Artificial Intelligence based Technique for Solar Irradiance Prediction Model with Improved Performance

Solar energy is one of today's most widely adopted renewable energy sources. The use of solar energy in power grids has many advantages, including the decrease in emissions and greenhouse gases, the decrease in power losses and costs, the ability to directly charge dc loads like laptops, electric vehicles, and phones without the need for converters, and an increase in power quality. Techniques based on artificial intelligence are superior in terms of their ability to provide accurate forecasts or predictions of solar energy. This allows photovoltaic power plants to participate in energy auctions earlier, allowing for more cost-effective resource planning. This research presents efficient machine and deep learning-based techniques to improve forecasting performance. The dataset will be taken from the base paper or the publicly available machine learning repository. The simulation will be done using the python spyder IDE 3.7 version. The simulated result will be shown in terms of performance parameter improvement like root means square error, mean absolute error etc.