Prediction of Rooftop Photovoltaic Solar Potential Using Machine Learning - K. Mukilan; K. Thaiyalnayaki; Yagya Dutta Dwivedi; J. Samson Isaac; Amarjeet Poonia; Arvind Sharma; Essam A. Al-Ammar; Saikh Mohammad Wabaidur; B. B. Subramanian; Adane Kassa (International Journal of Photoenergy)
Solar energy forecasting accuracy is essential for increasing the quantity of renewable energy that can be integrated into the existing electrical grid control systems. The availability of data at unprecedented levels of granularity allows for the development of data-driven algorithms to improve the estimation of solar energy generation and production. In this paper, we develop a prediction of solar potential across large photovoltaic panels from the roof tops using a machine learning method. The Restricted Boltzmann Machine (RBM) is the machine learning method used in the study to predict or forecast the solar potential in rooftops. The machine learning model is supplied with training dataset to get trained with the dataset for conversion into the model and then tested with the test dataset for validating the model. The results of simulation are conducted on R-package over various libraries to predict the rooftop solar potential. The results of simulation shows that the proposed method achieves higher rate of prediction accuracy than the other methods. The results of the simulation show that the proposed method achieves a higher rate of prediction accuracy of 99% than the other methods.