The ever-growing secondary market of photovoltaic (PV) systems (i.e., the transaction of solar plants ownership) calls for reliable and high-quality long-term PV degradation forecasts to mitigate the financial risks. However, when long-term PV performance degradation forecasts are required after a short time with limited degradation history, the existing physical and data-driven methods often provide unrealistic degradation scenarios. Therefore, we present a new data-driven method to forecast PV lifetime after a small performance degradation of only 3%. To achieve an accurate and reliable forecast, the developed method addresses the fundamental challenges that usually affect long-term degradation evaluation such as data treatment, choosing a good degradation model, and understanding the different degradation patterns. In the paper, we propose and describe an algorithm for degradation trend evaluation, a new concept of multiple "time- and degradation pattern-dependent" degradation factors. The proposed method has been calibrated and validated using different PV modules and systems data of 5 to 35 years of field exposure. The model has been benchmarked against existing statistical models evaluating 11 experimental PV systems with different technologies. The key advantage of our model over statistical ones is the ability to perform more reliable forecasts with limited degradation history. With an average relative uncertainty of 7.0%, our model is outstanding in consistency for different forecasting time horizons. Moreover, the model is applicable to all PV technologies. The proposed method will aid in making reliable financial decisions and also in adequately planning operation and maintenance activities.