Power Output Reconstruction of Photovoltaic Curtailment

The use of renewable energy sources in the grid's energy mix has recently gained popularity. Especially as solar photovoltaic (PV) generation production has almost zero emissions during its operations, they are preferred over fuel-based electricity production. However, expanding PV generation in grid capacity increases the chance of PV curtailment occurrence. Not to mention that microgrids supported with large-scale PV generation almost certainly create PV curtailment regularly. As the forecast of PV production is one of the electricity grid operation cornerstones, the prediction model should be as accurate as possible. The latest trend is utilizing machine learning (ML) models to predict PV output, thanks to their excellent learning and regression capabilities. However, its performance can be highly influenced by measurements used during the model design. Unfortunately, only some of the research on this topic deals with the PV curtailment problem resulting in underperforming ML models. This paper proposes a novel approach to identify and replace curtailed PV measurements. The methodology includes the physical model as a baseline of truly producable energy, which is then investigated and corrected as a piecewise linear system using Pearson correlation and weather measurements. Through real-life comparative scenarios, the suggested data reconstruction method provides increased accuracy of supervised ML-based solar power prediction