Due to the variability and instability of photovoltaic (PV) output, the accurate prediction of PV output power plays a major role in energy market for PV operators to optimize their profits in energy market. In order to predict PV output, environmental parameters such as temperature, humidity, rainfall and win speed are gathered as indicators and different machine learning models are built for each solar panel inverters. In this paper, we propose two different kinds of solar prediction schemes for one-hour ahead forecasting of solar output using Support Vector Machine (SVM) and Random Forest (RF).

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