基于BP神经网络灰色回归组合模型的年最大负荷预测
Annual Peak Load Forecasting Based on Combination Model of Back Propagation Neural Network and Grey Regression
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摘要:
为克服单一电力负荷预测模型的局限性，改善预测结果，提出了一种基于BP神经网络灰色回归组合模型的年最大负荷预测方法。在BP神经网络预测模型中，采用Levenberg-Marquardt算法对参数迭代过程进行优化；在灰色预测模型中，采用加政策因子处理法对原始数列进行改造以强化数列的递增趋势；在回归预测模型中，采用逐步线性回归法剔除对因变量影响较小的自变量。最后利用方差-协方差法对三种预测模型进行加权组合。以广州市2007—2016年实际数据对组合预测模型进行验证，并对广州市2017—2019年的年最大负荷进行预测。结果表明：所提方法预测精度较高且误差在工程允许范围之内，具有一定的工程实用价值。 Abstract:In order to overcome the limitation of single power load forecasting model and improve the predicted results, an annual peak load forecasting method based on combination model of back propagation neural network and grey regression is proposed. Levenberg-Marquardt algorithm is used to optimize the parameter iterative process in back propagation neural network forecasting model; original sequence is reformed by using policy factor treatment method in the grey forecasting model, which can strengthen increasing trend of the sequence; stepwise linear regression method is used to eliminate the independent variables that have a small effect on the dependent variable in the regression forecasting model. The three forecasting models are finally weighted combined by using variance-covariance method. The combined forecasting model is tested with the actual data of Guangzhou from 2007 to 2016, the peak load of Guangzhou from 2017 to 2019 is forecasted as well. The results show that the prediction accuracy of the method proposed in this paper is relatively high and the errors are within the permissible range in engineering, indicating the method is valuable in engineering.
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