Short-Term Load Forecasting Method Based on EWT and IDBSCAN

Accurate load forecasting is of great significance to the safety and stability of power grid operation. This paper proposes a combined prediction method based on empirical wavelet transform (EWT) and improved density-based spatial clustering of applications with noise (IDBSCAN). The EWT is used to decompose the load to obtain various intrinsic mode functions (IMFs). Then, each IMF is predicted with a rational method. The low frequency and intermediate frequency components are predicted by long short-term memory (LSTM). High frequency components have uncertain characteristics. Therefore, they are clustered by IDBSCAN, according to meteorological factors. The processing model is selected based on the characteristics of each class sample. Finally, the prediction results of each component are superimposed to obtain the total prediction result. Experiments are conducted based on the measured load data of a Chinese city. A comparison between the proposed method, EWT–LSTM, EWT-Elman, and empirical mode decomposition (EMD)-LSTM is made. The results indicate that the proposed method has higher prediction accuracy and reflects the randomness of the actual load.

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