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The study presents a novel framework integrating feature selection (FS) and machine learning (ML) techniques to forecast inland national energy consumption (EC) in the United Kingdom across all energy sources. This innovative framework strategically combines three FS approaches with five interpretable ML models using Shapley Additive Explanations (SHAP), with the dual goal of enhancing accuracy and transparency in EC predictions. By meticulously selecting the most pertinent features from diverse features—including meteorological conditions, socioeconomic parameters, and historical consumption patterns of different primary fuels—the proposed framework enhances the robustness of the forecasting model. This is achieved through benchmarking three FS approaches: ensemble filter, wrapper, and a hybrid ensemble filter-wrapper. In addition, we introduce a novel ensemble filter FS, synthesizing …