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Title: Optimization of the ANNs Models Performance in the Short-Term Forecasting of the Wind Power of Wind Farms
Authors: Velázquez Medina, Sergio Leandro 
Portero-Ajenjo, Ulises
UNESCO Clasification: 332205 Fuentes no convencionales de energía
330609 Transmisión y distribución
3306 Ingeniería y tecnología eléctricas
Keywords: Artificial neural networks (ANN)
Wind power forecasting
Model performance
Wind farm power output
Issue Date: 2021
Publisher: IntechOpen 
Abstract: Due to the low dispatchability of wind power, the massive integration of this energy source in electrical systems requires short-term and very short-term wind farm power output forecasting models to be as efficient and stable as possible. A study is conducted in the present paper of potential improvements to the performance of artificial neural network (ANN) models in terms of efficiency and stability. Generally, current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station, in addition to selecting a fixed number of time periods prior to the forecasting. In this respect, new ANN models are proposed in this paper, which are developed by: varying the number of prior 1-h periods (periods prior to the prediction hour) chosen for the input layer parameters; and/or incorporating in the input layers data from a second weather station in addition to the wind farm reference station. It has been found that the model performance is always improved when data from a second weather station are incorporated. The mean absolute relative error (MARE) of the new models is reduced by up to 7.5%. Furthermore, the longer the forecast horizon, the greater the degree of improvement.
ISBN: 978-1-78985-213-4
DOI: 10.5772/intechopen.97190
Source: Theory of Complexity / Edited by Ricardo López-Ruiz. Chapter 5
Appears in Collections:Capítulo de libro
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