Title: Non-intrusive household load identification based on enhanced random forests learning

Authors: Peiming Luo; Samson S. Yu; Guidong Zhang; Jian Zhou

Addresses: School of Automation, Guangdong University of Technology, Guangzhou, 510006, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, 200240, China ' Deakin University, Victoria, 3220, Australia ' School of Automation, Guangdong University of Technology, Guangzhou, 510006, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, 200240, China ' School of Automation, Guangdong University of Technology, Guangzhou, 510006, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, 200240, China

Abstract: Non-intrusive household load identification is an important way to achieve intelligent power consumption management. Random forests (RF), an ensemble learning method, has wide applicability and high robustness, which has been employed in the field of load identification. An enhanced random forests (ERF) algorithm is proposed in this work to solve the problem that the traditional random forest algorithm ignores the difference of ability of decision tree classification and the unfairness of voting. First, the Bayesian information criterion (BIC) is used to detect and identify switching events. Second, the best feature set is selected according to the time-frequency characteristics of the load information. Finally, the enhanced random forest algorithm is used to establish the load identification model. Experimental results show that ERF can achieve higher recognition accuracy of load categories than RF, and the accuracy can reach more than 90%.

Keywords: non-intrusive household load identification; BIC-based algorithm; enhanced random forests; decision tree; ensemble learning.

DOI: 10.1504/IJSCIP.2022.129580

International Journal of System Control and Information Processing, 2022 Vol.4 No.1, pp.56 - 67

Received: 19 Sep 2022
Accepted: 11 Dec 2022
Published online: 14 Mar 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article