Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System

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ID: 67201
2019
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Abstract
Non-intrusive load monitoring (NILM) is an effective method to optimize energy consumption patterns. Since the concept of NILM was proposed, extensive research has focused on energy disaggregation or load identification. The traditional method is to disaggregate mixed signals, and then identify the independent load. This paper proposes a multi-label classification method using Random Forest (RF) as a learning algorithm for non-intrusive load identification. Multi-label classification can be used to determine which categories data belong to. This classification can help to identify the operation states of independent loads from mixed signals without disaggregation. The experiments are conducted in real environment and public data set respectively. Several basic electrical features are selected as the classification feature to build the classification model. These features are also compared to select the most suitable features for classification by feature importance parameters. The classification accuracy and F-score of the proposed method can reach 0.97 and 0.98, respectively.
Reference Key
wu2019multilabelprocesses Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wu, Xin;Gao, Yuchen;Jiao, Dian;
Journal processes
Year 2019
DOI
DOI not found
URL
https://www.mdpi.com/2227-9717/7/6/337
Keywords
chemistry Biology (General) Medicine (General) Medicine Chemical technology Technology Science food processing and manufacture nutrition. foods and food supply transportation engineering geology transportation and communications stratigraphy

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