An Ultra-short-term Wind Power Forecasting Method with Special Error Distribution

Abstract

Abstract The randomness and uncontrollability of wind energy have brought many difficulties to the development of wind power generation. How to obtain accurate forecasting results of wind power has become an increasingly important topic these years. This paper proposes an ultra-short-term wind power forecasting method using Elman neural network with an enhanced gradient descent training algorithm, which is able to compute the probabilistic distribution function (PDF) of the forecasting error by utilizing two kinds of series expansion for the purpose of regarding it as the loss function of the forecasting model. To validate the accuracy and efficiency of the purposed method, the conventional least mean square error (LMSE) based model is served as a benchmark in the comparison of simulation results. At last, historical wind power statistics of one month collected from a wind farm in the northern Hebei Province of China are used to perform single-point and probabilistic predictions in order to verify the effectiveness of the purposed method.