Wind learning potential assessment based on wind direction modelling and machine learning

Title:

Wind learning potential assessment based on wind direction modelling and machine learning

Creator:

Krömer, P., Mišák, S., Stuchlý , J., and Platoš, J.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:ed3fe305-11bc-4d1b-b587-7a5fdca0d625
uuid:ed3fe305-11bc-4d1b-b587-7a5fdca0d625
doi:10.14311/NNW.2016.26.030

Subject:

differential evolution, wind direction modeling, evolutionary fuzzy rules, wind energy potentiac assessment, estimation, and optimization

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2016 Volume:26 | Number:6

Harvested from:

CDK

Metadata only:

false