Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation-Reference-Cited by-同舟云学术

Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation

Published:2019-10 Issue: Volume:191 Page:138-150
ISSN:0038-092X
Container-title:Solar Energy
language:en
Short-container-title:Solar Energy

Author:

Bakker Kilian,Whan Kirien,Knap Wouter,Schmeits Maurice

Publisher

Elsevier BV

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment

Reference44 articles.

1. Solar irradiance forecast using aerosols measurements: a data driven approach;Alfadda;Sol. Energy,2018

2. PV power forecast using a nonparametric PV model;Almeida;Sol. Energy,2015

3. Review of photovoltaic power forecasting;Antonanzas;Sol. Energy,2016

4. Generalized random forests;Athey;Ann. Stat.,2016

5. Bakker, K., 2019. Improving solar radiation forecasts using advanced statistical post-processing methods. MSc thesis, Utrecht University, https://dspace.library.uu.nl/handle/1874/374529.

Cited by 59 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Experimental development of a method of short and medium-term photovoltaic generation forecasting using multivariate statistics and mathematical modeling;Energy Reports;2024-12

2. Estimating the value of ECMWF EPS for photovoltaic power forecasting;Solar Energy;2024-09

3. Evaluation of Scikit-Learn Machine Learning Algorithms for Improving CMA-WSP v2.0 Solar Radiation Prediction;Atmosphere;2024-08-19

4. Site-Specific Deterministic Temperature and Dew Point Forecasts with Explainable and Reliable Machine Learning;Applied Sciences;2024-07-19

5. Evaluating classification tools for the prediction of in-vitro microbial pyruvate yield from organic carbon sources;PLOS ONE;2024-07-11