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

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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.

Original languageEnglish
Title of host publication2020 17th International Conference on the European Energy Market, EEM 2020
PublisherIEEE
Number of pages6
ISBN (Electronic)9781728169194
DOIs
Publication statusPublished - 2020
Event17th International Conference on the European Energy Market, EEM 2020 - Stockholm, Sweden
Duration: 16 Sep 202018 Sep 2020

Publication series

NameInternational Conference on the European Energy Market, EEM
Volume2020-September
ISSN (Print)2165-4077
ISSN (Electronic)2165-4093

Conference

Conference17th International Conference on the European Energy Market, EEM 2020
Country/TerritorySweden
CityStockholm
Period16/09/2018/09/20

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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