This paper presents a methodology for evaluating solar and wind energy using artificial intelligence (AI) technologies. The authors propose a general approach for assessing renewable energy sources, focusing on solar and wind energy, by integrating AI methods for data processing and optimization of energy system control. The methodology includes data preprocessing, forecasting, and the use of AI-based models to improve the accuracy of energy generation predictions. The study highlights the importance of AI in addressing the challenges of renewable energy forecasting, such as uncertainty in energy generation and the need for accurate predictions to optimize energy system operations. The authors implemented a hybrid forecasting model that combines exponential smoothing, random forest, and LSTM neural network models to predict solar power generation. The model was tested using data from a photovoltaic panel in Moscow, and the results showed high accuracy. The hybrid model outperformed traditional methods in terms of prediction accuracy and reliability. The study also discusses the use of AI in energy systems, including digital twins, which allow for the simulation and optimization of energy systems with renewable energy sources. The methodology includes data preprocessing techniques such as data cleaning, normalization, and dimensionality reduction to improve the quality of input data for AI models. The study also evaluates the performance of different AI models using metrics such as MAE, MAPE, RMSE, and R². The results show that the proposed hybrid model provides accurate predictions and is effective in forecasting solar energy generation. The paper concludes that AI technologies are essential for the efficient and reliable use of renewable energy sources. The proposed methodology provides a comprehensive framework for evaluating and forecasting solar and wind energy, which can be applied in various energy systems to improve the integration of renewable energy sources. The study emphasizes the importance of AI in addressing the challenges of renewable energy forecasting and optimizing energy system operations.This paper presents a methodology for evaluating solar and wind energy using artificial intelligence (AI) technologies. The authors propose a general approach for assessing renewable energy sources, focusing on solar and wind energy, by integrating AI methods for data processing and optimization of energy system control. The methodology includes data preprocessing, forecasting, and the use of AI-based models to improve the accuracy of energy generation predictions. The study highlights the importance of AI in addressing the challenges of renewable energy forecasting, such as uncertainty in energy generation and the need for accurate predictions to optimize energy system operations. The authors implemented a hybrid forecasting model that combines exponential smoothing, random forest, and LSTM neural network models to predict solar power generation. The model was tested using data from a photovoltaic panel in Moscow, and the results showed high accuracy. The hybrid model outperformed traditional methods in terms of prediction accuracy and reliability. The study also discusses the use of AI in energy systems, including digital twins, which allow for the simulation and optimization of energy systems with renewable energy sources. The methodology includes data preprocessing techniques such as data cleaning, normalization, and dimensionality reduction to improve the quality of input data for AI models. The study also evaluates the performance of different AI models using metrics such as MAE, MAPE, RMSE, and R². The results show that the proposed hybrid model provides accurate predictions and is effective in forecasting solar energy generation. The paper concludes that AI technologies are essential for the efficient and reliable use of renewable energy sources. The proposed methodology provides a comprehensive framework for evaluating and forecasting solar and wind energy, which can be applied in various energy systems to improve the integration of renewable energy sources. The study emphasizes the importance of AI in addressing the challenges of renewable energy forecasting and optimizing energy system operations.