<![CDATA[The demand for renewable energy in Indonesia continues to increase in line with the government's efforts to promote a sustainable energy transition. One of the rapidly growing technologies is On-Grid Solar Power Plants (PLTS), which rely on solar energy as their primary source. However, variations in solar irradiation and environmental factors cause fluctuations in the system's performance, potentially affecting its efficiency and reliability. Therefore, a robust method is needed to accurately predict system performance, supporting maintenance and operational optimization. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) method as a time series analysis approach to predict the Performance Ratio (PR) of PLTS based on historical data and solar irradiation variables. SARIMA was chosen because stationarity tests revealed a significant seasonal pattern that conventional ARIMA models cannot effectively handle. By considering seasonal factors, SARIMA provides a more accurate estimation of PR trends and fluctuations. This research aims to detect potential anomalies early, identify recurring operational patterns, and improve PLTS system monitoring efficiency. Model evaluation results show that SARIMA has higher accuracy than ARIMA in capturing seasonal patterns in PR data. Implementing this model can assist PLTS operators in making more data-driven decisions, optimizing maintenance strategies, and ensuring the reliability of renewable energy systems. These findings contribute to the development of more efficient energy management strategies and support the sustainability of solar energy utilization in Indonesia.]]>
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