基于层次分析法和径向基函数神经网络的中长期负荷预测综合模型A Comprehensive Model for Long-and Medium-Term Load Forecasting Based on Analytic Hierarchy Process and Radial Basis Function Neural Network
李春祥;牛东晓;孟丽敏;
摘要(Abstract)：
中长期负荷预测是电力系统规划与运行的基础工作,提出基于3指标量,即指标总量、指标增长量和指标增长率的综合模型。首先构建层次分析(analytic hierarchy process,AHP)模型,分别对3个指标量进行分析评价,优选出每个指标量的最优预测模型,然后利用径向基函数(radial basic function,RBF)神经网络对3个最优模型的预测结果进行拟合,并将GDP因素也作为神经网络输入数据之一,输出最终的预测结果。AHP模型中综合考虑了模型预测误差和模型拟合度,并成功地加入了人工干预的因素,依据专家经验判断模型的信任度和预测结果趋势可信度。AHP模型采用与预测时刻最近的历史数据进行分析,因此具有较好的实时性。实验结果表明该综合模型具有较高的预测精度,实际应用效果较好。
关键词(KeyWords)： 负荷预测;层次分析法;径向基函数神经网络;三指标量;综合模型
基金项目(Foundation): 国家自然科学基金资助项目(70671039)~~
作者(Author): 李春祥;牛东晓;孟丽敏;
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