Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data. (1st February 2022)

• Record Type: Journal Article
• Authors: Tang, Wenjun; Wang, Hao; Lee, Xian-Long; Yang, Hong-Tzer
• Is Part Of: Energy. Volume 240(2022)
• Publication Date: 2022-02-01
• Subjects: Consumption pattern -- Socioeconomic -- Smart meter -- Clustering -- Feature selection -- Machine learning; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.122500
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The smart meter data analysis contributes to better planning and operations for the power system. This study aims to identify the drivers of residential energy consumption patterns from the socioeconomic perspective based on the consumption and demographic data using machine learning. We model consumption patterns by representative loads and reveal the relationship between load patterns and socioeconomic characteristics. Specifically, we analyze the real-world smart meter data and extract load patterns by clustering in a robust way. We further identify the influencing socioeconomic attributes on load patterns to improve our method's interpretability. The relationship between consumers' load patterns and selected socioeconomic features is characterized via machine learning models. The findings are as follows. (1) Twelve load clusters, consisting of six for weekdays and six for weekends, exhibit a diverse pattern of lifestyle and a difference between weekdays and weekends. (2) Among various socioeconomic features, age and education level are suggested to influence the load patterns. (3) Our proposed analytical model using feature selection and machine learning is proved to be more effective than XGBoost and conventional neural network model in mapping the relationship between load patterns and socioeconomic features. Highlights: The relationship between residential load and demographics is investigated. Residential energy consumption behavior is analyzed by the K-Medoid Algorithm. Feature selection is used to identify demographic features affecting load patterns. Deep learning is developed to reveal load patterns using socioeconomic information. Our method is validated using real-world smart meter data and demographic surveys.