A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China. (March 2017) - Dong, Qingli; Sun, Yuhuan; Li, Peizhi
Abstract: As a crucial issue in the wind power industry, it is a tough and challenging task to predict the wind power accurately because of its nonlinearity, non-stationary and chaos. In this paper, we propose a novel hybrid model, which combines an integrated processing strategy and an optimized local linear fuzzy neural network, to forecast the wind power. First, discrete wavelet transform and singular spectrum analysis are used to filter out the noises and extract the trends from original wind power series, respectively. Then, the novel no-negative-constraint-combination theory together with the CS algorithm are adopted to integrate these two subseries obtained from the first step to retain strengths of each method. Based on the phase space reconstruction model, we could determine the most proper structure of the input sets and the output sets. Next, the local linear fuzzy neural network, with the initial rule consequent parameters optimized by the seeker optimization algorithm, is utilized to make wind power forecasts for a selected number of forward time steps. The numerical results from two experiments demonstrate that the proposed hybrid model is an effective approach to predict wind power, and the accuracy of prediction is highly improved compared with conventional forecasting models. Highlights: The hybrid forecasting approach can improve the accuracy of wind power forecasting significantly. A novel integrated strategy has been developed to process the datasets and select the proper structure of input sets. Traditional local linear neural-fuzzy model is optimized by seeker optimization algorithm. The hybrid model outperforms than the traditional methods.