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At present, there are many problems in the research on building system regulation and control, such as conflicting scheduling methods and underutilization of demand-side response resources, which will hinder the stable operation of power grids and the development of high penetration rate of clean energy. This paper proposes a two-layer optimal control strategy for intelligent buildings considering demand-side response. Firstly, the mathematical modeling of the intelligent building energy system is carried out, and then different demand response scenarios and power balance constraints are set. A two-layer optimal control model based on Lasso-GRNN neural network was established for day-ahead optimal control and intraday dynamic control. Finally, the effectiveness of the proposed strategy is verified by simulation analysis.

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Research on the Optimal Control Strategy of Smart Buildings Considering Demand Response

  • 会议论文

  • 发表日期：

    2025年

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  • Jingwen Chen,
  • Cheng Qian,
  • Songsong Chen,
  • Feixiang Gong,
  • Yaoxian Liu

摘要

At present, there are many problems in the research on building system regulation and control, such as conflicting scheduling methods and underutilization of demand-side response resources, which will hinder the stable operation of power grids and the development of high penetration rate of clean energy. This paper proposes a two-layer optimal control strategy for intelligent buildings considering demand-side response. Firstly, the mathematical modeling of the intelligent building energy system is carried out, and then different demand response scenarios and power balance constraints are set. A two-layer optimal control model based on Lasso-GRNN neural network was established for day-ahead optimal control and intraday dynamic control. Finally, the effectiveness of the proposed strategy is verified by simulation analysis.