| Summary: | Artificial Intelligence methods (AI) have been widely applied in building energy consumption prediction. As data-intensive methods, lacking sufficient input features will significantly impede prediction performance. For some buildings where the building energy management systems (BEMs) are underperformed, limited information can be extracted. In this study, a feature engineering framework that combines feature construction and selection was developed to deal with limited feature problems. Empirical mode decomposition and Boruta feature selection were applied with the purpose of generating new informative features and selecting all relevant features, respectively. The proposed strategy was then tested using some popular machine learning algorithms for three different buildings. The results indicated that the proposed strategy was able to extend the feature dimensions and determine all relevant features from the extended feature space, which resulted to a significant improvement in the prediction performance. Unlike most other existing studies whereby observed performance enhancements may be marginal and restricted to few of the tested algorithms, the features selected here consistently improved the outcomes of all the machine learning algorithms tested for all 3 buildings.
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