Methods of Aircraft Landing Speed

Science.gov (United States)

Diallo, Ousmane H.

2012-01-01

Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

Effects on Buildings of Surface Curvature Caused by Underground Coal Mining

Directory of Open Access Journals (Sweden)

Haifeng Hu

2016-08-01

Full Text Available Ground curvature caused by underground mining is one of the most obvious deformation quantities in buildings. To study the influence of surface curvature on buildings and predict the movement and deformation of buildings caused by ground curvature, a prediction model of the influence function on mining subsidence was used to establish the relationship between surface curvature and wall deformation. The prediction model of wall deformation was then established and the surface curvature was obtained from mining subsidence prediction software. Five prediction lines were set up in the wall from bottom to top and the predicted deformation of each line was used to calculate the crack positions in the wall. Thus, the crack prediction model was obtained. The model was verified by a case study from a coalmine in Shanxi, China. The results show that when the ground curvature is positive, the crack in the wall is shaped like a “V”; when the ground curvature is negative, the crack is shaped like a “∧”. The conclusion provides the basis for a damage evaluation method for buildings in coalmine areas.

Building a 3D geomechanical model of a gas field for geohazard prediction

NARCIS (Netherlands)

Orlic, B.; Eijs, R. van; Zijl, W.; Wees, J.D. van

2002-01-01

Land subsidence, triggered earthquakes and wellbore instabilities are some examples of geohazards caused by or related to the production of subsurface natural resources and storage of energy residues in the deep subsurface. The main objective of geomechanical modelling is to effectively predict

ARMAGH OBSERVATORY – HISTORIC BUILDING INFORMATION MODELLING FOR VIRTUAL LEARNING IN BUILDING CONSERVATION

Directory of Open Access Journals (Sweden)

M. Murphy

2017-08-01

Full Text Available In this paper the recording and design for a Virtual Reality Immersive Model of Armagh Observatory is presented, which will replicate the historic buildings and landscape with distant meridian markers and position of its principal historic instruments within a model of the night sky showing the position of bright stars. The virtual reality model can be used for educational purposes allowing the instruments within the historic building model to be manipulated within 3D space to demonstrate how the position measurements of stars were made in the 18th century. A description is given of current student and researchers activities concerning on-site recording and surveying and the virtual modelling of the buildings and landscape. This is followed by a design for a Virtual Reality Immersive Model of Armagh Observatory use game engine and virtual learning platforms and concepts.

Toward a virtual building laboratory

Energy Technology Data Exchange (ETDEWEB)

Klems, J.H.; Finlayson, E.U.; Olsen, T.H.; Banks, D.W.; Pallis, J.M.

1999-03-01

In order to achieve in a timely manner the large energy and dollar savings technically possible through improvements in building energy efficiency, it will be necessary to solve the problem of design failure risk. The most economical method of doing this would be to learn to calculate building performance with sufficient detail, accuracy and reliability to avoid design failure. Existing building simulation models (BSM) are a large step in this direction, but are still not capable of this level of modeling. Developments in computational fluid dynamics (CFD) techniques now allow one to construct a road map from present BSM's to a complete building physical model. The most useful first step is a building interior model (BIM) that would allow prediction of local conditions affecting occupant health and comfort. To provide reliable prediction a BIM must incorporate the correct physical boundary conditions on a building interior. Doing so raises a number of specific technical problems and research questions. The solution of these within a context useful for building research and design is not likely to result from other research on CFD, which is directed toward the solution of different types of problems. A six-step plan for incorporating the correct boundary conditions within the context of the model problem of a large atrium has been outlined. A promising strategy for constructing a BIM is the overset grid technique for representing a building space in a CFD calculation. This technique promises to adapt well to building design and allows a step-by-step approach. A state-of-the-art CFD computer code using this technique has been adapted to the problem and can form the departure point for this research.

Next Day Building Load Predictions based on Limited Input Features Using an On-Line Laterally Primed Adaptive Resonance Theory Artificial Neural Network.

Energy Technology Data Exchange (ETDEWEB)

Jones, Christian Birk [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Photovoltaic and Grid Integration Group; Robinson, Matt [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Mechanical Engineering; Yasaei, Yasser [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Electrical and Computer Engineering; Caudell, Thomas [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Electrical and Computer Engineering; Martinez-Ramon, Manel [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Electrical and Computer Engineering; Mammoli, Andrea [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Mechanical Engineering

2016-07-01

Optimal integration of thermal energy storage within commercial building applications requires accurate load predictions. Several methods exist that provide an estimate of a buildings future needs. Methods include component-based models and data-driven algorithms. This work implemented a previously untested algorithm for this application that is called a Laterally Primed Adaptive Resonance Theory (LAPART) artificial neural network (ANN). The LAPART algorithm provided accurate results over a two month period where minimal historical data and a small amount of input types were available. These results are significant, because common practice has often overlooked the implementation of an ANN. ANN have often been perceived to be too complex and require large amounts of data to provide accurate results. The LAPART neural network was implemented in an on-line learning manner. On-line learning refers to the continuous updating of training data as time occurs. For this experiment, training began with a singe day and grew to two months of data. This approach provides a platform for immediate implementation that requires minimal time and effort. The results from the LAPART algorithm were compared with statistical regression and a component-based model. The comparison was based on the predictions linear relationship with the measured data, mean squared error, mean bias error, and cost savings achieved by the respective prediction techniques. The results show that the LAPART algorithm provided a reliable and cost effective means to predict the building load for the next day.

NEW UPPER AND LOWER BOUNDS LINE OF SIGHT PATH LOSS MODELS FOR MOBILE PROPAGATION IN BUILDINGS

Directory of Open Access Journals (Sweden)

Supachai Phaiboon

2017-11-01

Full Text Available This paper proposes a method to predict line-of-sight (LOS path loss in buildings. We performed measurements in two different type of buildings at a frequency of 1.8 GHz and propose new upper and lower bounds path loss models which depend on max and min values of sample path loss data. This makes our models limit path loss within the boundary lines. The models include time-variant effects such as people moving and cars in parking areas with their influence on wave propagation that is very high.  The results have shown that the proposed models will be useful for the system and cell design of indoor wireless communication systems.

Large urban fire environment: trends and model city predictions

International Nuclear Information System (INIS)

Larson, D.A.; Small, R.D.

1983-01-01

The urban fire environment that would result from a megaton-yield nuclear weapon burst is considered. The dependence of temperatures and velocities on fire size, burning intensity, turbulence, and radiation is explored, and specific calculations for three model urban areas are presented. In all cases, high velocity fire winds are predicted. The model-city results show the influence of building density and urban sprawl on the fire environment. Additional calculations consider large-area fires with the burning intensity reduced in a blast-damaged urban center

System Dynamics as Model-Based Theory Building

OpenAIRE

Schwaninger, Markus; Grösser, Stefan N.

2008-01-01

This paper introduces model-based theory building as a feature of system dynamics (SD) with large potential. It presents a systemic approach to actualizing that potential, thereby opening up a new perspective on theory building in the social sciences. The question addressed is if and how SD enables the construction of high-quality theories. This contribution is based on field experiment type projects which have been focused on model-based theory building, specifically the construction of a mi...

Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction

Science.gov (United States)

Su, X.

2017-12-01

A satellite cloud image contains much weather information such as precipitation information. Short-time cloud movement forecast is important for precipitation forecast and is the primary means for typhoon monitoring. The traditional methods are mostly using the cloud feature matching and linear extrapolation to predict the cloud movement, which makes that the nonstationary process such as inversion and deformation during the movement of the cloud is basically not considered. It is still a hard task to predict cloud movement timely and correctly. As deep learning model could perform well in learning spatiotemporal features, to meet this challenge, we could regard cloud image prediction as a spatiotemporal sequence forecasting problem and introduce deep learning model to solve this problem. In this research, we use a variant of Gated-Recurrent-Unit(GRU) that has convolutional structures to deal with spatiotemporal features and build an end-to-end model to solve this forecast problem. In this model, both the input and output are spatiotemporal sequences. Compared to Convolutional LSTM(ConvLSTM) model, this model has lower amount of parameters. We imply this model on GOES satellite data and the model perform well.

A review of building information modelling

Science.gov (United States)

Wang, Wen; Han, Rui

2018-05-01

Building Information Modelling (BIM) is widely seen as a catalyst for innovation and productivity. It is becoming standard for new construction and is the most significant technology changing how we design, build, use and manage the building. It is a dominant technological trend in the software industry and although the theoretical groundwork was laid in the previous century, it is a popular topic in academic research. BIM is discussed in this study, which results can provide better and more comprehensive choices for building owners, designers, and developers in future.

Evaluation of main control room habitability in Japanese LWR (2). Evaluation for applicability of existing atmospheric dispersion models to building wake dispersion by using wind tunnel experiment

International Nuclear Information System (INIS)

Fukuda, Ryo; Fujita, Yuko; Yoneda, Jiro; Okabayashi, Kazuki; Tabuse, Shigehiko; Watada, Masayuki

2009-01-01

It is necessary to predict the concentration field behind the containment vessel building for the evaluation of main control room habitability in case of the emergency. The concentration field behind the building is very complicated phenomena and the exact prediction of concentration would be very difficult even if philosophical numerical simulation was used. Instead the simple and analytical prediction models (ARCON96, Gifford and Murphy-Campe etc.) have been used for the assessment of main control room habitability. In order to evaluate the previous models, the wind tunnel experiment was carried out. Recent regulatory models of ADMS4 developed by UK-CERC and AERMOD by US-EPA were also compared with this experimental data. Only both the containment vessel and reactor buildings of the typical PWR plant was scaled in 1/200 and the atmospheric stability C-D between C and D of Pasquill-Gifford categories was reproduced as a neutral condition in the wind tunnel experiment. In the wind experiment, the meandering effect for 1 hour was taken into consideration by the so-called overlapping method that a scaled model in the test section of a wind tunnel was rotated. By the rotation of the scaled model, wind directional fluctuations were relatively generated in the test section. The model was rotated at a various speed which was inversely proportional to each frequency of occurrence of a wind direction. Tracer gas was sampled during the rotation of the building model. As a result, we got the 1 hr.-averaged concentration taking a meandering effect into consideration. In this experiment, it is assumed that the frequency distribution of wind direction is Gaussian and horizontal plume width for 1 hr. was expanded to about 1.8 times of plume width based on Pasquill-Gifford chart by 1/5 power law due to the meandering effect. From the experiment, it was found as follows; It seems that meandering effect was not important in the near field behind a building, because strong

Building a three-dimensional model of CYP2C9 inhibition using the Autocorrelator: an autonomous model generator.

Science.gov (United States)

Lardy, Matthew A; Lebrun, Laurie; Bullard, Drew; Kissinger, Charles; Gobbi, Alberto

2012-05-25

In modern day drug discovery campaigns, computational chemists have to be concerned not only about improving the potency of molecules but also reducing any off-target ADMET activity. There are a plethora of antitargets that computational chemists may have to consider. Fortunately many antitargets have crystal structures deposited in the PDB. These structures are immediately useful to our Autocorrelator: an automated model generator that optimizes variables for building computational models. This paper describes the use of the Autocorrelator to construct high quality docking models for cytochrome P450 2C9 (CYP2C9) from two publicly available crystal structures. Both models result in strong correlation coefficients (R² > 0.66) between the predicted and experimental determined log(IC₅₀) values. Results from the two models overlap well with each other, converging on the same scoring function, deprotonated charge state, and predicted the binding orientation for our collection of molecules.

