Abstract
This work proposes a short-term electrical load demand forecaster for the Nigerian power distribution firms in Abuja, Benin, and Enugu. Using artificial neural network, the forecaster is created. Hour of the day, calendar day, day of the week (Sunday-Saturday), load demand of the previous day, load demand of the previous week, and average load demand of the preceding 24 h are the inputs to the neural network. The historical load demand for 2017–2020 includes hourly resolved dates and load demand for Abuja, Benin, and Enugu distribution firms for training purposes, while data for 2020 was used for testing the algorithm. The results generated a mean average percentage error ranging from 0.16 to 0.35. This forecaster is essential to Nigeria's efforts to expand access to power in accordance with Sustainable Development Goal 7.
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Adeyemi-Kayode, T.M., Orovwode, H.E., Adoghe, A.U., Misra, S., Agrawal, A. (2023). Short-Term Load Demand Forecasting Using Artificial Neural Network. In: Singh, Y., Singh, P.K., Kolekar, M.H., Kar, A.K., Gonçalves, P.J.S. (eds) Proceedings of International Conference on Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 1001. Springer, Singapore. https://doi.org/10.1007/978-981-19-9876-8_14
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DOI: https://doi.org/10.1007/978-981-19-9876-8_14
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