Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting

Schulz, Benedikt; Ayari, Mehrez El; Lerch, Sebastian; Baran, Sándor

doi:10.1016/j.solener.2021.03.023

Cited by 33 publications

(18 citation statements)

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“…In this study, the skew normal distributions are applied on the control forecast but one may consider other ensemble forecasts to combine. Moreover, more and more meteorological variables are postprocessed with numerous distributions, see e.g., Schulz et al [64], Baran et al [65]; in this context, the more distributions one tries, the better the results will be. For future work, one may try to implement EMOS schemes where the choice of a parametric distribution is related on forecasted weather regimes.…”

Section: Discussionmentioning

confidence: 99%

Skewed and Mixture of Gaussian Distributions for Ensemble Postprocessing

Taillardat

2021

Atmosphere

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The implementation of statistical postprocessing of ensemble forecasts is increasingly developed among national weather services. The so-called Ensemble Model Output Statistics (EMOS) method, which consists of generating a given distribution whose parameters depend on the raw ensemble, leads to significant improvements in forecast performance for a low computational cost, and so is particularly appealing for reduced performance computing architectures. However, the choice of a parametric distribution has to be sufficiently consistent so as not to lose information on predictability such as multimodalities or asymmetries. Different distributions are applied to the postprocessing of the European Centre for Medium-range Weather Forecast (ECMWF) ensemble forecast of surface temperature. More precisely, a mixture of Gaussian and skewed normal distributions are tried from 3- up to 360-h lead time forecasts, with different estimation methods. For this work, analytical formulas of the continuous ranked probability score have been derived and appropriate link functions are used to prevent overfitting. The mixture models outperform single parametric distributions, especially for the longest lead times. This statement is valid judging both overall performance and tolerance to misspecification.

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Section: Discussionmentioning

confidence: 99%

Skewed and Mixture of Gaussian Distributions for Ensemble Postprocessing

Taillardat

2021

Atmosphere

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“…The proposed probabilistic forecasting model could reflect the effects of atmospheric conditions on forecast errors [27] Two-day-ahead_30 min NWP-based solar irradiance forecast…”

Section: Refmentioning

confidence: 99%

“…In order to probabilistically forecast PV generation, some works proposed using numerical weather prediction (NWP) models under various scenarios. References [26][27][28] are some examples that employed NWP to obtain ensemble forecasts for solar irradiance and build an uncertainty model for PV generation. An NWP adopts some measured meteorological inputs, such as temperature, humidity and pressure.…”

Section: Refmentioning

confidence: 99%

Applications of Probabilistic Forecasting in Smart Grids: A Review

Khajeh

Laaksonen

2022

Applied Sciences

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This paper reviews the recent studies and works dealing with probabilistic forecasting models and their applications in smart grids. According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment. In this way, it firstly discusses the common methods employed to predict the distribution of variables. Then, it reviews how the recent literature used these forecasting methods and for which uncertain parameters they wanted to obtain distributions. Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed. In the next stage, this paper provides an overview related to scenario generation of uncertain parameters using their distributions and how these scenarios are adopted for optimal decision-making. In this regard, this paper discusses three types of optimization problems aiming to capture uncertainties and reviews the related papers. Finally, we propose some future applications of probabilistic forecasting based on the flexibility challenges of power systems in the near future.

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“…Gneiting et al (2005) use a Gaussian distribution for temperature and sea level pressure forecasts. More complex variables variables like precipitation or solar irradiance have been modelled via zero-censored distributions, whose mixed discrete-continuous nature enables point masses for the events of no rain or no irradiance (see, e.g., Scheuerer, 2014;Schulz et al, 2021).…”

Section: Ensemble Model Output Statistics (Emos)mentioning

confidence: 99%

Statistical and machine learning methods for postprocessing ensemble forecasts of wind gusts

Schulz¹,

Lerch²

2021

Preprint

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<p>We conduct a systematic and comprehensive comparison of state-of-the-art postprocessing methods for ensemble forecasts of wind gusts. The compared approaches range from well-established techniques to novel neural network-based methods. Our study is based on a 6-year dataset of forecasts from the convection&#8208;permitting COSMO&#8208;DE ensemble prediction system, with hourly lead times up to 21 hours and forecasts of 57 meteorological variables, and corresponding observations from 175<strong> </strong>weather stations over Germany. We find that simpler methods such as ensemble model output statistics (EMOS), member-by-member postprocessing and a novel isotonic distributional regression approach, which utilize ensemble forecasts of wind gusts as sole inputs, already result in improvement in terms of the mean CRPS of up to 40% compared to the raw ensemble predictions. This can be substantially improved upon by more complex machine learning methods such as gradient boosting-based extensions of EMOS, quantile regression forests, and variants of neural network-based approaches that are capable of incorporating additional information from the large variety of available predictor variables.</p>

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Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting

Cited by 33 publications

References 50 publications

Skewed and Mixture of Gaussian Distributions for Ensemble Postprocessing

Skewed and Mixture of Gaussian Distributions for Ensemble Postprocessing

Applications of Probabilistic Forecasting in Smart Grids: A Review

Statistical and machine learning methods for postprocessing ensemble forecasts of wind gusts

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