Wind power prediction using a novel model on wavelet decomposition-support vector machines-improved atomic search algorithm

Abstract Wind power output is highly volatile and intermittent owing to the characteristics of wind energy. Large-scale wind power integration affects the stability of entire power system. Accurate wind power prediction facilitates to utilize wind energy, improve power supply quality and maintain stable operation of power grid. A hybrid prediction model combining wavelet decomposition, support vector machine and improved atom search algorithm is proposed to predict wind power output. Dynamic sinusoidal wave adaptive weight is introduced to improve the position update equation, and crossover and mutation operations are added to the end of each iteration to improve the search ability of atomic search algorithm. In the prediction process, the original wind power data is processed by wavelet decomposition and the non-stationary signal is decomposed into several detail sequences of different frequencies and approximate sequence to extract important wind power features to reduce the prediction error caused by data fluctuation. Prediction results of actual wind farm data prove the proposed model has prominent advantages in predicting performance. Three evaluation indexes of mean absolute error, mean absolute percent error and root mean square error decreased by at least 1.14%, 2.60% and 1.52%. The proposed method is conductive to reduce wind curtailment and improve the economic benefit of wind farm.