基于GA优化SVM的风电功率的超短期预测

Ultra-short-term wind power forecasting based on SVM optimized by GA

研究风电功率预测技术对于减轻其输出电能的随机性对电力系统的影响具有重要意义。首先结合风电监控系统数据库中的历史功率数据和环境参数形成样本数据,同时采用遗传算法优化该模型的核函数类型、核函数参数及错误惩罚因子等参数,建立了GA-SVM模型,提高了模型参数组合优化选择的效率和预测精度。最后结合实例验证,并与标准SVM方法和BP神经网络方法比较。预测效果表明:所提出的GA-SVM优化模型在超短期风电功率预测上具有更优的学习能力和泛化能力。

Research on wind power prediction technology is of great significance for mitigating the effects of randomness on the output power of the power generated by the system. First of all, the sample data is formed combined with wind power monitoring system of history in the database data and environmental parameters, at the same time genetic algorithm is used to optimize the model parameters, such as kernel function type, the kernel function parameter and error warning factor. The GA-SVM model is established which improves the efficiency of the model parameter combination optimization choice and the prediction precision. At last, based on the example verification, the standard SVM method and BP neural network method are compared. Prediction results show that the proposed GA-SVM optimization model on the ultra-short-term wind power prediction has a better learning ability and generalization ability.