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Short Term Electricity Load Forecasting Through Machine Learning

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (AICV 2020)

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

Abstract

Essentially, Electricity Load Forecasting is an approximation of upcoming active loads from a variety of load buses before the active loads occur. Also, it is an important factor for power system energy management. Accurate and precise load forecasting can help to reduce the capacity of the power system, to make unit commitment decisions, and to increase the dependability of power systems. Hence, this paper presents a generalized regression Neural Network (GRNN) based approach for Short Term Load Forecasting (STLF). The results showed that the performance of GRNN with 30 neurons is better of short-term load forecasting in comparison with 10 neurons. For 10 neurons, the Mean Absolute Percentage Error (MAPE) was 2.10% and Mean Absolute Error (MAE) was 306.21 MWh. However, for 30 neurons it was observed that MAPE is 1.81% and MAE 268.48 MWh.

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Author information

Authors and Affiliations

  1. Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh, Saudi Arabia

    Ahmad Taher Azar

  2. Faculty of Computer and Artificial Intelligence, Benha University, Benha, Egypt

    Ahmad Taher Azar

  3. Center for Pattern Analysis and Machine Intelligence (CPAMI), University of Waterloo, Waterloo, Canada

    Alaa Khamis

  4. Faculty of Engineering, Cairo University, Giza, Egypt

    Nashwa Ahmad Kamal

  5. Hofstra University, Hempstead, NY, USA

    Brian Galli

Authors
  1. Ahmad Taher Azar
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  2. Alaa Khamis
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  3. Nashwa Ahmad Kamal
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  4. Brian Galli
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Corresponding author

Correspondence to Ahmad Taher Azar .

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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Azar, A.T., Khamis, A., Kamal, N.A., Galli, B. (2020). Short Term Electricity Load Forecasting Through Machine Learning. 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_40

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