Electrical Load Short Term Prediction by Artificial Neural Network

H. Awad; H. M. Mahmoud; S. A. Alfatah; E. Allam; A. A. Amin; F. A. Abdullah

doi:10.53797/icccmjssh.v5isp.16.2026

Authors

H. Awad Department of Electrical Technology at the faculty of Technology and Education, Helwan University, EGYPT
H. M. Mahmoud Department of Electrical Technology at the faculty of Technology and Education, Helwan University, EGYPT
S. A. Alfatah Department of Electrical Technology at the faculty of Technology and Education, Helwan University, EGYPT
E. Allam Department of Electrical Technology at the faculty of Technology and Education, Helwan University, EGYPT
A. A. Amin Delta Technological University, Menofia, EGYPT
F. A. Abdullah Reseacrcher, Mattaria Technological College, Mattaria, Cairo, EGYPT

DOI:

https://doi.org/10.53797/icccmjssh.v5isp.16.2026

Keywords:

Artificial neural network, generic algorithm, levenberg marquardt, short-term electric load forecasting

Abstract

Power generation facilities face a technical and economical challenge in the planning, operation, and management of electrical power systems. In the past, we have employed numerous techniques to forecast short-term electrical loads, but they have demonstrated several shortcomings, including their inability to accurately handle non-linear data. In order to forecast short-term electricity loads, we have created a novel artificial intelligence-based technique in this research study called the artificial neural networks (ANN) method.The neural network was trained, tested, and validated, and the predicted loads were produced by using the temperatures and historical data for peak loads in October 2023, which were represented by the highest electrical load for one day of the month and the temperature data per hour. We are researching and evaluating how electrical loads relate variables, meteorological conditions the results showed negligible mean square error (MSE) and exceptional realism and accuracy in determining future forecast values for this type of non-linear relationship compared to other forecasting methods.

Downloads

Download data is not yet available.

References

Abdolrasol, M. G., Hussain, S. S., Ustun, T. S., Sarker, M. R., Hannan, M. A., Mohamed, R., ... & Milad, A. (2021). Artificial neural networks based optimization techniques: A review. Electronics, 10(21), 2689. https://doi.org/10.3390/electronics10212689

Chen, C. S., Tzeng, Y. M., & Hwang, J. C. (1996). The application of artificial neural networks to substation load forecasting. Electric Power Systems Research, 38(2), 153-160. https://doi.org/10.1016/S0378-7796(96)01077-2

Feinberg, E. A., & Genethliou, D. (2005). Load forecasting. In Applied mathematics for restructured electric power systems: optimization, control, and computational intelligence (pp. 269-285). Boston, MA: Springer US. https://doi.org/10.1007/0-387-23471-3_12

Hambali, A. O. J., Akinyemi, M., & JYusuf, N. (2016). Electric power load forecast using decision tree algorithms. Comput. Inf. Syst. Dev. Inform. Allied Res. J, 7(4), 29-42. http://www.cisdijournal.net/

Lipu, M. H., Miah, M. S., Hannan, M. A., Hussain, A., Sarker, M. R., Ayob, A., ... & Mahmud, M. S. (2021). Artificial intelligence based hybrid forecasting approaches for wind power generation: Progress, challenges and prospects. IEEe Access, 9, 102460-102489. https://doi.org/10.1109/ACCESS.2021.3097102

Moturi, C. A., & Kioko, F. K. (2013). Use of artificial neural networks for short-term electricity load forecasting of Kenya national grid power system. International Journal of Computer Applications, 63(2).https://doi.org/10.5120/10439-5123

Olagoke, M. D., Ayeni, A. A., & Hambali, M. A. (2016). Short term electric load forecasting using neural network and genetic algorithm. International Journal of Applied Information Systems, 10(4), 22-28. https://doi.org/10.5120/ijais2016451490

Sheikh, S. K., & Unde, M. G. (2012). Short term load forecasting using ANN technique. International Journal of Engineering Sciences & Emerging Technologies, 1(2), 97-107.

Srinivasan, D., Liew, A. C., & Chen, J. S. (1991, November). A novel approach to electrical load forecasting based on a neural network. In [Proceedings] 1991 IEEE International Joint Conference on Neural Networks (pp. 1172-1177). IEEE. https://doi.org/10.1109/IJCNN.1991.170555

Zhao, H., Ren, Z., & Huang, W. (1997). Short term load forecasting considering weekly period based on PAR. Proceedings-Chinese Society of Electrical Engineering, 17, 211-213.