COMPLEMENTARITY OF HISTORIC BUILDING INFORMATION MODELLING AND GEOGRAPHIC INFORMATION SYSTEMS

Directory of Open Access Journals (Sweden)

X. Yang

2016-06-01

Full Text Available In this paper, we discuss the potential of integrating both semantically rich models from Building Information Modelling (BIM and Geographical Information Systems (GIS to build the detailed 3D historic model. BIM contributes to the creation of a digital representation having all physical and functional building characteristics in several dimensions, as e.g. XYZ (3D, time and non-architectural information that are necessary for construction and management of buildings. GIS has potential in handling and managing spatial data especially exploring spatial relationships and is widely used in urban modelling. However, when considering heritage modelling, the specificity of irregular historical components makes it problematic to create the enriched model according to its complex architectural elements obtained from point clouds. Therefore, some open issues limiting the historic building 3D modelling will be discussed in this paper: how to deal with the complex elements composing historic buildings in BIM and GIS environment, how to build the enriched historic model, and why to construct different levels of details? By solving these problems, conceptualization, documentation and analysis of enriched Historic Building Information Modelling are developed and compared to traditional 3D models aimed primarily for visualization.

Structured building model reduction toward parallel simulation

Energy Technology Data Exchange (ETDEWEB)

Dobbs, Justin R. [Cornell University; Hencey, Brondon M. [Cornell University

2013-08-26

Building energy model reduction exchanges accuracy for improved simulation speed by reducing the number of dynamical equations. Parallel computing aims to improve simulation times without loss of accuracy but is poorly utilized by contemporary simulators and is inherently limited by inter-processor communication. This paper bridges these disparate techniques to implement efficient parallel building thermal simulation. We begin with a survey of three structured reduction approaches that compares their performance to a leading unstructured method. We then use structured model reduction to find thermal clusters in the building energy model and allocate processing resources. Experimental results demonstrate faster simulation and low error without any interprocessor communication.

Modelling the heat dynamics of buildings using stochastic

DEFF Research Database (Denmark)

Andersen, Klaus Kaae; Madsen, Henrik

2000-01-01

This paper describes the continuous time modelling of the heat dynamics of a building. The considered building is a residential like test house divided into two test rooms with a water based central heating. Each test room is divided into thermal zones in order to describe both short and long term...... variations. Besides modelling the heat transfer between thermal zones, attention is put on modelling the heat input from radiators and solar radiation. The applied modelling procedure is based on collected building performance data and statistical methods. The statistical methods are used in parameter...

Summary of best guidelines and validation of CFD modeling in livestock buildings to ensure prediction quality

DEFF Research Database (Denmark)

Rong, Li; Nielsen, Peter Vilhelm; Bjerg, Bjarne Schmidt

2016-01-01

scale pig barns was simulated to show the procedures of validating a CFD simulation in livestock buildings. After summarizing the guideline and/or best practice for CFD modeling, the authors addressed the issues related to numerical methods and the governing equations, which were limited to RANS models....... Although it is not necessary to maintain the same format of reporting the CFD modeling as presented in this paper, the authors would suggest including all the information related to the selection of turbulence models, difference schemes, convergence criteria, boundary conditions, geometry simplification......, simulating domain etc. This information is particularly important for the readers to evaluate the quality of the CFD simulation results....

Modeling volatile organic compounds sorption on dry building materials using double-exponential model

International Nuclear Information System (INIS)

Deng, Baoqing; Ge, Di; Li, Jiajia; Guo, Yuan; Kim, Chang Nyung

2013-01-01

A double-exponential surface sink model for VOCs sorption on building materials is presented. Here, the diffusion of VOCs in the material is neglected and the material is viewed as a surface sink. The VOCs concentration in the air adjacent to the material surface is introduced and assumed to always maintain equilibrium with the material-phase concentration. It is assumed that the sorption can be described by mass transfer between the room air and the air adjacent to the material surface. The mass transfer coefficient is evaluated from the empirical correlation, and the equilibrium constant can be obtained by linear fitting to the experimental data. The present model is validated through experiments in small and large test chambers. The predicted results accord well with the experimental data in both the adsorption stage and desorption stage. The model avoids the ambiguity of model constants found in other surface sink models and is easy to scale up

Modeling of Failure Prediction Bayesian Network with Divide-and-Conquer Principle

Directory of Open Access Journals (Sweden)

Zhiqiang Cai

2014-01-01

Full Text Available For system failure prediction, automatically modeling from historical failure dataset is one of the challenges in practical engineering fields. In this paper, an effective algorithm is proposed to build the failure prediction Bayesian network (FPBN model with data mining technology. First, the conception of FPBN is introduced to describe the state of components and system and the cause-effect relationships among them. The types of network nodes, the directions of network edges, and the conditional probability distributions (CPDs of nodes in FPBN are discussed in detail. According to the characteristics of nodes and edges in FPBN, a divide-and-conquer principle based algorithm (FPBN-DC is introduced to build the best FPBN network structures of different types of nodes separately. Then, the CPDs of nodes in FPBN are calculated by the maximum likelihood estimation method based on the built network. Finally, a simulation study of a helicopter convertor model is carried out to demonstrate the application of FPBN-DC. According to the simulations results, the FPBN-DC algorithm can get better fitness value with the lower number of iterations, which verified its effectiveness and efficiency compared with traditional algorithm.

Use of a viscoelastic model for the seismic response of base-isolated buildings

International Nuclear Information System (INIS)

Uras, R.A.

1994-01-01

Due to recent developments in elastomer technology, seismic isolation using elastomer bearings is rapidly becoming an acceptable design tool to enhance structural seismic margins and to protect people and equipment from earthquake damage. With proper design of isolators, high-energy seismic input motions are transformed into low-frequency, low energy harmonic motions and the accelerations acting on the isolated building are significantly reduced. Several alternatives exist for the modeling of the isolators. This study is concerned with the use of a viscoelastic model to predict the seismic response of base-isolated buildings. The in-house finite element computer code has been modified to incorporate a viscoelastic spring element, and several simulations are performed. Then, the computed results have been compared with the corresponding observed data recorded at the test facility

A View on Future Building System Modeling and Simulation

Energy Technology Data Exchange (ETDEWEB)

Wetter, Michael

2011-04-01

This chapter presents what a future environment for building system modeling and simulation may look like. As buildings continue to require increased performance and better comfort, their energy and control systems are becoming more integrated and complex. We therefore focus in this chapter on the modeling, simulation and analysis of building energy and control systems. Such systems can be classified as heterogeneous systems because they involve multiple domains, such as thermodynamics, fluid dynamics, heat and mass transfer, electrical systems, control systems and communication systems. Also, they typically involve multiple temporal and spatial scales, and their evolution can be described by coupled differential equations, discrete equations and events. Modeling and simulating such systems requires a higher level of abstraction and modularisation to manage the increased complexity compared to what is used in today's building simulation programs. Therefore, the trend towards more integrated building systems is likely to be a driving force for changing the status quo of today's building simulation programs. Thischapter discusses evolving modeling requirements and outlines a path toward a future environment for modeling and simulation of heterogeneous building systems.A range of topics that would require many additional pages of discussion has been omitted. Examples include computational fluid dynamics for air and particle flow in and around buildings, people movement, daylight simulation, uncertainty propagation and optimisation methods for building design and controls. For different discussions and perspectives on the future of building modeling and simulation, we refer to Sahlin (2000), Augenbroe (2001) and Malkawi and Augenbroe (2004).

CFD methodology development for Singapore Green Mark Building application

NARCIS (Netherlands)

Chiu, P.H.; Raghavan, V.S.G.; Poh, H.J.; Tan, E.; Gabriela, O.; Wong, N.H.; van Hooff, T.; Blocken, B.; Li, R.; Leong-Kok, S.M.

2017-01-01

In the recent decade, investigation on the total building performance has become increasingly important for the environmental modelling community. With the advance of integrated design and modelling tool and Building Information Modelling (BIM) development, it is now possible to simulate and predict

Commercial Building Energy Baseline Modeling Software: Performance Metrics and Method Testing with Open Source Models and Implications for Proprietary Software Testing

Energy Technology Data Exchange (ETDEWEB)

Price, Phillip N.; Granderson, Jessica; Sohn, Michael; Addy, Nathan; Jump, David

2013-09-01

The overarching goal of this work is to advance the capabilities of technology evaluators in evaluating the building-level baseline modeling capabilities of Energy Management and Information System (EMIS) software. Through their customer engagement platforms and products, EMIS software products have the potential to produce whole-building energy savings through multiple strategies: building system operation improvements, equipment efficiency upgrades and replacements, and inducement of behavioral change among the occupants and operations personnel. Some offerings may also automate the quantification of whole-building energy savings, relative to a baseline period, using empirical models that relate energy consumption to key influencing parameters, such as ambient weather conditions and building operation schedule. These automated baseline models can be used to streamline the whole-building measurement and verification (M&V) process, and therefore are of critical importance in the context of multi-measure whole-building focused utility efficiency programs. This report documents the findings of a study that was conducted to begin answering critical questions regarding quantification of savings at the whole-building level, and the use of automated and commercial software tools. To evaluate the modeling capabilities of EMIS software particular to the use case of whole-building savings estimation, four research questions were addressed: 1. What is a general methodology that can be used to evaluate baseline model performance, both in terms of a) overall robustness, and b) relative to other models? 2. How can that general methodology be applied to evaluate proprietary models that are embedded in commercial EMIS tools? How might one handle practical issues associated with data security, intellectual property, appropriate testing ‘blinds’, and large data sets? 3. How can buildings be pre-screened to identify those that are the most model-predictable, and therefore those

Prediction-error variance in Bayesian model updating: a comparative study

Science.gov (United States)

Asadollahi, Parisa; Li, Jian; Huang, Yong

2017-04-01

In Bayesian model updating, the likelihood function is commonly formulated by stochastic embedding in which the maximum information entropy probability model of prediction error variances plays an important role and it is Gaussian distribution subject to the first two moments as constraints. The selection of prediction error variances can be formulated as a model class selection problem, which automatically involves a trade-off between the average data-fit of the model class and the information it extracts from the data. Therefore, it is critical for the robustness in the updating of the structural model especially in the presence of modeling errors. To date, three ways of considering prediction error variances have been seem in the literature: 1) setting constant values empirically, 2) estimating them based on the goodness-of-fit of the measured data, and 3) updating them as uncertain parameters by applying Bayes' Theorem at the model class level. In this paper, the effect of different strategies to deal with the prediction error variances on the model updating performance is investigated explicitly. A six-story shear building model with six uncertain stiffness parameters is employed as an illustrative example. Transitional Markov Chain Monte Carlo is used to draw samples of the posterior probability density function of the structure model parameters as well as the uncertain prediction variances. The different levels of modeling uncertainty and complexity are modeled through three FE models, including a true model, a model with more complexity, and a model with modeling error. Bayesian updating is performed for the three FE models considering the three aforementioned treatments of the prediction error variances. The effect of number of measurements on the model updating performance is also examined in the study. The results are compared based on model class assessment and indicate that updating the prediction error variances as uncertain parameters at the model

NASA Prediction of Worldwide Energy Resource High Resolution Meteorology Data For Sustainable Building Design

Science.gov (United States)

Chandler, William S.; Hoell, James M.; Westberg, David; Zhang, Taiping; Stackhouse, Paul W., Jr.

2013-01-01

A primary objective of NASA's Prediction of Worldwide Energy Resource (POWER) project is to adapt and infuse NASA's solar and meteorological data into the energy, agricultural, and architectural industries. Improvements are continuously incorporated when higher resolution and longer-term data inputs become available. Climatological data previously provided via POWER web applications were three-hourly and 1x1 degree latitude/longitude. The NASA Modern Era Retrospective-analysis for Research and Applications (MERRA) data set provides higher resolution data products (hourly and 1/2x1/2 degree) covering the entire globe. Currently POWER solar and meteorological data are available for more than 30 years on hourly (meteorological only), daily, monthly and annual time scales. These data may be useful to several renewable energy sectors: solar and wind power generation, agricultural crop modeling, and sustainable buildings. A recent focus has been working with ASHRAE to assess complementing weather station data with MERRA data. ASHRAE building design parameters being investigated include heating/cooling degree days and climate zones.

