To improve the accuracy of midterm power load forecasting, a forecasting model is proposed by combing kernel principal component analysis (KPCA) with back propagation neural network. First, the dimension of the input space is reduced by KPCA, then input the data set to the neural network model, optimized by particle swarm optimization. The monthly average of daily peak loads is forecasted to modify the daily forecast values and output the daily peak load in the end. Using the data provided by European Network on Intelligent Technologies to test the model, the mean absolute percent error of load forecasting model is only 1.39%. The feasibility and validity of the model have been proven.

中文翻译：

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基于核主成分分析和粒子群优化的BP神经网络的中期负荷预测模型。

为了提高中期电力负荷预测的准确性，提出了一种将核主成分分析（KPCA）与反向传播神经网络相结合的预测模型。首先，通过KPCA减小输入空间的维数，然后将数据集输入到神经网络模型中，并通过粒子群优化对其进行优化。预测每日峰值负荷的月平均值以修改每日预测值并最终输出每日峰值负荷。使用欧洲智能技术网络提供的数据对模型进行测试，负荷预测模型的平均绝对百分比误差仅为1.39％。该模型的可行性和有效性已得到证明。