Predictive Modeling of Solar Power Generation Using Deep Learning for Real-Time Optimization in Photovoltaic System

This research addresses the need for precise solar power generation forecasting and optimization of energy management within photovoltaic systems; this is important as there is a greater demand for renewable energy sources. Reliable predictions of the output of solar power would enhance stability in the grid and thus ensure efficient distribution of the energy. This research uses deep learning techniques, the Long Short-Term memory (LSTM) model, to predict solar power generation from several environmental variables, including solar irradiation, air temperature, wind speed, relative humidity, air pressure, module power, and module temperature. To validate the performance of the model, rigorous use of metrics like Mean absolute error (MAE), Root Mean Square Error (RMSE), and Mean absolute percentage error (MAPE) was made. The proposed LSTM model gave an MAE of 0.25 kW, an RMSE of 0.35 kW, and a MAPE of 5%. This was better compared to other models like Gated recurrent unit (GRU), recurrent neural network (RNN), and Artificial neural network (ANN). A real-time implementation of the LSTM model depicted the fact that it is feasible to use the model to make correct predictions of the amount of solar power produced throughout the day. At peak periods, such as midday hours, accuracy levels hit 98% in some cases, whereas the model is compelling enough to pinpoint and respond accordingly to changing solar power production volumes for optimizing load management and energy supply. This research verifies the deep learning model and its efficiency in energy forecasting, with intense stress on the practical application of the LSTM model in real-time energy management, contributing a lot to the advancement of renewable energy integration and optimizing power systems. Finally, this research provides valuable insight into improving the operational efficiency of photovoltaic systems in order to make the future energy scenario more sustainable.