TY  - JOUR
  - Bechrakis, DA,Deane, JP,McKeogh, EJ
  - 2004
  - April
  - Energy
  - Wind resource assessment of an area using short term data correlated to a long term data set
  - Validated
  - ()
  - wind energy assessment correlation simulation neural networks TERRAIN
  - 76
  - 725
  - 732
  - A method of estimating the annual wind energy potential of a selected site using short term measurements related to one year's recorded wind data at another reference site is presented. The proposed method utilizes the 1-year measured wind speed of one site to extrapolate the annual wind speed at a new site, using an artificial neural network (ANN). In this study, concurrent measurements from target and reference sites over periods of 1-month and 2-month were used to "train" the ANN. Topographical details or other meteorological data are not required for this approach. After derivation of the simulated wind speed time series for the target site, its mean value and its corresponding Weibull distribution parameters are calculated. The derived Weibull distribution of the simulated wind speed is used to make an assessment of the annual wind energy resource in the new area with respect to a particular wind turbine model. Three pairs of measuring stations in the southwest of Ireland were examined, where the wind potential is high and technically exploitable. Analysis of the measurements showed a reasonable cross-correlation coefficient of the wind speed between the sites. Results indicate that with this method, only a short time period of wind data acquisition in a new area might provide the information required for a satisfactory assessment of the annual wind energy resource. To evaluate the accuracy of the method, simulation results of the 1-month and 2-month training periods are compared to the corresponding actual values recorded at the sites. Also, a comparison with the results of a commercial wind energy assessment software package is presented showing similar results. (C) 2004 Published by Elsevier Ltd.
  - DOI 10.1016/j.solener.2004.01.004
DA  - 2004/04
ER  - 

@article{V43337505,
   = {Bechrakis,  DA and Deane,  JP and McKeogh,  EJ },
   = {2004},
   = {April},
   = {Energy},
   = {Wind resource assessment of an area using short term data correlated to a long term data set},
   = {Validated},
   = {()},
   = {wind energy assessment correlation simulation neural networks TERRAIN},
   = {76},
  pages = {725--732},
   = {{A method of estimating the annual wind energy potential of a selected site using short term measurements related to one year's recorded wind data at another reference site is presented. The proposed method utilizes the 1-year measured wind speed of one site to extrapolate the annual wind speed at a new site, using an artificial neural network (ANN). In this study, concurrent measurements from target and reference sites over periods of 1-month and 2-month were used to "train" the ANN. Topographical details or other meteorological data are not required for this approach. After derivation of the simulated wind speed time series for the target site, its mean value and its corresponding Weibull distribution parameters are calculated. The derived Weibull distribution of the simulated wind speed is used to make an assessment of the annual wind energy resource in the new area with respect to a particular wind turbine model. Three pairs of measuring stations in the southwest of Ireland were examined, where the wind potential is high and technically exploitable. Analysis of the measurements showed a reasonable cross-correlation coefficient of the wind speed between the sites. Results indicate that with this method, only a short time period of wind data acquisition in a new area might provide the information required for a satisfactory assessment of the annual wind energy resource. To evaluate the accuracy of the method, simulation results of the 1-month and 2-month training periods are compared to the corresponding actual values recorded at the sites. Also, a comparison with the results of a commercial wind energy assessment software package is presented showing similar results. (C) 2004 Published by Elsevier Ltd.}},
   = {DOI 10.1016/j.solener.2004.01.004},
  source = {IRIS}
}