The accurate forecasting of photovoltaic (PV) energy production is a critical challenge in the transition to renewable energy. Machine learning models designed for energy forecasting require vast and heterogeneous datasets to achieve robust performance. However, real-world limitations often result in incomplete or insufficient data, particularly when dealing with the inherent variability of solar irradiance and environmental conditions. This study proposes a Conditional Time Series Generative Adversarial Network (cTimeGAN) framework tailored to augment PV-related sensor data, enabling improved training and performance of forecasting models. The methodology leverages the ability of cTimeGAN to generate synthetic, contextually rich datasets that mirror the temporal, structural, and environmental variations present in real-world PV systems. Unlike traditional data augmentation techniques, cTimeGAN conditions the generation process on multiple factors such as weather patterns, system configuration, and seasonal profiles. This allows the network to synthesize time series that are both statistically coherent and physically plausible. The resulting synthetic data not only enhances the diversity of training sets but also supports scenario-based simulations, helping models generalize across rare or extreme operating conditions. Preliminary evaluation on benchmark datasets shows promising generative performance in terms of realism and predictive utility. Future work will explore the integration of the synthetic data into operational PV forecasting pipelines to assess its actual impact on predictive accuracy and robustness. The proposed framework has the potential to reduce dependency on extensive data collection campaigns, accelerate model deployment in new environments, and improve anomaly detection and resilience in PV energy systems.