| 引用本文: | 于 越,葛磊蛟,金朝阳,王 玥,丁 磊.考虑天气特征与多变量相关性的配电网短期负荷预测[J].电力系统保护与控制,2024,52(6):131-141.[点击复制] |
| YU Yue,GE Leijiao,JIN Zhaoyang,WANG Yue,DING Lei.Short-term load prediction method of distribution networks considering weather featuresand multivariate correlations[J].Power System Protection and Control,2024,52(6):131-141[点击复制] |
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| 摘要: |
| 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。 |
| 关键词: 配电网 短期负荷预测 天气特征 最大信息系数 Informer框架 |
| DOI：10.19783/j.cnki.pspc.231329 |
| 投稿时间：2023-10-16修订日期：2023-11-24 |
| 基金项目:国家自然科学基金项目资助(U22B20101) |
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| Short-term load prediction method of distribution networks considering weather featuresand multivariate correlations |
| YU Yue1,GE Leijiao2,JIN Zhaoyang1,WANG Yue1,DING Lei1 |
| (1. Key Laboratory of Power System Intelligent Dispatch and Control (Shandong University), Ministry of Education,
Jinan 250061, China; 2. Key Laboratory of Smart Grid of Ministry of Education (Tianjin University), Tianjin 300072, China) |
| Abstract: |
| 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. |
| Key words: distribution network short-term electricity load forecasting weather features maximal information coefficient Informer framework |
