Hunan Electric Power ›› 2026, Vol. 46 ›› Issue (1): 1-7.doi: 10.3969/j.issn.1008-0198.2026.01.001

• Power Grid Operation and Control •     Next Articles

Day-Ahead Scheduling of Building Integrated Energy System Based on Feedforward Neural Network

WANG Zeye1, LI Xueying2, ZHOU Yujie3, JING Tianjun1   

  1. 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;
    2. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China;
    3. College of Engineering, City University of Hong Kong, Hong Kong 999077, China
  • Received:2025-09-26 Revised:2025-10-25 Online:2026-02-25 Published:2026-03-10

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Abstract: With the increase of building integrated energy systems(BIES) and its increasingly prominent role in improving energy utilization efficiency, it is necessary to study how to enhance the coupling degree of equipment within BIES and its coordination with the power grid, thereby reducing the daily comprehensive operating cost of BIES. Therefore, a BIES day-ahead optimization scheduling model based on feedforward neural network(FNN) is proposed. Firstly, the structure of BIES with rooftop photovoltaics is analyzed. Secondly, a day-ahead PV output prediction method and process based on FNN are proposed, providing reliable PV power generation data for BIES optimization scheduling. Finally, with the objective of minimizing the daily comprehensive operating cost composed of electricity purchase cost, gas purchase cost, and equipment operation and maintenance cost, a day-ahead scheduling model for BIES is proposed. The result of the calculation examples show that the proposed PV output prediction method can effectively improve the prediction accuracy of PV power generation output, and the propose day-ahead optimization scheduling model can effectively improve the operational economy of BIES.

Key words: BIES(integrated energy system of building), FNN(feedforward neural network), optimization scheduling

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