Research on Energy-saving Regulation Mechanism of Residential Air-conditioning Loads Based on Multi-dimensional Information

Abstract

With the increasing difference between peak and valley electricity loads in China, the proportion of air conditioning load during peak summer season has reached 40% -50%, becoming a key factor affecting the safe operation of the power grid. How to scientifically identify the factors affecting air conditioning energy consumption and evaluate their energy-saving and emission reduction potential is an important prerequisite for achieving flexible load regulation. This article proposes a research framework that integrates the Analytic Hierarchy Process (AHP) of Information Entropy with an explanatory structural model. By decoupling complex coupling factors, a system for identifying and analyzing the relationship between factors affecting air conditioning energy consumption is constructed. In response to the problem of traditional methods being difficult to quantify user needs, this article uses stratified sampling and principal component analysis to extract four core demand dimensions from 14 factors: thermal comfort, energy efficiency, ease of use and economic satisfaction; Further combining long short-term neural networks, a potential evaluation model that balances user satisfaction and energy-saving effects was established. The research results indicate that the analysis system constructed in this article can effectively identify 15 main factors and 6 root factors from 38 influencing factors; When the air conditioning temperature is set to 26℃, it can achieve 10% power savings while maintaining high user satisfaction. The research results provide scientific decision-making basis for intelligent air conditioning regulation in demand side management, and have important practical value for promoting energy conservation and emission reduction in the construction industry.