中圖分類號： TN911.72；TM714
文獻(xiàn)標(biāo)識碼： A
DOI：10.16157/j.issn.0258-7998.212334
中文引用格式： 王毅，王蕭陽，李松濃，等. 基于單分類結(jié)合模糊寬度學(xué)習(xí)的負(fù)荷辨識方法[J].電子技術(shù)應(yīng)用，2022，48(5)：51-55，60.
英文引用格式： Wang Yi，Wang Xiaoyang，Li Songnong，et al. Load identification method based on one class classification combined with fuzzy broad learning[J]. Application of Electronic Technique，2022，48(5)：51-55，60.

Load identification method based on one class classification combined with fuzzy broad learning

Abstract： Non-Intrusive Load Monitoring(NILM) is a key technology for smart electricity consumption, which helps strengthen load-side management and improve electricity efficiency. With the rapid increase of power load types and quantities, when unknown electrical appliances outside the training sample are connected to the model, it will cause the model to misjudge and reduce the accuracy of load identification. In order to improve the stability and accuracy of the load identification model, a power load identification method combining single classification and fuzzy broad learning is proposed. The one-class K-nearest neighbor method is used to screen samples to detect unknown electrical appliances and control the risk of misjudgment. Considering the recognition rate and model complexity, the fuzzy broad learning system is used to classify and recognize the screened samples. The experimental results show that the algorithm proposed in this paper can effectively detect unknown electrical appliances, prevent model misjudgment, and get better results for both single-load and multi-load switching.

Key words : non-intrusive load identification；steady-state feature of load current；fuzzy broad learning system；one class K-nearest neighbor；TS fuzzy syste