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- 2017
基于支持向量回归的短期负荷预测
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Abstract:
摘要： 对短期负荷特性进行分析,选取与负荷相关的气象因素、日期类型、前几日负荷作为最大(最小)负荷预测回归模型的输入。夏冬两季休息日的负荷特性与春秋两季不一致,根据气象因素修正日期类型对应的数值。采用最小二乘支持向量机(least squares support vector machine, LSSVM)建立气象因素和日期类型与最大(最小)负荷的映射关系。利用相似日法计算日负荷变化系数,在预测最大负荷和最小负荷基础上,计算预测日各点负荷。算例分析验证了本研究预测模型的有效性。
Abstract: The characters of short term load were studied and the influence factors of daily load in summer and winter was analysed. The meteorological factors, such as date type and pevious load, were selected as the input of maximum incremental load forecasting regression model. The value corresponding to date type was modified based on meteorological factor due to the inconsistent load characteristic in different seasons. The least squares support vector machine(LS-SVM)was utilized to model mapping relationship between input factors and maximum incremental load. Numerical tests demonstrated the efficiency of the proposed method
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