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A deep-learning approach for modeling the demand function of air conditioning resources with respect to the electricity prices

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  • Gao, Chenge
  • Guo, Ye
  • Xu, Yinliang
  • Huang, Jieming
  • Zhang, Fan
  • Hu, Wuhua
  • Liu, Qiang

Abstract

This study considers the problem of modeling the demand function of air conditioning (AC) resources with respect to electricity prices. We propose a deep-learning approach based on the staircase properties of demand functions and their sensitivity to price and temperature fluctuations. The model integrates two expert networks which capture common features for ACs in the same area – one for price and one for temperature – dynamically weighted by gate units adjusted based on historical data, allowing the model to adaptively balance the influence of both factors. Furthermore, trainable staircase activation functions in the output layer enable flexible modeling across diverse AC resources with substantially lower sample requirements. Simulations on two cases, New York City and Shenzhen, demonstrate that the proposed method achieves accurate performance for a wide range of AC resources and reduces the cost when participating in the market.

Suggested Citation

  • Gao, Chenge & Guo, Ye & Xu, Yinliang & Huang, Jieming & Zhang, Fan & Hu, Wuhua & Liu, Qiang, 2025. "A deep-learning approach for modeling the demand function of air conditioning resources with respect to the electricity prices," Applied Energy, Elsevier, vol. 392(C).
    • Handle: RePEc:eee:appene:v:392:y:2025:i:c:s030626192500707x
    DOI: 10.1016/j.apenergy.2025.125977
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