A Hybrid Short-Term Building Electrical Load Forecasting Model Combining the Periodic Pattern, Fuzzy System, and Wavelet Transform

Accurate forecasting and scientific analysis of building electrical load can improve the level of building energy management to meet the requirements of energy saving. To further strengthen the forecasting accuracy, this study presents a hybrid model for building electrical load forecasting. The proposed method combines the fuzzy inference system and the periodicity knowledge together to generate accurate forecasting results. In this method, in order to better reflect the actual characteristic of the electrical load, the wavelet transform method is firstly utilized to filter the original building electrical load data. Then, the daily periodic pattern is extracted from such filtered electrical load data, and the residual data are obtained through removing the daily periodic pattern. Further, the residual data-driven forecasting model is constructed by the functionally weighted single-input-rule-modules connected fuzzy inference system (FWSIRM-FIS). This FWSIRM-FIS model is used to provide the compensation to the periodic component. In other words, the daily periodic component and the residual forecasting are combined to achieve the final forecasting result. Specifically, in order to assure the forecasting performance of the FWSIRM-FIS model, the subtraction clustering method is employed to construct the SIRMs while the least square estimation is utilized to optimize the parameters in the functional weights of the FWSIRM-FIS. Finally, in this paper, two real-world experiments are made and detailed comparisons with four traditional models are given. Experimental and comparison results demonstrate that the proposed hybrid model has the smallest forecasting errors and can achieve the best performance.