Marginal Weibull Diffusion Model for Wind Speed Modeling and Short-Term Forecasting

Alain Bensoussan; Alexandre Brouste

doi:10.1007/978-3-319-99052-1_1

Marginal Weibull Diffusion Model for Wind Speed Modeling and Short-Term Forecasting

Alain Bensoussan
, Alexandre Brouste^*

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

Abstract

We propose a dynamical model for the wind speed which is a Markov diffusion process with Weibull marginal distribution. It presents several advantages, namely nice modeling features both in terms of marginal probability density function and temporal correlation. The characteristics can be interpreted in terms of shape and scale parameters of a Weibull law which is convenient for practitioners to analyze the results. We calibrate the parameters with the maximum quasi-likelihood method and use the model to generate and forecast the wind speed. We have tested the model on wind-speed datasets provided by the National Renewable Energy Laboratory. The model fits well the data and we obtain a very good performance in point and probabilistic forecasting in the short-term in comparison to the benchmark.

Original language	English
Title of host publication	Renewable Energy
Subtitle of host publication	Forecasting and Risk Management 2017
Editors	Philippe Drobinski, Mathilde Mougeot, Dominique Picard, Riwal Plougonven, Peter Tankov
Publisher	Springer New York
Chapter	1
Pages	3-22
ISBN (Electronic)	9783319990521
ISBN (Print)	9783319990514
DOIs	https://doi.org/10.1007/978-3-319-99052-1_1
Publication status	Published - 2018
Event	Conference of Forecasting and Risk Management for Renewable Energy (FOREWER) 2017 - Paris, France Duration: 7 Jun 2017 → 9 Jun 2017 https://www.sites.google.com/site/forewerproject/updates/forecastingandriskmanagementforrenewableenergyparisjune7-92017 (For reference) https://sites.google.com/site/conferenceforewer/home (For reference)

Publication series

Name	Springer Proceedings in Mathematics and Statistics
Volume	254
ISSN (Print)	2194-1009
ISSN (Electronic)	2194-1017

Conference

Conference	Conference of Forecasting and Risk Management for Renewable Energy (FOREWER) 2017
Place	France
City	Paris
Period	7/06/17 → 9/06/17
Internet address	https://www.sites.google.com/site/forewerproject/updates/forecastingandriskmanagementforrenewableenergyparisjune7-92017 (For reference) https://sites.google.com/site/conferenceforewer/home (For reference)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Research Keywords

Statistical modeling
Ergodic diffusions
Wind speed forecasts

Access Output

10.1007/978-3-319-99052-1_1

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

Bensoussan, A., & Brouste, A. (2018). Marginal Weibull Diffusion Model for Wind Speed Modeling and Short-Term Forecasting. In P. Drobinski, M. Mougeot, D. Picard, R. Plougonven, & P. Tankov (Eds.), Renewable Energy: Forecasting and Risk Management 2017 (pp. 3-22). (Springer Proceedings in Mathematics and Statistics; Vol. 254). Springer New York. https://doi.org/10.1007/978-3-319-99052-1_1

Bensoussan, Alain ; Brouste, Alexandre. / Marginal Weibull Diffusion Model for Wind Speed Modeling and Short-Term Forecasting. Renewable Energy: Forecasting and Risk Management 2017. editor / Philippe Drobinski ; Mathilde Mougeot ; Dominique Picard ; Riwal Plougonven ; Peter Tankov. Springer New York, 2018. pp. 3-22 (Springer Proceedings in Mathematics and Statistics).

@inproceedings{14fbb1116ff3429d98b2b6dba9b2808a,

title = "Marginal Weibull Diffusion Model for Wind Speed Modeling and Short-Term Forecasting",

abstract = "We propose a dynamical model for the wind speed which is a Markov diffusion process with Weibull marginal distribution. It presents several advantages, namely nice modeling features both in terms of marginal probability density function and temporal correlation. The characteristics can be interpreted in terms of shape and scale parameters of a Weibull law which is convenient for practitioners to analyze the results. We calibrate the parameters with the maximum quasi-likelihood method and use the model to generate and forecast the wind speed. We have tested the model on wind-speed datasets provided by the National Renewable Energy Laboratory. The model fits well the data and we obtain a very good performance in point and probabilistic forecasting in the short-term in comparison to the benchmark.",

keywords = "Statistical modeling, Ergodic diffusions, Wind speed forecasts",

author = "Alain Bensoussan and Alexandre Brouste",

year = "2018",

doi = "10.1007/978-3-319-99052-1\_1",

language = "English",

isbn = "9783319990514",

series = "Springer Proceedings in Mathematics and Statistics",

publisher = "Springer New York",

pages = "3--22",

editor = "Philippe Drobinski and Mathilde Mougeot and Dominique Picard and Riwal Plougonven and Peter Tankov",

booktitle = "Renewable Energy",

address = "United States",

note = "Conference of Forecasting and Risk Management for Renewable Energy (FOREWER) 2017 ; Conference date: 07-06-2017 Through 09-06-2017",

url = "https://www.sites.google.com/site/forewerproject/updates/forecastingandriskmanagementforrenewableenergyparisjune7-92017, https://sites.google.com/site/conferenceforewer/home",

}

Bensoussan, A & Brouste, A 2018, Marginal Weibull Diffusion Model for Wind Speed Modeling and Short-Term Forecasting. in P Drobinski, M Mougeot, D Picard, R Plougonven & P Tankov (eds), Renewable Energy: Forecasting and Risk Management 2017. Springer Proceedings in Mathematics and Statistics, vol. 254, Springer New York, pp. 3-22, Conference of Forecasting and Risk Management for Renewable Energy (FOREWER) 2017, Paris, France, 7/06/17. https://doi.org/10.1007/978-3-319-99052-1_1

Marginal Weibull Diffusion Model for Wind Speed Modeling and Short-Term Forecasting. / Bensoussan, Alain; Brouste, Alexandre.
Renewable Energy: Forecasting and Risk Management 2017. ed. / Philippe Drobinski; Mathilde Mougeot; Dominique Picard; Riwal Plougonven; Peter Tankov. Springer New York, 2018. p. 3-22 (Springer Proceedings in Mathematics and Statistics; Vol. 254).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Marginal Weibull Diffusion Model for Wind Speed Modeling and Short-Term Forecasting

AU - Bensoussan, Alain

AU - Brouste, Alexandre

PY - 2018

Y1 - 2018

N2 - We propose a dynamical model for the wind speed which is a Markov diffusion process with Weibull marginal distribution. It presents several advantages, namely nice modeling features both in terms of marginal probability density function and temporal correlation. The characteristics can be interpreted in terms of shape and scale parameters of a Weibull law which is convenient for practitioners to analyze the results. We calibrate the parameters with the maximum quasi-likelihood method and use the model to generate and forecast the wind speed. We have tested the model on wind-speed datasets provided by the National Renewable Energy Laboratory. The model fits well the data and we obtain a very good performance in point and probabilistic forecasting in the short-term in comparison to the benchmark.

AB - We propose a dynamical model for the wind speed which is a Markov diffusion process with Weibull marginal distribution. It presents several advantages, namely nice modeling features both in terms of marginal probability density function and temporal correlation. The characteristics can be interpreted in terms of shape and scale parameters of a Weibull law which is convenient for practitioners to analyze the results. We calibrate the parameters with the maximum quasi-likelihood method and use the model to generate and forecast the wind speed. We have tested the model on wind-speed datasets provided by the National Renewable Energy Laboratory. The model fits well the data and we obtain a very good performance in point and probabilistic forecasting in the short-term in comparison to the benchmark.

KW - Statistical modeling

KW - Ergodic diffusions

KW - Wind speed forecasts

UR - https://www.scopus.com/pages/publications/85059661559

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85059661559&origin=recordpage

U2 - 10.1007/978-3-319-99052-1_1

DO - 10.1007/978-3-319-99052-1_1

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783319990514

T3 - Springer Proceedings in Mathematics and Statistics

SP - 3

EP - 22

BT - Renewable Energy

A2 - Drobinski, Philippe

A2 - Mougeot, Mathilde

A2 - Picard, Dominique

A2 - Plougonven, Riwal

A2 - Tankov, Peter

PB - Springer New York

T2 - Conference of Forecasting and Risk Management for Renewable Energy (FOREWER) 2017

Y2 - 7 June 2017 through 9 June 2017

ER -

Bensoussan A, Brouste A. Marginal Weibull Diffusion Model for Wind Speed Modeling and Short-Term Forecasting. In Drobinski P, Mougeot M, Picard D, Plougonven R, Tankov P, editors, Renewable Energy: Forecasting and Risk Management 2017. Springer New York. 2018. p. 3-22. (Springer Proceedings in Mathematics and Statistics). Epub 2018 Dec 28. doi: 10.1007/978-3-319-99052-1_1

Marginal Weibull Diffusion Model for Wind Speed Modeling and Short-Term Forecasting

Abstract

Publication series

Conference

UN SDGs

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this