考虑积温效应的支持向量回归最大日负荷预测

Qirong Lin, Qiaoqiao Wang, Guilin Zhang, Yu Shi, Hongxia Liu, Lijun Deng
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引用次数: 2

摘要

最大日负荷预测在电力系统调度中具有重要意义。本文首先对电力负荷特性进行了分析。其次,选取最大负荷和天气因素作为最大增量负荷预测回归模型的输入,利用最小二乘支持向量机(LS-SVM)建立输入与输出之间的映射关系;然后,根据夏季负荷变化规律,提出了休息日的修正日期类型归一化。同时考虑了积温的影响,减小了模型的预测误差。最后,通过数值试验验证了该模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Maximum daily load forecasting based on support vector regression considering accumulated temperature effect
Maximum daily load forecasting is of great significance in power system dispatching. First, electric load characteristics are analysed in this paper. Second, maximum load and weather factors are selected as the input of the maximum incremental load forecasting regression model, and the mapping relationship between input and output is established by least squares support vector machine (LS-SVM). Then, the modified date type normalization of rest days is proposed according to load change regulation in summer. Moreover, the effect of accumulated temperature is considered to reduce the model prediction error. Finally, numerical tests demonstrated the efficiency of the proposed model.
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