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

Abstract

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