Probabilistic forecasting of photovoltaic power supply — A hybrid approach using D-vine copulas to model spatial dependencies. (15th December 2021) - Schinke-Nendza, A.; von Loeper, F.; Osinski, P.; Schaumann, P.; Schmidt, V.; Weber, C.
Abstract: The fast growth of installed photovoltaic capacity is leading to an increasing impact of variable photovoltaic generation on the overall electricity industry, affecting all stakeholders in this sector. As a consequence, the importance of appropriate photovoltaic power forecasts for planning and decision support is rising, to cope with the resulting uncertainty. In particular, probabilistic forecasts are becoming increasingly important to assess the underlying risks, e.g., depicting the effect of adverse combinations. Whereas deterministic forecasts, while having the advantage of being more detailed, suffer from reflecting only an average expectation. Therefore, this paper proposes a comprehensive hybrid approach to generate deterministic and probabilistic photovoltaic power forecasts, while introducing several improvements for intra-day and day-ahead modelling and forecasting applications. In this context, several pre- and post-processing steps have been combined for the deterministic model, while the spatial interrelation of the forecasting errors is taken into account by applying D-vine copulas for the probabilistic forecasts. The reliability of the proposed hybrid approach is validated, using a comprehensive case study with high-resolution numerical weather predictions and real-world measurement data over several years for multiple photovoltaic units. Furthermore, the proposed model is benchmarked against various combinations of a photovoltaic power model (with and without statistical post-processing) and typical probabilistic models. As part of the evaluation the Energy score, Variogram-based score and Diebold–Mariano test are applied to evaluate the proposed model and highlight the strong performance of the proposed hybrid approach. Highlights: Benchmark of nonparametric statistical and parametric physical forecasting techniques. Application of sophisticated model chain with several pre- and post-processing steps. Comparison on intra-day and day-ahead deterministic and probabilistic forecasts. Extensive case study benchmarking forecasts for multiple photovoltaic units and years. Improved modelling of spatially correlated forecasting errors with vine copulas.