CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production

Climate change is pushing an increasing number of nations to use green energy resources, particularly solar power as an applicable substitute to traditional power sources. However, photovoltaic power generation is highly weather-dependent, relying mostly on solar irradiation that is highly unstable, and unpredictable which makes power generation challenging. Accurate photovoltaic power predictions can substantially improve the operation of solar power systems. This is vital for supplying prime electricity to customers and ensuring the resilience of power plants' operation. This research is motivated by the recent adoption and advances in DL models and their successful use in the sector of energy. The suggested model merges two deep learning architectures, the long short-term memory (LSTM) and convolutional neural network (CNN). Using a real-world dataset from Rabat, Morocco, as a case study to illustrate the effectiveness of the suggested topology. According to error metrics, MAE, MAPE, and RMSE, the suggested architecture CNN-LSTM performance exceeds that of standard machine learning and single DL models in terms of prediction, precision, and stability.