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Building energy management systems (BEMS) have somewhat standardised building energy consumption data formats, thereby enhancing their compatibility with the relevant ML-based prediction algorithms. However, data shortage remains a significant limiter to accurate building energy consumption prediction. Against this backdrop, it would seem logical to believe that a potentially viable remedy would be to rationalise the features extracted from available data, to guarantee better representation of building energy consumption. It is envisaged that this approach will help address the challenges of redundant and unrelated information clouding the features, which may undermine the performance of current ML-based methods. Currently, no research has systematically investigated the application and/or impact of feature selection on building energy consumption prediction. Hence, the overarching purpose of this …