A survey on advanced machine learning and deep learning techniques assisting in renewable energy generation.

Abstract

The sustainability of the earth depends on renewable energy. Forecasting the output of renewable energy has a big impact on how we operate and manage our power networks. Accurate forecasting of renewable energy generation is crucial to ensuring grid dependability and permanence and reducing the risk and cost of the energy market and infrastructure. Although there are several approaches to forecasting solar radiation on a global scale, the two most common ones are machine learning algorithms and cloud pictures combined with physical models. The objective is to present a summary of machine learning-based techniques for solar irradiation forecasting in this context. Renewable energy is being used more and more in the world's energy grid. Numerous strategies, including hybrids, physical models, statistical approaches, and artificial intelligence techniques, have been developed to anticipate the use of renewable energy. This paper examines methods for forecasting renewable energy based on deep learning and machine learning. Review and analysis of deep learning and machine learning forecasts for renewable energy come first. The second paragraph describes metaheuristic optimization techniques for renewable energy. The third topic was the open issue of projecting renewable energy. I will wrap up with a few potential future job objectives.