Prediction of photovoltaic power generation based on LSTM and transfer learning digital twin

Abstract

Abstract This research offers a digital twin model for solar power production power prediction based on long short term memory network (LSTM), and then applies this model to other models with limited operational time and inadequate data through transfer learning. The prediction for the solar system’s electrical output. Due to the effect of sun irradiation, temperature, and other random elements, photovoltaic power output is very intermittent and fluctuating, making it impossible to anticipate photovoltaic power with precision. Synchronization and real-time updating of physical entities, thereby obtaining more accurate forecasting results than traditional forecasting methods, while utilizing knowledge learned from PV systems with sufficient historical data to assist PV systems with limited historical data in establishing a digital twin of power generation forecasting model, not only can obtain accurate prediction results but also save training time for the model. In this study, the PV historical data of three distinct sites from the open source websites of Queensland University and Shanxi Jinneng Clean Energy Company are used to validate the validity of the suggested technique.