In recent years, the global shift toward sustainable energy sources, such as photovoltaics (PV) has accelerated alongside the electrification of transportation. This transition is primarily driven by the urgent need to mitigate global warming and reduce greenhouse gas emissions associated with fossil fuels and internal combustion engine vehicles. However, the intermittent nature of PV power presents challenges for energy management in EV charging stations, making accurate day-ahead forecasting essential. This paper proposes a Physics-Informed Neural Network (PINN)-based PV forecasting method that integrates historical data with the physical principles of PV systems. Case studies across four seasons show that the proposed method achieves the lowest Root Mean Squared Error (RMSE) in three out of four seasons, with RMSEs ranging from 0.1065 to 0.1366. Furthermore, comparative analysis shows that the PINN model surpasses traditional forecasting benchmarks, including the Persistence model, ANN, LSTM, RNN, and others—establishing itself as a promising and robust approach for future PV forecasting research.