SwePub
Sök i LIBRIS databas

  Utökad sökning

onr:"swepub:oai:DiVA.org:hj-63087"
 

Sökning: onr:"swepub:oai:DiVA.org:hj-63087" > Evaluation of confo...

Evaluation of conformal-based probabilistic forecasting methods for short-term wind speed forecasting

Althoff, Simon (författare)
Lund University, Sweden
Szabadváry, Johan H. (författare)
Algorithma AB, Sweden
Anderson, Jonathan (författare)
Algorithma AB, Sweden
visa fler...
Carlsson, Lars (författare)
Jönköping University,JTH, Avdelningen för datavetenskap,Centre for Reliable Machine Learning, University of London, UK; Algorithma AB, Sweden
visa färre...
 (creator_code:org_t)
ML Research Press, 2023
2023
Engelska.
Ingår i: Proceedings of Machine Learning Research. - : ML Research Press. ; , s. 100-115
  • Konferensbidrag (refereegranskat)
Abstract Ämnesord
Stäng  
  • We apply Conformal Predictive Distribution Systems (CPDS) and a non-exchangeable version of the traditional Conformal Prediction (NECP) method to short-term wind speed forecasting to generate probabilistic forecasts. These are compared to the more traditional Quantile Regression Forest (QRF) method. A short-term forecast is available from a few hours before the forecasted time period and is only extended a couple days into the future. The methods are supplied ensemble forecasts as input and additionally the Conformal methods are supplied with post-processed point forecasts for generating the probability distributions. In the NECP case we propose a method of producing probability distributions by creating sequentially larger prediction intervals. The methods are compared through a teaching schedule, to mimic a real-world setting. For each model update in the teaching schedule a grid-search approach is applied to select each method’s optimal hyperparameters, respectively. The methods are tested out of the box with tweaks to few hyperparameters. We also introduce a normalized nonconformity score and use it with the conformal method that handles data that violates the exchangeability assumption. The resulting probability distributions are compared to actual wind measurements through Continuous Ranked Probability Scores (CRPS) as well as their validity and efficiency of certain prediction intervals. Our results suggest that the conformal based methods, with the pre-trained underlying model, produce slightly more conservative but more efficient probability distributions than QRF at a lower computational cost. We further propose how the conformal-based methods could be improved for the application to real-world scenarios.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Nyckelord

conformal predictive distribution systems
exchangeable
non exchangeable
normalized nonconformity
quantile regression forests
short-term wind forecast
Probability distributions
Wind speed
Conformal predictive distribution system
Distribution systems
Predictive distributions
Quantile regression
Quantile regression forest
Regression forests
Short term wind forecast
Weather forecasting

Publikations- och innehållstyp

ref (ämneskategori)
kon (ämneskategori)

Till lärosätets databas

Hitta mer i SwePub

Av författaren/redakt...
Althoff, Simon
Szabadváry, Joha ...
Anderson, Jonath ...
Carlsson, Lars
Om ämnet
NATURVETENSKAP
NATURVETENSKAP
och Data och informa ...
Artiklar i publikationen
Av lärosätet
Jönköping University

Sök utanför SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy