The Experts below are selected from a list of 195 Experts worldwide ranked by ideXlab platform
Subodh Paudel - One of the best experts on this subject based on the ideXlab platform.
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a relevant data selection method for Energy consumption prediction of Low Energy Building based on support vector machine
Energy and Buildings, 2017Co-Authors: Subodh Paudel, Mohamed Elmitri, Stephane Couturier, P H Nguyen, Rene I G Kamphuis, Bruno Lacarriere, Olivier Le CorreAbstract:Abstract Low Energy Buildings (LEBs) are being considered as a promising solution for the built environment to satisfy high-Energy efficiency standards. The technology is based on Lowering the overall heat transmission coefficient value (U-value) of the Buildings envelope and increasing a heat capacity thus creating a higher thermal inertia. However, LEB introduces a large time constant compared to conventional Building due to which it sLows the rate of heat transfer between interior of Building and outdoor environment and alters the indoor climate regardless of sudden changes in climatic conditions. Therefore, it is challenging to estimate and predict thermal Energy demand for such LEBs. This work focuses on artificial intelligence (AI) model to predict Energy consumption of LEB. Two kinds of AI modeling approaches: “all data” and “relevant data” are considered. The “all data” uses all available training data and “relevant data” uses a small representative day dataset and addresses the complexity of Building non-linear dynamics by introducing past day climatic impacts behavior. This extraction is based on dynamic time warping pattern recognition methods. The case study consists of a French residential LEB. The numerical results showed that “relevant data” modeling approach that relies on small representative data selection has higher accuracy (R 2 = 0.98; RMSE = 3.4) than “all data” modeling approach (R 2 = 0.93; RMSE = 7.1) to predict heating Energy load.
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a relevant data selection method for Energy consumption prediction of Low Energy Building based on support vector machine
Energy and Buildings, 2017Co-Authors: Subodh Paudel, Mohamed Elmitri, Rene I G Kamphuis, Stephane Couturie, P H Nguye, Uno Lacarriere, Olivier Le CorreAbstract:Abstract Low Energy Buildings (LEBs) are being considered as a promising solution for the built environment to satisfy high-Energy efficiency standards. The technology is based on Lowering the overall heat transmission coefficient value (U-value) of the Buildings envelope and increasing a heat capacity thus creating a higher thermal inertia. However, LEB introduces a large time constant compared to conventional Building due to which it sLows the rate of heat transfer between interior of Building and outdoor environment and alters the indoor climate regardless of sudden changes in climatic conditions. Therefore, it is challenging to estimate and predict thermal Energy demand for such LEBs. This work focuses on artificial intelligence (AI) model to predict Energy consumption of LEB. Two kinds of AI modeling approaches: “all data” and “relevant data” are considered. The “all data” uses all available training data and “relevant data” uses a small representative day dataset and addresses the complexity of Building non-linear dynamics by introducing past day climatic impacts behavior. This extraction is based on dynamic time warping pattern recognition methods. The case study consists of a French residential LEB. The numerical results showed that “relevant data” modeling approach that relies on small representative data selection has higher accuracy (R 2 = 0.98; RMSE = 3.4) than “all data” modeling approach (R 2 = 0.93; RMSE = 7.1) to predict heating Energy load.
Olivier Le Corre - One of the best experts on this subject based on the ideXlab platform.
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a relevant data selection method for Energy consumption prediction of Low Energy Building based on support vector machine
Energy and Buildings, 2017Co-Authors: Subodh Paudel, Mohamed Elmitri, Stephane Couturier, P H Nguyen, Rene I G Kamphuis, Bruno Lacarriere, Olivier Le CorreAbstract:Abstract Low Energy Buildings (LEBs) are being considered as a promising solution for the built environment to satisfy high-Energy efficiency standards. The technology is based on Lowering the overall heat transmission coefficient value (U-value) of the Buildings envelope and increasing a heat capacity thus creating a higher thermal inertia. However, LEB introduces a large time constant compared to conventional Building due to which it sLows the rate of heat transfer between interior of Building and outdoor environment and alters the indoor climate regardless of sudden changes in climatic conditions. Therefore, it is challenging to estimate and predict thermal Energy demand for such LEBs. This work focuses on artificial intelligence (AI) model to predict Energy consumption of LEB. Two kinds of AI modeling approaches: “all data” and “relevant data” are considered. The “all data” uses all available training data and “relevant data” uses a small representative day dataset and addresses the complexity of Building non-linear dynamics by introducing past day climatic impacts behavior. This extraction is based on dynamic time warping pattern recognition methods. The case study consists of a French residential LEB. The numerical results showed that “relevant data” modeling approach that relies on small representative data selection has higher accuracy (R 2 = 0.98; RMSE = 3.4) than “all data” modeling approach (R 2 = 0.93; RMSE = 7.1) to predict heating Energy load.
