基于改进人工鱼群优化支持向量机的短期风电功率预测

王永翔; 陈国初

王永翔,陈国初.基于改进人工鱼群优化支持向量机的短期风电功率预测[J].电测与仪表,2016,53(3):.
WANG Yong-xiang,CHEN Guo-chu.Short-term Wind Power Prediction Based on Improved Artificial Fish Swarm Algorithm Optimization Support Vector Machine[J].Electrical Measurement & Instrumentation,2016,53(3):.

基于改进人工鱼群优化支持向量机的短期风电功率预测

Short-term Wind Power Prediction Based on Improved Artificial Fish Swarm Algorithm Optimization Support Vector Machine

DOI：

中文关键词: 人工鱼群算法支持向量机变系数因子功率预测

英文关键词:artificial fish swarm algorithm support vector machine variable coefficient power prediction

基金项目:上海市教委科研创新项目（13YZ140）、上海市自然科学基金项目（11ZR1413900）、上海市教委重点学科（J51901）

作者中文名	作者英文名	单位
王永翔	WANG Yong-xiang	上海电机学院电气学院
陈国初	CHEN Guo-chu	上海电机学院电气学院

摘要点击次数: 2004

中文摘要:

针对人工鱼群算法中固定的视野和步长导致算法寻优速度变慢、易陷入局部最优等问题,引入了一个变系数因子来自适应调节人工鱼在聚群、追尾和觅食行为中的视野和步长；此外,为了降低算法后期运算复杂度以获得更多有效的人工鱼,加入一种人工鱼群最大迭代次数淘汰机制。将改进后的人工鱼群算法用来优化支持向量机中的核函数参数和惩罚参数,并应用到风电场短期风电功率预测中。通过实验仿真对比得出改进的人工鱼群优化支持向量机在短期风电功率预测中有较好的效果。

英文摘要:

Due to the fixed vision and step of artificial fish swarm algorithm resulting in algorithm optimization speed slow, easy to fall into local optimum value, it introduces a variable coefficient factor for adapting the vision and step in artificial fish’s swarm, rear end and foraging behavior; in addition, to reduce computational complexity of late algorithm and obtain more effective artificial fish, adding a maximum number of iterations elimination mechanism. Then, using improved artificial fish swarm algorithm to optimize the kernel function and penalty coefficient of support vector machine, and applied it in short-term wind power prediction. The simulation results show that the improved artificial fish swarm algorithm optimization support vector machine has a better effect on short-term wind power prediction.

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