Temperature Forecasting in Morocco Using Machine Learning: Optimization for Solar Energy Applications

In the current context of an energy transition, solar potential is an invaluable resource for producing renewable energy in Morocco. However, the efficiency for installation of solar panels requires accurate prediction of temperature. This paper purports to research new information about Geographic Information Systems coupled with Machine Learning techniques for temperature forecasting in Morocco. In this work, we compare two models: the Random Forest (RF) one with the XGBoost one, based on a set of factors: PVOUT, GIT, OPTA, GHI, DNI, DIF, and DEM. Our results present a promising outlook for optimizing the solar panel installation process, using the value that a pixel has as a target for our prediction. Initial results indicate that the RF model has some promising levels of precision up to a level of 0.9971 R2, whereas XGBoost reached 0.977775. These results give good insights into the optimization for the solar panel installation at the pixel level for our purpose of predictions.