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The International Conference on Artificial Intelligence and Computer Vision

AICV 2020: Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) pp 237–246Cite as

Prediction of the Electrical Load for Egyptian Energy Management Systems: Deep Learning Approach

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1153))

Abstract

In the era of the Internet of Things (IoT) and machine learning, energy utilities have now the possibility of providing many benefits to the customers. Using advanced metering infrastructure is an important step to optimize and manage energy consumption. Smart meters are deployed in millions of households worldwide, representing an opportunity for monitoring systems and data analytics. Egypt has started its path into smart metering and needs to use big data analytics to endow the Egyptian grid with intelligence. In this paper, we investigate the role of big data analytics and available machine learning technologies that mine the data to generate insights into smart grid management process. We investigate a deep learning-based methodology such as long short-term memory (LSTM) network model to perform prediction of the electrical load for energy management systems. The LSTM model presents a decrease of 14% in forecasting error compared to deep forward neural networks.

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Author information

Authors and Affiliations

  1. Faculty of Information Technology and Computers Science, University of Sinai, North Sinai, Egypt

    Mohammed E. El-Telbany

Authors
  1. Mohammed E. El-Telbany
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Corresponding author

Correspondence to Mohammed E. El-Telbany .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Information, Giza, Egypt

    Aboul-Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Faculty of Computers and Information, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadajalara, Jalisco, Mexico

    Diego Oliva

  5. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

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Cite this paper

El-Telbany, M.E. (2020). Prediction of the Electrical Load for Egyptian Energy Management Systems: Deep Learning Approach. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_23

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