考虑天气特征与多变量相关性的配电网短期负荷预测
针对配电网短期负荷预测受到众多复杂天气特征等随机不确定性因素影响,以及传统预测模型难以有效分析不同特征序列之间的相关性等问题,提出一种考虑天气特征与多变量相关性的配电网短期负荷预测方法.首先,提出多变量快速最大信息系数(multi-variable rapid maximal information coefficient,MVRapidMIC)提取相关性高的天气特征序列.其次,引入探索性因子分析法(exploratory factor analysis,EFA),对高相关性特征序列进行降维处理.最后,将维度分段(dimension-segment-wise,DSW)机制和两阶段注意力(two...
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| Published in | 电力系统保护与控制 Vol. 52; no. 6; pp. 131 - 141 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
电网智能化调度与控制教育部重点实验室(山东大学),山东 济南 250061%智能电网教育部重点实验室(天津大学),天津 300072
16.03.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1674-3415 |
| DOI | 10.19783/j.cnki.pspc.231329 |
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| Abstract | 针对配电网短期负荷预测受到众多复杂天气特征等随机不确定性因素影响,以及传统预测模型难以有效分析不同特征序列之间的相关性等问题,提出一种考虑天气特征与多变量相关性的配电网短期负荷预测方法.首先,提出多变量快速最大信息系数(multi-variable rapid maximal information coefficient,MVRapidMIC)提取相关性高的天气特征序列.其次,引入探索性因子分析法(exploratory factor analysis,EFA),对高相关性特征序列进行降维处理.最后,将维度分段(dimension-segment-wise,DSW)机制和两阶段注意力(two-stage attention,TSA)机制与Informer模型结合,提高预测模型对不同特征序列相关性的分析能力.通过DTU 7K 47节点实际配电网的历史负荷数据开展仿真测试,验证所提方法的预测精度、鲁棒性和时效性. |
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| AbstractList | 针对配电网短期负荷预测受到众多复杂天气特征等随机不确定性因素影响,以及传统预测模型难以有效分析不同特征序列之间的相关性等问题,提出一种考虑天气特征与多变量相关性的配电网短期负荷预测方法.首先,提出多变量快速最大信息系数(multi-variable rapid maximal information coefficient,MVRapidMIC)提取相关性高的天气特征序列.其次,引入探索性因子分析法(exploratory factor analysis,EFA),对高相关性特征序列进行降维处理.最后,将维度分段(dimension-segment-wise,DSW)机制和两阶段注意力(two-stage attention,TSA)机制与Informer模型结合,提高预测模型对不同特征序列相关性的分析能力.通过DTU 7K 47节点实际配电网的历史负荷数据开展仿真测试,验证所提方法的预测精度、鲁棒性和时效性. |
| Abstract_FL | To address challenges in short-term load forecasting for distribution networks,challenges such as the impact of complex weather features and the difficulty in analyzing correlations between different feature sequences using traditional models,a method considering those issues is proposed.First,the method presents a multi-variable rapid maximal information coefficient(MVRapidMIC)to extract highly correlated weather feature sequences.Exploratory factor analysis(EFA)is then employed for dimensionality reduction on these sequences.Finally,the dimension-segment-wise(DSW)and two-stage attention(TSA)mechanisms are integrated with the Informer model to enhance the model's ability to analyze correlations between different feature sequences.Simulation tests using historical load data from the DTU 7K 47-bus distribution system validate the forecasting accuracy,robustness,and timeliness of the method. |
| Author | 葛磊蛟 丁磊 王玥 于越 金朝阳 |
| AuthorAffiliation | 电网智能化调度与控制教育部重点实验室(山东大学),山东 济南 250061%智能电网教育部重点实验室(天津大学),天津 300072 |
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| Author_FL | YU Yue WANG Yue GE Leijiao JIN Zhaoyang DING Lei |
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| DocumentTitle_FL | Short-term load prediction method of distribution networks considering weather features and multivariate correlations |
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| Keywords | short-term electricity load forecasting weather features 天气特征 最大信息系数 distribution network 配电网 maximal information coefficient Informer framework 短期负荷预测 Informer框架 |
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| Publisher | 电网智能化调度与控制教育部重点实验室(山东大学),山东 济南 250061%智能电网教育部重点实验室(天津大学),天津 300072 |
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| Snippet | 针对配电网短期负荷预测受到众多复杂天气特征等随机不确定性因素影响,以及传统预测模型难以有效分析不同特征序列之间的相关性等问题,提出一种考虑天气特征与多变量相关性... |
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| Title | 考虑天气特征与多变量相关性的配电网短期负荷预测 |
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