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A platform for research: civil engineering, architecture and urbanism
Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network
Highlights Novel pseudo dynamic transitional model is introduced. Orthogonal array design is applied to the pseudo dynamic model. A large building is considered for case study. Minimum energy consumption error is achieved and is 0.02% for learning phase and 2.39% for validation phase. Application for energy operator to manage the heating load for dynamic control of heat production system.
Abstract 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 time dependent attributes of operational power level characteristics and its effect in the overall model performance is outlined. Pseudo dynamic model is applied to a case study of French Institution building and compared its results with static and other pseudo dynamic neural network models. The results show the coefficients of correlation in static and pseudo dynamic neural network model of 0.82 and 0.89 (with energy consumption error of 0.02%) during the learning phase, and 0.61 and 0.85 during the prediction phase, respectively. Further, orthogonal array design is applied to the pseudo dynamic model to check the schedule of occupancy profile and operational heating power level characteristics. The results show the new schedule and provide the robust design for pseudo dynamic model. Due to prediction in short time horizon, it finds application for Energy Services Company (ESCOs) to manage the heating load for dynamic control of heat production system.
Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network
Highlights Novel pseudo dynamic transitional model is introduced. Orthogonal array design is applied to the pseudo dynamic model. A large building is considered for case study. Minimum energy consumption error is achieved and is 0.02% for learning phase and 2.39% for validation phase. Application for energy operator to manage the heating load for dynamic control of heat production system.
Abstract 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 time dependent attributes of operational power level characteristics and its effect in the overall model performance is outlined. Pseudo dynamic model is applied to a case study of French Institution building and compared its results with static and other pseudo dynamic neural network models. The results show the coefficients of correlation in static and pseudo dynamic neural network model of 0.82 and 0.89 (with energy consumption error of 0.02%) during the learning phase, and 0.61 and 0.85 during the prediction phase, respectively. Further, orthogonal array design is applied to the pseudo dynamic model to check the schedule of occupancy profile and operational heating power level characteristics. The results show the new schedule and provide the robust design for pseudo dynamic model. Due to prediction in short time horizon, it finds application for Energy Services Company (ESCOs) to manage the heating load for dynamic control of heat production system.
Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network
Paudel, Subodh (author) / Elmtiri, Mohamed (author) / Kling, Wil L. (author) / Corre, Olivier Le (author) / Lacarrière, Bruno (author)
Energy and Buildings ; 70 ; 81-93
2013-11-09
13 pages
Article (Journal)
Electronic Resource
English
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