郑州大学学报（理学版）2017，Vol.49Issue(2)：120-126,7.DOI:10.13705/j.issn.1671-6841.2016189
基于相似日搜索的改进LMD与ESN相结合的短期电力负荷预测模型
The Short-term Power Load Forecasting Model of CombingILMD and ESN Based on Similar Days Searching
摘要
Abstract
Short-term power load was easily influenced by natural factors and social factors, which made load forecast more difficult.In order to improve the accuracy of short-term power load prediction, the forecasting mode of combing improved local mean decomposition (ILMD) and echo state network (ESN) based on similar days searching was proposed.Firstly, the days most similar to the forecasted date were selected by fuzzy cluster analysis.A data sequence was formed by uniting the similar days' hourly loads together according to their time orders.Then, the ILMD was used to decompose the data sequence into several independent components, and an ESN was established for each component, separately.Each network was trained with similar daily load data.Using each trained network to predict the value of the corresponding component, the final result of prediction was the accumulation of all components predict values.Experiments showed that this method could effectively improve the prediction accuracy.关键词
负荷预测/局部均值分解/回声状态网络/相似日/模糊聚类Key words
load forecasting/ILMD/ESN/similar days/fuzzy cluster analysis分类
信息技术与安全科学引用本文复制引用
张亚丽,胡伯轩,李莎莎,罗勇..基于相似日搜索的改进LMD与ESN相结合的短期电力负荷预测模型[J].郑州大学学报（理学版）,2017,49(2):120-126,7.基金项目
河南省青年骨干教师项目(2015GGJS-148) （2015GGJS-148）
河南省产学研合作项目(152107000058) （152107000058）
河南省重点科技攻关项目(152102210036). （152102210036）
