Parametric Evaluation of Different ANN Architectures: Forecasting Wind Power Across Different Time Horizons

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Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA 2018
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)978-1-5386-5844-4
DOIs
Publication statusPublished - 2018
Event2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA - Lima, Peru
Duration: 18 Sep 201821 Sep 2018

Conference

Conference2018 IEEE PES Transmission and Distribution Conference and Exhibition - Latin America, T and D-LA
CountryPeru
CityLima
Period18/09/1821/09/18

Keywords

  • Artificial Neural Networks
  • Forecasting
  • MIGRATE
  • Wind Energy

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