The rapidly growing global energy demand, environmental concerns, and the urgent need to reduce carbon footprints have made sustainable household energy consumption a critical priority. This study aims to analyze household energy data to predict the electricity self-sufficiency rate (ESSR) of households and extract meaningful insights to enhance it.

To achieve this, the researchers employed LightGBM (Light Gradient Boosting Machine), SHAP (SHapley Additive exPlanations), and correlation-heatmap-based approaches to analyze 12 months of energy and questionnaire survey data collected from over 200 smart houses in Kitakyushu, Japan.

First, they used LightGBM to predict the ESSR of households and identify key features impacting the prediction model. The analysis revealed that the most significant features included housing type, average monthly electricity bill, presence of a floor heating system, average monthly gas bill, electricity tariff plan, electrical capacity, number of TVs, cooking equipment used, number of washing and drying machines, and the frequency of viewing home energy management systems (HEMSs).

Additionally, the LightGBM classifier was implemented to extract the most significant features and establish a statistical correlation between these features and the ESSR. Notably, the LightGBM-based model can also predict the ESSR of households that did not participate in the questionnaire survey. While this model provides a global view of feature importance, it lacks detailed explanations for individual predictions. To address this, the researchers utilized SHAP analysis to determine the impact-wise order of key features influencing the ESSR and evaluated the contribution of each feature to the model’s predictions.

A heatmap was employed to analyze the correlation among household variables and the ESSR. The performance of the classification model was assessed using a confusion matrix, which indicated a good F1 score (Weighted Avg) of 0.90. The findings discussed in this article offer valuable insights for energy policymakers aiming to develop energy self-sufficient houses.