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An accurate electricity load prediction is important to optimizing building electricity load performance. However, building electricity load prediction is complex due to many influencing factors. This study develops a hybrid algorithm that combines clustering approach, empirical mode decomposition, and support vector regression to develop a prediction model for building electricity load. k-shape clustering is used to extract similar building electricity load pattern, and empirical mode decomposition is employed to decompose electricity load data into several Intrinsic Mode Functions (IMF). Finally, a prediction model using support vector regression is built for each IMF individually, and the prediction result of all IMFs is combined to obtain an aggregated output of electricity load. Numerical testing demonstrated that the proposed method can accurately predict the electricity load in the building.

Improvement of Building Electricity Load Prediction Accuracy Using Hybrid k-Shape Clustering EMD Based Support Vector Regression