Modeling and short-term prediction of HVAC system with a clustering algorithm

Abstract Energy consumption and air quality index (AQI) prediction is important for efficient heating, ventilation, and air conditioning (HVAC) system operation and management. A data-mining approach is presented in this paper for modeling and short-term prediction of the complicated non-linear system. The multilayer perceptron (MLP) ensemble performs best among the data mining algorithms discussed in this paper. A clustering-based method from preprocessing input data to construct the prediction models is proposed to decreases the prediction errors and the computational cost. The effectiveness of the proposed method is validated through a practical case study with both modeling and short-term prediction. The analytical results showed that the method was capable of reducing the prediction errors for modeling and short-term prediction by 11.05% and 12.21%, respectively, comparing with the models built without clustering method.

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