Modeling Electricity Consumption and Production in Smart Homes using LSTM Networks

Bachici Miroslav-Andrei () and Gellert Arpad ()
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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

International Journal of Advanced Statistics and IT&C for Economics and Life Sciences, 2020, vol. 10, issue 1, 80-89

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.

Keywords: electricity prediction; Long Short-Term Memory; smart home; energy management system; photovoltaics (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:ijsiel:v:10:y:2020:i:1:p:80-89:n:7

DOI: 10.2478/ijasitels-2020-0009

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