基于季节调整和Holt-Winters的月度负荷预测方法

针对负荷序列中异常数据会导致模型误设或参数估计发生偏差的问题，提出利用季节调整方法，先对原始负荷序列进行季节调整，获得消除离群值、节假日影响的季节调整后序列和季节成分序列；然后用改进的Holt-Winters方法对季节调整后成分进行预测，用虚拟回归方法预测季节成分序列；最后对各成分预测结果重构得到最终预测结果的月度负荷预测方法。通过实例检验，提出的方法能明显提高预测精度，预测效果要优于季节性Holt-Winters、SARIMA、神经网络、支持向量机等模型。

A Hybrid Monthly Load Forecasting Method Based on Seasonal Adjustment and Holt-Winters

Load forecasting plays an important role in the planning and economic and secure operation of power systems. However, the abnormal data in load series will result in forecasting model misspecification or incorrect model parameters estimation. So a hybrid monthly load forecasting model based on seasonal adjustment and improved holt-winters is built to solve such problems. Firstly, after seasonal adjustment, the final seasonally adjusted series where outliers or holidays effects have been removed and seasonal component series can be obtained simultaneously; secondly, the improved Holt-Winters method is used to forecast final seasonally adjusted component, and virtual regression equation is used to forecast seasonal component. Finally, the final forecasting result can be obtained by using forecasting result of seasonal component and seasonal adjusted component jointly. The case calculation results show that the proposed method can significantly improve the prediction accuracy and the forecasting performance is better than seasonal Holt-Winters, SARIMA, neural network, and support vector machine. In summary, the proposed model can be practically applied as a monthly load forecasting tool.