scholarly journals Sensitivity Analysis of Common Input Parameters in Tools for Modeling Energy in Homes

2016 ◽  
Vol 13 (2) ◽  
Author(s):  
Sheikh Tijan Tabban ◽  
Nelson Fumo
Keyword(s):  
Sensitivity Analysis ◽  
Energy Consumption ◽  
Reference Model ◽  
Residential Buildings ◽  
Input Parameter ◽  
Heating Equipment ◽  
Water Heating ◽  
Common Input ◽  
Heating And Cooling ◽  
Input Parameters

Energy models of buildings can be developed and used for analysis of energy consumption. A model offers the opportunity to simulate a building under specific conditions for analysis of energy efficiency measures or optimum design. Due to the great amount of information needed to develop an energy model of a building, the number of inputs can be reduced by making variable the most relevant input parameters and making the others to take common or standard values. In this study, an analysis of input parameters required by computational tools to estimate energy consumption in homes was done in two stages. In the first stage, common input parameters were identified for three software and three webtools based on the criteria that the input parameter should be common for at least two software and at least one webtool. In the second stage, a sensitivity analysis was performed on the inputs identified in the first stage. The software BEopt, developed by the National Renewable Energy Laboratory, was used as the source of typical input parameters to be compared, and to perform the simulations for the sensitivity analysis. The base or reference model to perform simulations for the sensitivity analysis corresponds to a model developed with information from a research house located on the campus of the University of Texas at Tyler and default inputs for the BEopt B-10 reference benchmark. Results show that besides the location, and consequently the weather, common parameters are building orientation, air leakage, space conditioning settings, space conditioning schedule, water heating equipment, and terrain. Among these parameters, the sensitivity analysis identified the largest variations in energy consumption for variations on space conditioning schedule (heating and cooling setpoints), followed by the type of water heating equipment. KEYWORDS: Residential Buildings; Energy Consumption; Energy Analysis; Input Parameters; Building Simulation; Source Energy

scholarly journals Towards Sustainable Energy Retrofitting, a Simulation for Potential Energy Use Reduction in Residential Buildings in Palestine

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3876
Author(s):  
Sameh Monna ◽  
Adel Juaidi ◽  
Ramez Abdallah ◽  
Aiman Albatayneh ◽  
Patrick Dutournie ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Energy Savings ◽  
Residential Buildings ◽  
Residential Building ◽  
Simulation Models ◽  
Water Heating ◽  
Heating And Cooling ◽  
Space Cooling ◽  
Cooling Loads

Since buildings are one of the major contributors to global warming, efforts should be intensified to make them more energy-efficient, particularly existing buildings. This research intends to analyze the energy savings from a suggested retrofitting program using energy simulation for typical existing residential buildings. For the assessment of the energy retrofitting program using computer simulation, the most commonly utilized residential building types were selected. The energy consumption of those selected residential buildings was assessed, and a baseline for evaluating energy retrofitting was established. Three levels of retrofitting programs were implemented. These levels were ordered by cost, with the first level being the least costly and the third level is the most expensive. The simulation models were created for two different types of buildings in three different climatic zones in Palestine. The findings suggest that water heating, space heating, space cooling, and electric lighting are the highest energy consumers in ordinary houses. Level one measures resulted in a 19–24 percent decrease in energy consumption due to reduced heating and cooling loads. The use of a combination of levels one and two resulted in a decrease of energy consumption for heating, cooling, and lighting by 50–57%. The use of the three levels resulted in a decrease of 71–80% in total energy usage for heating, cooling, lighting, water heating, and air conditioning.

Seismic hazard sensitivity analysis: A multi-parameter approach

1991 ◽  
Vol 81 (3) ◽  
pp. 796-817
Author(s):  
Nitzan Rabinowitz ◽  
David M. Steinberg
Keyword(s):  
Sensitivity Analysis ◽  
Seismic Hazard ◽  
Subjective Probability ◽  
Probability Distributions ◽  
Input Parameter ◽  
The Other ◽  
Probabilistic Assessment ◽  
Input Parameters ◽  
Parameter Approach

Abstract We propose a novel multi-parameter approach for conducting seismic hazard sensitivity analysis. This approach allows one to assess the importance of each input parameter at a variety of settings of the other input parameters and thus provides a much richer picture than standard analyses, which assess each input parameter only at the default settings of the other parameters. We illustrate our method with a sensitivity analysis of seismic hazard for Jerusalem. In this example, we find several input parameters whose importance depends critically on the settings of other input parameters. This phenomenon, which cannot be detected by a standard sensitivity analysis, is easily diagnosed by our method. The multi-parameter approach can also be used in the context of a probabilistic assessment of seismic hazard that incorporates subjective probability distributions for the input parameters.

scholarly journals Application of sensitivity analysis and genopt to optimize the energy performance of a building in Morocco

2018 ◽  
Vol 7 (4) ◽  
pp. 2068 ◽  
Author(s):  
Abdelhadi Serbouti ◽  
Mourad Rattal ◽  
Abdellah Boulal ◽  
Mohammed Harmouchi ◽  
Azeddine Mouhsen
Keyword(s):  
Sensitivity Analysis ◽  
Residential Buildings ◽  
Energy Performance ◽  
High Energy ◽  
Performance Requirement ◽  
Input Parameters ◽  
Sensitivity Analysis Method ◽  
Python Programming ◽  
Industry Sectors ◽  
High Energy Consumption

The worldwide demographic and economic growth increases the global need for energy and directly contributes to climate change. In Morocco, the residential real estate is the third largest consumer of energy after transport and industry sectors. Thus, the aim of this study is to help engineers improve the energy performance of residential buildings by coupling the TRNSYS software both with a sensitivity analysis method and with an optimization tool. In fact, sensitivity analysis allows reducing the number of input parameters of any studied model, by ranking their degree of impact on any chosen output, and then discard the parameters with the least influence on that output. To do so, we developed algorithms in Python programming language to combine the open source library SALib, available in Github platform, with the TRNSYS software. Then, the chosen input parameters can be optimized through coupling the generic optimization program Genopt with TRNSYS. This article will also explain how these tools were applied to reduce the heating & air-conditioning needs of a high-energy consumption building in Morocco, while studying the variation of nineteen input parameters in TRNSYS. The main aim is to meet the energy performance requirement of the Moroccan thermal regulation for buildings.  

scholarly journals Pyrolysis modeling, sensitivity analysis, and optimization techniques for combustible materials: A review

2019 ◽  
Vol 37 (4-6) ◽  
pp. 377-433
Author(s):  
Tatenda Nyazika ◽  
Maude Jimenez ◽  
Fabienne Samyn ◽  
Serge Bourbigot
Keyword(s):  
Sensitivity Analysis ◽  
Material Properties ◽  
Input Parameter ◽  
Fire Behavior ◽  
Sensitivity Study ◽  
Optimization Techniques ◽  
Decomposition Reactions ◽  
Input Parameters ◽  
Gradient Based ◽  
Complete Set

Over the past years, pyrolysis models have moved from thermal models to comprehensive models with great flexibility including multi-step decomposition reactions. However, the downside is the need for a complete set of input data such as the material properties and the parameters related to the decomposition kinetics. Some of the parameters are not directly measurable or are difficult to determine and they carry a certain degree of uncertainty at high temperatures especially for materials that can melt, shrink, or swell. One can obtain input parameters by searching through the literature; however, certain materials may have the same nomenclature but the material properties may vary depending on the manufacturer, thereby inducing uncertainties in the model. Modelers have resorted to the use of optimization techniques such as gradient-based and direct search methods to estimate input parameters from experimental bench-scale data. As an integral part of the model, a sensitivity study allows to identify the role of each input parameter on the outputs. This work presents an overview of pyrolysis modeling, sensitivity analysis, and optimization techniques used to predict the fire behavior of combustible solids when exposed to an external heat flux.

scholarly journals Analysis of Inputs Parameters Used to Estimate Enteric Methane Emission Factors Applying a Tier 2 Model: Case Study of Native Cattle in Senegal

2021 ◽  
Author(s):  
Séga Ndao
Keyword(s):  
Sensitivity Analysis ◽  
Methane Emission ◽  
Input Parameter ◽  
Emission Factors ◽  
Future Research ◽  
Tier 2 ◽  
Lactating Cows ◽  
Enteric Methane ◽  
Input Parameters ◽  
Native Cattle

