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Statistical analysis of energy consumption patterns on the heat demand of buildings in district heating systems
Highlights The paper studied the impacts of energy consumption patterns on heat consumption. Time and building types were used to reflect various ECPs. Results show that time does not represent a good reflection of ECP. Categorising buildings according to function is an effective way to reflect ECPs. 13.7–338.2MWh of electricity could be saved from pump work according to this study.
Abstract Precise prediction of heat demand is crucial for optimising district heating (DH) systems. Energy consumption patterns (ECPs) represent a key parameter in developing a good mathematical model to predict heat demand. This study quantitatively investigated the impacts of ECPs on heat consumption. Two key factors, namely, time and type of buildings, were used to reflect various ECPs in DH systems, and a Gaussian mixture model (GMM) was developed to examine their impacts on heat consumption. The model was trained and validated using the measured data from a real DH system. Results show that the factor of time does not represent a good reflection of ECP. In contrast, categorising buildings according to their function is an effective way to reflect ECPs. Based on the defined building types, i.e., commercial, apartment and office, the average absolute deviation of the predicted heat load was about 4–8%.
Statistical analysis of energy consumption patterns on the heat demand of buildings in district heating systems
Highlights The paper studied the impacts of energy consumption patterns on heat consumption. Time and building types were used to reflect various ECPs. Results show that time does not represent a good reflection of ECP. Categorising buildings according to function is an effective way to reflect ECPs. 13.7–338.2MWh of electricity could be saved from pump work according to this study.
Abstract Precise prediction of heat demand is crucial for optimising district heating (DH) systems. Energy consumption patterns (ECPs) represent a key parameter in developing a good mathematical model to predict heat demand. This study quantitatively investigated the impacts of ECPs on heat consumption. Two key factors, namely, time and type of buildings, were used to reflect various ECPs in DH systems, and a Gaussian mixture model (GMM) was developed to examine their impacts on heat consumption. The model was trained and validated using the measured data from a real DH system. Results show that the factor of time does not represent a good reflection of ECP. In contrast, categorising buildings according to their function is an effective way to reflect ECPs. Based on the defined building types, i.e., commercial, apartment and office, the average absolute deviation of the predicted heat load was about 4–8%.
Statistical analysis of energy consumption patterns on the heat demand of buildings in district heating systems
Ma, Zhanyu (author) / Li, Hailong (author) / Sun, Qie (author) / Wang, Chao (author) / Yan, Aibin (author) / Starfelt, Fredrik (author)
Energy and Buildings ; 85 ; 464-472
2014-09-25
9 pages
Article (Journal)
Electronic Resource
English
AB , apartment building , AD , absolute deviation , CB , commercial building , DH , district heating , ECP , energy consumption pattern , EEA , European Environmental Agency , GH , Ggreenhouse gas , GMM , gaussian mixture model , OB , office building , PDF , probability density function , District heating (DH) , Heat demand , Gaussian mixture model (GMM) , Energy consumption pattern (ECP)
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