Volume 53

Usage Behavior Analysis of Household Appliances Based on a Panel Study in Shenzhen, China Jiajing GAO, Jialin Wu, Yi Zhang

Abstract

The temporal mismatch between renewable energy generation and energy consumption in residential buildings is highly dependent on user behavior, which is crucial for future home energy management and demand-side response solutions aimed at increasing renewable energy consumption. This study leveraged a custom-developed personal energy tracking app to conduct both a questionnaire survey on social attributes and a self-monitoring tracking survey of residents in Guangdong, China. 373 data on their 24-hour energy usage behaviors were collected and utilized clustering algorithms to extract typical usage times and patterns for various appliances. Additionally, the study examined the significance and importance of social characteristics affecting residents’ usage behaviors. The findings provide valuable insights for further investigation into residents’ willingness to participate in flexible energy regulation, the formulation of incentive policies, and the design of home energy management strategies.

Keywords energy usage behavior, home energy management, app development, panel study, k-means clustering, random forest