Non-intrusive load identification method based on XGBoost and GRU by stacking ensemble learning. (14th July 2021)

• Record Type: Journal Article
• Authors: Yi, Lingzhi; Liu, Ning; Liu, Jiangyong; Fan, Lv; Liu, Jiankang; Song, Huina
• Is Part Of: International journal of advanced mechatronic systems. Volume 9:Number 2(2021)
• Page Start: 109
• Page End: 119
• Publication Date: 2021-07-14
• Subjects: non-intrusive -- ensemble learning -- model combination -- XGBoost -- GRU -- load identification; Mechatronics -- Periodicals; 629.89
• Languages: English
• ISSNs: 1756-8412

Abstract

Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. In this paper, aiming at the low accuracy of single model load identification, a non-invasive load identification method based on the combination of XGBoost and GRU model is proposed. Considering the difference of data observation and training principles, the stacking based load identification model embedded various machine learning algorithms was proposed to utilise their diversified strength. Firstly, to reduce the impact of unbalanced samples on load identification, SMOTE algorithm is used to balance the samples; secondly, a stacking integrated model is constructed based on the idea of ensemble learning and model combination, which is established to improve the poor performance of load identification for single model. Finally, the results indicate the proposed stacking ensemble learning model has better identification performance compared with the traditional single models on the public data set PLAID.