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

  • Roham TorabiEmail author
  • Sandy Rodrigues
  • Nuno Cafôfo
  • Lucas Pereira
  • Filipe Quintal
  • Nuno Nunes
  • Fernando Morgado-Dias
Advances in Bio-Inspired Intelligent Systems


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.


Roof-top PV system Prediction Artificial neural network Monitoring system 



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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© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Sciences and TechnologyUniversity of CoimbraCoimbraPortugal
  2. 2.Madeira Interactive Technology InstituteFunchalPortugal
  3. 3.Instituto Superior TécnicoUniversity of LisbonLisbonPortugal
  4. 4.University of MadeiraFunchalPortugal

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