基于改进TLBO优化LSSVM的风电功率短期预测

为提高风电功率短期预测的精度,提出一种基于改进TLBO优化LSSVM的风电功率短期预测方法。首先对基本TLBO算法中的‘教’阶段进行改进,在采用自适应教学因子的同时改变所有搜索个体的平均值,从而能够自适应的提高TLBO在整个搜索空间的性能;然后改进TLBO算法的‘学’阶段,为维持种群的多样性,避免TLBO算法过早收敛和陷入局部最优,在学习阶段引入高斯变异算子;最后用改进的TLBO优化构建的LSSVM预测模型。以上海北沿风电场和莱州风电场实测数据为例,仿真结果表明,与PSO和TLBO优化LSSVM相比,改进的TLBO优化LSSVM方法对短期风电功率预测具有更好的稳定性和更高的准确性。

Short-term prediction of wind power based on improved TLBO optimization LSSVM

In order to improve the accuracy of short-term forecasting of wind power, a short-term forecasting method based on improved TLBO optimization LSSVM is proposed. Firstly, to improve TLBO algorithm of "teach" stage, the adaptive teaching factor is used, at the same time, changing average of all search individuals, which can enhance the performance of TLBO in the whole search space; Then, the ''learning'' stage of TLBO algorithm is improved to maintain the diversity of the population and avoid the premature convergence and local optimization of TLBO algorithm, and the gaussian mutation operator is introduced in the learning stage. Finally, the improved LSSVM prediction model is optimized with improved TLBO. Take the measured data of beiyan wind farm in Shanghai as an example, the simulation results show that with the PSO and TLBO compared to optimizing LSSVM, improved TLBO optimizing LSSVM method for short-term wind power prediction has better stability and higher accuracy