E3S Web Conf.
Volume 197, 202075th National ATI Congress – #7 Clean Energy for all (ATI 2020)
|Number of page(s)||11|
|Section||Environmental Sustainability and Renewable Energy Sources|
|Published online||22 October 2020|
On the wake effect in wind farm power forecasting: a new data-driven approach
University of Messina, C.da Di Dio, Messina, 98166, Italy
2 University of Catania, V.le A. Doria 6, Catania 95125, Italy
* Corresponding author: firstname.lastname@example.org
Wind power generation differs from other energy sources, such as thermal, solar or hydro, due to the inherent stochastic nature of wind. For this reason wind power forecasting, especially for wind farms, is a complex task that cannot be accurately solved with traditional statistical methods or needs large computational systems if physical models are used. Recently, the so-called learning approaches are considered a good compromise among the previous methods since they are able to integrate physical phenomena such as wake effects without presenting heavy computational loads. The present work deals with an innovative method to forecast wind power generation in a wind farm with a combination of GISbased methods, neural network approach and a wake physical model. This innovative method was tested with a wind farm located in Sicily (Italy), used as a case study. It consists of 30 identical wind turbines (850 kW each one), located at different heights, for an overall Power peak of 25 MW. The time series dataset consists of one year with a sampling time of 10 minutes considering wind speeds and wind directions. The output of this innovative model leaded to good results, especially for medium-term overall energy production forecast for the case study.
Key words: wind power / neural networks / GIS / wake modelling
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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