Bootstrap prediction and Bayesian prediction under misspecified models

OpenAIRE

Fushiki, Tadayoshi

2005-01-01

We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

Predictive Finite Rate Model for Oxygen-Carbon Interactions at High Temperature

Science.gov (United States)

Poovathingal, Savio

An oxidation model for carbon surfaces is developed to predict ablation rates for carbon heat shields used in hypersonic vehicles. Unlike existing empirical models, the approach used here was to probe gas-surface interactions individually and then based on an understanding of the relevant fundamental processes, build a predictive model that would be accurate over a wide range of pressures and temperatures, and even microstructures. Initially, molecular dynamics was used to understand the oxidation processes on the surface. The molecular dynamics simulations were compared to molecular beam experiments and good qualitative agreement was observed. The simulations reproduced cylindrical pitting observed in the experiments where oxidation was rapid and primarily occurred around a defect. However, the studies were limited to small systems at low temperatures and could simulate time scales only of the order of nanoseconds. Molecular beam experiments at high surface temperature indicated that a majority of surface reaction products were produced through thermal mechanisms. Since the reactions were thermal, they occurred over long time scales which were computationally prohibitive for molecular dynamics to simulate. The experiments provided detailed dynamical data on the scattering of O, O2, CO, and CO2 and it was found that the data from molecular beam experiments could be used directly to build a model. The data was initially used to deduce surface reaction probabilities at 800 K. The reaction probabilities were then incorporated into the direct simulation Monte Carlo (DSMC) method. Simulations were performed where the microstructure was resolved and dissociated oxygen convected and diffused towards it. For a gas-surface temperature of 800 K, it was found that despite CO being the dominant surface reaction product, a gas-phase reaction forms significant CO2 within the microstructure region. It was also found that surface area did not play any role in concentration of

Vision-based building energy diagnostics and retrofit analysis using 3D thermography and building information modeling

Science.gov (United States)

Ham, Youngjib

The emerging energy crisis in the building sector and the legislative measures on improving energy efficiency are steering the construction industry towards adopting new energy efficient design concepts and construction methods that decrease the overall energy loads. However, the problems of energy efficiency are not only limited to the design and construction of new buildings. Today, a significant amount of input energy in existing buildings is still being wasted during the operational phase. One primary source of the energy waste is attributed to unnecessary heat flows through building envelopes during hot and cold seasons. This inefficiency increases the operational frequency of heating and cooling systems to keep the desired thermal comfort of building occupants, and ultimately results in excessive energy use. Improving thermal performance of building envelopes can reduce the energy consumption required for space conditioning and in turn provide building occupants with an optimal thermal comfort at a lower energy cost. In this sense, energy diagnostics and retrofit analysis for existing building envelopes are key enablers for improving energy efficiency. Since proper retrofit decisions of existing buildings directly translate into energy cost saving in the future, building practitioners are increasingly interested in methods for reliable identification of potential performance problems so that they can take timely corrective actions. However, sensing what and where energy problems are emerging or are likely to emerge and then analyzing how the problems influence the energy consumption are not trivial tasks. The overarching goal of this dissertation focuses on understanding the gaps in knowledge in methods for building energy diagnostics and retrofit analysis, and filling these gaps by devising a new method for multi-modal visual sensing and analytics using thermography and Building Information Modeling (BIM). First, to address the challenges in scaling and

Energy policy for integrating the building environmental performance model of an air conditioned building in a subtropical climate

International Nuclear Information System (INIS)

Mui, K.W.

2006-01-01

For an air conditioned building, the major electricity consumption is by the heating, and air conditioning (HVAC) system. As energy saving strategies may be in conflict with the criteria of indoor air quality and thermal comfort, a concept of the building environmental performance model (BEPM) has been developed to optimize energy consumption in HVAC systems without any deterioration of the indoor air quality and thermal comfort. The BEPM is divided into two main modules: the adaptive comfort temperature (ACT) module and the new demand control ventilation (nDCV) module. This study aims to enhance and prompt the conventional operation of the air side systems by incorporating temperature reset with the adaptive comfort temperature control and the new demand control ventilation system in high rise buildings in Hong Kong. A new example weather year (1991) was established as a reference to compute the energy use of HVAC systems in buildings in order to obtain more representative data for predicting annual energy consumption. A survey of 165 Hong Kong office buildings was conducted and it provided valuable information on the existing HVAC design values in different grades of private commercial buildings in Hong Kong. It was found that the actual measured values of indoor temperature were lower than the design ones. Furthermore, with the new example weather year and the integration of the BEPM into Grade A private office buildings in Hong Kong, the total energy saving of the air conditioning systems was calculated (i.e. a saving of HK$122 million in electrical consumption per year) while the thermal comfort for the occupants was also maintained

Methodology for Modeling Building Energy Performance across the Commercial Sector

Energy Technology Data Exchange (ETDEWEB)

Griffith, B.; Long, N.; Torcellini, P.; Judkoff, R.; Crawley, D.; Ryan, J.

2008-03-01

This report uses EnergyPlus simulations of each building in the 2003 Commercial Buildings Energy Consumption Survey (CBECS) to document and demonstrate bottom-up methods of modeling the entire U.S. commercial buildings sector (EIA 2006). The ability to use a whole-building simulation tool to model the entire sector is of interest because the energy models enable us to answer subsequent 'what-if' questions that involve technologies and practices related to energy. This report documents how the whole-building models were generated from the building characteristics in 2003 CBECS and compares the simulation results to the survey data for energy use.

Heterotic model building: 16 special manifolds

International Nuclear Information System (INIS)

He, Yang-Hui; Lee, Seung-Joo; Lukas, Andre; Sun, Chuang

2014-01-01

We study heterotic model building on 16 specific Calabi-Yau manifolds constructed as hypersurfaces in toric four-folds. These 16 manifolds are the only ones among the more than half a billion manifolds in the Kreuzer-Skarke list with a non-trivial first fundamental group. We classify the line bundle models on these manifolds, both for SU(5) and SO(10) GUTs, which lead to consistent supersymmetric string vacua and have three chiral families. A total of about 29000 models is found, most of them corresponding to SO(10) GUTs. These models constitute a starting point for detailed heterotic model building on Calabi-Yau manifolds in the Kreuzer-Skarke list. The data for these models can be downloaded http://www-thphys.physics.ox.ac.uk/projects/CalabiYau/toricdata/index.html.

An expandable software model for collaborative decision making during the whole building life cycle

International Nuclear Information System (INIS)

Papamichael, K.; Pal, V.; Bourassa, N.; Loffeld, J.; Capeluto, G.

2000-01-01

Decisions throughout the life cycle of a building, from design through construction and commissioning to operation and demolition, require the involvement of multiple interested parties (e.g., architects, engineers, owners, occupants and facility managers). The performance of alternative designs and courses of action must be assessed with respect to multiple performance criteria, such as comfort, aesthetics, energy, cost and environmental impact. Several stand-alone computer tools are currently available that address specific performance issues during various stages of a building's life cycle. Some of these tools support collaboration by providing means for synchronous and asynchronous communications, performance simulations, and monitoring of a variety of performance parameters involved in decisions about a building during building operation. However, these tools are not linked in any way, so significant work is required to maintain and distribute information to all parties. In this paper we describe a software model that provides the data management and process control required for collaborative decision making throughout a building's life cycle. The requirements for the model are delineated addressing data and process needs for decision making at different stages of a building's life cycle. The software model meets these requirements and allows addition of any number of processes and support databases over time. What makes the model infinitely expandable is that it is a very generic conceptualization (or abstraction) of processes as relations among data. The software model supports multiple concurrent users, and facilitates discussion and debate leading to decision making. The software allows users to define rules and functions for automating tasks and alerting all participants to issues that need attention. It supports management of simulated as well as real data and continuously generates information useful for improving performance prediction and

NOAA ESRI Grid - seafloor hardbottom occurrence predictions model in New York offshore planning area from Biogeography Branch

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This dataset represents hard bottom occurrence predictions from a spatial model developed for the New York offshore spatial planning area. This model builds upon the...

Indoor Air Quality Building Education and Assessment Model

Science.gov (United States)

The Indoor Air Quality Building Education and Assessment Model (I-BEAM), released in 2002, is a guidance tool designed for use by building professionals and others interested in indoor air quality in commercial buildings.

Climate change and high-resolution whole-building numerical modelling

NARCIS (Netherlands)

Blocken, B.J.E.; Briggen, P.M.; Schellen, H.L.; Hensen, J.L.M.

2010-01-01

This paper briefly discusses the need of high-resolution whole-building numerical modelling in the context of climate change. High-resolution whole-building numerical modelling can be used for detailed analysis of the potential consequences of climate change on buildings and to evaluate remedial

Working group report: Flavor physics and model building

Indian Academy of Sciences (India)

cO Indian Academy of Sciences. Vol. ... This is the report of flavor physics and model building working group at ... those in model building have been primarily devoted to neutrino physics. ..... [12] Andrei Gritsan, ICHEP 2004, Beijing, China.

Application of 6D Building Information Model (6D BIM) for Business-storage Building in Slovenia

Science.gov (United States)

Pučko, Zoran; Vincek, Dražen; Štrukelj, Andrej; Šuman, Nataša

2017-10-01

The aim of this paper is to present an application of 6D building information modelling (6D BIM) on a real business-storage building in Slovenia. First, features of building maintenance in general are described according to the current Slovenian legislation, and also a general principle of BIM is given. After that, step-by-step activities for modelling 6D BIM are exposed, namely from Element list for maintenance, determination of their lifetime and service measures, cost analysing and time analysing to 6D BIM modelling. The presented 6D BIM model is designed in a unique way in which cost analysis is performed as 5D BIM model with linked data to use BIM Construction Project Management Software (Vico Office), integrated with 3D BIM model, whereas time analysis as 4D BIM model is carried out as non-linked data with the help of Excel (without connection to 3D BIM model). The paper is intended to serve as a guide to the building owners to prepare 6D BIM and to provide an insight into the relevant dynamic information about intervals and costs for execution of maintenance works in the whole building lifecycle.

Dispersion model for airborne radioactive particulates inside a process building

International Nuclear Information System (INIS)

Perkins, W.C.; Stoddard, D.H.

1984-02-01

An empirical model, predicting the spread of airborne radioactive particles after they are released inside a building, has been developed. The basis for this model is a composite of data for dispersion of airborne activity recorded during 12 case incidents. These incidents occurred at the Savannah River Plant (SRP) during approximately 90 plant-years of experience with the chemical and metallurgical processing of purified neptunium and plutonium. The model illustrates that the multiple-air-zone concept, used in the designs of many nuclear facilities, can be an efficient safety feature to limit the spread of airborne activity from a release. This study also provides some insight into an apparently anomalous behavior of airborne particulates, namely, their migration against the prevailing flow of ventilation air. 2 references, 12 figures, 4 tables

Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage

International Nuclear Information System (INIS)

Fiorentini, Massimo; Wall, Josh; Ma, Zhenjun; Braslavsky, Julio H.; Cooper, Paul

2017-01-01

Highlights: • A comprehensive approach to managing thermal energy in residential buildings. • Solar-assisted HVAC system with on-site energy generation and storage. • Mixed logic-dynamical building model identified using experimental data. • Design and implementation of a logic-dynamical model predictive control strategy. • MPC applied to the Net-Zero Energy house winner of the Solar Decathlon China 2013. - Abstract: This paper describes the development, implementation and experimental investigation of a Hybrid Model Predictive Control (HMPC) strategy to control solar-assisted heating, ventilation and air-conditioning (HVAC) systems with on-site thermal energy generation and storage. A comprehensive approach to the thermal energy management of a residential building is presented to optimise the scheduling of the available thermal energy resources to meet a comfort objective. The system has a hybrid nature with both continuous variables and discrete, logic-driven operating modes. The proposed control strategy is organized in two hierarchical levels. At the high-level, an HMPC controller with a 24-h prediction horizon and a 1-h control step is used to select the operating mode of the HVAC system. At the low-level, each operating mode is optimised using a 1-h rolling prediction horizon with a 5-min control step. The proposed control strategy has been practically implemented on the Building Management and Control System (BMCS) of a Net Zero-Energy Solar Decathlon house. This house features a sophisticated HVAC system comprising of an air-based photovoltaic thermal (PVT) collector and a phase change material (PCM) thermal storage integrated with the air-handling unit (AHU) of a ducted reverse-cycle heat pump system. The simulation and experimental results demonstrated the high performance achievable using an HMPC approach to optimising complex multimode HVAC systems in residential buildings, illustrating efficient selection of the appropriate operating modes

Predictive Modelling and Time: An Experiment in Temporal Archaeological Predictive Models

OpenAIRE

David Ebert

2006-01-01

One of the most common criticisms of archaeological predictive modelling is that it fails to account for temporal or functional differences in sites. However, a practical solution to temporal or functional predictive modelling has proven to be elusive. This article discusses temporal predictive modelling, focusing on the difficulties of employing temporal variables, then introduces and tests a simple methodology for the implementation of temporal modelling. The temporal models thus created ar...

