Short-Term Power Prediction of a Wind Farm Based on Empirical Mode Decomposition and Mayfly Algorithm–Back Propagation Neural Network - Gong, Zeweiyi; Ma, Xianlong; Xiao, Ni; Cao, Zhanguo; Zhou, Shuai; Wang, Yaolong; Guo, Chenjun; Yu, Hong (Frontiers in Energy Research, 2022)
With the improvement of energy consumption structure, the installed capacity of wind power increases gradually. However, the inherent intermittency and instability of wind energy bring severe challenges to the dispatching operation. Wind power forecasting is one of the main solutions. In this work, a new combined wind power prediction model is proposed. Firstly, quartile method is used for data cleaning, namely identifying and eliminating the abnormal data. Then, the wind power data sequence is decomposed by empirical mode decomposition to eliminate non-stationary characteristics. Finally, the wind generator data is trained by MA-BP network to establish the wind power prediction model. And the simulation tests verify the prediction effect of proposed method.