TY - GEN A1 - Nguyen, Thi Ngoc A1 - Müsgens, Felix T1 - What drives the accuracy of PV output forecasts? T2 - Applied Energy N2 - In this paper, 180 papers on photovoltaic (PV) output forecasting were reviewed and a database of forecast errors was extracted for statistical analysis. The paper shows that among the forecast models, hybrid models are most likely to become the primary form of PV output forecasting in the future. The use of data processing techniques is positively correlated with the forecast quality, while the lengths of the forecast horizons and out-of-sample test sets have negative effects on the forecast accuracy. The paper also found that the use of data normalization, the wavelet transform, and the inclusion of clear sky index and numerical weather prediction variables are the most effective data processing techniques. Furthermore, the paper found some evidence of “cherry picking” in the reporting of errors and we recommend that the test sets be at least one year long to avoid any distortion in the performance of the models. KW - Solar energy forecasting KW - Photovoltaics forecasting KW - Evaluating forecasts KW - Forecasting practice KW - Model selection KW - Statistical analysis Y1 - 2022 U6 - https://doi.org/10.1016/j.apenergy.2022.119603 SN - 1872-9118 SN - 0306-2619 VL - 323 ER - TY - GEN A1 - Nguyen, Thi Ngoc A1 - Müsgens, Felix T1 - A Meta-Analysis of Solar Forecasting Based on Skill Score T2 - arXiv N2 - We conduct the first comprehensive meta-analysis of deterministic solar forecasting based on skill score, screening 1,447 papers from Google Scholar and reviewing the full texts of 320 papers for data extraction. A database of 4,687 points was built and analyzed with multivariate adaptive regression spline modelling, partial dependence plots, and linear regression. The marginal impacts on skill score of ten factors were quantified. The analysis shows the non-linearity and complex interaction between variables in the database. Forecast horizon has a central impact and dominates other factors' impacts. Therefore, the analysis of solar forecasts should be done separately for each horizon. Climate zone variables have statistically significant correlation with skill score. Regarding inputs, historical data and spatial temporal information are highly helpful. For intra-day, sky and satellite images show the most importance. For day-ahead, numerical weather predictions and locally measured meteorological data are very efficient. All forecast models were compared. Ensemble-hybrid models achieve the most accurate forecasts for all horizons. Hybrid models show superiority for intra-hour while image-based methods are the most efficient for intra-day forecasts. More training data can enhance skill score. However, over-fitting is observed when there is too much training data (longer than 2000 days). There has been a substantial improvement in solar forecast accuracy, especially in recent years. More improvement is observed for intra-hour and intra-day than day-ahead forecasts. By controlling for the key differences between forecasts, including location variables, our findings can be applied globally. Y1 - 2023 U6 - https://doi.org/10.48550/arXiv.2208.10536 SP - 1 EP - 55 ER -