Deep Neural Networks for Forecasting Build Energy Load

Chaymae Makri, Said Guedira, Imad El Harraki, Soumia El Hani

2021

Abstract

The consumption of electric power is progressing rapidly with the increase in the human population and technology development. Therefore, for a stable power supply, accurate prediction of power consumption is essential. In recent years, deep neural networks have been one of the main tools in developing methods for predicting energy consumption. This paper aims to propose a method for predicting energy demand in the case of the residential sector. This method uses a deep learning algorithm based on long short-term memory (LSTM). We applied the built model to the electricity consumption data of a house. To evaluate the proposed approach, we compared the performance of the prediction results with Multilayer Perceptron, Recurrent Neural Network, and other methods. The experimental results demonstrate that the proposed method has higher prediction performance and excellent generalization capability.

Paper Citation

in Harvard Style

Makri C., Guedira S., El Harraki I. and El Hani S. (2021). Deep Neural Networks for Forecasting Build Energy Load. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 306-310. DOI: 10.5220/0010733100003101

in Bibtex Style

@conference{bml21,
author={Chaymae Makri and Said Guedira and Imad El Harraki and Soumia El Hani},
title={Deep Neural Networks for Forecasting Build Energy Load},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={306-310},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010733100003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - Deep Neural Networks for Forecasting Build Energy Load
SN - 978-989-758-559-3
AU - Makri C.
AU - Guedira S.
AU - El Harraki I.
AU - El Hani S.
PY - 2021
SP - 306
EP - 310
DO - 10.5220/0010733100003101