Study on wind power prediction based on improved double wavelet transform and quantile regression forest

Abstract

Abstract The random fluctuation of a wind power system will bring significant challenges to its stable operation when connected to the grid[1]. To reduce the impact of microgrid wind power integration on electric power systems and in response to the certain prediction inaccuracy of BP, LSSVM, and ARIMA-based prediction models in case of rapid fluctuation of actual power, a method of decomposing the original wind power sequence into several characteristic subcomponents through the improved double wavelet transform algorithm was proposed in this study to weaken the fluctuation of the wind power sequence. Specifically, a short-term wind power probability density prediction model based on the improved double wavelet algorithm and quantile regression method was established. Then, the original wind power sequence was decomposed into a series of components with different frequencies using the double wavelet transform method. Suitable components were chosen to construct a QRF prediction model to acquire the prediction results.