Мақола

Optimising energy efficiency enhancing NILM through high- resolution data analytics

Carlos Rodríguez-NavarroDepartamento de Ingeniería Escuela Superior de Ingeniería, Universidad de Almería Carretera Sacramento s/n, La Cañada de San Urbano, 04120 Almeria (Spain)F. PortilloDepartamento de Ingeniería Escuela Superior de Ingeniería, Universidad de Almería Carretera Sacramento s/n, La Cañada de San Urbano, 04120 Almeria (Spain)Laura Castro‐SantosDepartamento de Enxeñaría Naval e Industrial Centro de Investigación en Tecnoloxías Navais e Industriais (CITENI), Escola Politécnica de Enxeñaría de Ferrol, Universidade da Coruña Campus Industrial de Ferrol, Esteiro, 15471 Ferrol (Spain)Almudena Filgueira‐VizosoDepartamento de Química Centro de Investigación en Tecnoloxías Navais e Industriais (CITENI), Escola Politécnica de Enxeñaría de Ferrol, Universidade da Coruña Campus Industrial de Ferrol, Esteiro, 15471 Ferrol (Spain)Francisco G. MontoyaDepartamento de Ingeniería Escuela Superior de Ingeniería, Universidad de Almería Carretera Sacramento s/n, La Cañada de San Urbano, 04120 Almeria (Spain)Alfredo AlcaydeDepartamento de Ingeniería Escuela Superior de Ingeniería, Universidad de Almería Carretera Sacramento s/n, La Cañada de San Urbano, 04120 Almeria (Spain)

Renewable Energy and Power Quality Journaljournal2024en

ABI

Аннотация

This study introduces a novel approach to enhancing energy efficiency by building a high-resolution dataset for Non-Intrusive Load Monitoring, addressing the challenges of monitoring a wide range of devices. To achieve optimal energy efficiency, it is essential to have advanced monitoring of electrical variables. In this context, the second version of openZmeter is presented, which supports up to 4 devices, each capable of 160 measurements, including voltage, current and power harmonics on each channel. To carry out this purpose, the Non-Intrusive Load Monitoring Toolkit is adapted for the new openZmeter v2. The main objective of this study is to offer a new pragmatic approach to generating artificial intelligence models to optimise energy use, analysing the results through an exhaustive experimental analysis under various casuistry and conditions. Numerous comparative studies are provided using classical disaggregation algorithms with different requirements. Conclusively, the research emphasises the transformative potential of artificial intelligence for energy efficiency strategies, offering insights for scholars and practitioners.

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Мавзулар

Air Quality Monitoring and Forecasting Neural Networks and Applications Energy Load and Power Forecasting

Идентификаторлар

DOI: 10.52152/4013

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