Comparing Artificial Intelligence Techniques for Predicting Energy Consumption and Renewable Energy Production

Due to the variability in energy production from renewable sources such as solar and wind, maintaining the stability of power grids in renewable energy systems presents several challenges. Accurate prediction of both renewable energy generation and energy consumption is crucial to addressing these issues effectively. Artificial intelligence (AI) techniques can significantly enhance the accuracy of these predictions by analyzing large and complex datasets. A review of previous research highlights the importance of selecting the most appropriate AI technique, as different approaches have been proposed and evaluated using various performance metrics. This study used a multi-criteria decision-making method to rank AI prediction techniques. Extreme Gradient Boosting, Random Forest, Long Short-Term Memory, and Artificial Neural Networks emerged as the highest-ranked among the evaluated techniques. These four techniques were applied to data sets to predict energy consumption and solar energy production.