Noninvasive load identification based on RF-KNN

Diancheng Yao; Changchun Chi; Jie Guo

doi:10.1117/12.3034826

11 July 2024 Noninvasive load identification based on RF-KNN

Diancheng Yao, Changchun Chi, Jie Guo

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132102C (2024) https://doi.org/10.1117/12.3034826
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

To solve the problem of low load recognition accuracy caused by high load feature dimension in non-invasive load recognition, a non-invasive load recognition method combining random forest (RF) and K-nearest neighbor (KNN) was proposed. Firstly, feature extraction is carried out on the current data collected by the equipment during stable operation. Then, RF is used to score the feature importance of the load characteristics. Finally, the data with high load feature scores are selected and input into the KNN model to complete load identification and classification. The experimental results show that the random forest (RF) feature selection for high-dimensional load features can effectively improve the load identification accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Diancheng Yao, Changchun Chi, and Jie Guo "Noninvasive load identification based on RF-KNN", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132102C (11 July 2024); https://doi.org/10.1117/12.3034826

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