2573667
doi
10.1109/TDC-LA.2018.8511757
oai:zenodo.org:2573667
user-eu
Preece, Robin
University of Manchester
Rueda Torres, Jose
TU Delft
van der Meijden, Mart
TU Delft
Parametric Evaluation of Different ANN Architectures: Forecasting Wind Power Across Different Time Horizons
Sewdien, Vinay
TenneT TSO BV
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>The participation of volatile wind energy resources in the generation mix of power systems is increasing. It is therefore becoming more and more crucial for system operators to accurately predict the wind power generation across different short term horizons (5 to 60 minutes ahead) in order to adequately balance the system and maintain system security. This paper presents a comprehensive assessment of the influence of different parameters in artificial neural networks, such as the amount of historic data, batch size, number of hidden layers, number of neurons per hidden layer, and the amount of training data on the short term forecast accuracy. In order to identify the parameters which are most influential with respect to forecast accuracy, a sensitivity study isolating the various factors on a one-at-a-time basis has been performed. To minimize the forecast error across the investigated forecast horizons, the developed neural networks use the feed forward back propagation algorithm. From the investigated cases it is concluded that a neural network with two hidden layers is most suitable for wind forecasting on the timeframes considered. Furthermore, with increasing forecast horizons (from 5 to 60 minutes ahead), better performance is achieved when neural networks contain increased neurons in the hidden layers and have enlarged training data sets</p>
Zenodo
2018-10-29
info:eu-repo/semantics/conferencePaper
2573666
user-eu
award_title=Massive InteGRATion of power Electronic devices; award_number=691800; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/691800; funder_id=00k4n6c32; funder_name=European Commission;
1579534268.019564
790213
md5:925b91641ee7466e61301a3cf475a564
https://zenodo.org/records/2573667/files/Parametric Evaluation of Different ANN Architectures.pdf
public