DenPow Model for Solar PV Power Generation Forecasting
Keywords:
Solar power plant, Forecasting, Regression, Deep neural network, Performance.Abstract
Due to environment dependent variation in solar power generation, in a day or throughout year, there is need of forecasting. This can help for planning at the distribution centre for selection of feed from different power plants. This can help to reduce the generation losses at the hydro power plants by putting load on solar plants during their peak generation hours. The historic power generation records of entire year can be used for time ahead forecasting of the power generation. The data used contains fields such as power generated, temperature of module and environment, irradiation, wind speed and rainfall. This paper addresses the forecasting challenge of solar power generation with 'DenPow' model. The model shows maximum error of 3% in predicted value compared to ground truth. The model outperforms over artificial neural network (ANN), recurrent neural network (RNN), RNN model with the combination of Long Short Term Memory (LSTM) and Auto-GRU layers and with combination of CNN and LSTM methods.
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