JACIII Vol.22 No.1 pp. 133-140
doi: 10.20965/jaciii.2018.p0133


Application of Modular Network Self-Organization Map to Temporal and Spatial Projection of Wind Speed with Wind Data at Biased Positions

Mitsuharu Hayashi and Ken Nagasaka

Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology
2-24-16 Nakamachi, Koganei-shi, Tokyo 184-8588, Japan

April 25, 2016
November 6, 2017
January 20, 2018
wind power generation, off-shore, Artificial Neural Network (ANN), Modular Network Self-Organization Map (mnSOM), projection

Wind generation is one of the fastest growing resources among renewable energies worldwide including Japan. As Japan is an island country surrounded by ocean, the on-shore landscape topography suitable for wind generation is limited. Therefore, based on the wind map until the year 2030, it is expected that new off-shore wind generation installations will be more suitable. For this reason, it is very important to determine the wind characteristics of the candidate areas for installing wind generation; however, in most off-shore installation sites, availability of weather condition data is poor and significant time and cost are required to accurately measure pin-point wind/weather conditions data. In this study, our goal is to project the wind speed of an unseen area (where weather condition data are not available) by mapping the seen areas (where weather condition data are available) around the target area using a modularized Artificial Neural Network (ANN) referred to as a Self-Organization Map (SOM). By learning the correlation between modularized ANNs of seen and unseen areas, the result of this temporal and spatial projection is the prediction of wind speed of a target area. We believe that the proposed technique will significantly reduce the amount of time and cost involved in selection of off-shore installation sites. Moreover, it should contribute to accelerated development and implementation of off-shore wind power generation in the future.

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Last updated on Feb. 21, 2018