A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting

• Published in: Journal of forecasting Vol. 39; no. 5; pp. 737 - 756
• Main Authors: Fan, Guo‐Feng, Guo, Yan‐Hui, Zheng, Jia‐Mei, Hong, Wei‐Chiang
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Periodicals Inc 01.08.2020
• Subjects: back‐propagation neural network (BPNN); Decomposition; Electricity generation; empirical mode decomposition (EMD); Employment; Forecasting; general regression; Information theory; mid‐short‐term load forecasting; Neural networks; Power; Simulation; support vector regression (SVR)
• ISSN: 0277-6693; 1099-131X
• DOI: 10.1002/for.2655

Since load forecasting plays a decisive role in the safe and stable operation of power systems, it is particularly important to explore forecasting methods accurately. In this article, the hybrid empirical mode decomposition (EMD) and support vector regression (SVR) with back‐propagation neural network (BPNN), namely the EMDHR‐SVR‐BPNN model, is proposed. Information theory is mainly used to solve the data tendency problem, and the EMD method is used to solve the data volatility problem. There is no interaction between these two methods; thus these two models can complement each other through generalized regression of orthogonal decomposition. Taking the load data from the New South Wales (NSW, Australia) market as an example, the obtained simulation results are compared with other models. It is concluded that the proposed EMDHR‐SVR‐BPNN model not only improves the forecasting accuracy but also has good fitting ability. It can reflect the changing tendency of data in a timely manner, providing a strong basis for the electricity generation of the power sector in the future, thus reducing electricity waste. The proposed EMDHR‐SVR‐BPNN model has potential for employment in mid‐short term load forecasting.