A multi‐objective optimization metaheuristic hybrid technique for forecasting the electricity consumption of the UAE: A grey wolf approach

Abstract

By implementing a multi‐objective optimization approach in forecasting, we introduce three optimization models grey wolf optimizer, genetic algorithm, and differential evolution algorithm combined with multilayer perceptron neural networks and support vector machines to predict electricity consumption in the UAE. The hybrid models' accuracy and efficiency were evaluated using various forecasting metrics. This study's contributions are threefold: it is the first to employ such a sophisticated hybrid approach, particularly using the recently introduced grey wolf optimizer, it compares optimization techniques with the established Pearson correlation‐based method for dimensionality reduction and it represents one of the most extensive macroeconomic forecasts in the UAE using multi‐objective heuristic hybrid optimization methods. Our findings indicate that the grey wolf optimizer significantly outperforms all other models, followed by the genetic algorithm.