Forecast of photovoltaic power generation using deep-learning algorithms: evaluation of LSTM, LSTM-autoencoder, and LSTM-attention-mechanism

Abstract

In this work, we evaluate the performance of photovoltaic (PV) power generation forecast using various hybrid deep-learning algorithms, including long and short term memory (LSTM), LSTM with an Autoencoder ( LSTM-Autoencoder ) and LSTM with an attention mechanism (LSTM-Attention). We show that the LSTM-Attention model is significantly more accurate in predicting the hourly power generation of a PV plant with 162kW capacity than the other two reference counterparts. After 100 epochs training, the model achieves a superior Root Mean Square Error (RMSE) below 0.01, Mean Absolute Error (MAE) below 0.005, the Absolute Deviation (AD) below 0.02, and the Mean Absolute Percantage Error (MAPE) is around 35%. Moreover, since the correlation coefficient is up to 92%, this hybrid model not only can be used in solar power generation prediction in PV plants, but potentially can also be extended to other renewable energy sources such as predicting wind power or tide power generation.