A novel short-term multi-input-multi-output prediction model of wind speed and wind power with LSSVM based on quantum-behaved particle swarm optimization algorithm

With the rapid development of wind power, the installed capacity of wind power is also growing continuously. The intermittency and uncertainty of wind power may pose danger on the safety of the power system, thus Research of short-term load forecasting has important practical application value in the field of power network dispatching. 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 the wind power prediction model and quantum-behaved particle swarm optimization (QPSO) algorithm is used to optimize the most important parameters which influence the least squares support vector machine regression model. In the proposed QPSO-LSSVM, the kernel parameter σ and regularization parameter γ are considered as the position vector of particles and quantum mechanics is introduced in particle swarm optimization (PSO) algorithm to effectively solve the contradiction between expanding search and finding optimal solution. A multiinput-multi-output (MIMO) short-term prediction 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, meanwhile, several error indicators are selected to analyze the results of the three models. Prediction analysis results show that the QPSO-LSSVM model can achieve higher prediction accuracy and confirm the effectiveness and feasibility of the method. Copyright © 2017, AIDIC Servizi S.r.l.