| 引用本文: | 邓 威,郭钇秀,李 勇,朱 亮,刘定国.基于特征选择和Stacking集成学习的配电网网损预测[J].电力系统保护与控制,2020,48(15):108-115.[点击复制] |
| DENG Wei,GUO Yixiu,LI Yong,ZHU Liang,LIU Dingguo.Power losses prediction based on feature selection and Stacking integrated learning[J].Power System Protection and Control,2020,48(15):108-115[点击复制] |
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| 摘要: |
| 针对配电网能量管理和节能降损的要求，为了提高配电网网损分析与评估的有效性，提出了一种基于特征选择和Stacking集成学习的配电网网损预测方法。首先基于特征选择的主要方法，通过相关性分析法、最大信息系数法和基于树模型的特征选择法对特征进行综合分析，得到各种特征对网损预测的重要性，选择重要特征作为配电网网损预测模型的输入特征。在此基础上，介绍Stacking集成学习原理，考虑融合多种预测模型的优势特点，建立Stacking集成学习配电网网损预测模型，最后通过仿真验证得到网损预测结果。该仿真数据来源于湖南省10kV配电网某线路44个台区的真实数据，网损预测结果表明该方法能够有效提升配电网网损预测的准确性和鲁棒性，相比于单一预测模型具有更好的预测精度和泛化能力。
关键词：特征选择；模型融合；集成学习；网损预测 |
| 关键词: 特征选择 模型融合 集成学习 网损预测 |
| DOI：DOI: 10.19783/j.cnki.pspc.191097 |
| 投稿时间：2019-09-08修订日期：2019-10-09 |
| 基金项目:国家自然科学基金项目资助(51822702)；国网湖南省电力有限公司科研项目资助(5216A517000U)“配电网损耗分析及降损增效辅助决策技术研究”；长沙市杰出青年创新项目资助(KQ1802029) |
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| Power losses prediction based on feature selection and Stacking integrated learning |
| DENG Wei,GUO Yixiu,LI Yong,ZHU Liang,LIU Dingguo |
| (1. State Grid Hunan Power Company Limited Research Institute, Changsha 410007, China; 2. College of Electrical and
Information Engineering, Hunan University, Changsha 410082, China; 3. State Grid Hunan Electric
Power Company Limited, Changsha 410004, China) |
| Abstract: |
| In order to improve the efficiency of distribution network losses analysis and evaluation on a distribution network, this paper proposes a distribution network loss prediction method based on feature selection and Stacking integrated learning. First, based on the main method of feature selection, through the correlation analysis method, the maximum information coefficient method and the tree-based model, the input features are comprehensively analyzed and the importance of various features for the network losses prediction is obtained. The important features are selected as the input of the distribution network losses prediction model. We then introduce Stacking integrated learning theory, which considers the advantages of combining multiple prediction models. We establish the Stacking integrated learning distribution network losses prediction model and finally obtain the results through simulation. The simulation data of the paper is derived from the real data of 44 stations in a line of a 10 kV distribution network in Hunan Province. The results show that the method can effectively improve the accuracy and robustness of distribution network losses prediction. Compared with a single prediction model, it has higher prediction accuracy and better generalization capabilities.This work is supported by National Natural Science Foundation of China (No. 51822702), and Science and Technology Project of State Grid Hunan Electric Power Company (No. 5216A517000U), and the Excellent Innovation Youth Program of Changsha of China (No. KQ1802029). |
| Key words: feature selection model fusion integrated learning losses prediction |