Mathematical models for indoor radon prediction

International Nuclear Information System (INIS)

Malanca, A.; Pessina, V.; Dallara, G.

1995-01-01

It is known that the indoor radon (Rn) concentration can be predicted by means of mathematical models. The simplest model relies on two variables only: the Rn source strength and the air exchange rate. In the Lawrence Berkeley Laboratory (LBL) model several environmental parameters are combined into a complex equation; besides, a correlation between the ventilation rate and the Rn entry rate from the soil is admitted. The measurements were carried out using activated carbon canisters. Seventy-five measurements of Rn concentrations were made inside two rooms placed on the second floor of a building block. One of the rooms had a single-glazed window whereas the other room had a double pane window. During three different experimental protocols, the mean Rn concentration was always higher into the room with a double-glazed window. That behavior can be accounted for by the simplest model. A further set of 450 Rn measurements was collected inside a ground-floor room with a grounding well in it. This trend maybe accounted for by the LBL model

Prediction models and control algorithms for predictive applications of setback temperature in cooling systems

International Nuclear Information System (INIS)

Moon, Jin Woo; Yoon, Younju; Jeon, Young-Hoon; Kim, Sooyoung

2017-01-01

Highlights: • Initial ANN model was developed for predicting the time to the setback temperature. • Initial model was optimized for producing accurate output. • Optimized model proved its prediction accuracy. • ANN-based algorithms were developed and tested their performance. • ANN-based algorithms presented superior thermal comfort or energy efficiency. - Abstract: In this study, a temperature control algorithm was developed to apply a setback temperature predictively for the cooling system of a residential building during occupied periods by residents. An artificial neural network (ANN) model was developed to determine the required time for increasing the current indoor temperature to the setback temperature. This study involved three phases: development of the initial ANN-based prediction model, optimization and testing of the initial model, and development and testing of three control algorithms. The development and performance testing of the model and algorithm were conducted using TRNSYS and MATLAB. Through the development and optimization process, the final ANN model employed indoor temperature and the temperature difference between the current and target setback temperature as two input neurons. The optimal number of hidden layers, number of neurons, learning rate, and moment were determined to be 4, 9, 0.6, and 0.9, respectively. The tangent–sigmoid and pure-linear transfer function was used in the hidden and output neurons, respectively. The ANN model used 100 training data sets with sliding-window method for data management. Levenberg-Marquart training method was employed for model training. The optimized model had a prediction accuracy of 0.9097 root mean square errors when compared with the simulated results. Employing the ANN model, ANN-based algorithms maintained indoor temperatures better within target ranges. Compared to the conventional algorithm, the ANN-based algorithms reduced the duration of time, in which the indoor temperature

BIM-enabled Conceptual Modelling and Representation of Building Circulation

OpenAIRE

Lee, Jin Kook; Kim, Mi Jeong

2014-01-01

This paper describes how a building information modelling (BIM)-based approach for building circulation enables us to change the process of building design in terms of its computational representation and processes, focusing on the conceptual modelling and representation of circulation within buildings. BIM has been designed for use by several BIM authoring tools, in particular with the widely known interoperable industry foundation classes (IFCs), which follow an object-oriented data modelli...

Construction Worker Fatigue Prediction Model Based on System Dynamic

Directory of Open Access Journals (Sweden)

Wahyu Adi Tri Joko

2017-01-01

Full Text Available Construction accident can be caused by internal and external factors such as worker fatigue and unsafe project environment. Tight schedule of construction project forcing construction worker to work overtime in long period. This situation leads to worker fatigue. This paper proposes a model to predict construction worker fatigue based on system dynamic (SD. System dynamic is used to represent correlation among internal and external factors and to simulate level of worker fatigue. To validate the model, 93 construction workers whom worked in a high rise building construction projects, were used as case study. The result shows that excessive workload, working elevation and age, are the main factors lead to construction worker fatigue. Simulation result also shows that these factors can increase worker fatigue level to 21.2% times compared to normal condition. Beside predicting worker fatigue level this model can also be used as early warning system to prevent construction worker accident

Development of safety enhancement technology of containment building

International Nuclear Information System (INIS)

Seo, Jeong Moon; Choun, Y. S.; Choi, I. K.

2002-04-01

This study consists of four research areas, (1) Seismic safety assessment, (2) Aging assessment of a containment building, (3) Prediction of long-term behavior and analysis of a containment building, (4) Performance verification of a containment building. In the seismic safety assessment area, responses of a containment building were monitored and the analysis method was verified. Also performed are the identification of earthquake characteristics and improvement of the seismic fragility analysis method. In the area of aging assessment of a containment building, we developed aging management code SLMS and database. Aging tests were performed for containment building materials and aging models were developed. Techniques for investigation, detection, and evaluation of aging were developed. In the area of prediction of long-term behavior and analysis of a containment building, we developed a non-linear structural analysis code NUCAS and material models. In the area of performance verification of a containment building, we analyzed the crack behavior of a containment wall and the behavior of the containment under internal pressure. We also improved the ISI methods for prestressed containment

Whole-Building Hygrothermal Modeling in IEA Annex 41

DEFF Research Database (Denmark)

Rode, Carsten; Woloszyn, Monika

2007-01-01

. The IEA Annex 41 project runs from 2004–2007, coming to conclusion just before the Thermal Performance of the Exterior Envelopes of Whole Buildings X conference. The Annex 41 project and its Subtask 1 do not aim to produce one state-of-the-art hygrothermal simulation model for whole buildings, but rather...... the modeling, free scientific contributions have been invited from specific fields that need the most attention in order to better accomplish the integral building simulations. This paper will give an overview of the advances in whole-building hygrothermal simulation that have been accomplished and presented...

Modelling the effects of phase change materials on the energy use in buildings. Results of Experiments and System Dynamics Modelling

Energy Technology Data Exchange (ETDEWEB)

Prins, J.

2012-02-15

The current era is in need for more and more sustainable energy solutions. Phase Change Materials (PCM's) are a solution for a more sustainable build environment because they can help to reduce the energy use of buildings during heating and cooling of the indoor air. This paper presents the results of recent experiments that have been executed with test boxes. In addition a System Dynamics model has been developed to find out how PCM's can be used efficiently without testing in reality. The first experiment, in which PCM's were applied in a concrete floor, shows a reduction of peak temperatures with 4C {+-} 0.7C on maximum temperatures and over 1.5C {+-} 0.7C on minimum temperatures during warm periods. The model confirmed these findings, although the predicted reductions were slightly. During the second experiment more PCM's were applied by mounting them into the walls using gypsum plasterboard to increase the latent heat capacity. Remarkably, both the experimental set-up as the model showed that the increase of PCM's (of almost 98%) causes hardly any difference compared to the first situation. Adapting the exterior in a way to absorb more solar energy, increases the average indoor temperature but decreases the reduction of peak temperatures. Again the model confirmed these findings of the experiment. These results show that the effect of PCM's varies on different climatological contexts and with different construction components physics. This means no straight forward advice on the use of PCM's for a building design can be given. The solution for this problem is provided by the model, showing that the effects of PCM's can be modelled in order to use PCM's in an effective way in different climatological contexts and with different characteristics of construction components. The research shows that a simple model is already capable of predicting PCM performance in test boxes with reasonable accuracy. Therefore it can be

A Comparison of Two Strategies for Building an Exposure Prediction Model.

Science.gov (United States)

Heiden, Marina; Mathiassen, Svend Erik; Garza, Jennifer; Liv, Per; Wahlström, Jens

2016-01-01

Cost-efficient assessments of job exposures in large populations may be obtained from models in which 'true' exposures assessed by expensive measurement methods are estimated from easily accessible and cheap predictors. Typically, the models are built on the basis of a validation study comprising 'true' exposure data as well as an extensive collection of candidate predictors from questionnaires or company data, which cannot all be included in the models due to restrictions in the degrees of freedom available for modeling. In these situations, predictors need to be selected using procedures that can identify the best possible subset of predictors among the candidates. The present study compares two strategies for selecting a set of predictor variables. One strategy relies on stepwise hypothesis testing of associations between predictors and exposure, while the other uses cluster analysis to reduce the number of predictors without relying on empirical information about the measured exposure. Both strategies were applied to the same dataset on biomechanical exposure and candidate predictors among computer users, and they were compared in terms of identified predictors of exposure as well as the resulting model fit using bootstrapped resamples of the original data. The identified predictors were, to a large part, different between the two strategies, and the initial model fit was better for the stepwise testing strategy than for the clustering approach. Internal validation of the models using bootstrap resampling with fixed predictors revealed an equally reduced model fit in resampled datasets for both strategies. However, when predictor selection was incorporated in the validation procedure for the stepwise testing strategy, the model fit was reduced to the extent that both strategies showed similar model fit. Thus, the two strategies would both be expected to perform poorly with respect to predicting biomechanical exposure in other samples of computer users. © The

Dispersion model for airborne particulates inside a building

International Nuclear Information System (INIS)

Perkins, W.C.; Stoddard, D.H.

1985-01-01

An empirical model has been developed for the spread of airborne radioactive particles after they are released inside a building. The model has been useful in performing safety analyses of actinide materials facilities at the Savannah River Plant (SRP). These facilities employ the multiple-air-zone concept; that is, ventilation air flows from rooms or areas of least radioactive material hazard, through zones of increasing hazard, to a treatment system. A composite of the data for dispersion of airborne activity during 12 actual case incidents at SRP forms the basis for this model. These incidents occurred during approximately 90 plant-years of experience at SRP with the chemical and metallurgical processing of purified neptunium and plutonium after their recovery from irradiated uranium. The model gives ratios of the airborne activity concentrations in rooms and corridors near the site of the release. The multiple-air-zone concept has been applied to many designs of nuclear facilities as a safety feature to limit the spread of airborne activity from a release. The model illustrates the limitations of this concept: it predicts an apparently anomalous behavior of airborne particulates; namely, a small migration against the flow of the ventilation air

PREDICTION OF DENGUE FEVER EPIDEMIC SPREADING USING DYNAMICS TRANSMISSION VECTOR MODEL

Directory of Open Access Journals (Sweden)

Retno Widyaningrum

2014-05-01

Full Text Available Increasing number of dengue cases in Surabaya shows that its city has high potential of dengue fever epidemic. Although some policies were designed by Surabaya Health Department, such as fogging and mosquito’s nest eradication, but these efforts still out of target because of inaccurate predictions. Ineffectiveness eradication of dengue fever epidemic is caused by lack of information and knowledge on environmental conditions in Surabaya. Developing spread and prediction system to minimize dengue fever epidemic is necessary to be conducted immediately. Spread and prediction system can improve eradication and prevention accuracy. The transmission dynamics vector simulation will be used as an approach to draw a complex system ofmosquito life cycle in which involve a lot offactors. Dynamics transmission model used to build model in mosquito model (oviposition rate and pre adult mosquito, infected and death cases in dengue fever. The model of mosquito and infected population can represent system. The output of this research is website of spread and prediction system of dengue fever epidemics to predict growth rate of Aedes Aegypti mosquito, infected, and death population because of dengue fever epidemics. The deviation of infected population is 0,519. The model of death cases in dengue fever is less precision with the deviation 1,229. Death cases model need improvement by adding some variables that influence to dengue fever death cases. Spread ofdengue fever prediction will help the government, health department to decide the best policies in minimizing the spread ofdengue fever epidemics.

