Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/341824
Type: Artigo
Title: Load disaggregation using microscopic power features and pattern recognition
Author: de Souza, Wesley Angelino
Garcia, Fernando Deluno
Marafao, Fernando Pinhabel
Pereira da Silva, Luiz Carlos
Simoes, Marcelo Godoy
Abstract: A new generation of smart meters are called cognitive meters, which are essentially based on Artificial Intelligence (AI) and load disaggregation methods for Non-Intrusive Load Monitoring (NILM). Thus, modern NILM may recognize appliances connected to the grid during certain periods, while providing much more information than the traditional monthly consumption. Therefore, this article presents a new load disaggregation methodology with microscopic characteristics collected from current and voltage waveforms. Initially, the novel NILM algorithm-called the Power Signature Blob (PSB)-makes use of a state machine to detect when the appliance has been turned on or off. Then, machine learning is used to identify the appliance, for which attributes are extracted from the Conservative Power Theory (CPT), a contemporary power theory that enables comprehensive load modeling. Finally, considering simulation and experimental results, this paper shows that the new method is able to achieve 95% accuracy considering the applied data set
Subject: Inteligência artificial
Aprendizado de máquina
Country: Suiça
Editor: MDPI
Rights: Aberto
Identifier DOI: 10.3390/en12142641
Address: https://www.mdpi.com/1996-1073/12/14/2641
Date Issue: 2019
Appears in Collections:FEEC - Artigos e Outros Documentos

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