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

Author

Listed:
  • Zhengchao Xie

    (Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, China
    State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, 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)

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.

Suggested Citation

  • Zhengchao Xie & Xiao Wang & Lijun Zheng & Hao Chang & Fei Wang, 2022. "A Period-Based Neural Network Algorithm for Predicting Building Energy Consumption of District Heating," Energies, MDPI, vol. 15(17), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6338-:d:902296
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    References listed on IDEAS

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