Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor

Marco Beccali, Serporta, Cirrincione, Antonino Marvuglia

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)

Abstract

Wind energy evaluation is an important goal in the conversion of energy systems to more environmentally friendly solutions. In this paper, we present a novel approach to wind speed spatial estimation on the isle of Sicily (Italy): an incremental self-organizing neural network (Generalized Mapping Regressor - GMR) is coupled with exploratory data analysis techniques in order to obtain a map of the spatial distribution of the average wind speed over the entire region. First, the topographic surface of the island was modelled using two different neural techniques and by exploiting the information extracted from a digital elevation model of the region. Then, GMR was used for automatic modelling of the terrain roughness. Afterwards, a statistical analysis of the wind data allowed for the estimation of the parameters of the Weibull wind probability distribution function. In the last sections of the paper, the expected values of the Weibull distributions were regionalized using the GMR neural network
Original languageEnglish
Pages (from-to)884-893
Number of pages9
JournalApplied Energy
Volume87
Publication statusPublished - 2010

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • General Energy
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law

Fingerprint

Dive into the research topics of 'Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor'. Together they form a unique fingerprint.

Cite this