基于WRF模式和PSO-LSSVM的风电场短期风速订正

风速预测是风电场风电功率预测的基础与前提，以数值天气预报(WRF模式)为基础进行风速预测，为了提高WRF模式预测的准确性，采用最小二乘支持向量机(Least Squares Support Vector Machine, LSSVM)对WRF模式输出的风速进行订正。同时，为提高LSSVM算法的精确度和减小拟合过程的复杂度，采用粒子群优化算法(Particle Swarm Optimization, PSO)对其参数进行优化。试验结果表明:采用LSSVM订正可以进一步减小WRF模式预测风速的误差，再经过PSO优化后，相对均方根误差和相对平均绝对误差降低了5%~10%，均方根误差下降了0.5 m/s。与未经优化的LSSVM以及极限学习机(ELM)算法对比分析后得出，粒子群优化最小二乘支持向量机(PSO-LSSVM)对WRF模式预测的风速有较好的订正效果，能进一步提高风速预测的准确性。

Modification technology research of short-term wind speed in wind farm based on WRF model and PSO-LSSVM method

Wind speed forecasting is the base and precondition of wind power prediction of wind farm. The Numerical Weather Prediction (WRF) model is used to predict wind speed. In order to improve the accuracy of WRF model, the Least Square Support Vector Machine (LSSVM) is used to correct the wind speed of the output of the WRF model. At the same time, in order to improve the accuracy of the LSSVM model and reduce the complexity of the fitting process, Particle Swarm Algorithm (PSO) is used to optimize the parameters. Experimental results show that the LSSVM can further reduce the error of WRF model in predicting wind speed sequence, and the relative root mean square error and the relative to the average absolute error are reduced by 5%~10%, the RMS error decreased by 0.5 m/s. Compared with without optimized LSSVM and ELM, PSO-LSSVM has a better correction effect in wind speed predicting by WRF to improve the accuracy of wind speed forecasting. This work is supported by National Natural Science Foundation of China (No. 41675156).