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Economic Load-Reduction Strategy of Central Air Conditioning Based on Convolutional Neural Network and Pre-Cooling

Author

Listed:
  • Siyue Lu

    (State Grid Beijing Electric Power Research Institute, Beijing 100075, China)

  • Baoqun Zhang

    (State Grid Beijing Electric Power Research Institute, Beijing 100075, China)

  • Longfei Ma

    (State Grid Beijing Electric Power Research Institute, Beijing 100075, China)

  • Hui Xu

    (State Grid Beijing Electric Power Research Institute, Beijing 100075, China)

  • Yuantong Li

    (Department of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Shaobing Yang

    (Department of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Central air conditioning in large buildings is an important demand-response resource due to its large load power and strong controllability. Demand-response-oriented air conditioning load modeling needs to calculate the room temperature. The room temperature calculation models commonly used in the existing research cannot easily and accurately calculate the room temperature change of large buildings. Therefore, in order to obtain the temperature change of a large building and its corresponding power potential, this paper first proposes a building model based on CNN (convolutional neural network). Then, in order to fully apply the demand-response potential of the central air conditioning load, this paper puts forward an evaluation method of the load-reduction potential of the central air conditioning cluster based on pre-cooling and develops an economic load-reduction strategy according to the different energy consumption of different buildings in the pre-cooling stage. Finally, multiple building examples with different building parameters and temperature comfort ranges are set up, and the economic advantages of the proposed strategy are illustrated by Cplex solution examples.

Suggested Citation

  • Siyue Lu & Baoqun Zhang & Longfei Ma & Hui Xu & Yuantong Li & Shaobing Yang, 2023. "Economic Load-Reduction Strategy of Central Air Conditioning Based on Convolutional Neural Network and Pre-Cooling," Energies, MDPI, vol. 16(13), pages 1-22, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5035-:d:1182342
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    References listed on IDEAS

    as
    1. Saeid Bashash & Kai Lun Lee, 2019. "Automatic Coordination of Internet-Connected Thermostats for Power Balancing and Frequency Control in Smart Microgrids," Energies, MDPI, vol. 12(10), pages 1-23, May.
    2. Zexu Chen & Jing Shi & Zhaofang Song & Wangwang Yang & Zitong Zhang, 2022. "Genetic Algorithm Based Temperature-Queuing Method for Aggregated IAC Load Control," Energies, MDPI, vol. 15(2), pages 1-16, January.
    3. Siyue Lu & Teng Li & Xuefeng Yan & Shaobing Yang, 2022. "Evaluation of Photovoltaic Consumption Potential of Residential Temperature-Control Load Based on ANP-Fuzzy and Research on Optimal Incentive Strategy," Energies, MDPI, vol. 15(22), pages 1-21, November.
    4. Lin Pan & Sheng Wang & Jiying Wang & Min Xiao & Zhirong Tan, 2022. "Research on Central Air Conditioning Systems and an Intelligent Prediction Model of Building Energy Load," Energies, MDPI, vol. 15(24), pages 1-31, December.
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