基于GOA-SVM的短期负荷预测

支持向量机是借助于凸优化技术的统计学习方法,与传统神经网络相比,泛化错误率低并且结果易于解释。将支持向量机用于负荷预测时,参数选择不准确会导致预测性能较差。提出一种基于蚱蜢优化算法的支持向量机短期负荷预测方法,以某地区负荷、天气等历史数据对SVM进行训练,并通过GOA优化选取支持向量机参数,然后以得到的最优参数建立GOA-SVM负荷预测模型。算例分析表明,GOA-SVM预测模型比GA-SVM和PSO-SVM模型有更好的收敛性能,且预测精度更高。

Short-term load forecasting based on GOA-SVM

Support vector machine (SVM) is a statistical learning method based on structural risk minimization principle. Compared with traditional neural network, the generalization error rate is low and the result is easy to explain. When SVM is applied to load forecasting, the inaccurate selection of parameters will result in poor prediction performance. A short-term load forecasting method of support vector machine based on grasshopper optimization algorithm is proposed. SVM is trained with historical data such as load and weather in a certain area. The parameters of support vector machine are selected by GOA, and then the GOA-SVM load forecasting model is established with the optimal parameters obtained. The example analysis shows that the GOA-SVM model has better convergence performance and higher prediction accuracy than GA-SVM and PSO-SVM models.