Research on Building Energy Consumption Prediction Based on Improved PSO Fusion LSSVM Model

Suli Zhang (), Yiting Chang, Hui Li and Guanghao You
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Suli Zhang: School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, China
Yiting Chang: School of Energy and Power Engineering, Changchun Institute of Technology, Changchun 130012, China
Hui Li: School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China
Guanghao You: School of Energy and Power Engineering, Changchun Institute of Technology, Changchun 130012, China

Energies, 2024, vol. 17, issue 17, 1-17

Abstract: In urban building management, accurate prediction of building energy consumption is significant in realizing energy conservation and improving energy efficiency. Due to the complexity and variability of energy consumption data, existing prediction models face the challenge of difficult parameter selection, which directly affects their accuracy and application. To solve this problem, this study proposes an improved particle swarm algorithm (IPSO) for optimizing the parameters of the least squares support vector machine (LSSVM) and constructing an energy consumption prediction model based on IPSO-LSSVM. The model fully combines the advantages of LSSVM in terms of nonlinear fitting and generalization ability and uses the IPSO algorithm to adjust the parameters precisely. By analyzing the sample data characteristics and validating them on two different types of building energy consumption datasets, the results of the study show that, compared with traditional baseline models such as back-propagation neural networks (BP) and support vector regression (SVR), the model proposed in this study is more accurate and efficient in parameter selection and significantly reduces the prediction error rate. This improved approach not only improves the accuracy of building energy consumption prediction but also enhances the robustness and adaptability of the model, which provides reliable methodological support for the development of more effective energy-saving strategies and optimization of energy use to achieve the goal of energy-saving and consumption reduction and provides a new solution for the future management of building energy consumption.

Keywords: building energy consumption; prediction; improved particle swarm optimization; least squares support vector machine (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: 2024
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