Experimental and analytical studies of a deeply embedded reactor building model considering soil-building interaction. Pt. 1

International Nuclear Information System (INIS)

Tanaka, H.; Ohta, T.; Uchiyama, S.

1979-01-01

The purpose of this paper is to describe the dynamic characteristics of a deeply embedded reactor building model derived from experimental and analytical studies which considers soil-building interaction behaviour. The model building is made of reinforced concrete. It has two stories above ground level and a basement, resting on sandy gravel layer at a depth of 3 meters. The backfill around the building was made to ground level. The model building is simplified and reduced to about one-fifteenth (1/15) of the prototype. It has bearing wall system for the basement and the first story, and frame system for the second. (orig.)

Characterization of the wind loads and flow fields around a gable-roof building model in tornado-like winds

Energy Technology Data Exchange (ETDEWEB)

Hu, Hui; Yang, Zifeng; Sarkar, Partha [Iowa State University, Department of Aerospace Engineering, Ames, IA (United States); Haan, Fred [Iowa State University, Department of Aerospace Engineering, Ames, IA (United States); Rose-Hulman Institute of Technology, Department of Mechanical Engineering, Terre Haute, IN (United States)

2011-09-15

An experimental study was conducted to quantify the characteristics of a tornado-like vortex and to reveal the dynamics of the flow-structure interactions between a low-rise, gable-roof building model and swirling, turbulent tornado-like winds. The experimental work was conducted by using a large-scale tornado simulator located in the Aerospace Engineering Department of Iowa State University. In addition to measuring the pressure distributions and resultant wind loads acting on the building model, a digital Particle Image Velocimetry system was used to conduct detailed flow field measurements to quantify the evolution of the unsteady vortices and turbulent flow structures around the gable-roof building model in tornado-like winds. The effects of important parameters, such as the distance between the centers of the tornado-like vortex and the test model and the orientation angles of the building model related to the tornado-like vortex, on the evolutions of the wake vortices and turbulent flow structures around the gable-roof building model as well as the wind loads induced by the tornado-like vortex were assessed quantitatively. The detailed flow field measurements were correlated with the surface pressure and wind load measurements to elucidate the underlying physics to gain further insight into flow-structure interactions between the gable-roof building model and tornado-like winds in order to provide more accurate prediction of wind damage potential to built structures. (orig.)

A Prediction Model for ROS1-Rearranged Lung Adenocarcinomas based on Histologic Features

OpenAIRE

Zhou, Jianya; Zhao, Jing; Zheng, Jing; Kong, Mei; Sun, Ke; Wang, Bo; Chen, Xi; Ding, Wei; Zhou, Jianying

2016-01-01

Aims To identify the clinical and histological characteristics of ROS1-rearranged non-small-cell lung carcinomas (NSCLCs) and build a prediction model to prescreen suitable patients for molecular testing. Methods and Results We identified 27 cases of ROS1-rearranged lung adenocarcinomas in 1165 patients with NSCLCs confirmed by real-time PCR and FISH and performed univariate and multivariate analyses to identify predictive factors associated with ROS1 rearrangement and finally developed predi...

A Probabilistic Model for Exteriors of Residential Buildings

KAUST Repository

Fan, Lubin; Wonka, Peter

2016-01-01

We propose a new framework to model the exterior of residential buildings. The main goal of our work is to design a model that can be learned from data that is observable from the outside of a building and that can be trained with widely available

Modeling urban building energy use: A review of modeling approaches and procedures

Energy Technology Data Exchange (ETDEWEB)

Li, Wenliang; Zhou, Yuyu; Cetin, Kristen; Eom, Jiyong; Wang, Yu; Chen, Gang; Zhang, Xuesong

2017-12-01

With rapid urbanization and economic development, the world has been experiencing an unprecedented increase in energy consumption and greenhouse gas (GHG) emissions. While reducing energy consumption and GHG emissions is a common interest shared by major developed and developing countries, actions to enable these global reductions are generally implemented at the city scale. This is because baseline information from individual cities plays an important role in identifying economical options for improving building energy efficiency and reducing GHG emissions. Numerous approaches have been proposed for modeling urban building energy use in the past decades. This paper aims to provide an up-to-date review of the broad categories of energy models for urban buildings and describes the basic workflow of physics-based, bottom-up models and their applications in simulating urban-scale building energy use. Because there are significant differences across models with varied potential for application, strengths and weaknesses of the reviewed models are also presented. This is followed by a discussion of challenging issues associated with model preparation and calibration.

Economic aspects and models for building codes

DEFF Research Database (Denmark)

Bonke, Jens; Pedersen, Dan Ove; Johnsen, Kjeld

It is the purpose of this bulletin to present an economic model for estimating the consequence of new or changed building codes. The object is to allow comparative analysis in order to improve the basis for decisions in this field. The model is applied in a case study.......It is the purpose of this bulletin to present an economic model for estimating the consequence of new or changed building codes. The object is to allow comparative analysis in order to improve the basis for decisions in this field. The model is applied in a case study....

Research on the Prediction Model of CPU Utilization Based on ARIMA-BP Neural Network

Directory of Open Access Journals (Sweden)

Wang Jina

2016-01-01

Full Text Available The dynamic deployment technology of the virtual machine is one of the current cloud computing research focuses. The traditional methods mainly work after the degradation of the service performance that usually lag. To solve the problem a new prediction model based on the CPU utilization is constructed in this paper. A reference offered by the new prediction model of the CPU utilization is provided to the VM dynamic deployment process which will speed to finish the deployment process before the degradation of the service performance. By this method it not only ensure the quality of services but also improve the server performance and resource utilization. The new prediction method of the CPU utilization based on the ARIMA-BP neural network mainly include four parts: preprocess the collected data, build the predictive model of ARIMA-BP neural network, modify the nonlinear residuals of the time series by the BP prediction algorithm and obtain the prediction results by analyzing the above data comprehensively.

Intelligent seismic risk mitigation system on structure building

Science.gov (United States)

Suryanita, R.; Maizir, H.; Yuniorto, E.; Jingga, H.

2018-01-01

Indonesia located on the Pacific Ring of Fire, is one of the highest-risk seismic zone in the world. The strong ground motion might cause catastrophic collapse of the building which leads to casualties and property damages. Therefore, it is imperative to properly design the structural response of building against seismic hazard. Seismic-resistant building design process requires structural analysis to be performed to obtain the necessary building responses. However, the structural analysis could be very difficult and time consuming. This study aims to predict the structural response includes displacement, velocity, and acceleration of multi-storey building with the fixed floor plan using Artificial Neural Network (ANN) method based on the 2010 Indonesian seismic hazard map. By varying the building height, soil condition, and seismic location in 47 cities in Indonesia, 6345 data sets were obtained and fed into the ANN model for the learning process. The trained ANN can predict the displacement, velocity, and acceleration responses with up to 96% of predicted rate. The trained ANN architecture and weight factors were later used to build a simple tool in Visual Basic program which possesses the features for prediction of structural response as mentioned previously.

Active load management in an intelligent building using model predictive control strategy

DEFF Research Database (Denmark)

Zong, Yi; Kullmann, Daniel; Thavlov, Anders

2011-01-01

This paper introduces PowerFlexHouse, a research facility for exploring the technical potential of active load management in a distributed power system (SYSLAB) with a high penetration of renewable energy and presents in detail on how to implement a thermal model predictive controller for load...... shifting in PowerFlexHouse heaters' power consumption scheme. With this demand side control study, it is expected that this method of demand response can dramatically raise energy efficiencies and improve grid reliability, when there is a high penetration of intermittent energy resources in the power...

Dynamic analysis of reactor containment building using axisymmetric finite element model

International Nuclear Information System (INIS)

Thakkar, S.K.; Dubey, R.N.

1989-01-01

The structural safety of nuclear reactor building during earthquake is of great importance in view of possibility of radiation hazards. The rational evaluation of forces and displacements in various portions of structure and foundation during strong ground motion is most important for safe performance and economic design of the reactor building. The accuracy of results of dynamic analysis is naturally dependent on the type of mathematical model employed. Three types of mathematical models are employed for dynamic analysis of reactor building beam model axisymmetric finite element model and three dimensional model. In this paper emphasis is laid on axisymmetric model. This model of containment building is considered a reinfinement over conventional beam model of the structure. The nuclear reactor building on a rocky foundation is considered herein. The foundation-structure interaction is relatively less in this condition. The objective of the paper is to highlight the significance of modelling of non-axisymmetric portion of building, such as reactor internals by equivalent axisymmetric body, on the structural response of the building

THE EFFECT OF BUILDING FAÇADE MODEL ON LIGHT DISTRIBUTION (CASE STUDY: MENARA PHINISI BUILDING OF UNM

Directory of Open Access Journals (Sweden)

Nurul Jamala

2017-12-01

Full Text Available Global warming issues influence the temperature of the earth surface. It is an impact on energy consumption, especially in buildings. Utilization of daylight is one of the factors that need to be considered, in order to minimize energy consumption as a source of artificial lighting. This study analyzed the distribution of light on the Menara Phinisi building of Makassar State University. Quantitative research method that is to describe the data of simulation in Autodesk Ecotect program. The research objective was to determine the effect of the building facade model on the value of illumination inside the building. Results of the study concluded that the decrease percentage of the distribution of light on the building facade using and not using the facade is 3,16% or 236 lux. Distribution of light in horizontal and diagonal facade models differ in the amount of 2,5%. Design analysis of the building serves as a guide for analyzing the influence of the building facade model so that it can create energy efficient buildings.

Rolling force prediction for strip casting using theoretical model and artificial intelligence

Institute of Scientific and Technical Information of China (English)

CAO Guang-ming; LI Cheng-gang; ZHOU Guo-ping; LIU Zhen-yu; WU Di; WANG Guo-dong; LIU Xiang-hua

2010-01-01

Rolling force for strip casting of 1Cr17 ferritic stainless steel was predicted using theoretical model and artificial intelligence.Solution zone was classified into two parts by kiss point position during casting strip.Navier-Stokes equation in fluid mechanics and stream function were introduced to analyze the rheological property of liquid zone and mushy zone,and deduce the analytic equation of unit compression stress distribution.The traditional hot rolling model was still used in the solid zone.Neural networks based on feedforward training algorithm in Bayesian regularization were introduced to build model for kiss point position.The results show that calculation accuracy for verification data of 94.67% is in the range of+7.0%,which indicates that the predicting accuracy of this model is very high.

Building a risk-targeted regional seismic hazard model for South-East Asia

Science.gov (United States)

Woessner, J.; Nyst, M.; Seyhan, E.

