With the increasing demand for energy different forms of electrical energy yield arise. Among them there is the photovoltaic solar energy, a renewable, abundant and low cost energy source whose applications depend directly on the solar irradiation intensity. In this project one proposes the use of Supervised Machine Learning techniques in order to implement models capable of predicting solar irradiation from a known dataset in specific sites within the State of São Paulo, Brazil. Once such dataset is obtained different techniques will be applied aiming to carry the preprocessing and Feature Engineering steps to optimize the Supervised Machine Learning algorithms. Models' capability of prediction will be evaluated in two moments: during the adjustment stage and, subsequently, in the validation stage. Results will be tabulated and analyzed regarding the models' hyper parameters, the difference between predicted and observed values and error metrics. Implementation will be carried out using the Python programming language in addition to diverse machine learning, data manipulation and visualization libraries.
News published in Agência FAPESP Newsletter about the scholarship: