Performance analysis of ARIMA Model for wind speed forecasting in Jerusalem, Palestine

Abstract

Palestine lacks sufficient conventional energy sources that meet the daily needs of the Palestinian people, and consequently, it heavily relies on neighboring countries for its supply with energy compensations. Wind energy is recognized as an abundant, effective, and eco-friendly power source, but it poses several challenges in harnessing due to the inherent variability of wind characteristics. The main objective of this research study is to delve into the wind energy landscape in Palestine, and to offer some insights into the feasibility of wind speed forecasting for implementing sustainable energy solutions, with a special focus on ARIMA; a widely used statistical method for time series forecasting. It specifically explores the potential of using ARIMA models to forecast wind speed using a data captured from a meteorological station located in east Jerusalem, Palestine for a duration of 2 years—January 1, 2021 to December 31, 2022. To find the optimal values of ARIMA parameters (p, d, q) for the considered study site, a set of experiments were conducted and the model's forecasting accuracy was evaluated using three metrics: RMSE, MAE, and the coefficient of determination (R2). The results have shown that ARIMA (21,2) emerges as the most accurate structure with an input period that demonstrates superior estimation with minimal RMSE (1.74), minimal MAE (1.58) and higher R2 (0.76) values. This means that the optimal estimation is achieved when an autoregressive process is based on the previous two lagged observations and the moving average process incorporates the dependency between the observation and the residual error from a second-order moving average applied to the lagged observations. These findings give valuable insights into the feasibility and precision of wind speed forecasting models for sustainable energy solutions, and emphasize the potential for harnessing wind energy in the region as clarified by ARIMA forecasting accuracy.