Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data

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. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study uses machine learning to identify the drivers of residential energy consumption patterns from the socioeconomic perspective based on smart meter data. The findings reveal the diversity of load patterns and the difference between weekdays and weekends, and suggest that age and education level may influence load patterns. The proposed analytical model using feature selection and machine learning proves to be more effective in mapping the relationship between load patterns and socioeconomic features than XGBoost and conventional neural network models.