2015-12-01

The last decade has tragically shown the social and economic vulnerability of countries in South-East Asia to earthquake hazard and risk. While many disaster mitigation programs and initiatives to improve societal earthquake resilience are under way with the focus on saving lives and livelihoods, the risk management sector is challenged to develop appropriate models to cope with the economic consequences and impact on the insurance business. We present the source model and ground motions model components suitable for a South-East Asia earthquake risk model covering Indonesia, Malaysia, the Philippines and Indochine countries. The source model builds upon refined modelling approaches to characterize 1) seismic activity from geologic and geodetic data on crustal faults and 2) along the interface of subduction zones and within the slabs and 3) earthquakes not occurring on mapped fault structures. We elaborate on building a self-consistent rate model for the hazardous crustal fault systems (e.g. Sumatra fault zone, Philippine fault zone) as well as the subduction zones, showcase some characteristics and sensitivities due to existing uncertainties in the rate and hazard space using a well selected suite of ground motion prediction equations. Finally, we analyze the source model by quantifying the contribution by source type (e.g., subduction zone, crustal fault) to typical risk metrics (e.g.,return period losses, average annual loss) and reviewing their relative impact on various lines of businesses.

Model for Refurbishment of Heritage Buildings

DEFF Research Database (Denmark)

Rasmussen, Torben Valdbjørn

2014-01-01

the Heritage Agency, the Danish Working Environment Authority and the owner as a team cooperated in identifying feasible refurbishments. In this case, the focus centered on restoring and identifying potential energy savings and deciding on energy upgrading measures for the listed complex. The refurbished...... with the requirements for the use of the building. The model focuses on the cooperation and dialogue between authorities and owners, who refurbish heritage buildings. The developed model was used for the refurbishment of the listed complex, Fæstningens Materialgård. Fæstningens Materialgård is a case study where...

Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications.

Science.gov (United States)

Cichosz, Simon Lebech; Johansen, Mette Dencker; Hejlesen, Ole

2015-10-14

Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short- and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies. © 2015 Diabetes Technology Society.

BIM-Enabled Conceptual Modelling and Representation of Building Circulation

Directory of Open Access Journals (Sweden)

Jin Kook Lee

2014-08-01

Full Text Available This paper describes how a building information modelling (BIM-based approach for building circulation enables us to change the process of building design in terms of its computational representation and processes, focusing on the conceptual modelling and representation of circulation within buildings. BIM has been designed for use by several BIM authoring tools, in particular with the widely known interoperable industry foundation classes (IFCs, which follow an object-oriented data modelling methodology. Advances in BIM authoring tools, using space objects and their relations defined in an IFC's schema, have made it possible to model, visualize and analyse circulation within buildings prior to their construction. Agent-based circulation has long been an interdisciplinary topic of research across several areas, including design computing, computer science, architectural morphology, human behaviour and environmental psychology. Such conventional approaches to building circulation are centred on navigational knowledge about built environments, and represent specific circulation paths and regulations. This paper, however, places emphasis on the use of ‘space objects’ in BIM-enabled design processes rather than on circulation agents, the latter of which are not defined in the IFCs' schemas. By introducing and reviewing some associated research and projects, this paper also surveys how such a circulation representation is applicable to the analysis of building circulation-related rules.

Modelling the heat dynamics of a monitored Test Reference Environment for Building Integrated Photovoltaic systems using stochastic differential equations

DEFF Research Database (Denmark)

Lodi, C.; Bacher, Peder; Cipriano, J.

2012-01-01

reduce the ventilation thermal losses of the building by pre-heating the fresh air. Furthermore, by decreasing PV module temperature, the ventilation air heat extraction can simultaneously increase electrical and thermal energy production of the building. A correct prediction of the PV module temperature...... and heat transfer coefficients is fundamental in order to improve the thermo-electrical production.The considered grey-box models are composed of a set of continuous time stochastic differential equations, holding the physical description of the system, combined with a set of discrete time measurement......This paper deals with grey-box modelling of the energy transfer of a double skin Building Integrated Photovoltaic (BIPV) system. Grey-box models are based on a combination of prior physical knowledge and statistics, which enable identification of the unknown parameters in the system and accurate...

Building Models to Predict Hint-or-Attempt Actions of Students

Science.gov (United States)

Castro, Francisco Enrique Vicente; Adjei, Seth; Colombo, Tyler; Heffernan, Neil

2015-01-01

A great deal of research in educational data mining is geared towards predicting student performance. Bayesian Knowledge Tracing, Performance Factors Analysis, and the different variations of these have been introduced and have had some success at predicting student knowledge. It is worth noting, however, that very little has been done to…

Modelling energy demand in the buildings sector within the EU

Energy Technology Data Exchange (ETDEWEB)

O Broin, Eoin

2012-11-01

fuel mixes are applied in three scenarios. The rates for expansion of floor area and increases in living standards are the same for all the scenarios. The model outputs predict that if energy efficiency remains at the current level, then expansion of the building floor area and other increases in living standards would increase final energy demand in the EU by almost 70 % by 2050. The other two scenarios reveal the levels of improvements in efficiency that are needed to maintain energy demand at current rates or reduce it by 20 %. The results of the modelling provide a conceptual framework for the development of fiscal and regulatory policy decisions in relation to energy prices and various categories of energy efficiency measures, with the overall objective of meeting future demand for energy services of the building sector within the EU in a sustainable manner.

Window opening behaviour: simulations of occupant behaviour in residential buildings using models based on a field survey

DEFF Research Database (Denmark)

Valentina, Fabi; Andersen, Rune Korsholm; Corgnati, Stefano Paolo

2012-01-01

Window opening behaviour has been shown to have a significant impact on airflow rates and hence energy consumption. Nevertheless, the inhabitant behaviour related to window opening in residential buildings is currently poorly investigated through both field surveys and building energy simulations....... In particular, reliable information regarding user behaviour in residential buildings is crucial for suitable prediction of building performance (energy consumption, indoor environmental quality, etc.). To face this issue, measurements of indoor climate and outdoor environmental parameters and window “opening...... and closing” actions were performed in 15 dwellings from January to August 2008 in Denmark. Probabilistic models of inhabitants’ window “opening and closing” behaviour were developed and implemented in the energy simulation software IDA ICE to improve window opening and closing strategies in simulations...

A model predictive framework of Ground Source Heat Pump coupled with Aquifer Thermal Energy Storage System in heating and cooling equipment of a building

NARCIS (Netherlands)

Rostampour Samarin, V.; Bloemendal, J.M.; Keviczky, T.

2017-01-01

This paper presents a complete model of a building heating and cooling equipment and a ground source heat pump (GSHP) coupled with an aquifer thermal energy storage (ATES) system. This model contains detailed
mathematical representations of building thermal dynamics, ATES system dynamics, heat

Construction of ground-state preserving sparse lattice models for predictive materials simulations

Science.gov (United States)

Huang, Wenxuan; Urban, Alexander; Rong, Ziqin; Ding, Zhiwei; Luo, Chuan; Ceder, Gerbrand

2017-08-01

First-principles based cluster expansion models are the dominant approach in ab initio thermodynamics of crystalline mixtures enabling the prediction of phase diagrams and novel ground states. However, despite recent advances, the construction of accurate models still requires a careful and time-consuming manual parameter tuning process for ground-state preservation, since this property is not guaranteed by default. In this paper, we present a systematic and mathematically sound method to obtain cluster expansion models that are guaranteed to preserve the ground states of their reference data. The method builds on the recently introduced compressive sensing paradigm for cluster expansion and employs quadratic programming to impose constraints on the model parameters. The robustness of our methodology is illustrated for two lithium transition metal oxides with relevance for Li-ion battery cathodes, i.e., Li2xFe2(1-x)O2 and Li2xTi2(1-x)O2, for which the construction of cluster expansion models with compressive sensing alone has proven to be challenging. We demonstrate that our method not only guarantees ground-state preservation on the set of reference structures used for the model construction, but also show that out-of-sample ground-state preservation up to relatively large supercell size is achievable through a rapidly converging iterative refinement. This method provides a general tool for building robust, compressed and constrained physical models with predictive power.

Indoor Air Quality Building Education and Assessment Model Forms

Science.gov (United States)

The Indoor Air Quality Building Education and Assessment Model (I-BEAM) is a guidance tool designed for use by building professionals and others interested in indoor air quality in commercial buildings.

Building optimal regression tree by ant colony system-genetic algorithm: Application to modeling of melting points

Energy Technology Data Exchange (ETDEWEB)

Hemmateenejad, Bahram, E-mail: hemmatb@sums.ac.ir [Department of Chemistry, Shiraz University, Shiraz (Iran, Islamic Republic of); Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of); Shamsipur, Mojtaba [Department of Chemistry, Razi University, Kermanshah (Iran, Islamic Republic of); Zare-Shahabadi, Vali [Young Researchers Club, Mahshahr Branch, Islamic Azad University, Mahshahr (Iran, Islamic Republic of); Akhond, Morteza [Department of Chemistry, Shiraz University, Shiraz (Iran, Islamic Republic of)

2011-10-17

Highlights: {yields} Ant colony systems help to build optimum classification and regression trees. {yields} Using of genetic algorithm operators in ant colony systems resulted in more appropriate models. {yields} Variable selection in each terminal node of the tree gives promising results. {yields} CART-ACS-GA could model the melting point of organic materials with prediction errors lower than previous models. - Abstract: The classification and regression trees (CART) possess the advantage of being able to handle large data sets and yield readily interpretable models. A conventional method of building a regression tree is recursive partitioning, which results in a good but not optimal tree. Ant colony system (ACS), which is a meta-heuristic algorithm and derived from the observation of real ants, can be used to overcome this problem. The purpose of this study was to explore the use of CART and its combination with ACS for modeling of melting points of a large variety of chemical compounds. Genetic algorithm (GA) operators (e.g., cross averring and mutation operators) were combined with ACS algorithm to select the best solution model. In addition, at each terminal node of the resulted tree, variable selection was done by ACS-GA algorithm to build an appropriate partial least squares (PLS) model. To test the ability of the resulted tree, a set of approximately 4173 structures and their melting points were used (3000 compounds as training set and 1173 as validation set). Further, an external test set containing of 277 drugs was used to validate the prediction ability of the tree. Comparison of the results obtained from both trees showed that the tree constructed by ACS-GA algorithm performs better than that produced by recursive partitioning procedure.

Unstructured meshing and parameter estimation for urban dam-break flood modeling: building treatments and implications for accuracy and efficiency

Science.gov (United States)

Schubert, J. E.; Sanders, B. F.

2011-12-01

Urban landscapes are at the forefront of current research efforts in the field of flood inundation modeling for two major reasons. First, urban areas hold relatively large economic and social importance and as such it is imperative to avoid or minimize future damages. Secondly, urban flooding is becoming more frequent as a consequence of continued development of impervious surfaces, population growth in cities, climate change magnifying rainfall intensity, sea level rise threatening coastal communities, and decaying flood defense infrastructure. In reality urban landscapes are particularly challenging to model because they include a multitude of geometrically complex features. Advances in remote sensing technologies and geographical information systems (GIS) have promulgated fine resolution data layers that offer a site characterization suitable for urban inundation modeling including a description of preferential flow paths, drainage networks and surface dependent resistances to overland flow. Recent research has focused on two-dimensional modeling of overland flow including within-curb flows and over-curb flows across developed parcels. Studies have focused on mesh design and parameterization, and sub-grid models that promise improved performance relative to accuracy and/or computational efficiency. This presentation addresses how fine-resolution data, available in Los Angeles County, are used to parameterize, initialize and execute flood inundation models for the 1963 Baldwin Hills dam break. Several commonly used model parameterization strategies including building-resistance, building-block and building hole are compared with a novel sub-grid strategy based on building-porosity. Performance of the models is assessed based on the accuracy of depth and velocity predictions, execution time, and the time and expertise required for model set-up. The objective of this study is to assess field-scale applicability, and to obtain a better understanding of advantages

PARAMO: a PARAllel predictive MOdeling platform for healthcare analytic research using electronic health records.

