Solar Photovoltaic Power Output Forecasting using Deep Learning Models: A Case Study of Zagtouli PV Power Plant

Abstract

Forecasting solar PV power output holds significant importance in the realm of energy management, particularly due to the intermittent nature of solar irradiation. Currently, most forecasting studies employ statistical methods. However, deep learning models have the potential for better forecasting. This study utilises Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU) and hybrid LSTM-GRU deep learning techniques to analyse, train, validate, and test data from the Zagtouli Solar Photovoltaic (PV) plant located in Ouagadougou (longitude:12.30702o and latitude:1.63548o), Burkina Faso. The study involved three evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2). The RMSE evaluation criteria gave 10.799(LSTM), 11.695(GRU) and 10.629(LSTM-GRU) giving the LSTM-GRU model as the best for RMSE evaluation. The MAE evaluation provided 2.09, 2.1 and 2.0 for the LSTM, GRU and LSTM-GRU models respectively, showing that the LSTM-GRU model is superior for MAE evaluation. The R2 criteria similarly showed the LSTM-GRU model to be best with 0.999 compared to 0.998 for LSTM and 0.997 for GRU. It becomes evident that the hybrid LSTM-GRU model exhibits superior predictive capabilities compared to the other two models. These results indicate that the hybrid LSTM-GRU model has the potential to reliably predict the solar PV power output. It is therefore recommended that the authorities in charge of the solar PV Plant in Ouagadougou should consider switching to the deep learning LSTM-GRU model.