Research on Central Air Conditioning Systems and an Intelligent Prediction Model of Building Energy Load

Lin Pan (), Sheng Wang (), Jiying Wang (), Min Xiao and Zhirong Tan
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Lin Pan: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Sheng Wang: GREE, State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation, GREE Electric Appliances Inc. of Zhuhai, Zhuhai 519070, China
Jiying Wang: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Min Xiao: School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430063, China
Zhirong Tan: School of Navigation, Wuhan University of Technology, Wuhan 430063, China

Energies, 2022, vol. 15, issue 24, 1-31

Abstract: The central air conditioning system provides city dwellers with an efficient and comfortable environment. Meanwhile, coinciding with their use, the building electricity load is increased, as central air conditioners consume a lot of electricity. It has become necessary to control central air conditioners for storage and to analyze the energy saving optimization of central air conditioner operation. This study investigates the energy consumption background of central air conditioning systems, and proposes an intelligent load prediction method. With a back propagation (BP) neural network, we use the data collected in the actual project to build the cooling load prediction model for central air conditioning. The network model is also trained using the Levenberg–Marquardt (LM) algorithm, and the established model is trained, tested, and predicted by importing a portion of the sample data, which is filtered by preprocessing. The experimental results show that most of the data errors for training, testing, and prediction are within 10%, indicating that the accuracy achievable by the model can meet the practical requirements, and can be used in real engineering projects.

Keywords: energy consumption; central air conditioning system; cooling load forecasting; 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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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