Science.gov (United States)

Ng, Kenney; Ghoting, Amol; Steinhubl, Steven R; Stewart, Walter F; Malin, Bradley; Sun, Jimeng

2014-04-01

Healthcare analytics research increasingly involves the construction of predictive models for disease targets across varying patient cohorts using electronic health records (EHRs). To facilitate this process, it is critical to support a pipeline of tasks: (1) cohort construction, (2) feature construction, (3) cross-validation, (4) feature selection, and (5) classification. To develop an appropriate model, it is necessary to compare and refine models derived from a diversity of cohorts, patient-specific features, and statistical frameworks. The goal of this work is to develop and evaluate a predictive modeling platform that can be used to simplify and expedite this process for health data. To support this goal, we developed a PARAllel predictive MOdeling (PARAMO) platform which (1) constructs a dependency graph of tasks from specifications of predictive modeling pipelines, (2) schedules the tasks in a topological ordering of the graph, and (3) executes those tasks in parallel. We implemented this platform using Map-Reduce to enable independent tasks to run in parallel in a cluster computing environment. Different task scheduling preferences are also supported. We assess the performance of PARAMO on various workloads using three datasets derived from the EHR systems in place at Geisinger Health System and Vanderbilt University Medical Center and an anonymous longitudinal claims database. We demonstrate significant gains in computational efficiency against a standard approach. In particular, PARAMO can build 800 different models on a 300,000 patient data set in 3h in parallel compared to 9days if running sequentially. This work demonstrates that an efficient parallel predictive modeling platform can be developed for EHR data. This platform can facilitate large-scale modeling endeavors and speed-up the research workflow and reuse of health information. This platform is only a first step and provides the foundation for our ultimate goal of building analytic pipelines

Modelling Technology for Building Fire Scene with Virtual Geographic Environment

Science.gov (United States)

Song, Y.; Zhao, L.; Wei, M.; Zhang, H.; Liu, W.

2017-09-01

Building fire is a risky activity that can lead to disaster and massive destruction. The management and disposal of building fire has always attracted much interest from researchers. Integrated Virtual Geographic Environment (VGE) is a good choice for building fire safety management and emergency decisions, in which a more real and rich fire process can be computed and obtained dynamically, and the results of fire simulations and analyses can be much more accurate as well. To modelling building fire scene with VGE, the application requirements and modelling objective of building fire scene were analysed in this paper. Then, the four core elements of modelling building fire scene (the building space environment, the fire event, the indoor Fire Extinguishing System (FES) and the indoor crowd) were implemented, and the relationship between the elements was discussed also. Finally, with the theory and framework of VGE, the technology of building fire scene system with VGE was designed within the data environment, the model environment, the expression environment, and the collaborative environment as well. The functions and key techniques in each environment are also analysed, which may provide a reference for further development and other research on VGE.

Automatic Generation of 3D Building Models with Multiple Roofs

Institute of Scientific and Technical Information of China (English)

Kenichi Sugihara; Yoshitugu Hayashi

2008-01-01

Based on building footprints (building polygons) on digital maps, we are proposing the GIS and CG integrated system that automatically generates 3D building models with multiple roofs. Most building polygons' edges meet at right angles (orthogonal polygon). The integrated system partitions orthogonal building polygons into a set of rectangles and places rectangular roofs and box-shaped building bodies on these rectangles. In order to partition an orthogonal polygon, we proposed a useful polygon expression in deciding from which vertex a dividing line is drawn. In this paper, we propose a new scheme for partitioning building polygons and show the process of creating 3D roof models.

A Unified Building Model for 3D Urban GIS

Directory of Open Access Journals (Sweden)

Ihab Hijazi

2012-07-01

Full Text Available Several tasks in urban and architectural design are today undertaken in a geospatial context. Building Information Models (BIM and geospatial technologies offer 3D data models that provide information about buildings and the surrounding environment. The Industry Foundation Classes (IFC and CityGML are today the two most prominent semantic models for representation of BIM and geospatial models respectively. CityGML has emerged as a standard for modeling city models while IFC has been developed as a reference model for building objects and sites. Current CAD and geospatial software provide tools that allow the conversion of information from one format to the other. These tools are however fairly limited in their capabilities, often resulting in data and information losses in the transformations. This paper describes a new approach for data integration based on a unified building model (UBM which encapsulates both the CityGML and IFC models, thus avoiding translations between the models and loss of information. To build the UBM, all classes and related concepts were initially collected from both models, overlapping concepts were merged, new objects were created to ensure the capturing of both indoor and outdoor objects, and finally, spatial relationships between the objects were redefined. Unified Modeling Language (UML notations were used for representing its objects and relationships between them. There are two use-case scenarios, both set in a hospital: “evacuation” and “allocating spaces for patient wards” were developed to validate and test the proposed UBM data model. Based on these two scenarios, four validation queries were defined in order to validate the appropriateness of the proposed unified building model. It has been validated, through the case scenarios and four queries, that the UBM being developed is able to integrate CityGML data as well as IFC data in an apparently seamless way. Constraints and enrichment functions are

Techniques for building timing-predictable embedded systems

CERN Document Server

Guan, Nan

2016-01-01

This book describes state-of-the-art techniques for designing real-time computer systems. The author shows how to estimate precisely the effect of cache architecture on the execution time of a program, how to dispatch workload on multicore processors to optimize resources, while meeting deadline constraints, and how to use closed-form mathematical approaches to characterize highly variable workloads and their interaction in a networked environment. Readers will learn how to deal with unpredictable timing behaviors of computer systems on different levels of system granularity and abstraction. Introduces promising techniques for dealing with challenges associated with deploying real-time systems on multicore platforms; Provides a complete picture of building timing-predictable computer systems, at the program level, component level and system level; Leverages different levels of abstraction to deal with the complexity of the analysis.

Alternatives to quintessence model building

International Nuclear Information System (INIS)

Avelino, P.P.; Beca, L.M.G.; Pinto, P.; Carvalho, J.P.M. de; Martins, C.J.A.P.

2003-01-01

We discuss the issue of toy model building for the dark energy component of the universe. Specifically, we consider two generic toy models recently proposed as alternatives to quintessence models, respectively known as Cardassian expansion and the Chaplygin gas. We show that the former is entirely equivalent to a class of quintessence models. We determine the observational constraints on the latter, coming from recent supernovae results and from the shape of the matter power spectrum. As expected, these restrict the model to a behavior that closely matches that of a standard cosmological constant Λ

Performance and robustness of hybrid model predictive control for controllable dampers in building models

Science.gov (United States)

Johnson, Erik A.; Elhaddad, Wael M.; Wojtkiewicz, Steven F.

2016-04-01

A variety of strategies have been developed over the past few decades to determine controllable damping device forces to mitigate the response of structures and mechanical systems to natural hazards and other excitations. These "smart" damping devices produce forces through passive means but have properties that can be controlled in real time, based on sensor measurements of response across the structure, to dramatically reduce structural motion by exploiting more than the local "information" that is available to purely passive devices. A common strategy is to design optimal damping forces using active control approaches and then try to reproduce those forces with the smart damper. However, these design forces, for some structures and performance objectives, may achieve high performance by selectively adding energy, which cannot be replicated by a controllable damping device, causing the smart damper performance to fall far short of what an active system would provide. The authors have recently demonstrated that a model predictive control strategy using hybrid system models, which utilize both continuous and binary states (the latter to capture the switching behavior between dissipative and non-dissipative forces), can provide reductions in structural response on the order of 50% relative to the conventional clipped-optimal design strategy. This paper explores the robustness of this newly proposed control strategy through evaluating controllable damper performance when the structure model differs from the nominal one used to design the damping strategy. Results from the application to a two-degree-of-freedom structure model confirms the robustness of the proposed strategy.

Antibody structural modeling with prediction of immunoglobulin structure (PIGS)

DEFF Research Database (Denmark)

Marcatili, Paolo; Olimpieri, Pier Paolo; Chailyan, Anna

2014-01-01

Antibodies (or immunoglobulins) are crucial for defending organisms from pathogens, but they are also key players in many medical, diagnostic and biotechnological applications. The ability to predict their structure and the specific residues involved in antigen recognition has several useful...... applications in all of these areas. Over the years, we have developed or collaborated in developing a strategy that enables researchers to predict the 3D structure of antibodies with a very satisfactory accuracy. The strategy is completely automated and extremely fast, requiring only a few minutes (∼10 min...... on average) to build a structural model of an antibody. It is based on the concept of canonical structures of antibody loops and on our understanding of the way light and heavy chains pack together....

Modelling the distribution of 222Rn concentration in a multi level, general purpose building

International Nuclear Information System (INIS)

Toro, Laszlo; Noditi, Mihaela; Gheorghe, Raluca; Gheorghe, Dan

2008-01-01

The importance of 222 Rn (radon) in the indoor air related to the exposure form natural sources is relatively well documented. About 30% of the individual effective dose from natural sources is coming from the inhalation of 222 Rn and his short lived daughters. In unfavorable conditions given by the soil porosity and the existence of upward air movement in the soil there is a possibility to have unusually high radon concentration in houses even on soil with 'normal' 226 Ra content. Some construction solutions (high indoor spaces) should generate a significant indoor-outdoor negative pressure differences and consequently upward air currents (stack effect) which will facilitate the entrance of radon in the building. This effect will multiply the possibility of migration of radon in the building. The difficulty of the prediction of radon migration in the soil-building system increase the importance of the mathematical modelling of the behavior of radon-soil emission, infiltration and migration in the building - in areas with high radon potential. For one level simple buildings there are several models in the literature but the information regarding the multilevel building models are relatively scarce. Two different approaches used to describe the behavior of the radon gas in large (mainly high) buildings have been analyzed: Direct approach: computational fluid dynamics, solving the transport equations for the whole building (the domain of the solution of the transport and flow equations is delimited by the building envelope - the external walls); the openings (internal and external) and ventilation are defined by the boundary conditions. This approach is quite complex, the equations are solved (numerically) for highly inhomogeneous medium but is based on the fundamental processes governing the transport. In the same time it gives the possibility to obtain a concentration pattern in every part of the building. Multi-zone approach treating the building as interconnected

Updating of a dynamic finite element model from the Hualien scale model reactor building

International Nuclear Information System (INIS)

Billet, L.; Moine, P.; Lebailly, P.

1996-08-01

The forces occurring at the soil-structure interface of a building have generally a large influence on the way the building reacts to an earthquake. One can be tempted to characterise these forces more accurately bu updating a model from the structure. However, this procedure requires an updating method suitable for dissipative models, since significant damping can be observed at the soil-structure interface of buildings. Such a method is presented here. It is based on the minimization of a mechanical energy built from the difference between Eigen data calculated bu the model and Eigen data issued from experimental tests on the real structure. An experimental validation of this method is then proposed on a model from the HUALIEN scale-model reactor building. This scale-model, built on the HUALIEN site of TAIWAN, is devoted to the study of soil-structure interaction. The updating concerned the soil impedances, modelled by a layer of springs and viscous dampers attached to the building foundation. A good agreement was found between the Eigen modes and dynamic responses calculated bu the updated model and the corresponding experimental data. (authors). 12 refs., 3 figs., 4 tabs

Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables

Directory of Open Access Journals (Sweden)

Feng Zhong-xiang

2014-01-01

Full Text Available In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

Combined prediction model of death toll for road traffic accidents based on independent and dependent variables.

Science.gov (United States)

Feng, Zhong-xiang; Lu, Shi-sheng; Zhang, Wei-hua; Zhang, Nan-nan

2014-01-01

In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

Empirical Model Building Data, Models, and Reality

CERN Document Server

Thompson, James R

2011-01-01

Praise for the First Edition "This...novel and highly stimulating book, which emphasizes solving real problems...should be widely read. It will have a positive and lasting effect on the teaching of modeling and statistics in general." - Short Book Reviews This new edition features developments and real-world examples that showcase essential empirical modeling techniques Successful empirical model building is founded on the relationship between data and approximate representations of the real systems that generated that data. As a result, it is essential for researchers who construct these m

PREDICTIVE MODELS FOR SUPPORT OF INCIDENT MANAGEMENT PROCESS IN IT SERVICE MANAGEMENT

Directory of Open Access Journals (Sweden)

Martin SARNOVSKY

2018-03-01

Full Text Available ABSTRACT The work presented in this paper is focused on creating of predictive models that help in the process of incident resolution and implementation of IT infrastructure changes to increase the overall support of IT management. Our main objective was to build the predictive models using machine learning algorithms and CRISP-DM methodology. We used the incident and related changes database obtained from the IT environment of the Rabobank Group company, which contained information about the processing of the incidents during the incident management process. We decided to investigate the dependencies between the incident observation on particular infrastructure component and the actual source of the incident as well as the dependency between the incidents and related changes in the infrastructure. We used Random Forests and Gradient Boosting Machine classifiers in the process of identification of incident source as well as in the prediction of possible impact of the observed incident. Both types of models were tested on testing set and evaluated using defined metrics.

