Benefits of spatiotemporal modeling for short-term wind power forecasting at both individual and aggregated levels-Reference-Cited by-同舟云学术

Benefits of spatiotemporal modeling for short-term wind power forecasting at both individual and aggregated levels

Published:2018-02-13 Issue:3 Volume:29 Page:e2493
ISSN:1180-4009
Container-title:Environmetrics
language:en
Short-container-title:Environmetrics

Author:

Lenzi Amanda¹,Steinsland Ingelin²,Pinson Pierre³

Affiliation:

1. Department of Applied Mathematics and Computer Science; Technical University of Denmark; 2800 Kongens Lyngby Denmark

2. Department of Mathematical Sciences; Norwegian University of Science and Technology; N-7491 Trondheim Norway

3. Department of Electrical Engineering; Technical University of Denmark; 2800 Kgs. Lyngby Denmark

Funder

Strategiske Forskningsråd

Norges Forskningsråd

Publisher

Wiley

Subject

Ecological Modelling,Statistics and Probability

Reference46 articles.

1. Wind Power in Power Systems

2. Spatial and Spatio-temporal Bayesian Models with R-INLA

3. Submodel selection and evaluation in regression. The X-random case;Breiman;International Statistical Review/Revue Internationale de Statistique,1992

4. Probabilistic wind power forecasts using local quantile regression;Bremnes;Wind Energy,2004

5. Scoring probabilistic forecasts: The importance of being proper;Bröcker;Weather and Forecasting,2007

Cited by 16 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. A benchmarking framework for performance evaluation of statistical wind power forecasting models;Sustainable Energy Technologies and Assessments;2023-06

2. Pricing Wind Power Uncertainty in the Electricity Market;IEEE Access;2023

3. Estimating Wind Power Uncertainty using Quantile Smoothing Splines Regression;2022 57th International Universities Power Engineering Conference (UPEC);2022-08-30

4. Lidar-based minute-scale offshore wind speed forecasts analysed under different atmospheric conditions;Meteorologische Zeitschrift;2022-02-03

5. Incorporating Spatial and Temporal Correlations to Improve Aggregation of Decentralized Day-Ahead Wind Power Forecasts;IEEE Access;2022