A Period-Based Neural Network Algorithm for Predicting Building Energy Consumption of District Heating

Zhengchao Xie, Xiao Wang (), Lijun Zheng, Hao Chang and Fei Wang
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Zhengchao Xie: Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, China
Xiao Wang: Zhejiang Gas & Thermoelectricity Design Institute Co., Ltd., Hangzhou 310030, China
Lijun Zheng: Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, China
Hao Chang: Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, China
Fei Wang: State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China

Energies, 2022, vol. 15, issue 17, 1-22

Abstract: Northern China is vigorously promoting cogeneration and clean heating technologies. The accurate prediction of building energy consumption is the basis for heating regulation. In this paper, the daily, weekly, and annual periods of building energy consumption are determined by Fourier transformation. Accordingly, a period-based neural network (PBNN) is proposed to predict building energy consumption. The main innovation of PBNN is the introduction of a new data structure, which is a time-discontinuous sliding window. The sliding window consists of the past 24 h, 24 h for the same period last week, and 24 h for the same period the previous year. When predicting the building energy consumption for the next 1 h, 12 h, and 24 h, the prediction errors of the PBNN are 2.30%, 3.47%, and 3.66% lower than those of the traditional sliding window PBNN (TSW-PBNN), respectively. The training time of PBNN is approximately half that of TSW-PBNN. The time-discontinuous sliding window reduces the energy consumption prediction error and neural network model training time.

Keywords: period-based neural network; energy consumption; sliding window structure; Fourier transform (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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