Solar Power Prediction with Artificial Intelligence
Enas Raafat Maamoun Shouman
Abstract
Solar power prediction is a critical aspect of optimizing renewable energy integration and ensuring efficient grid management. The chapter explore the application of artificial intelligence (AI) techniques for accurate solar power forecasting. The AI models considered include Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest, and Gradient Boosting. These models are selected based on their ability to capture complex patterns and non-linear relationships present in the solar energy data. The solar power forecasting process involves data preprocessing, feature selection, model training, and evaluation. Data preprocessing techniques are applied to handle missing values and normalize the data to improve model performance. Feature selection methods are utilized to identify the most relevant features that influence solar power generation. The AI models are trained using historical data, where they learn the relationships between input features and solar power generation. Model evaluation is carried out using metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) to assess the accuracy of the forecasts. Furthermore, the forecasted results are visualized through line plots and error plots to provide valuable insights to stakeholders. A comprehensive report detailing the forecasting process, methodology, and results is generated, allowing decision-makers to make informed choices based on the forecasted solar energy data.