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.
|Title of host publication||2020 17th International Conference on the European Energy Market, EEM 2020|
|Number of pages||6|
|Publication status||Published - 2020|
|Event||17th International Conference on the European Energy Market, EEM 2020 - Stockholm, Sweden|
Duration: 16 Sep 2020 → 18 Sep 2020
|Name||International Conference on the European Energy Market, EEM|
|Conference||17th International Conference on the European Energy Market, EEM 2020|
|Period||16/09/20 → 18/09/20|
Bibliographical noteGreen 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.
- convolutional neural network
- deep learning
- multilayer perceptron
- numerical weather forecasting
- wind energy