Forecasting the Solar Power with Differentiation of Data Figures using Neural Networks

The industrial sector relies heavily on solar power forecasting because it is dependable and readily available. High solar energy penetration is essential for the financial sustainability of power systems. The fact that solar energy is actually renewable is the most significant benefit of all the features of solar panels. Forecast models incorporate information from numerical weather prediction (NWP) factors such as cloud cover, sunshine, radiation, precipitation, etc. However, there is NTP Uncertainty in the assumption of the atmosphere’s starting state. Advanced predictions are made in the meantime by machine learning algorithms. We need to estimate and compare the accuracy rates between Ann algorithm and Rnn algorithm for the outgoing solar power in this study using the dataset from the previous month.In the upcoming month, the crucial machine learning algorithm will be created. The most extensively used “multi-layer Neural Network” model of ANN and RNN should anticipate and compare electricity costs. Our research aims to accurately forecast solar power. The technique of forecasting the solar energy collected from solar panels for future use is attained by machine learning algorithms. The prediction and comparison will be carried out using the Artificial Neural Network and Recurrent Neural Network concepts in Machine Learning.