Delegates are invited to meet and discuss with the poster presenters during the poster presentation sessions between 10:30-11:30 and 16:00-17:00 on Thursday, 19 November 2015.
Lead Session Chair:
Stephan Barth, ForWind - Center for Wind Energy Research, Germany
YUKA KIKUCHI (1) F TAKESHI ISHIHARA (1)
(1) The University of Tokyo, Tokyo, Japan
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Presenter's biographyBiographies are supplied directly by presenters at EWEA 2015 and are published here unedited
Ms. Yuka Kikuchi is an assistant professor in the University of Tokyo. She studied civil engineering and took Ph. D. in the university of Tokyo. Her research topic is mainly establishing engineering cost model for wind farms in Japan.
PosterDownload poster (11.21 MB)
Prediction of offshore wind climate by using mesoscale model in japan coastal region
Japan has large offshore wind energy potential, most of which are located in coastal region when bottom mounted foundation is considered. In outer sea, accurate wind speed prediction was done by using mesoscale model WRF with 1 % annual average wind speed prediction error. However, the effect of the land and sea surface temperature may cause large prediction error in Japan coastal region, since Japan is mountainous and has strong ocean current. In this study, the wind speed prediction was carried out at coastal region in Japan and validated measurement considering the effect of nudging, land use and sea temperature.
In this study, wind speed prediction was performed with mesoscale model WRF ver 3.4.1 for one year at Choshi located 3.1 km east of the Japan coast considering the effect of nudging, land use and sea surface temperature. The prediction was compared with measurement data in order to validate the prediction accuracy.
Main body of abstract
Offshore wind climate assessment was carried out by using mesoscale model WRF ver. 3.4.1 and validated by measurement data at a test site located 3.1 km offshore, Choshi. It was found that wind climate in Japan coastal region was strongly influenced by nudging, land use, and sea surface temperature. A method to identify optimal nudging using offshore and aerological observations was proposed. The land use datasets were created using max area sampling method according to horizontal resolution of mesoscale model. The sea surface temperature datasets were corrected by using observations. As a result, relative error of annual wind speed was reduced from 7.3 % to 2.2 % and correlation coefficient between predicted and measured wind speed was improved from 0.80 to 0.84.
The proposed method reduced the relative error of annual wind speed from 7.3 % to 2.2 % and correlation coefficient between predicted and measured wind speed was improved from 0.80 to 0.84 by considering the nudging, land use and sea surface temperature effects on wind speed prediction.
The effects of nudging, land use and sea surface temperature were important to predict the accurate wind speeds in Japan coastal region.