Finding Representative Wind Power Scenarios and their Probabilities for Stochastic Models
Finding Representative Wind Power Scenarios and their Probabilities for Stochastic Models
dc.contributor.author | Jean Sumaili | en |
dc.contributor.author | Hrvoje Keko | en |
dc.contributor.author | Vladimiro Miranda | en |
dc.date.accessioned | 2017-11-16T13:29:33Z | |
dc.date.available | 2017-11-16T13:29:33Z | |
dc.date.issued | 2011 | en |
dc.description.abstract | This paper analyzes the application of clustering techniques for wind power scenario reduction. The results have shown the unimodal structure of the scenario generated under a Monte Carlo process. The unimodal structure has been confirmed by the modes found by the information theoretic learning mean shift algorithm. The paper also presents a new technique able to represent the wind power forecasting uncertainty by a set of representative scenarios capable of characterizing the probability density function of the wind power forecast. From an initial large set of sampled scenarios, a reduced discrete set of representative scenarios associated with a probability of occurrence can be created finding the areas of high probability density. This will allow the reduction of the computational burden in stochastic models that require scenario representation. | en |
dc.identifier.uri | http://repositorio.inesctec.pt/handle/123456789/2309 | |
dc.language | eng | en |
dc.relation | 4811 | en |
dc.relation | 208 | en |
dc.relation | 5164 | en |
dc.rights | info:eu-repo/semantics/openAccess | en |
dc.title | Finding Representative Wind Power Scenarios and their Probabilities for Stochastic Models | en |
dc.type | conferenceObject | en |
dc.type | Publication | en |