Solar PV Power Generation Forecasting Using an Attention-Based Bidirectional LSTM Network

To provide the reliability and efficiency of the solar energy system. The solar photovoltaic (PV) power generation should be predicted accurately. The generation of solar power is not a linear process and it changes with time depending on the environmental factors. Therefore, it is quite difficult to forecast solar power production properly. We, in this regard, introduce a new hybrid deep learning (DL) model, SP-BiLSTM-ATT, a composite of Bidirectional Long Short-Term Memory model and Attention mechanism to forecast the solar power generation. The model proposed is tested with the help of a solar power generation dataset that has been acquired via the Kaggle data repository. The performance of the proposed model is correlated to the performance of other well-known regression models, such as Decision Tree (dt), K-Nearest Neighbors (KNN), Random Forest (RF) and standard LSTM model. The model proposed has a much greater accuracy when compared to the conventional models with an R2 which is 0.95. The fact that the precision of the suggested model was steadily increased in all the experiments validates the reliability of the model.