Prediction of the Electricity Demand in the Market: An Application of Optimization and Machine Learning

In this study, the combination of Gray Wolf Optimization and Artificial neural networks (GWO-ANN) algorithm was applied to predict the long-term electricity demand in Iraq, considering the nonlinear trend and uncertainties in the variables affecting it. The results indicate that the population and gross domestic product are significant explanatory variables for long-term energy demand, consistent with previous studies. Compared to other intelligent methods, the GWO-ANN algorithm requires less data for modeling and optimally designs the ANN structure. The modeling and forecasting model outperform the ANN in simulating and predicting the long-term energy demand. Based on the most likely scenario, the predicted electricity demand in Iraq will reach approximately 415 GWh. Electricity is a critical factor in the development of societies and is utilized in various economic sectors.