Photovoltaic Power Forecasting and Performance Assessment for Off-Grid Systems

The variable nature of photovoltaic (PV) power generation presents a significant challenge for the reliable operation of standalone PV systems, demanding accurate short-term forecasting to improve energy management and describing battery utilization. This study suggests a multivariable long short-term memory (LSTM) network for 10 min ahead PV power forecasting using one year of operational data collected from an off-grid PV system with batteries at a 5 min sampling interval. The model integrates historical PV power, solar irradiance, load demand, battery current, battery voltage, and dynamics of PV generations. The proposed model achieved a daylight forecasting accuracy of R2 = 0.728, demonstrating good agreement between the measured and predicted PV power profiles while effectively capturing daily generation patterns under varying conditions. Residual analysis indicated that the prediction errors were centred around zero, confirming the absence of significant systematic bias. Moreover, the annual battery state of charge (SOC) remained within the operational limits of 20–90 percent, demonstrating stable battery operation throughout the entire assessment period. Along with the PV forecasting results, these findings provide predictive and operational insights into the performance of the off-grid PV system.