Energy Consumption Forecasting of Urban Residential Buildings in Cold Regions of China

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 0.264 GJ/m2 and 0.252 GJ/m2, 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 13.4 kgce/m2 with a relative error of 6.52%.