Forecasting wind generation and assessing influence of climat parameters on wind power curve
UDC: 621.311
DOI: 10.33285/2073-9028-2022-1(306)-120-131
Authors:
ZUBAKIN VASILY A.
1,
VELICHKO ARSENY I.1
1 Gubkin Russian State University of Oil and Gas (National Research University), Moscow, Russian Federation
Keywords: renewable energy sources, wind energy forecast, machine learning, wind farm, wholesale electricity and capacity market
Annotation:
The article is devoted to the forecasting of wind power generation. Existing forecasting methods are analyzed and assessed. The power curve of the wind turbine is modeled using various machine learning algorithms to assess the influence of climatic parameters on the wind power curve.
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