Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques

This paper presents a data mining (DM) based approach to developing ensemble models for predicting next-day energy consumption and peak power demand, with the aim of improving the prediction accuracy. This approach mainly consists of three steps. Firstly, outlier detection, which merges feature extraction, clustering analysis, and the generalized extreme studentized deviate (GESD), is performed to remove the abnormal daily energy consumption profiles. Secondly, the recursive feature elimination (RFE), an embedded variable selection method, is applied to select the optimal inputs to the base prediction models developed separately using eight popular predictive algorithms. The parameters of each model are then obtained through leave-group-out cross validation (LGOCV). Finally, the ensemble model is developed and the weights of the eight predictive models are optimized using genetic algorithm (GA).

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