Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/55460
Title: Performance sensitivity of a wind farm power curve model to different signals of the input layer of ANNs: Case studies in the Canary Islands
Authors: Velázquez Medina, Sergio 
Carta González, José Antonio 
Portero Ajenjo, Ulises
UNESCO Clasification: 332202 Generación de energía
Keywords: Wake
Optimization
Energy
Issue Date: 2019
Journal: Complexity 
Abstract: Improving the estimation of the power output of a wind farm enables greater integration of this type of energy source in electrical systems. The development of accurate models that represent the real operation of a wind farm is one way to attain this objective. A wind farm power curve model is proposed in this paper which is developed using artificial neural networks, and a study is undertaken of the influence on model performance when parameters such as the meteorological conditions (wind speed and direction) of areas other than the wind farm location are added as signals of the input layer of the neural network. Using such information could be of interest, either to study possible improvements that could be obtained in the performance of the original model, which uses exclusively the meteorological conditions of the area where the wind farm is located, or simply because no reliable meteorological data for the area of the wind farm are available. In the study developed it is deduced that the incorporation of meteorological data from an additional weather station other than that of the wind farm site can improve by up to 17.6% the performance of the original model.
URI: http://hdl.handle.net/10553/55460
ISSN: 1076-2787
DOI: 10.1155/2019/2869149
Source: Complexity [ISSN 1076-2787], v. 2019
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