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Wind power / Wind power forecasting / Cluster analysis / Uncertainty / Randomness / Normal distribution / Scenario / Statistics / Mesoscale meteorology / Wind


CLUSTERING-BASED WIND POWER SCENARIO REDUCTION TECHNIQUE J. Sumaili1, H. Keko1, V. Miranda1, A. Botterud2 and J. Wang2 1 Instituto de Engenharia de Sistemas e Computadores do Porto (INESC Porto)
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Document Date: 2011-10-28 06:45:41

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City

Porto / CLUSTERING / Honolulu / Stockholm / /

Company

UChicago Argonne LLC / National Renewable Energy Laboratory / E. Canestrelli / Argonne National Laboratory / /

Country

United States / Portugal / Sweden / /

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Facility

National Renewable Energy Laboratory / /

IndustryTerm

stochastic programming algorithm / i7640M processor / unserved energy / excessive computing effort / stochastic programming algorithms / evolutionary optimization algorithm / adaptive recombination operator / /

MarketIndex

set 1000 / /

Organization

Instituto de Engenharia de Sistemas / U.S. Department of Energy / World Congress / Universidade do Porto / In Computational Intelligence / Computational Intelligence / /

Person

J. Sumaili / V / Springer / /

Position

editor / representative / /

ProgrammingLanguage

Python / /

ProvinceOrState

Illinois / Hawaii / /

Technology

stochastic programming algorithm / evolutionary optimization algorithm / i7640M processor / clustering algorithm / EPSO algorithm / html / recombination / stochastic programming algorithms / been achieved using a Core i7640M processor / /

URL

www.tcpdf.org / http /

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