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a relevant data selection method for Energy consumption prediction of Low Energy Building based on support vector machine
Energy and Buildings, 2017Co-Authors: Subodh Paudel, Mohamed Elmitri, Rene I G Kamphuis, Stephane Couturie, P H Nguye, Uno Lacarriere, Olivier Le CorreAbstract:Abstract Low Energy Buildings (LEBs) are being considered as a promising solution for the built environment to satisfy high-Energy efficiency standards. The technology is based on Lowering the overall heat transmission coefficient value (U-value) of the Buildings envelope and increasing a heat capacity thus creating a higher thermal inertia. However, LEB introduces a large time constant compared to conventional Building due to which it sLows the rate of heat transfer between interior of Building and outdoor environment and alters the indoor climate regardless of sudden changes in climatic conditions. Therefore, it is challenging to estimate and predict thermal Energy demand for such LEBs. This work focuses on artificial intelligence (AI) model to predict Energy consumption of LEB. Two kinds of AI modeling approaches: “all data” and “relevant data” are considered. The “all data” uses all available training data and “relevant data” uses a small representative day dataset and addresses the complexity of Building non-linear dynamics by introducing past day climatic impacts behavior. This extraction is based on dynamic time warping pattern recognition methods. The case study consists of a French residential LEB. The numerical results showed that “relevant data” modeling approach that relies on small representative data selection has higher accuracy (R 2 = 0.98; RMSE = 3.4) than “all data” modeling approach (R 2 = 0.93; RMSE = 7.1) to predict heating Energy load.
Saso Medved - One of the best experts on this subject based on the ideXlab platform.
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free cooling of a Building using pcm heat storage integrated into the ventilation system
Solar Energy, 2007Co-Authors: Ciril Arka, Saso MedvedAbstract:Abstract This article presents a study of the free cooling of a Low-Energy Building using a latent-heat thermal Energy storage (LHTES) device integrated into a mechanical ventilation system. The cylindrical LHTES device was filled with spheres of encapsulated RT20 paraffin, a phase-change material (PCM). A numerical model of the LHTES was developed to identify the parameters that have an influence on the LHTES’s thermal response, to determine the optimum phase-change temperature and to form the LHTES’s temperature-response function. The last of these defines the LHTES’s outlet-air temperature for a periodic variation of the inlet ambient-air temperature and the defined operating conditions. The temperature-response function was then integrated into the TRNSYS Building thermal response model. Numerical simulations showed that a PCM with a melting temperature between 20 and 22 °C is the most suitable for free cooling in the case of a continental climate. The analyses of the temperatures in a Low-Energy Building showed that free cooling with an LHTES is an effective cooling technique. Suitable thermal comfort conditions in the presented case-study Building could be achieved using an LHTES with 6.4 kg of PCM per square metre of floor area.
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efficiency of free cooling using latent heat storage integrated into the ventilation system of a Low Energy Building
International Journal of Refrigeration-revue Internationale Du Froid, 2007Co-Authors: Ciril Arka, Oris Vidrih, Saso MedvedAbstract:This article presents the results of an investigation into the free cooling efficiency in a heavyweight and lightweight Low Energy Building using a mechanical ventilation system with two latent heat thermal Energy storages (LHTESs), one for cooling the fresh supply air and the other for cooling the re-circulated indoor air. Both LHTESs contain sphere encapsulated PCM (paraffin RT20). Using a developed and experimentally verified numerical model of the LHTES, the temperature response functions, based on the heat storage size, the air fLow rates and the PCM's thermal properties, are established in the form of a Fourier series and empirical equations and used in the TRNSYS Building thermal response model. Several mechanical ventilation, night cooling and free cooling operation modes were analysed and compared. It was found that the free cooling technique enables a reduction in the size of the mechanical ventilation system, provides more favourable temperatures and therefore enables better thermal comfort conditions, and in our studied case also fresh air for the occupants.
Rene I G Kamphuis - One of the best experts on this subject based on the ideXlab platform.
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a relevant data selection method for Energy consumption prediction of Low Energy Building based on support vector machine
Energy and Buildings, 2017Co-Authors: Subodh Paudel, Mohamed Elmitri, Stephane Couturier, P H Nguyen, Rene I G Kamphuis, Bruno Lacarriere, Olivier Le CorreAbstract:Abstract Low Energy Buildings (LEBs) are being considered as a promising solution for the built environment to satisfy high-Energy efficiency standards. The technology is based on Lowering the overall heat transmission coefficient value (U-value) of the Buildings envelope and increasing a heat capacity thus creating a higher thermal inertia. However, LEB introduces a large time constant compared to conventional Building due to which it sLows the rate of heat transfer between interior of Building and outdoor environment and alters the indoor climate regardless of sudden changes in climatic conditions. Therefore, it is challenging to estimate and predict thermal Energy demand for such LEBs. This work focuses on artificial intelligence (AI) model to predict Energy consumption of LEB. Two kinds of AI modeling approaches: “all data” and “relevant data” are considered. The “all data” uses all available training data and “relevant data” uses a small representative day dataset and addresses the complexity of Building non-linear dynamics by introducing past day climatic impacts behavior. This extraction is based on dynamic time warping pattern recognition methods. The case study consists of a French residential LEB. The numerical results showed that “relevant data” modeling approach that relies on small representative data selection has higher accuracy (R 2 = 0.98; RMSE = 3.4) than “all data” modeling approach (R 2 = 0.93; RMSE = 7.1) to predict heating Energy load.
