Solar Radiation monthly prediction and forecasting using Machine Learning technique

The whole world is trying to reduce the CO2 emission by using renewable energy resources (RERs) instead of fossil fuels especially for electricity generation. One of the main RERs for electricity is the sun. Soler panels are used are used for capturing the energy of the sun using photovoltaic (PV) cells. The output of PV’s depends on the weather and climate. So, it is very important to know how much energy can be harvested in upcoming years or months by predicting the solar energy for a particular area of earth, using historical data. In this work, two machine learning techniques are used to forecast solar radiation using historical data. This will help the energy companies in planning and designing of long-term goals for sustainable electricity generation.