  Renewable Energies (RE) have gained more attention in recent years since they
offer clean and sustainable energy. One of the major sustainable development
goals (SDG-7) set by the United Nations (UN) is to achieve affordable and clean
energy for everyone. Among the world's all renewable resources, solar energy is
considered as the most abundant and can certainly fulfill the target of SDGs.
Solar energy is converted into electrical energy through Photovoltaic (PV)
panels with no greenhouse gas emissions. However, power generated by PV panels
is highly dependent on solar radiation received at a particular location over a
given time period. Therefore, it is challenging to forecast the amount of PV
output power. Predicting the output power of PV systems is essential since
several public or private institutes generate such green energy, and need to
maintain the balance between demand and supply. This research aims to forecast
PV system output power based on weather and derived features using different
machine learning models. The objective is to obtain the best-fitting model to
precisely predict output power by inspecting the data. Moreover, different
performance metrics are used to compare and evaluate the accuracy under
different machine learning models such as random forest, XGBoost, KNN, etc.
