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A study on forecasting electricity production and consumption in smart cities and factories

Author

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  • Gellert, Arpad
  • Florea, Adrian
  • Fiore, Ugo
  • Palmieri, Francesco
  • Zanetti, Paolo

Abstract

The electrical power sector must undergo a thorough metamorphosis to achieve the ambitious targets in greenhouse gas reduction set forth in the Paris Agreement of 2015. Reducing uncertainty about demand and, in case of renewable electricity generation, supply is important for the determination of spot electricity prices. In this work we propose and evaluate a context-based technique to anticipate the electricity production and consumption in buildings. We focus on a household with photovoltaics and energy storage system. We analyze the efficiency of Markov chains, stride predictors and also their combination into a hybrid predictor in modelling the evolution of electricity production and consumption. All these methods anticipate electric power based on previous values. The main goal is to determine the best method and its optimal configuration which can be integrated into a (possibly hardware-based) intelligent energy management system. The role of such a system is to adjust and synchronize through prediction the electricity consumption and production in order to increase self-consumption, reducing thus the pressure over the power grid. The experiments performed on datasets collected from a real system show that the best evaluated predictor is the Markov chain configured with an electric power history of 100 values, a context of one electric power value and the interval size of 1.

Suggested Citation

  • Gellert, Arpad & Florea, Adrian & Fiore, Ugo & Palmieri, Francesco & Zanetti, Paolo, 2019. "A study on forecasting electricity production and consumption in smart cities and factories," International Journal of Information Management, Elsevier, vol. 49(C), pages 546-556.
    • Handle: RePEc:eee:ininma:v:49:y:2019:i:c:p:546-556
    DOI: 10.1016/j.ijinfomgt.2019.01.006
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    Citations

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    Cited by:

    1. Dwivedi, Yogesh K. & Hughes, Laurie & Kar, Arpan Kumar & Baabdullah, Abdullah M. & Grover, Purva & Abbas, Roba & Andreini, Daniela & Abumoghli, Iyad & Barlette, Yves & Bunker, Deborah & Chandra Kruse,, 2022. "Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action," International Journal of Information Management, Elsevier, vol. 63(C).
    2. Bachici Miroslav-Andrei & Gellert Arpad, 2020. "Modeling Electricity Consumption and Production in Smart Homes using LSTM Networks," International Journal of Advanced Statistics and IT&C for Economics and Life Sciences, Sciendo, vol. 10(1), pages 80-89, December.

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