| 引用本文: | 邱磊鑫,余 涛,彭秉刚.基于异构基Stacking机制的居民负荷特征图像识别方法[J].电力系统保护与控制,2022,50(20):97-105.[点击复制] |
| QIU Leixin,YU Tao,PENG Binggang.Image recognition method of resident load characteristics based onheterogeneous basis Stacking mechanism[J].Power System Protection and Control,2022,50(20):97-105[点击复制] |
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| 摘要: |
| 负荷识别技术能将不同电器类型有效区分开，对于用电策略制定、需求响应具有重要意义。针对当前负荷识别技术无法有效实现负荷特征融合以及不同识别器模型结合的问题，提出一种基于异构基Stacking机制的居民用电负荷识别特征图像集成学习方法。该方法通过构建特征图像实现特征融合，利用卷积神经网络充分挖掘特征图像中蕴含的深层次特征，解决传统方法对特征挖掘不够深入的问题。同时引入集成学习Stacking方法将多种异质负荷识别模型结合，综合各种模型的优势，解决传统方法模型单一化的问题。最后使用公开数据集PLAID进行验证并在实验室电器设备上完成工程应用。结果表明，所提方法具有较高的识别准确率和应用价值。 |
| 关键词: 负荷识别 集成学习 特征图像 卷积神经网络 特征筛选 |
| DOI：DOI:?10.19783/j.cnki.pspc.220048 |
| 投稿时间：2022-01-11修订日期：2022-03-14 |
| 基金项目:国家自然科学基金委员会-国家电网公司智能电网联合基金项目资助(U2066212) |
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| Image recognition method of resident load characteristics based onheterogeneous basis Stacking mechanism |
| QIU Leixin,YU Tao,PENG Binggang |
| (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China) |
| Abstract: |
| Load identification technology can effectively distinguish different electrical types. This is of significance for power consumption strategy formulation and demand response. There is a problem in that the present load identification technology cannot effectively realize the load feature fusion and the combination of different recognizer models. Thus an integrated learning method of residential power load identification feature image based on heterogeneous basis Stacking mechanism is proposed. This method realizes feature fusion by constructing feature images and fully excavates the deep-seated features contained in feature images using a convolutional neural network, so as to solve the problem that the traditional methods are not deep enough in feature mining. At the same time, the ensemble learning Stacking method is introduced to combine a variety of heterogeneous load identification models to integrate the advantages of various models and overcome the problem of the simplification of traditional methods. Finally, the public data set PLAID is used to verify and complete the engineering application on laboratory electrical equipment. The results show that this method has high recognition accuracy and application value.
This work is supported by the Joint Project of the Commission of National Natural Science Foundation of China- Smart Grid of State Grid Corporation of China (No. U2066212). |
| Key words: load identification integrated learning feature image convolutional neural network feature selection |
