A New Combinatory Approach for Wind Power Forecasting

Abstract

Wind power generation is considerably dependent on the weather condition and it is correlated closely with the air density, wind speed, and its direction through considering the transformer tap change cost. Therefore, an accurate prediction model is required to forecast and adopt with the complicated signals. In this article, an accurate prediction method trained with improved wavelet transform (IWT) to decompose the original signal to subsignals, new feature selection based on the maximum dependence, maximum relevancy, and minimum redundancy to filter the signal and select the best candidate inputs and a synthetic forecasting engine with minimum forecasting error is proposed. The proposed method of forecasting engine composed two-dimensional convolution neural network (TDCNN) and trained by improved optimization algorithm based on particle swarm optimization. The proposed improved optimization algorithm will fine-tune the weights of TDCNN to increase the prediction accuracy of the forecast engine. The training optimization processes and effectual fast classification are the main duty of the proposed intelligent algorithm. The efficiency of the suggested prediction approach is widely evaluated and compared to other prediction approaches using the practical power market data. A simulation study is conducted over the trained model and the results are discussed in detail. Obtained numerical results and also analysis in short-term and long-term forecasting horizons validate the high performance and advantages of the introduced approach.