Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network

Sukanta Basu; Simon J. Watson; Eric Lacoa Arends; Bedassa Cheneka

doi:10.1109/EEM49802.2020.9221979

Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network

Sukanta Basu, Simon J. Watson, Eric Lacoa Arends, Bedassa Cheneka

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

2 Citations (Scopus)

102 Downloads (Pure)

Abstract

A hybrid neural network model, comprising of a convolutional neural network and a multilayer perceptron network, has been developed for day-ahead forecasting of regional scale wind power production. This model requires operational weather forecasts as input and also has the capability to ingest data from ensemble forecasts. Even though the training of the model requires significant computational cost, the actual forecasting can be done within a few minutes on any recent personal computer. The proposed model has demonstrated noteworthy performance at a recent international forecasting competition.

Original language	English
Title of host publication	2020 17th International Conference on the European Energy Market, EEM 2020
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	9781728169194
DOIs	https://doi.org/10.1109/EEM49802.2020.9221979
Publication status	Published - 2020
Event	17th International Conference on the European Energy Market, EEM 2020 - Stockholm, Sweden Duration: 16 Sept 2020 → 18 Sept 2020

Publication series

Name	International Conference on the European Energy Market, EEM
Volume	2020-September
ISSN (Print)	2165-4077
ISSN (Electronic)	2165-4093

Conference

Conference	17th International Conference on the European Energy Market, EEM 2020
Country/Territory	Sweden
City	Stockholm
Period	16/09/20 → 18/09/20

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

convolutional neural network
deep learning
multilayer perceptron
numerical weather forecasting
wind energy

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10.1109/EEM49802.2020.9221979

09221979Final published version, 1.28 MB

Cite this

Basu, S., Watson, S. J., Lacoa Arends, E., & Cheneka, B. (2020). Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network. In 2020 17th International Conference on the European Energy Market, EEM 2020 Article 9221979 (International Conference on the European Energy Market, EEM; Vol. 2020-September). IEEE. https://doi.org/10.1109/EEM49802.2020.9221979

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abstract = "A hybrid neural network model, comprising of a convolutional neural network and a multilayer perceptron network, has been developed for day-ahead forecasting of regional scale wind power production. This model requires operational weather forecasts as input and also has the capability to ingest data from ensemble forecasts. Even though the training of the model requires significant computational cost, the actual forecasting can be done within a few minutes on any recent personal computer. The proposed model has demonstrated noteworthy performance at a recent international forecasting competition.",

keywords = "convolutional neural network, deep learning, multilayer perceptron, numerical weather forecasting, wind energy",

author = "Sukanta Basu and Watson, {Simon J.} and {Lacoa Arends}, Eric and Bedassa Cheneka",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 17th International Conference on the European Energy Market, EEM 2020 ; Conference date: 16-09-2020 Through 18-09-2020",

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doi = "10.1109/EEM49802.2020.9221979",

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Basu, S, Watson, SJ, Lacoa Arends, E & Cheneka, B 2020, Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network. in 2020 17th International Conference on the European Energy Market, EEM 2020., 9221979, International Conference on the European Energy Market, EEM, vol. 2020-September, IEEE, 17th International Conference on the European Energy Market, EEM 2020, Stockholm, Sweden, 16/09/20. https://doi.org/10.1109/EEM49802.2020.9221979

Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network. / Basu, Sukanta; Watson, Simon J.; Lacoa Arends, Eric et al.
2020 17th International Conference on the European Energy Market, EEM 2020. IEEE, 2020. 9221979 (International Conference on the European Energy Market, EEM; Vol. 2020-September).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Basu S, Watson SJ, Lacoa Arends E, Cheneka B. Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network. In 2020 17th International Conference on the European Energy Market, EEM 2020. IEEE. 2020. 9221979. (International Conference on the European Energy Market, EEM). doi: 10.1109/EEM49802.2020.9221979

Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network

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Keywords

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