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Home Energy Forecast Performance Tool for Smart Living Services Suppliers under an Energy 4.0 and CPS Framework

Author

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  • Filipe Martins Rodrigues

    (Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro, 1959-007 Lisboa, Portugal
    IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal)

  • Carlos Cardeira

    (IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal)

  • João M. F. Calado

    (Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro, 1959-007 Lisboa, Portugal
    IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal)

  • Rui Melicio

    (IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
    ICT, Universidade de Évora, Rua Romão Ramalho, 59, 7000-645 Évora, Portugal)

Abstract

Industry 4.0 is a paradigm consisting of cyber-physical systems based on the interconnection between all sorts of machines, sensors, and actuators, generally known as things. The combination of energy technology and information and technology communication (ICT) enables measurement, control, and automation to be performed across the distributed grid with high time resolution. Through digital revolution in the energy sector, the term Energy 4.0 emerges in the future electric sector. The growth outlook for appliance usage is increasing and the appearance of renewable energy sources on the electric grid requires strategies to control demand and peak loads. Potential feedback for energy performance is the use of smart meters in conjunction with smart energy management; well-designed applications will successfully inform, engage, empower, and motivate consumers. This paper presents several hands-on tools for load forecasting, comparing previous works and verifying which show the best energy forecasting performance in a smart monitoring system. Simulations were performed based on forecasting of the hours ahead of the load for several households. Special attention was given to the accuracy of the forecasting model for weekdays and weekends. The development of the proposed methods, based on artificial neural networks (ANN), provides more reliable forecasting for a few hours ahead and peak loads.

Suggested Citation

  • Filipe Martins Rodrigues & Carlos Cardeira & João M. F. Calado & Rui Melicio, 2022. "Home Energy Forecast Performance Tool for Smart Living Services Suppliers under an Energy 4.0 and CPS Framework," Energies, MDPI, vol. 15(3), pages 1-22, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:957-:d:736594
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    References listed on IDEAS

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