Abstract
The expansion in solar power is expected to be dramatic soon. A number of solar parks with high capacities are being set up to harness the potential of this renewable resource. However, the variability of solar power remains an important issue for grid integration of solar PV power plants. Changing weather conditions affect the PV output. Thus, developing methods for accurately forecasting solar PV output is essential for enabling large-scale PV deployment. This paper proposes a model for forecasting PV output based on weather classification, using a solar PV plant in Maharashtra, India, as the sample system. The input data is first classified using RBF-SVM into three types based on weather conditions, namely, sunny, rainy and cloudy. Then, the neural network model corresponding to that weather type is applied to forecast the solar PV output. The result obtained for the overall model is studied for its effectiveness and are compared with existing research.
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