In the context of the Paris Agreement, and considering the importance of methane emissions from cattle in West Africa, application of a Tier 2 method to estimate enteric methane emission factors is clearly pertinent. The current study has two purposes. Firstly, it aims to detect how much each input parameter contributes to the overall uncertainty of enteric methane emission factors for cattle. Secondly, it aims to identify which input parameters require additional research efforts for strengthening the evidence base, thus reducing the uncertainty of methane enteric emission factors. Uncertainty and sensitivity analysis methodologies were applied to input parameters in the calculation of enteric methane emission factors for lactating cows and adult male Senegalese native cattle using the IPCC Tier 2 model. The results show that the IPCC default input parameters, such as the coefficient for calculating net energy for maintenance (Cfi), digestible energy (DE) and the methane conversion rate (Ym) are the first, second and third most important input parameters, respectively, in terms of their contribution to uncertainty of the enteric methane emission factor. Sensitivity analysis demonstrated that future research in Senegal should prioritize the development of Ym, Cfi and DE in order to estimate enteric methane emission factors more accurately and to reduce the uncertainty of the national agricultural greenhouse gas inventory.

scholarly journals Effects of the geometry of residential buildings with a sunspace on their energy performance

2019 ◽  
Vol 17 (1) ◽  
pp. 105-118
Author(s):  
Ana Vukadinovic ◽  
Jasmina Radosavljevic ◽  
Amelija Djordjevic ◽  
Nemanja Petrovic
Keyword(s):  
Energy Consumption ◽  
Aspect Ratio ◽  
Shape Factor ◽  
Residential Buildings ◽  
Dynamic Modelling ◽  
Building Design ◽  
Energy Performance ◽  
Total Consumption ◽  
Heating And Cooling ◽  
Heating Energy Consumption

The increase in energy consumption in building design and construction and the issues related to environmental protection have steered many current researchers toward examining the ways to reduce total CO2 emissions, which resulted in the development of various measures to increase energy efficiency. One measure for more cost-efficient and rational use of energy resources in individual residential buildings is the application of passive solar systems with a sunspace. This paper presents the effects of the shape factor of a residential building with a passive sunspace on the total consumption of heating and cooling energy. The total amount of energy required for building heating and cooling was calculated by means of dynamic modelling using EnergyPlus software. The simulations were run according to the meteorological parameters for the city of Nis. For simulation purposes, models of residential buildings with a passive sunspace and square- and rectangle-shaped floors were designed. The variations between the models include different building shape factor, floor geometry, surface area of the southern fa?ade, and glazing percentage, i.e. window-to-wall ratio (WWR). Examination of the models with WWR=20%, WWR=40%, and WWR=60% revealed that the elongated shape of a building with the aspect ratio of 2.25:1, with the longer side of the fa?ade facing south, is the most favourable in terms of heating energy consumption. For the same WWRs, the elongated shape of a building with the aspect ratio of 1.56:1, with the longer side of the fa?ade facing south, is the most favourable in terms of cooling energy consumption. As WWR increases, so does the amount of energy required to cool the building. The biggest increase in heating energy consumption was observed in buildings with the aspect ratio 1:2.25, with the shorter side facing south.

scholarly journals Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

2020 ◽  
Vol 10 (11) ◽  
pp. 3829 ◽  
Author(s):  
Arash Moradzadeh ◽  
Amin Mansour-Saatloo ◽  
Behnam Mohammadi-Ivatloo ◽  
Amjad Anvari-Moghaddam
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Residential Buildings ◽  
Load Forecasting ◽  
Training Data ◽  
Support Vector ◽  
Cooling Load ◽  
Heating And Cooling ◽  
Cooling Loads ◽  
Heating And Cooling Load

Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.

scholarly journals Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

2021 ◽  
Vol 13 (22) ◽  
pp. 12442
Author(s):  
Amal A. Al-Shargabi ◽  
Abdulbasit Almhafdy ◽  
Dina M. Ibrahim ◽  
Manal Alghieth ◽  
Francisco Chiclana
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Mean Square Error ◽  
Deep Neural Networks ◽  
Prediction Models ◽  
Residential Buildings ◽  
Arid Climate ◽  
Mean Square ◽  
Heating And Cooling ◽  
Cooling Loads

