基于相关因素映射的小波回归分析短期负荷预测模型研究.
首发时间:2008-11-24
摘要:电力系统负荷的嵌套周期性和相关因素的冗余和不确定性是产生预测误差的主要来源,本文通过建立映射数据库,将相关因素量化,选择出一系列最佳相似日的负荷数据作为用于预测的历史负荷数据,消除或降低其它冗余因素的影响。再利用小波变换回归分析建模对贵州电网日96 点负荷实例进行预测, 与BP 神经网络法相比, 本模型预测精度高, 在短期负荷预测中具有有效性和可行性。
关键词: 相关因素映射 小波变换 回归分析 短期负荷预测 周期性
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Using Short- term Load Forecasting Based on Correlative Factor Mapping and WTRA
Abstract:Using the advantages of Correlative Factor Mapping theory for processing large data and eliminating redundant information, it finds the relevant factors to load. The forecasting model is established by means of Wavelet Transaction and Regression Analysis algorithm. It is applied to short-term load forecasting using the actual daily load 96 points data for GuiZhou power grid. The results demonstrate that the precision of the proposed forecasting models is better than that of BP and the proposed model is feasible and effective for short-term load forecasting.
Keywords: Correlative Factor Mapping Wavelet Transaction Regression Analysis Short-term Load Forecasting Periodicity
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No.2599836389912274****
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