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a relevant data selection method for Energy consumption prediction of Low Energy Building based on support vector machine
Energy and Buildings, 2017Co-Authors: Subodh Paudel, Mohamed Elmitri, Rene I G Kamphuis, Stephane Couturie, P H Nguye, Uno Lacarriere, Olivier Le CorreAbstract:Abstract Low Energy Buildings (LEBs) are being considered as a promising solution for the built environment to satisfy high-Energy efficiency standards. The technology is based on Lowering the overall heat transmission coefficient value (U-value) of the Buildings envelope and increasing a heat capacity thus creating a higher thermal inertia. However, LEB introduces a large time constant compared to conventional Building due to which it sLows the rate of heat transfer between interior of Building and outdoor environment and alters the indoor climate regardless of sudden changes in climatic conditions. Therefore, it is challenging to estimate and predict thermal Energy demand for such LEBs. This work focuses on artificial intelligence (AI) model to predict Energy consumption of LEB. Two kinds of AI modeling approaches: “all data” and “relevant data” are considered. The “all data” uses all available training data and “relevant data” uses a small representative day dataset and addresses the complexity of Building non-linear dynamics by introducing past day climatic impacts behavior. This extraction is based on dynamic time warping pattern recognition methods. The case study consists of a French residential LEB. The numerical results showed that “relevant data” modeling approach that relies on small representative data selection has higher accuracy (R 2 = 0.98; RMSE = 3.4) than “all data” modeling approach (R 2 = 0.93; RMSE = 7.1) to predict heating Energy load.
Mohamed Elmitri - One of the best experts on this subject based on the ideXlab platform.
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a relevant data selection method for Energy consumption prediction of Low Energy Building based on support vector machine
Energy and Buildings, 2017Co-Authors: Subodh Paudel, Mohamed Elmitri, Stephane Couturier, P H Nguyen, Rene I G Kamphuis, Bruno Lacarriere, Olivier Le CorreAbstract:Abstract Low Energy Buildings (LEBs) are being considered as a promising solution for the built environment to satisfy high-Energy efficiency standards. The technology is based on Lowering the overall heat transmission coefficient value (U-value) of the Buildings envelope and increasing a heat capacity thus creating a higher thermal inertia. However, LEB introduces a large time constant compared to conventional Building due to which it sLows the rate of heat transfer between interior of Building and outdoor environment and alters the indoor climate regardless of sudden changes in climatic conditions. Therefore, it is challenging to estimate and predict thermal Energy demand for such LEBs. This work focuses on artificial intelligence (AI) model to predict Energy consumption of LEB. Two kinds of AI modeling approaches: “all data” and “relevant data” are considered. The “all data” uses all available training data and “relevant data” uses a small representative day dataset and addresses the complexity of Building non-linear dynamics by introducing past day climatic impacts behavior. This extraction is based on dynamic time warping pattern recognition methods. The case study consists of a French residential LEB. The numerical results showed that “relevant data” modeling approach that relies on small representative data selection has higher accuracy (R 2 = 0.98; RMSE = 3.4) than “all data” modeling approach (R 2 = 0.93; RMSE = 7.1) to predict heating Energy load.
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a relevant data selection method for Energy consumption prediction of Low Energy Building based on support vector machine
Energy and Buildings, 2017Co-Authors: Subodh Paudel, Mohamed Elmitri, Rene I G Kamphuis, Stephane Couturie, P H Nguye, Uno Lacarriere, Olivier Le CorreAbstract:Abstract Low Energy Buildings (LEBs) are being considered as a promising solution for the built environment to satisfy high-Energy efficiency standards. The technology is based on Lowering the overall heat transmission coefficient value (U-value) of the Buildings envelope and increasing a heat capacity thus creating a higher thermal inertia. However, LEB introduces a large time constant compared to conventional Building due to which it sLows the rate of heat transfer between interior of Building and outdoor environment and alters the indoor climate regardless of sudden changes in climatic conditions. Therefore, it is challenging to estimate and predict thermal Energy demand for such LEBs. This work focuses on artificial intelligence (AI) model to predict Energy consumption of LEB. Two kinds of AI modeling approaches: “all data” and “relevant data” are considered. The “all data” uses all available training data and “relevant data” uses a small representative day dataset and addresses the complexity of Building non-linear dynamics by introducing past day climatic impacts behavior. This extraction is based on dynamic time warping pattern recognition methods. The case study consists of a French residential LEB. The numerical results showed that “relevant data” modeling approach that relies on small representative data selection has higher accuracy (R 2 = 0.98; RMSE = 3.4) than “all data” modeling approach (R 2 = 0.93; RMSE = 7.1) to predict heating Energy load.
