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
发布时间：2019-02-17 10:17
【摘要】：短期电力负荷容易受到自然因素及社会因素的影响,这使得负荷预测比较困难.为了提高短期负荷的预测精度,提出了基于相似日搜索的改进局部均值分解(ILMD)和回声状态网络(ESN)相结合的短期电力负荷预测模型.首先用模糊聚类分析将与预测日最相似的多个日期筛选出来.然后把这些相似日的整点负荷数据按照时间先后排成一组数据序列,用改进的LMD进行分解,对分解出的各个分量分别建立一个ESN网络,对每一个网络分别训练并进行预测.最后把每个网络的预测结果累加起来就是最终的预测值.实验证明此方法能有效提高预测精度.
[Abstract]:Short-term power load is easily affected by natural and social factors, which makes load forecasting more difficult. In order to improve the accuracy of short-term load forecasting, a short-term load forecasting model based on similarity day search based on improved local mean decomposition (ILMD) and echo state network (ESN) is proposed. Firstly, fuzzy clustering analysis is used to screen out the most similar dates to the predicted date. Then the load data of these similar days are arranged into a set of data sequence according to the time sequence, then decomposed by improved LMD, each component is decomposed into a ESN network, and each network is trained and predicted separately. Finally, the sum of the forecast results of each network is the final prediction value. Experiments show that this method can effectively improve the prediction accuracy.
【作者单位】： 河南省电力勘测设计院;郑州大学电气工程学院;河南省工程咨询公司;
【基金】：河南省青年骨干教师项目(2015GGJS-148) 河南省产学研合作项目(152107000058) 河南省重点科技攻关项目(152102210036)
【分类号】：TP183;TM715
本文编号：2425064
[Abstract]:Short-term power load is easily affected by natural and social factors, which makes load forecasting more difficult. In order to improve the accuracy of short-term load forecasting, a short-term load forecasting model based on similarity day search based on improved local mean decomposition (ILMD) and echo state network (ESN) is proposed. Firstly, fuzzy clustering analysis is used to screen out the most similar dates to the predicted date. Then the load data of these similar days are arranged into a set of data sequence according to the time sequence, then decomposed by improved LMD, each component is decomposed into a ESN network, and each network is trained and predicted separately. Finally, the sum of the forecast results of each network is the final prediction value. Experiments show that this method can effectively improve the prediction accuracy.
【作者单位】： 河南省电力勘测设计院;郑州大学电气工程学院;河南省工程咨询公司;
【基金】：河南省青年骨干教师项目(2015GGJS-148) 河南省产学研合作项目(152107000058) 河南省重点科技攻关项目(152102210036)
【分类号】：TP183;TM715
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