Residential energy consumption dynamics: A SHAP-based interpretation, k-means clustering, and predictive modelling

Abstract

A proper understanding of climatic consumption dynamics is critical for demand-side management, grid stability, and climate-sensitive residential energy planning. The non-linear interaction between meteorological conditions has necessitated intelligent predictive models. However, existing studies focused on machine learning (ML) models in their black-box nature, with limited interpretability of the impact of climatic-drivers, climatic-demand interactions, and hidden consumption regimes. This research fills this gap through an integrated framework that combines seasonal hypothesis-testing, k-means clustering, Shapley Additive exPlanations (SHAP)-based interpretability, and advanced predictive modeling using XGBoost, Random Forest (RF), long-short term memory (LSTM), Support Vector Machine (SVM), and Autoregressive Integrated moving average (ARIMA). The seasonal hypothesis-testing using ANOVA and Tukey's HSD revealed statistically significant differences in energy consumption across seasons, with peak-demand during winter and summer extremes. SHAP-based feature ranking identified temperature and humidity as the most influential drivers of electricity-demand. The k-means clustering revealed three distinct groups/clusters, which reflect the climatic-consumption scenarios. The ensemble learning (RF and XGBoost) exhibited the lowest training-error. RF had the best training performance with MSE, RMSE, and MAE values of 1.6484, 1.2839, and 0.8480. The data-driven insights in this study provide useful intelligence that supports critical decision-making through a proper understanding of the residential climatic-consumption dynamics.

Keywords

Ensemble learning / SHAP / Explainable AI (XAI) / Power consumption / K-means / Climate

CRediT authorship contribution statement

Oluwatobi Adeleke: Writing – review & editing, Writing – original draft, Validation, Investigation, Formal analysis, Data curation, Conceptualization. Oluwapelumi Joseph Adebowale: Writing – original draft, Validation, Investigation, Conceptualization. Idowu Ayoade: Writing – original draft, Visualization, Validation, Investigation, Formal analysis, Conceptualization. Iretioluwa Olawuyi: Writing – original draft, Visualization, Validation, Methodology, Investigation, Data curation. Tien-Chien Jen: Writing – review & editing, Writing – original draft, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

Declaration of competing interest

Tien-Chien Jen is an associate editor for Green Energy and Resources and was not involved in the editorial review of the decision to publish this article. Other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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