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Development of a Self-Calibrated Embedded System for Energy Management in Low Voltage

Author

Listed:
  • Eder Andrade da Silva

    (Latin American Institute of Technology, Infrastructure and Territory (ILATIT), Federal University of Latin American Integration (UNILA), Foz do Iguaçu 85867-000, PR, Brazil
    Research Group on Energy & Energy Sustainability (GPEnSE), Pernambuco 54518-430, PE, Brazil)

  • Carlos Alejandro Urzagasti

    (Latin American Institute of Technology, Infrastructure and Territory (ILATIT), Federal University of Latin American Integration (UNILA), Foz do Iguaçu 85867-000, PR, Brazil
    Research Group on Energy & Energy Sustainability (GPEnSE), Pernambuco 54518-430, PE, Brazil)

  • Joylan Nunes Maciel

    (Latin American Institute of Technology, Infrastructure and Territory (ILATIT), Federal University of Latin American Integration (UNILA), Foz do Iguaçu 85867-000, PR, Brazil
    Research Group on Energy & Energy Sustainability (GPEnSE), Pernambuco 54518-430, PE, Brazil)

  • Jorge Javier Gimenez Ledesma

    (Latin American Institute of Technology, Infrastructure and Territory (ILATIT), Federal University of Latin American Integration (UNILA), Foz do Iguaçu 85867-000, PR, Brazil
    Research Group on Energy & Energy Sustainability (GPEnSE), Pernambuco 54518-430, PE, Brazil)

  • Marco Roberto Cavallari

    (Research Group on Energy & Energy Sustainability (GPEnSE), Pernambuco 54518-430, PE, Brazil
    School of Electrical and Computer Engineering (FEEC), State University of Campinas (Unicamp), Campinas 13083-852, SP, Brazil)

  • Oswaldo Hideo Ando Junior

    (Latin American Institute of Technology, Infrastructure and Territory (ILATIT), Federal University of Latin American Integration (UNILA), Foz do Iguaçu 85867-000, PR, Brazil
    Research Group on Energy & Energy Sustainability (GPEnSE), Pernambuco 54518-430, PE, Brazil
    Academic Unit of Cabo de Santo Agostinho (UACSA), Federal Rural University of Pernambuco (UFRPE), Cabo de Santo Agostinho 54518-430, PE, Brazil)

Abstract

Due to the growing concern and search for energy sustainability, there has been an increase in recent years in solutions in the area of energy management and efficiency related to the Internet of Things (IoT), the home energy management system (HEMS), and the building energy management system (BEMS). The availability of the energy consumption pattern in real time is part of the necessity presented by this research. It is essential for perceiving and understanding the savings opportunities. In this context, this manuscript presents the development of a self-calibrated embedded system to measure, monitor, control, and forecast the consumption of electrical loads, enabling the improvement of energy efficiency through the management of loads performed by the demand side. The validation of the produced device was performed by comparing the readings of the device with the readings obtained through the evaluation system of the integrated circuit manufacturer ADE9153A ® , Analog Devices ® purchased in Brazil. The result obtained with the developed device featured errors smaller than ±0.1%, which were in addition smaller than ±1% with respect to the full scale, thus proving to be a viable solution for the proposed application.

Suggested Citation

  • Eder Andrade da Silva & Carlos Alejandro Urzagasti & Joylan Nunes Maciel & Jorge Javier Gimenez Ledesma & Marco Roberto Cavallari & Oswaldo Hideo Ando Junior, 2022. "Development of a Self-Calibrated Embedded System for Energy Management in Low Voltage," Energies, MDPI, vol. 15(22), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8707-:d:977946
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    References listed on IDEAS

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    1. Christos Athanasiadis & Dimitrios Doukas & Theofilos Papadopoulos & Antonios Chrysopoulos, 2021. "A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption," Energies, MDPI, vol. 14(3), pages 1-23, February.
    2. Regina Negri Pagani & João Luiz Kovaleski & Luis Mauricio Resende, 2015. "Methodi Ordinatio: a proposed methodology to select and rank relevant scientific papers encompassing the impact factor, number of citation, and year of publication," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2109-2135, December.
    3. Shaukat, N. & Ali, S.M. & Mehmood, C.A. & Khan, B. & Jawad, M. & Farid, U. & Ullah, Z. & Anwar, S.M. & Majid, M., 2018. "A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1453-1475.
    4. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
    5. Prasertsak Charoen & Nathavuth Kitbutrawat & Jasada Kudtongngam, 2022. "A Demand Response Implementation with Building Energy Management System," Energies, MDPI, vol. 15(3), pages 1-21, February.
    6. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 31-53.
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    Cited by:

    1. Nunes Maciel, Joylan & Javier Gimenez Ledesma, Jorge & Hideo Ando Junior, Oswaldo, 2024. "Hybrid prediction method of solar irradiance applied to short-term photovoltaic energy generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    2. Issam Hanafi & Bousselham Samoudi & Ahlem Ben Halima & Laurent Canale, 2022. "Hotspots and Tendencies of Energy Optimization Based on Bibliometric Review," Energies, MDPI, vol. 16(1), pages 1-22, December.

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