Forecasting Power Output of Photovoltaic Solar Plants Using Long-Short Term Memory (LSTM)

Renewable sources for power generation are being popular day by day and solar PV is mostly first choice. In addition, the energy output of solar PV is highly affected by weather conditions like temperature, irradiance, sky conditions etc. Therefore, an intelligent model based on weather conditions is essential for estimation of solar energy output to meet the needs of energy required. The prediction of PV power output is critical to security, operation, scheduling and energy management. Stability of power grid can also be increased if accuracy of power production in PV plants is further enhanced. This paper has worked on LSTM and used recurrent neural networks (RNN) for forecasting of power production and it is seen that the results of RNNs are nearly compatible with the realistic power production which is evident from less mean absolute error (MAE), mean absolute percentage error (MAPE), Root Mean Square Percentage Error (RMPSE) of magnitude. The comparison with different layers of LSTM model for each season of weather is analysed

Solar PV, Forecasting, Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Mean Absolute Percentage Error (MAPE).