Analysis and Application of Photovoltaic Grid Connection Optimization Model based on LightGBM Algorithm

With the increasing popularity of photovoltaic (PV) energy worldwide, the operational efficiency and stability of PV grid-connected systems have become critical issues that need to be addressed.PV grid-connected systems are affected by a variety of factors, such as light intensity, temperature, and aging of the equipment, which may lead to fluctuations in the efficiency of the system, and consequently, affect the stability and reliability of the entire power system. Therefore, how to effectively deal with these challenges and improve the operational efficiency and stability of PV grid-connected systems has become one of the hot topics in current research. In order to cope with the problem of fluctuating efficiency of PV grid-connected system, this paper proposes a PV grid-connected optimisation model based on LightGBM (Light Gradient Boosting Machine) algorithm. The model constructs a corresponding optimisation strategy by deeply analysing the key factors affecting the efficiency of PV grid-connection, such as weather conditions, equipment status, etc. The LightGBM algorithm, with its high efficiency and accuracy, plays an important role in model construction and prediction. The experimental results show that the LightGBM-based PV grid- connected optimisation model proposed in this paper achieves more than 95% in prediction accuracy, which is highly consistent with the actual operation data. More importantly, the operational efficiency of the PV grid-connected system is significantly improved by the optimisation suggestions provided by the model, with an average improvement of about 20%. This finding not only verifies the superiority of the model in prediction accuracy, but also demonstrates its great potential in improving the operational efficiency and stability of the system.