A deep-learning approach for modeling the demand function of air conditioning resources with respect to the electricity prices
Chenge Gao,
Ye Guo,
Yinliang Xu,
Jieming Huang,
Fan Zhang,
Wuhua Hu and
Qiang Liu
Applied Energy, 2025, vol. 392, issue C, No S030626192500707X
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.
Keywords: Air-conditioners; Deep learning; Distributed energy resources; Electricity markets (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030626192500707X
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:392:y:2025:i:c:s030626192500707x
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic
DOI: 10.1016/j.apenergy.2025.125977
Access Statistics for this article
Applied Energy is currently edited by J. Yan
More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().