Gray wolf optimization-based wind power load mid-long term forecasting algorithm

Abstract

Enhancing the utilization of new energy consumption relies significantly on mid-to-long-term load forecasting. However, current studies face limitations in effectively extracting and utilizing future climate forecast information. So, in this paper, we proposed a wind power mid-long-term load forecasting method considering different wind energy characteristics for effective future climate information prediction. Further, the wind energy resources climate forecast results data is taken as input to improve the adaptability of the prediction model and realize the screening of different forecast error characteristic data sets by building a wind energy feature mining model. Then, an adaptive prediction model is constructed based on the Gray wolf optimization algorithm (GWO) and the long short-term memory network (LSTM). The result obtained by the proposed model is compared with the existing research. From, the result analysis it is clear that the performance of the proposed prediction method is better than the existing research.