Solar Power

Solar power research focuses on improving the accuracy and reliability of solar energy generation forecasting to facilitate efficient grid integration and resource management. Current efforts utilize diverse machine learning models, including deep learning architectures like LSTM, convolutional neural networks (CNNs), and graph neural networks (GNNs), often incorporating weather data, air quality indices, and satellite imagery to enhance prediction accuracy. These advancements are crucial for optimizing energy distribution, reducing reliance on fossil fuels, and enabling more effective planning and operation of solar power systems. Furthermore, research is addressing challenges like intermittency through improved forecasting and the development of innovative anomaly detection methods for concentrated solar power plants.