The Role of Machine Learning Methods for Renewable Energy Forecasting

Abstract

Across the world, countries are placing greater emphasis on transitioning to cleaner energy sources, while also becoming increasingly concerned about the worsening climate crisis. With the cost-effectiveness and eco-friendly nature of renewable energy (RE) sources, there has been a considerable amount of interest. Nevertheless, the unpredictable nature of RE sources presents significant challenges to the security and stability of power grids, adding complexity to the operation and scheduling of power systems. Consequently, the widespread adoption of RE applications becomes more challenging. Accurately forecasting the efficiency of RE is essential for effective system management and operation. By improving the accuracy of these forecasts, we can minimise risks and enhance the stability and reliability of the network. Machine learning (ML) has the potential to greatly assist in achieving the future objectives of RE by comprehending complex correlations within data and providing accurate predictions. This review offers valuable insights into the prediction of RE generation using ML techniques. It explores a wide range of RE sources, such as solar, wind, hydroelectric, geothermal, biomass, and marine-based energies. In addition, the assessment offers a detailed analysis of the latest research findings, along with comprehensive information on performance metrics and ML techniques utilised in RE forecasting.