AI-driven Renewable Energy Generation Forecasting Using Machine Learning Techniques

Abstract

Solar and wind power depend heavily on weather, so the amount of electricity they will supply in the next few hours is difficult to know in advance. This makes planning harder for the people who operate power systems. Machine learning is widely used to predict renewable generation, and recent studies report good prediction accuracy. However, most of these studies stop at the prediction itself. They give a single number without saying how much it may vary; they do not explain which weather conditions led to that number; they work on stored data files instead of live weather information; they are trained only once, and they end with a comparison of error values rather than with advice that an operator can act on. This paper proposes a framework for renewable energy generation forecasting that tries to solve these practical problems. The proposed system is planned as five simple layers covering data collection from stored records and a live weather service, data preparation, a set of machine learning models with a comparison step, supporting features such as a confidence range, an explanation of the influencing factors and periodic model updating, and finally a web application with a dashboard, planning suggestions and a simple assistant. The paper explains the need for the work, the gaps found in existing studies, the proposed design, how the system is expected to work, and a stage-wise plan for building and testing it. The system has not yet been developed, so no results are reported. The expected benefits are described in general terms and include more useful forecasts, better understanding of the predictions, easier use by non-technical users, and a clearer path from a research model to a working tool.