Majlesi Journal of Electrical Engineering Electricity Demand Prediction by a Transformer-Based Model Volume 16 (2022) Issue 4, December 2022 2024 02 11 Electricity Demand Prediction by a Transformer-Based Model 10.30486/mjee.2022.696520 EN Ahmed Mohammed Mahmood Department of Optical Techniques, AlNoor University College, Iraq Musaddak Maher Abdul Zahra Al-Nisour University College, Baghdad, Iraq Waleed Hamed Medical technical college, Al-Farahidi University, Baghdad, Iraq Bashar S. Bashar Department of Medical instruments engineering techniques, Al-Farahidi University, Baghdad,10021, Iraq Alaa Hussein Abdulaal Medical Device Engineering, Ashur University College, Baghdad, Iraq Taif Alawsi Mazaya University College/ Dhi Qar, Iraq Ali Hussein Adhab Department of Medical Laboratory Technics, Al-Zahrawi University College, Karbala, Iraq Journal Article 2024 02 11 The frighteningly high levels of power consumption at present are caused mainly by the expanding global population and the accessibility of energy-hungry smart technologies. So far, various simulation tools, engineering- and AI-based methodologies have been utilized to anticipate power consumption effectively. While engineering approaches forecast using dynamic equations, AI-based methods forecast using historical data. The modeling of nonlinear electrical demand patterns is still lacking for durable solutions, however, the available approaches are only effective for resolving transient dependencies. Furthermore, because they are only based on historical data, the current methodologies are static in nature. In this research, we present a system based on deep learning to anticipate power consumption while accounting for long-term historical relationships. In our approach, a transformer-based model is used for the prediction of electricity demand on data collected from the regional facilities in Iraq. According to the conducted experiments, our approach claims competitive performance, achieving an error rate of 2.0 in predicting 1-day-ahead of electricity demand in the test samples.