Interpretable Energy Forecasting: Comparative Analysis of Voting Regression and NODE Models for Electricity Power Consumption Prediction

Accurate electricity power consumption estimation is vital for efficient energy management, demand-side response strategies and grid optimization. This study explores ensemble based machine learning-based and deep learning approaches for electricity power consumption prediction across three distinct zones by using a Voting Regression model including Linear Regression, Random Forest and K-Nearest Neighbors (KNN) and NODE (Neural Oblivious Decision Ensembles) model. Quantitative and qualitative experimental results show that Voting Regression model outperforms NODE model in overall and additionally, SHAP (SHapley Additive exPlanations) technique is applied on the Voting Regression model to analyze and explain the feature contributions and effects for the model prediction. Consequently, this study provides a reliable, explainable and data-driven framework for optimizing electricity power distribution and electricity consumption planning.