A benchmark for multivariate probabilistic solar irradiance forecasts
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A benchmark for multivariate probabilistic solar irradiance forecasts
Authors
Keywords
Benchmark, Forecast evaluation, Multivariate forecasting, Oahu solar measurement grid, Renewable energy, SURFRAD
Journal
SOLAR ENERGY
Volume 225, Issue -, Pages 286-296
Publisher
Elsevier BV
Online
2021-07-23
DOI
10.1016/j.solener.2021.07.010
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Clear-sky index space-time trajectories from probabilistic solar forecasts: Comparing promising copulas
- (2020) Dennis van der Meer et al. Journal of Renewable and Sustainable Energy
- Choice of clear-sky model in solar forecasting
- (2020) Dazhi Yang Journal of Renewable and Sustainable Energy
- Verification of deterministic solar forecasts
- (2020) Dazhi Yang et al. SOLAR ENERGY
- Worldwide performance assessment of 95 direct and diffuse clear-sky irradiance models using principal component analysis
- (2020) Xixi Sun et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- A guideline to solar forecasting research practice: Reproducible, operational, probabilistic or physically-based, ensemble, and skill (ROPES)
- (2019) Dazhi Yang Journal of Renewable and Sustainable Energy
- A universal benchmarking method for probabilistic solar irradiance forecasting
- (2019) Dazhi Yang SOLAR ENERGY
- Verification of solar irradiance probabilistic forecasts
- (2019) Philippe Lauret et al. SOLAR ENERGY
- Review on probabilistic forecasting of photovoltaic power production and electricity consumption
- (2018) D.W. van der Meer et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- Editorial: Submission of Data Article is now open
- (2018) Dazhi Yang et al. SOLAR ENERGY
- SolarData: An R package for easy access of publicly available solar datasets
- (2018) Dazhi Yang SOLAR ENERGY
- The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)
- (2017) Ronald Gelaro et al. JOURNAL OF CLIMATE
- Generation and evaluation of space–time trajectories of photovoltaic power
- (2016) Faranak Golestaneh et al. APPLIED ENERGY
- Assessing the Calibration of High-Dimensional Ensemble Forecasts Using Rank Histograms
- (2016) Thordis L. Thorarinsdottir et al. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
- On assessing calibration of multivariate ensemble forecasts
- (2016) Daniel S. Wilks QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
- Variogram-Based Proper Scoring Rules for Probabilistic Forecasts of Multivariate Quantities*
- (2015) Michael Scheuerer et al. MONTHLY WEATHER REVIEW
- Probabilistic Forecasting
- (2014) Tilmann Gneiting et al. Annual Review of Statistics and Its Application
- Wind Energy: Forecasting Challenges for Its Operational Management
- (2013) Pierre Pinson STATISTICAL SCIENCE
- Wind power forecasting uncertainty and unit commitment
- (2011) J. Wang et al. APPLIED ENERGY
- Evaluating the quality of scenarios of short-term wind power generation
- (2011) P. Pinson et al. APPLIED ENERGY
- Setting the Operating Reserve Using Probabilistic Wind Power Forecasts
- (2010) Manuel A. Matos et al. IEEE TRANSACTIONS ON POWER SYSTEMS
- Reliability diagrams for non-parametric density forecasts of continuous variables: Accounting for serial correlation
- (2010) Pierre Pinson et al. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
- Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds
- (2008) Tilmann Gneiting et al. TEST
- From probabilistic forecasts to statistical scenarios of short-term wind power production
- (2008) Pierre Pinson et al. WIND ENERGY
- REST2: High-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiation – Validation with a benchmark dataset
- (2007) Christian A. Gueymard SOLAR ENERGY
Become a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get StartedAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started