Abstract
Of the many kinds of renewable energy, wind power is low in cost and non-polluting, so it is especially well-suited to Taiwan. The Mai Liao Wind Farm is the most important wind farm in Taiwan, and forecasting the wind power output for national sustainable development continues to be a challenging research feature. In this study, we attempt to forecast the wind power data collected from the Mai Liao Wind Farm. Our forecast model is based on a Multi-Layer Perceptron Artificial Neural Network (MLP) model using the data collected at the Mai Liao Wind Farm over a period of five years from September 2002 to August 2007. We proposed a new algorithm, namely improved Simplified Swarm Optimization (iSSO), which improves Simplified Swarm Optimization (SSO) by justifying the weights and bias in training the MLP. The proposed iSSO combines Principal Component Analysis (PCA), Autocorrelation Function (AF) and Partial Autocorrelation Function (PAF) for the selection of features which increases the efficiency of the proposed model. The experimental results demonstrate that the performance of iSSO outperforms the other six most popular algorithms. © 2013 Elsevier Ltd. All rights reserved.