<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Modeling Electricity Consumption and Production in Smart Homes using LSTM Networks

Computer Science and Electrical and Electronics Engineering Department, Faculty of Engineering

International Journal of Advanced Statistics and IT&C for Economics and Life Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper presents a forecasting method of the electricity consumption and production in a household equipped with photovoltaic panels and a smart energy...

Modeling Electricity Consumption and Production in Smart Homes using LSTM Networks

Online veröffentlicht: 24. Dez. 2020

Seitenbereich: 80 - 89

DOI: https://doi.org/10.2478/ijasitels-2020-0009

Schlüsselwörter
electricity prediction, Long Short-Term Memory, smart home, energy management system, photovoltaics

© 2020 Miroslav-Andrei Bachici et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Modeling Electricity Consumption and Production in Smart Homes using LSTM Networks

Online veröffentlicht: 24. Dez. 2020

Seitenbereich: 80 - 89

DOI: https://doi.org/10.2478/ijasitels-2020-0009

Schlüsselwörterelectricity prediction, Long Short-Term Memory, smart home, energy management system, photovoltaics

© 2020 Miroslav-Andrei Bachici et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
electricity prediction, Long Short-Term Memory, smart home, energy management system, photovoltaics