A fuzzy-based model to implement the global safety buildings index assessment for agri-food buildings

Directory of Open Access Journals (Sweden)

Francesco Barreca

2014-06-01

Full Text Available The latest EU policies focus on the issue of food safety with a view to ensuring adequate and standard quality levels for the food produced and/or consumed within the EC. To that purpose, the environment where agricultural products are manufactured and processed plays a crucial role in achieving food hygiene. As a consequence, it is of the outmost importance to adopt proper building solutions which meet health and hygiene requirements as well as to use suitable tools to measure the levels achieved. Similarly, it is necessary to verify and evaluate the level of workers’ safety and welfare in their working environment. Workers’ safety has not only an ethical and social value but also an economic implication, since possible accidents or environmental stressors are the major causes of the lower efficiency and productivity of workers. Therefore, it is fundamental to design suitable models of analysis that allow assessing buildings as a whole, taking into account both health and hygiene safety as well as workers’ safety and welfare. Hence, this paper proposes an assessment model that, based on an established study protocol and on the application of a fuzzy logic procedure, allows assessing the global safety level of an agri-food building by means of a global safety buildings index. The model here presented is original since it uses fuzzy logic to evaluate the performances of both the technical and environmental systems of an agri-food building in terms of health and hygiene safety of the manufacturing process as well as of workers’ health and safety. The result of the assessment is expressed through a triangular fuzzy membership function which allows carrying out comparative analyses of different buildings. A specific procedure was developed to apply the model to a case study which tested its operational simplicity and the validity of its results. The proposed model allows obtaining a synthetic and global value of the building performance of

Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine

Directory of Open Access Journals (Sweden)

Hang-cheong Wong

2012-01-01

Full Text Available Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance vector machine (RVM, to build a reliable time-dependent lambda model which can be continually updated whenever a sample is added to, or removed from, the estimated lambda model. The paper also presents a new model predictive control (MPC algorithm for air-ratio regulation based on RVM. This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN and decremental least-squares support vector machine (DLSSVM. Moreover, the control algorithm has been implemented on a real car to test. Experimental results reveal that the control performance of the proposed relevance vector machine model predictive controller (RVMMPC is also superior to DRNNMPC, support vector machine-based MPC, and conventional proportional-integral (PI controller in production cars. Therefore, the proposed RVMMPC is a promising scheme to replace conventional PI controller for engine air-ratio control.

Geometry optimization method versus predictive ability in QSPR modeling for ionic liquids

Science.gov (United States)

Rybinska, Anna; Sosnowska, Anita; Barycki, Maciej; Puzyn, Tomasz

2016-02-01

Computational techniques, such as Quantitative Structure-Property Relationship (QSPR) modeling, are very useful in predicting physicochemical properties of various chemicals. Building QSPR models requires calculating molecular descriptors and the proper choice of the geometry optimization method, which will be dedicated to specific structure of tested compounds. Herein, we examine the influence of the ionic liquids' (ILs) geometry optimization methods on the predictive ability of QSPR models by comparing three models. The models were developed based on the same experimental data on density collected for 66 ionic liquids, but with employing molecular descriptors calculated from molecular geometries optimized at three different levels of the theory, namely: (1) semi-empirical (PM7), (2) ab initio (HF/6-311+G*) and (3) density functional theory (B3LYP/6-311+G*). The model in which the descriptors were calculated by using ab initio HF/6-311+G* method indicated the best predictivity capabilities ({{Q}}_{{EXT}}2 = 0.87). However, PM7-based model has comparable values of quality parameters ({{Q}}_{{EXT}}2 = 0.84). Obtained results indicate that semi-empirical methods (faster and less expensive regarding CPU time) can be successfully employed to geometry optimization in QSPR studies for ionic liquids.

Modelling of internal structure in seismic analysis of a PHWR building

International Nuclear Information System (INIS)

Reddy, G.R.; Vaze, K.K.; Kushawaha, H.S.; Ingle, R.K.; Subramanian, K.V.

1991-01-01

Seismic analysis of complex and large structures, consisting of thick shear walls, such as Reactor Building is very involved and time consuming. It is a standard practice to model the structure as a stick model to predict reasonably the dynamic behaviour of the structure. It is required to determine approximate equivalent sectional properties of Internal Structure for representation in the stick model. The restraint to warping can change the stress distribution thus affecting the centre of rigidity and torsional inertia, Hence, standard formulae does not hold good for determination of sectional properties of the Internal Structure. In this case the equivalent sectional properties for the Internal Structure are calculated using a Finite Element Model (FEM) of the Internal Structure and applying unit horizontal forces in each direction. A 3-D stick model is developed using the guidelines. Using the properties calculated by FEM and also by standard formulae, the responses of the 3-D stick model are compared. (J.P.N.)

Exploitation of Semantic Building Model in Indoor Navigation Systems

Science.gov (United States)

Anjomshoaa, A.; Shayeganfar, F.; Tjoa, A. Min

2009-04-01

There are many types of indoor and outdoor navigation tools and methodologies available. A majority of these solutions are based on Global Positioning Systems (GPS) and instant video and image processing. These approaches are ideal for open world environments where very few information about the target location is available, but for large scale building environments such as hospitals, governmental offices, etc the end-user will need more detailed information about the surrounding context which is especially important in case of people with special needs. This paper presents a smart indoor navigation solution that is based on Semantic Web technologies and Building Information Model (BIM). The proposed solution is also aligned with Google Android's concepts to enlighten the realization of results. Keywords: IAI IFCXML, Building Information Model, Indoor Navigation, Semantic Web, Google Android, People with Special Needs 1 Introduction Built environment is a central factor in our daily life and a big portion of human life is spent inside buildings. Traditionally the buildings are documented using building maps and plans by utilization of IT tools such as computer-aided design (CAD) applications. Documenting the maps in an electronic way is already pervasive but CAD drawings do not suffice the requirements regarding effective building models that can be shared with other building-related applications such as indoor navigation systems. The navigation in built environment is not a new issue, however with the advances in emerging technologies like GPS, mobile and networked environments, and Semantic Web new solutions have been suggested to enrich the traditional building maps and convert them to smart information resources that can be reused in other applications and improve the interpretability with building inhabitants and building visitors. Other important issues that should be addressed in building navigation scenarios are location tagging and end-user communication

IMPROVING TRADITIONAL BUILDING REPAIR CONSTRUCTION QUALITY USING HISTORIC BUILDING INFORMATION MODELING CONCEPT

Directory of Open Access Journals (Sweden)

T. C. Wu

2013-07-01

Full Text Available In addition to the repair construction project following the repair principles contemplated by heritage experts, the construction process should be recorded and measured at any time for monitoring to ensure the quality of repair. The conventional construction record methods mostly depend on the localized shooting of 2D digital images coupled with text and table for illustration to achieve the purpose of monitoring. Such methods cannot fully and comprehensively record the 3D spatial relationships in the real world. Therefore, the construction records of traditional buildings are very important but cannot function due to technical limitations. This study applied the 3D laser scanning technology to establish a 3D point cloud model for the repair construction of historical buildings. It also broke down the detailed components of the 3D point cloud model by using the concept of the historic building information modeling, and established the 3D models of various components and their attribute data in the 3DGIS platform database. In the construction process, according to the time of completion of each stage as developed on the construction project, this study conducted the 3D laser scanning and database establishment for each stage, also applied 3DGIS spatial information and attribute information comparison and analysis to propose the analysis of differences in completion of various stages for improving the traditional building repair construction quality. This method helps to improve the quality of repair construction work of tangible cultural assets of the world. The established 3DGIS platform can be used as a power tool for subsequent management and maintenance.

TACD: a transportable ant colony discrimination model for corporate bankruptcy prediction

Science.gov (United States)

Lalbakhsh, Pooia; Chen, Yi-Ping Phoebe

2017-05-01

This paper presents a transportable ant colony discrimination strategy (TACD) to predict corporate bankruptcy, a topic of vital importance that is attracting increasing interest in the field of economics. The proposed algorithm uses financial ratios to build a binary prediction model for companies with the two statuses of bankrupt and non-bankrupt. The algorithm takes advantage of an improved version of continuous ant colony optimisation (CACO) at the core, which is used to create an accurate, simple and understandable linear model for discrimination. This also enables the algorithm to work with continuous values, leading to more efficient learning and adaption by avoiding data discretisation. We conduct a comprehensive performance evaluation on three real-world data sets under a stratified cross-validation strategy. In three different scenarios, TACD is compared with 11 other bankruptcy prediction strategies. We also discuss the efficiency of the attribute selection methods used in the experiments. In addition to its simplicity and understandability, statistical significance tests prove the efficiency of TACD against the other prediction algorithms in both measures of AUC and accuracy.

Modelling energy demand in the Norwegian building stock

Energy Technology Data Exchange (ETDEWEB)

Sartori, Igor

2008-07-15

Energy demand in the building stock in Norway represents about 40% of the final energy consumption, of which 22% goes to the residential sector and 18% to the service sector. In Norway there is a strong dependency on electricity for heating purposes, with electricity covering about 80% of the energy demand in buildings. The building sector can play an important role in the achievement of a more sustainable energy system. The work performed in the articles presented in this thesis investigates various aspects related to the energy demand in the building sector, both in singular cases and in the stock as a whole. The work performed in the first part of this thesis on development and survey of case studies provided background knowledge that was then used in the second part, on modelling the entire stock. In the first part, a literature survey of case studies showed that, in a life cycle perspective, the energy used in the operating phase of buildings is the single most important factor. Design of low-energy buildings is then beneficial and should be pursued, even though it implies a somewhat higher embodied energy. A case study was performed on a school building. First, a methodology using a Monte Carlo method in the calibration process was explored. Then, the calibrated model of the school was used to investigate measures for the achievement of high energy efficiency standard through renovation work. In the second part, a model was developed to study the energy demand in a scenario analysis. The results showed the robustness of policies that included conservation measures against the conflicting effects of the other policies. Adopting conservation measures on a large scale showed the potential to reduce both electricity and total energy demand from present day levels while the building stock keeps growing. The results also highlighted the inertia to change of the building stock, due to low activity levels compared to the stock size. It also became clear that a deeper

Cost-derived indices for building design and construction ...

African Journals Online (AJOL)

Also as multiples of gfi, substructure cost index, sci and roofing cost index, rci could predict componental costs of substructure and roofing for phased development purposes. Keywords: Cost Indices, Building Design, Building Construction Journal of Modeling, Design and Management of Engineering Systems, Vol.

The Creation of Space Vector Models of Buildings From RPAS Photogrammetry Data

Directory of Open Access Journals (Sweden)

Trhan Ondrej

2017-06-01

Full Text Available The results of Remote Piloted Aircraft System (RPAS photogrammetry are digital surface models and orthophotos. The main problem of the digital surface models obtained is that buildings are not perpendicular and the shape of roofs is deformed. The task of this paper is to obtain a more accurate digital surface model using building reconstructions. The paper discusses the problem of obtaining and approximating building footprints, reconstructing the final spatial vector digital building model, and modifying the buildings on the digital surface model.

Predicting the future completing models of observed complex systems