A novel short-term multi-input–multi-output prediction model of wind speed and wind power with LSSVM based on improved ant colony algorithm optimization

Abstract

The intermittency and uncertainty of wind power may pose danger on the safety on the power system, thus the research of wind speed and wind power prediction methods has been widely concerned. The keys of wind power forecasting are the forecasting model selection and model optimization. In this paper, the least squares support vector machine (LSSVM) is chosen as the wind speed and wind power prediction model and improved ant colony optimization (IACO) algorithm is used to optimize the most important parameters which influence the least squares support vector machine (LSSVM) regression model. In the IACO-LSSVM method, the kernel parameter σ and regularization parameter γ were considered the position vector of ants, the improved pheromone updating method is used to effectively solve the contradiction between expanding search and finding optimal solution. A multi-input–multi-output (MIMO) short-term wind speed and wind power forecasting model is built and applied in a wind farm of Gansu province in order to predict wind speed and wind power. For comparative study, PSO-LSSVM model and SVM model are used for forecasting. Prediction analysis results show that the IACO-LSSVM model can achieve higher prediction accuracy and confirm the effectiveness and feasibility of the method.