Evaluating Power Usage Patterns: A Case Study on Time Series Modeling Forecasting in Erbil City, 2015–2024

Precise electricity consumption predictions are essential for efficient energy management, resource allocation, and power system stability, especially in expanding urban areas like Erbil. Time series models are crucial in this field because they can capture trends, seasonal variations, and structural shifts in energy use patterns. This study aims to forecast electricity consumption in Erbil for 2025 by leveraging the Autoregressive Integrated Moving Average (ARIMA) model, a widely recognized approach in time series analysis. 3,652 historical daily electricity use data observations from 2015 to 2024 were examined. Among the preprocessing procedures were first differencing to attain stationarity and logarithmic transformation to handle outliers. Evaluation criteria including the Akaike Information Criterion (AIC), Root Mean Square Error (RMSE), and Mean Squared Error (MSE) were given top priority during the model selection process. With an MSE of 0.0487347, the seasonal ARIMA(1,1,1)x(0,1,1)12 model was found to be the most successful. Yule-Walker equations, Maximum Likelihood, and Least Squares were among the optimization approaches used in parameter estimation; t-tests were used to confirm statistical significance. Monthly averages for 2025 ranged from 955 MW in January to 916 MW in December, indicating notable seasonal changes. The importance of time series models in tackling the difficulties of resource planning and energy demand forecasting is highlighted by this study, which offers a solid foundation for controlling Erbil's seasonal and structural consumption patterns.