Abstract Machine learning methods such as Support Vector Machine (SVM) and Neural Network (NN) as soft computing methods are widely used to solve nonlinear problems. Wavelet analysis and artificial intelligence machine learning will be combined to improve the self learning ability and prediction accuracy. Actual historical load data is decomposed into high and low frequency load sequence by using wavelet analysis. Utilizing SVM and NN machine learning methods, choosing the appropriate parameters such as network structures, penalty parameter and kernel function width by optimization program, the single branch predictions for each sequence are separately made, and each branch prediction results are reconstructed to achieve ultimate load forecasting. Application result shows that wavelet SVM has higher prediction accuracy.

中文翻译：

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小波支持向量机和神经网络软计算方法在电力负荷预测中的混合应用

摘要 机器学习方法，如支持向量机（SVM）和神经网络（NN）作为软计算方法被广泛用于解决非线性问题。将小波分析和人工智能机器学习相结合，提高自学习能力和预测精度。利用小波分析将实际历史负荷数据分解为高频和低频负荷序列。利用SVM和NN机器学习方法，通过优化程序选择合适的网络结构、惩罚参数和核函数宽度等参数，分别对每个序列进行单分支预测，重构每个分支预测结果，实现极限负荷预测. 应用结果表明小波支持向量机具有更高的预测精度。