Load Disaggregation Using Microscopic Power Features and Pattern Recognition

Autor(es)

de Souza, Wesley Angelino ^[1] ; Garcia, Fernando Deluno ^[2] ; Marafao, Fernando Pinhabel ^[2] ; Pereira da Silva, Luiz Carlos ^[3] ; Simoes, Marcelo Godoy ^[4]

Afiliação do(s) autor(es)

^[1] Fed Univ Sao Carlos UFSCar, Dept Comp Sci, BR-18052780 Sorocaba, SP - Brazil

^[2] Sao Paulo State Univ UNESP, Inst Sci & Technol Sorocaba, BR-18087180 Sorocaba, SP - Brazil

^[3] Univ Campinas UNICAMP, Sch Elect & Comp Engn FEEC, BR-13083852 Campinas, SP - Brazil

^[4] Colorado Sch Mines, Dept Elect Engn, Golden, CO 80401 - USA

• Tipo de documento: Artigo Científico
• Fonte: ENERGIES; v. 12, n. 14 JUL 2 2019.
• Citações Web of Science: 0

Resumo

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. (AU)

• Processo FAPESP: 16/08645-9 - Pesquisas interdisciplinares em redes inteligentes de energia elétrica
• Beneficiário: João Bosco Ribeiro do Val
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 12/19375-1 - Estudo de técnicas de análise e tecnologias para o desenvolvimento de medidores inteligentes de energia com foco em microrredes residenciais
• Beneficiário: Wesley Angelino de Souza
• Modalidade de apoio: Bolsas no Brasil - Doutorado