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Is the post-processing of global horizontal irradiance (GHI) forecasts necessary for issuing good photovoltaic (PV) power forecasts? Whenever this question is raised, the instinctive supposition always seems to be "yes," because GHI is the most important weather parameter governing the amount of PV power generated, and surely, the better the GHI forecasts are, the better the PV power forecasts should result. To attend to this question more scientifically and more formally, two classic deterministic-to-deterministic post-processing methods, namely, the model output statistics and kernel conditional density estimation, are applied at various stages of PV power forecasting, resulting in four distinct workflows. These different workflows are trained and tested on three PV plants in Hungary, using data from a four-year (2017–2020) period. Both ground-based GHI and satellite-retrieved GHI are used as the "truth" with which numerical weather prediction (NWP) GHI forecasts are post-processed. A very thorough deterministic forecast verification exercise is conducted following the best practices. It is found that contrary to the common supposition, post-processing GHI only leads to marginal, if that can be quantified at all, benefits, so long as the PV power forecasts are to be post-processed. This ought to be deemed as a very important finding, as it puts into question the "GHI forecasting + post-processing + irradiance-to-power conversion" workflow that has dominated solar forecasting for decades. • Post-processing is best performed on PV power instead of GHI • Model chains change the bias and variance of the input forecasts • Post-processing methods developed for GHI are transferable to PV power [ABSTRACT FROM AUTHOR]

• Five data availability scenarios are compared for different physical model chains. • Nominal power and module orientation are the most critical design parameters. • Unknown tilt and azimuth angles and row spacing should be estimated. • Absolute error is more sensitive to missing design parameters than squared error. • Physical power calculation is encouraged even if some design parameters are unknown. Accurate photovoltaic (PV) power forecasts are essential for the grid integration of the technology. Most solar forecasting studies deal only with solar irradiance forecasting, which highlights the importance of the irradiance to power conversion methods to enable the practical utilization of the advanced irradiance forecasting techniques. The PV power output is commonly calculated with physical model chains that rely on the main design parameters of the PV plants. This study presents an analysis of how the unavailability or uncertain knowledge of the design parameters affects the physical power forecast accuracy. The analysis is performed by verifying the power forecasts created by the bests of 32,400 model chains for 16 PV plants in Hungary for five data availability scenarios based on numerical weather prediction data. The results show that even though the knowledge of all relevant design parameters ensures the lowers errors, the critical parameters are only the nameplate capacities and the module orientation. The increase in mean absolute error and root mean square error is only 1.3% and 0.5%, respectively, if only these crucial parameters are known compared to the baseline scenario. If necessary, the tilt and azimuth angles can also be inferred, where the resulting inaccuracy depends on the estimation errors. The results reveal that the physical model chains can be used with a decent accuracy for power forecast calculations even if the design parameters are unknown, which highly broadens the applicability of the physical method. [ABSTRACT FROM AUTHOR]

Inspecting the literature, much effort has been placed on the verification of irradiance forecasts from numerical weather prediction (NWP) models, as such forecasts are thought to have profound implications on the photovoltaic (PV) power forecasts, which in turn affects grid operators' confidence in integrating such power into the electricity grid. However, perhaps due to the proprietary nature of PV plants and lack of access to state-of-the-art NWP model output, only few have had the chance to conduct head-to-head comparisons of global mesoscale and regional downscaled NWP models, in terms of how their irradiance forecast inaccuracies propagate to PV power forecasts. In this regard, this work presents such a study, in which irradiance and PV power forecasts from the European Centre for Medium-Range Weather Forecasts' High-Resolution (HRES) and Météo-France's Application of Research to Operations at Mesoscale (AROME) models are thoroughly verified against the ground-based measurements from 32 research-grade radiometry stations and 94 actual PV plants in Hungary. A wide range of techniques and case studies concerning verification is herein considered, including variance ratio analysis, Murphy–Winkler decomposition, point-versus-areal verification, and seasonal verification. Despite that the results are too numerous to be summarized in a few sentences, the overarching observation from the verification exercise is that the performance of irradiance forecasts can only be used to infer that of PV power forecasts to a certain extent, which contrasts the conventional wisdom. • Global ECMWF outperforms regional AROME for global horizontal irradiance forecasting. • The advantage of ECMWF is reduced if photovoltaic power forecasting is concerned. • Both numerical weather prediction models have the same accuracy for regional forecasting. • Photovoltaic power forecast errors are the highest in winter. [ABSTRACT FROM AUTHOR]

Under the two-step framework of photovoltaic (PV) power forecasting, that is, forecasting first the irradiance and then converting it to PV power, there are two chief ways in which one can account for the uncertainty embedded in the final PV power forecast. One of those is to produce probabilistic irradiance forecast through, for example, ensemble numerical weather prediction (NWP), and the other is to pass the irradiance forecast through a collection of different irradiance-to-power conversion sequences, which are known as model chains. This work investigates, for the first time, into the question: Whether pairing ensemble NWP with ensemble model chain is better than leveraging any individual method alone? Using data from 14 utility-scale ground-mounted PV plants in Hungary and the state-of-the-art global mesoscale NWP model of the European Centre for Medium-Range Weather Forecasts, it is herein demonstrated that the best probabilistic PV power forecast needs to consider both ensemble NWP and ensemble model chain. Furthermore, owing to the higher-quality probabilistic forecasts, the point forecast accuracy is also improved substantially through pairing. Overall, the recommended paring strategy achieves a mean-normalized continuous ranked probability score and a root mean square error of 18.4% and 42.1%, respectively. • New physical ensemble methods are presented for photovoltaic power forecasting. • The added value of ensemble numerical weather prediction is quantified. • Underdispersed raw ensembles can be effectively calibrated by linear quantile regression. • Improved probabilistic forecasting methods also lead to better deterministic forecasts. [ABSTRACT FROM AUTHOR]

Physical model chain is a step-by-step modeling framework for the conversion of irradiance to photovoltaic (PV) power. When a model chain is fed with irradiance forecasts, it provides the corresponding PV power forecasts. Despite its advantages, forecasting with model chains has yet to receive the attention that it deserves. In several recent works, however, the idea of model-chain-based solar forecasting has been formally modernized, though the framework was restricted to deterministic forecasting. In this work, the model-chain-based forecasting framework is extended to the probability space, in that, a calibrated ensemble of model chains is used to generate probabilistic PV power forecasts. Using two-year data from eight PV plants in Hungary, alongside professional weather forecasts issued by the Hungarian Meteorological Services, it is empirically shown that the raw model-chain ensemble forecasts tend to be underdispered, but adequate post-processing is able to improve calibration and reduce the continuous ranked probability score of raw ensembles by 20%. Given the fact that uncertainty quantification has a cardinal importance to grid integration, this probabilistic extension of the model-chain-based solar forecasting framework is thought beneficial. • The physical photovoltaic model chains are extended for probabilistic forecasting. • The raw ensemble of model chains results in underdispersed power forecasts. • Linear quantile regression is suitable for calibration of the model-chain ensemble. • The proposed method is also beneficial for deterministic power forecasting. [ABSTRACT FROM AUTHOR]