混合模型在短期负荷预测中的应用

Xin Jin, Jie Wu, Yao Dong, Dezhong Chi
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引用次数: 6

摘要

短期负荷预测因其广泛的应用而被视为一个重要的问题。本文利用从SA抽取的电力负荷数据,对灰色预测模型进行了短期负荷预测试验。然后将灰色预测模型得到的电力负荷残差序列作为原始数据,分别应用灰色预测模型和支持向量机(SVM)对后续的残差序列进行预测,将预测的残差序列加入到单一灰色预测模型的原始电力负荷预测中;使用灰色预测模型时,平均绝对百分比误差从11.97%降低到11.71%,使用SVM进行残差预测时,平均绝对百分比误差显著降低到5.45%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of a Hybrid Model to Short-Term Load Forecasting
Short-term load forecasting has been viewed as an important problem for its wide application. Grey forecasting model is tested by using electric load data sampled from SA for short-term load forecasting in this paper. Then by regarding the electric load residual series obtained from grey forecasting model as the original data, the grey forecasting model and the support vector machine (SVM) are applied to forecast the follow-up residual series respectively, by adding this forecasted residual series to the original forecasted electric load by single grey forecasting model, the mean absolute percentage error is reduced from 11.97% to 11.71% when using grey forecasting model and a significant reduce to 5.45% while using SVM in residual forecasting.
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