<![CDATA[Forecasting the output power of photovoltaic (PV) systems is crucial in managing renewable energy efficiently and sustainably. The availability of historical data and environmental variables, such as temperature and humidity, greatly influences prediction accuracy. However, in practice, historical data is often incomplete due to technical constraints or limited monitoring infrastructure, which results in decreased prediction quality and system efficiency. To overcome these challenges, this study proposed a comparative approach between two predictive models, namely SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous variables) as a classical statistical model, and WGAN-GP (Wasserstein Generative Adversarial Network with Gradient Penalty) as a generative deep learning model designed to handle incomplete data and capture nonlinear relationships. The datasets included PV power output from the monitoring system at Universitas Kristen Immanuel (UKRIM) Yogyakarta, along with temperature and humidity data from the Kalitirto weather station in Sleman, Yogyakarta. The research was conducted through several stages, namely: data collection, pre-processing, model training, and evaluation using MAE, MSE, RMSE, and MAPE metrics. The results show that the SARIMAX model using the Time-Series Cross-Validation (TSCV) achieves the best numerical performance (MAE = 0.085; RMSE = 0.145). However, this model fails to represent daily patterns realistically. In contrast, both the standard SARIMAX and WGAN-GP models are more consistent in representing seasonal patterns and daily fluctuations, even though their prediction errors were slightly higher in terms of numerical metrics. The findings advance scientific understanding of hybrid forecasting models and offer practical implications for improving energy reliability and decision-making in data-constrained environments.]]>
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