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Modeling Electricity Consumption and Production in Smart Homes using LSTM Networks

Author

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  • Bachici Miroslav-Andrei

    (Computer Science and Electrical and Electronics Engineering Department, Faculty of Engineering, “Lucian Blaga” University of Sibiu, Romania)

  • Gellert Arpad

    (Computer Science and Electrical and Electronics Engineering Department, Faculty of Engineering, “Lucian Blaga” University of Sibiu, Romania)

Abstract

This paper presents a forecasting method of the electricity consumption and production in a household equipped with photovoltaic panels and a smart energy management system. The prediction is performed with a Long Short-Term Memory recurrent neural network. The datasets collected during five months in a household are used for the evaluations. The recurrent neural network is configured optimally to reduce the forecasting errors. The results show that the proposed method outperforms an earlier developed Multi-Layer Perceptron, as well as the Autoregressive Integrated Moving Average statistical forecasting algorithm.

Suggested Citation

  • 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.
    • Handle: RePEc:vrs:ijsiel:v:10:y:2020:i:1:p:80-89:n:7
    DOI: 10.2478/ijasitels-2020-0009
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    References listed on IDEAS

    as
    1. Kavaklioglu, Kadir, 2011. "Modeling and prediction of Turkey's electricity consumption using Support Vector Regression," Applied Energy, Elsevier, vol. 88(1), pages 368-375, January.
    2. 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.
    3. Hernández, Luis & Baladrón, Carlos & Aguiar, Javier M. & Carro, Belén & Sánchez-Esguevillas, Antonio & Lloret, Jaime, 2014. "Artificial neural networks for short-term load forecasting in microgrids environment," Energy, Elsevier, vol. 75(C), pages 252-264.
    4. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
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