The dramatic growth in the number of buildings worldwide has led to an increase interest in predicting energy consumption, especially for the case of residential buildings. As the heating and cooling system highly affect the operation cost of buildings; it is worth investigating the development of models to predict the heating and cooling loads of buildings. In contrast to the majority of the existing related studies, which are based on historical energy consumption data, this study considers building characteristics, such as area and floor height, to develop prediction models of heating and cooling loads. In particular, this study proposes deep neural networks models based on several hyper-parameters: the number of hidden layers, the number of neurons in each layer, and the learning algorithm. The tuned models are constructed using a dataset generated with the Integrated Environmental Solutions Virtual Environment (IESVE) simulation software for the city of Buraydah city, the capital of the Qassim region in Saudi Arabia. The Qassim region was selected because of its harsh arid climate of extremely cold winters and hot summers, which means that lot of energy is used up for cooling and heating of residential buildings. Through model tuning, optimal parameters of deep learning models are determined using the following performance measures: Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, and coefficient of determination (R2). The results obtained with the five-layer deep neural network model, with 20 neurons in each layer and the Levenberg–Marquardt algorithm, outperformed the results of the other models with a lower number of layers. This model achieved MSE of 0.0075, RMSE 0.087, R and R2 both as high as 0.99 in predicting the heating load and MSE of 0.245, RMSE of 0.495, R and R2 both as high as 0.99 in predicting the cooling load. As the developed prediction models were based on buildings characteristics, the outcomes of the research may be relevant to architects at the pre-design stage of heating and cooling energy-efficient buildings.

Uncertainty Quantification in Support of Severe Accident Analysis Code User Confidence Using MELCOR-DAKOTA

2021 ◽  
Author(s):  
Emmanuel Boafo ◽  
Emmanuel Numapau Gyamfi
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Hydrogen Generation ◽  
Input Parameter ◽  
Distribution Functions ◽  
Physical Models ◽  
Severe Accident ◽  
Accident Analysis ◽  
Melt Flow Rate ◽  
Input Parameters

Abstract Uncertainty and Sensitivity analysis methods are often used in severe accident analysis for validating the complex physical models employed in the system codes that simulate such scenarios. This is necessitated by the large uncertainties associated with the physical models and boundary conditions employed to simulate severe accident scenarios. The input parameters are sampled within defined ranges based on assigned probability distribution functions (PDFs) for the required number of code runs/realizations using stochastic sampling techniques. Input parameter selection is based on their importance to the key FOM, which is determined by the parameter identification and ranking table (PIRT). Sensitivity analysis investigates the contribution of each uncertain input parameter to the uncertainty of the selected FOM. In this study, the integrated severe accident analysis code MELCOR was coupled with DAKOTA, an optimization and uncertainty quantification tool in order to investigate the effect of input parameter uncertainty on hydrogen generation. The methodology developed was applied to the Fukushima Daiichi unit 1 NPP accident scenario, which was modelled in another study. The results show that there is approximately 22.46% uncertainty in the amount of hydrogen generated as estimated by a single MELCOR run given uncertainty in selected input parameters. The sensitivity analysis results also reveal that MELCOR input parameters; COR_SC 1141(Melt flow rate per unit width at breakthrough candling) , COR_ZP (Porosity of fuel debris beds) and COR_EDR (Characteristic debris size in core region) contributed most significantly to the uncertainty in hydrogen generation.

Application of Phase Change Materials in Construction Materials for Thermal Energy Storage Systems in Buildings

2021 ◽  
pp. 1-20
Author(s):  
Eray Mertkan Meric ◽  
Savas Erdem ◽  
Ezgi Gurbuz
Keyword(s):  
Energy Consumption ◽  
Phase Change Materials ◽  
Residential Buildings ◽  
Construction Materials ◽  
Building Design ◽  
World Population ◽  
Total Consumption ◽  
Heating And Cooling ◽  
Thermal Energy Storage Systems ◽  
Developed World

There has been a continuous increase in world population and industrialization. In parallel, energy consumption grew substantially in the world. The residential buildings constitute the majority of the total consumption in the developed world. Therefore, energy saving and reducing heat losses of buildings are of major concern for our society. At this point, phase changing materials (PCMs) show up as one of the most useful materials in building design. In this study, PCMs, which are used at places such as facade of buildings, bricks, and inside of concrete in order to be able to reduce the energy consumption due to heating and cooling, to provide comfort temperature inside buildings, are extensively reviewed.

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