Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems

Solar power is an excellent alternative source that can significantly cut our dependency on nonrenewable and destructive fossil fuels. radiation (SR) be predicted with great precision, it may possible to drastically minimize the impact cost associated development of solar energy. To successfully implement power, all projects using energy must have access reliable sun data. However, deployment, administration, performance photovoltaic or thermal systems severely impacted by lack ambiguity this Methods for estimating predicting help solve these problems. Prediction techniques put use in real world to, example, keep grid functioning smoothly ensure supply electricity exactly matches demand at times. Recently developed forecasting methods include deep learning convolutional neural networks combined long short-term memory (CNN-LSTM) model. This study provides a comprehensive examination meteorological data, along CNN-LSTM methods, order design train most accurate SR artificial network model possible. Weather data was collected from NASA station included details such as current temperature, relative humidity, speed wind. research revealed highly correlated both temperature radiation. Furthermore, findings demonstrated algorithm outperformed other algorithm-trained models, evidenced score respective maximum coefficient determination (R²) > 95% minimum mean square error (MSE) 0.000987 testing step. In comparison different existing intelligence models. These scenarios basic implementation used supplement conventional SR, provide possibilities monitor low cost, encourage adoption data-driven management.