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

Siyue Lu, Baoqun Zhang, Longfei Ma, Hui Xu, Yuantong Li () and Shaobing Yang
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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

Energies, 2023, vol. 16, issue 13, 1-22

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.

Keywords: central air conditioning; air conditioning load control; demand response; load reduction; convolutional neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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