The power generation from photovoltaic plants depends on varying meteorological conditions. These meteorological conditions such as solar irradiance, temperature, and wind speed are nonlinear and stochastic, thus affecting the estimation of solar photovoltaic (PV) power. Accurate estimation of photovoltaic power is essential for enhancing the functioning of solar power installations. The paper aims to develop a novel deep learning-based photovoltaic power prediction model on different weather conditions. The proposed model utilises a two-stage deep learning framework for accurate solar PV power prediction, which combines the long short-term memory (LSTM) and convolutional neural network (CNN) deep learning architectures. The key role of CNN layer is to identify the weather conditions, i.e., sunny, partially cloudy, and extremely cloudy, while the LSTM layer learns the patterns of solar power generation that depend on weather variations to estimate photovoltaic power. The proposed hybrid models consider meteorological factors, such as wind speed, irradiance, temperature, and humidity, including cloud cover and UV index to provide precise solar PV power prediction. The presented hybrid model has better performance metrices having root mean square error of 0.0254, 0.03465, and 0.0824, mean square error of 0.000645, 0.00120, and 0.00679, R2 of 0.9898, 0.9872, and 0.9358, and mean average error of 0.0163, 0.0236, and 0.2521 for sunny, partially cloudy, and extremely cloudy weather conditions, respectively. The results demonstrate that presented deep learning-based novel solar PV power prediction model can accurately predict solar PV power based on instantaneous changes in generated power patterns and aid in the optimisation of PV power plant operations. The paper presents an effective methodology for prediction of solar power that can contribute to the improvement of solar power generation and management.
