Energy Consumption Forecasting of Urban Residential Buildings in Cold Regions of China
Publication: Journal of Energy Engineering
Volume 149, Issue 2
Abstract
Long-term predictions of the energy consumption of a building can be a reference in formulating energy conservation policies to achieve carbon neutrality. However, existing research on the prediction of energy consumption of urban buildings mainly adopts top-down or bottom-up single models that do not consider the coupling effect of macro and micro factors. Therefore, a coupled top-down and bottom-up prediction model has been proposed in this paper. The applicability of the proposed method was investigated based on residential buildings in Beijing, China. First, based on a top-down methodology, the energy consumption data of residential buildings in Beijing were investigated using an urban statistical yearbook, and the energy consumption of residential buildings under different energy-saving policies was analyzed. Second, micro factors, such as envelope parameters, personnel behavior, air conditioning, and electrical usage of typical residential buildings, were investigated. Subsequently, a simulation model of residential building energy consumption was constructed according to the survey data. The actual and simulated values for 2017 were and , respectively. Finally, pessimistic and optimistic scenarios are proposed using the Human Impact, Population, Affluence, Technology (IPAT) model. The energy consumption of residential buildings in Beijing for the next decade was predicted under different scenarios by adopting the gray prediction method and multiple regression analysis, which was verified using the back-propagation neural network algorithm. In the baseline scenario, the projected 2021 heating energy consumption value was with a relative error of 6.52%.
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Data Availability Statement
All data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research is supported by the 2019 Science Program of the Ministry of Housing and Urban-Rural Development of China (No. 2019-K-026).
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Journal of Energy Engineering
Volume 149 • Issue 2 • April 2023
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© 2023 American Society of Civil Engineers.
History
Received: Apr 14, 2022
Accepted: Nov 1, 2022
Published online: Jan 12, 2023
Published in print: Apr 1, 2023
Discussion open until: Jun 12, 2023
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