Socio-Demographic Information Estimation via Smart Meter Data Analysis and Feature Selection

In this study, we propose a novel method for predicting the number of household members using smart meter electricity consumption data. Utilizing various feature selection techniques, including SHAP values, mutual information, feature importance, and a proposed method involving pairwise grouping and selection based on importance testing, we aimed to enhance the model's predictive accuracy. The proposed method involves selecting time points based on specific time ranges and overlapping important features from other techniques. The analysis revealed that time periods such as morning, evening, and before bedtime are most informative for estimating household member numbers, while periods with lower activity, like lunchtime to early evening, are less useful.