Non-intrusive load monitoring and decomposition method based on decision tree
| Autor: | Dan Qu, Jiang Lin, Hongyan Li, Xianfeng Ding |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Equipment state
Computer science Particle Swarm Optimization (PSO) Applied Mathematics Decision tree learning lcsh:Mathematics 0211 other engineering and technologies Decision tree Particle swarm optimization 02 engineering and technology lcsh:QA1-939 01 natural sciences Reliability engineering 010309 optics Electrical equipment 0–1 programming model 0103 physical sciences Programming paradigm Decomposition method (queueing theory) Load characteristics lcsh:Industry Decision tree identification lcsh:HD2321-4730.9 Non-intrusive load detection 021102 mining & metallurgy |
| Zdroj: | Journal of Mathematics in Industry, Vol 10, Iss 1, Pp 1-14 (2020) |
| ISSN: | 2190-5983 |
| Popis: | In order to realize the problems of non-intrusive load monitoring and decomposition (NILMD) from two aspects of load identification and load decomposition, based on the load characteristics of the database, this paper firstly analyzes and identifies the equipment composition of mixed electrical equipment group by using the load decision tree algorithm. Then, a 0–1 programming model for the equipment status identification is established, and the Particle Swarm Optimization (PSO) is used to solve the model for equipment state recognition, and the equipment operating state in the equipment group is identified. Finally, a simulation experiment is carried out for the partial data of Question A in the 6th “teddy cup” data mining challenge competition. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |