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A global monitoring system for electricity consumption and production of household roof-top PV systems in Madeira

Abstract

This paper describes recent work on the development of a wireless-based remote monitoring system for household energy consumption and generation in Madeira Island, Portugal. It contains three different main sections: (1) a monitoring system for consumed and produced energy of residencies equipped with photovoltaic (PV) systems, (2) developing a tool to predict the electricity production, (3) and proposing a solution to detect the PV system malfunctions. With the later tool, the user (owner) or the energy management system can monitor its own PV system and make an efficient schedule use of electricity at the consumption side. In addition, currently, the owners of PV systems are notified about a failure in the system only when they receive the bill, whereas using the proposed method conveniently would notify owners prior to bill issue. The artificial neural network was employed as a tool together with the hardware-based monitoring system which allows a daily analysis of the performance of the system. The comparison of the predicted value of the produced electricity with the actual production for each day shows the validity of the method.

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Acknowledgements

This work was supported partially by the Funding Program + Conhecimento II, Incentive System to Research and Technological Development and Innovation of Madeira Region II, SmartSolar project MADFDR-01-0190-FEDER-000015.

This work was also supported by Agência Regional para o Desenvolvimento e Tecnologia through the PhD Studentship, Project M1420-09-5369-000001 and by the Portuguese Foundation for Science and Technology through Projeto Estrategico LA9 - UID/ EEA/50009/2013.

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Correspondence to Roham Torabi.

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Torabi, R., Rodrigues, S., Cafôfo, N. et al. A global monitoring system for electricity consumption and production of household roof-top PV systems in Madeira. Neural Comput & Applic 32, 15835–15844 (2020). https://doi.org/10.1007/s00521-018-3832-3

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Keywords

  • Roof-top PV system
  • Prediction
  • Artificial neural network
  • Monitoring system