1. bookVolume 10 (2020): Edition 1 (December 2020)
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Magazine
eISSN
2067-354X
Première parution
30 Jul 2019
Périodicité
2 fois par an
Langues
Anglais
access type Accès libre

Modeling Electricity Consumption and Production in Smart Homes using LSTM Networks

Publié en ligne: 24 Dec 2020
Volume & Edition: Volume 10 (2020) - Edition 1 (December 2020)
Pages: 80 - 89
Détails du magazine
License
Format
Magazine
eISSN
2067-354X
Première parution
30 Jul 2019
Périodicité
2 fois par an
Langues
Anglais
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

[1] S. Abdulkarim, Time series prediction with simple recurrent neural networks, Bayero Journal of Pure and Applied Sciences, Vol. 9, No. 1, 2016.10.4314/bajopas.v9i1.4Search in Google Scholar

[2] C. Fan, F. Xiao, S. Wang, Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques, Applied Energy, Vol. 127, 2014.10.1016/j.apenergy.2014.04.016Search in Google Scholar

[3] S. Feilmeier, Loads management based on photovoltaic and energy storage system, M.Sc. Thesis, Lucian Blaga University of Sibiu, 2015.Search in Google Scholar

[4] A. Gellert, A. Florea, U. Fiore, F. Palmieri, P. Zanetti, A study on forecasting electricity production and consumption in smart cities and factories, International Journal of Information Management, Elsevier, ISSN 0268-4012, Vol. 49, pp. 546-556, December 2019.10.1016/j.ijinfomgt.2019.01.006Search in Google Scholar

[5] A. Gellert, U. Fiore, A. Florea, R. Chis, Forecasting Electricity Consumption and Production in Smart Homes, Submitted to Pervasive and Mobile Computing, November 2020.Search in Google Scholar

[6] A. Gellert, A. Florea, Investigating a New Design Pattern for Efficient Implementation of Prediction Algorithms, Journal of Digital Information Management, Vol. 11, Issue 5, ISSN 0972-7272, pp. 366-377, October 2013.Search in Google Scholar

[7] A. Graves, Supervised Sequence Labelling with Recurrent Neural Networks, Studies in Computational Intelligence, Springer, ISBN: 978-3-642-24796-5, January 201210.1007/978-3-642-24797-2_2Search in Google Scholar

[8] K. Grolinger, A. L’Heureux, M. Capretz, L. Seewald, Energy Forecasting for Event Venues: Big Data and Prediction Accuracy, Energy and Buildings, Vol. 112, pp. 222-233, 2016.10.1016/j.enbuild.2015.12.010Search in Google Scholar

[9] L. Hernández, C. Baladrón, J. Aguiar, B. Carro, A. Sánchez-Esguevillas, J. Lloret, Artificial neural networks for short-term load forecasting in microgrids environment, Energy, Vol. 75, pp. 252-264, 2014.10.1016/j.energy.2014.07.065Search in Google Scholar

[10] S. Hochreiter, J. Schmidhuber, Long Short-Term Memory, Neural Computation, Vol. 9, No. 8, pp. 1735-1780, 1997.Search in Google Scholar

[11] K. Kavaklioglu, Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression, Applied Energy, Vol. 88, Issue 1, pp. 368-375, 2011.10.1016/j.apenergy.2010.07.021Search in Google Scholar

[12] T. Khatib, A. Mohamed, K. Sopian, M. Mahmoud, Solar Energy Prediction for Malaysia Using Artificial Neural Networks, International Journal of Photoenergy, Vol. 2012, 2012.10.1155/2012/419504Search in Google Scholar

[13] H. Khosravani, M.D.M. Castilla, M. Berenguel, A. Ruano, P. Ferreira, A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building, Energies, Vol. 9, Issue 1, 2016.10.3390/en9010057Search in Google Scholar

[14] C. Monteiro, L.A. Fernandez-Jimenez, I.J. Ramirez-Rosado, A. Muñoz-Jimenez, Pedro M. Lara-Santillan, Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques, Mathematical Problems in Engineering, Vol. 2013, 2013.10.1155/2013/767284Search in Google Scholar

[15] J. Park, D. Yi, S. Ji, Analysis of Recurrent Neural Network and Predictions, Symmetry, Vol. 12, No. 4, 2020.10.3390/sym12040615Search in Google Scholar

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