English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

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