TY - EJOUR
AU - Mahmood, Ahmed Mohammed
AU - Zahra, Musaddak Maher Abdul
AU - Hamed, Waleed
AU - Bashar, Bashar S.
AU - Abdulaal, Alaa Hussein
AU - Alawsi, Taif
AU - Adhab, Ali Hussein
PY - 2024
DA - February
TI - Electricity Demand Prediction by a Transformer-Based Model
T2 - Majlesi Journal of Electrical Engineering
VL - 16
L1 - https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/electricity-demand-prediction-by-a-transformer-based-model/
DO - 10.30486/mjee.2022.696520
N2 - 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.
IS - 4
PB - OICC Press
KW - ANN, Machine Learning, Electricity demand, self-attention, Power Consumption, Prediction, PV panel, Solar output, GUI 
EN -