loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Jannes Stubbemann ; Nils Andre Treiber and Oliver Kramer

Affiliation: University of Oldenburg, Germany

Keyword(s): Neural Networks, Resilient Propagation, Wind Power Prediction.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Economics, Business and Forecasting Applications ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Regression ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: Wind power prediction based on statistical learning has the potential to outperform classical physical weather prediction models. Neural networks have been successfully applied to wind prediction in the past. In this paper, we apply neural networks to the spatio-temporal prediction model we proposed in the past. We concentrate on a comparison between classical backpropagation and the more advanced resilient propagation (RPROP) variants. The analysis is based on time series data from the NREL western wind data set. The experimental results show that RPROP+ and iRPROP+ significantly outperform the classical backpropagation variants.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.142.98.108

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Stubbemann, J.; Andre Treiber, N. and Kramer, O. (2015). Resilient Propagation for Multivariate Wind Power Prediction. In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM; ISBN 978-989-758-077-2; ISSN 2184-4313, SciTePress, pages 333-337. DOI: 10.5220/0005284403330337

@conference{icpram15,
author={Jannes Stubbemann. and Nils {Andre Treiber}. and Oliver Kramer.},
title={Resilient Propagation for Multivariate Wind Power Prediction},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM},
year={2015},
pages={333-337},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005284403330337},
isbn={978-989-758-077-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM
TI - Resilient Propagation for Multivariate Wind Power Prediction
SN - 978-989-758-077-2
IS - 2184-4313
AU - Stubbemann, J.
AU - Andre Treiber, N.
AU - Kramer, O.
PY - 2015
SP - 333
EP - 337
DO - 10.5220/0005284403330337
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic