To develop a predictive model using machine learning algorithms to estimate the output of renewable energy sources such as solar and wind power.
To optimize the utilization of renewable energy resources in a smart grid system through predictive modeling and forecasting techniques.
To improve the efficiency and reliability of the smart grid by incorporating machine learning algorithms for better decision-making and control.
Literature review on the application of machine learning in renewable energy prediction and smart grid optimization.
Data collection and pre-processing of renewable energy output data for model training.
Implementation of machine learning algorithms such as neural networks or support vector machines for predictive modeling.
Evaluation of the model performance using appropriate metrics and comparison with existing methods.
Integration of the predictive model into a smart grid system for real-time optimization and control.
