General Information
    • ISSN: 1793-821X (Print)
    • Frequency: Quarterly (2013-2014); Bimonthly (Since 2015)
    • DOI: 10.18178/JOCET
    • Editor-in-Chief: Prof. Haider F. Abdul Amir
    • Executive Editor: Ms. Jennifer Zeng
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School of Science and Technology Universiti Malaysia Sabah, Malaysia.
I would like to express my appreciation to all authors, reviewers and editors.

JOCET 2018 Vol.6(4): 297-302 ISSN: 1793-821X
DOI: 10.18178/JOCET.2018.6.4.478

Grid-Style Modular Network Self-Organization Map for Detail Projection of Unknown Off-Shore Wind Speed

Mitsuharu Hayashi and Ken Nagasaka
Abstract—Wind generation is one of the fast growing and introduced resources among renewable energies through worldwide including Japan. As Japan, on the other hand, is an island country surrounded by ocean, the landscape topography suitable for wind generation is limited for the on-shore. Therefore, based on the wind map of up to year 2030, it is expected that new wind generation installation will be more suitable on off-shore rather than on-shore. For this reason, it is very important to determine the wind characteristics of the candidate area for installing wind generation, however in most cases of off-shore installation, existence of weather condition data is poor and needs lots of time and cost for measuring pin-point weather condition data. In this study, the goal of this research is to project a wind speed of an unseen area (where its weather condition data is not available) by mapping the seen areas (where their weather condition data are available) around the target area using the modularized Artificial Neural Network (SOM: Self-Organization Map). By learning the correlation between modularized ANNs of seen and unseen areas, the result of this temporal and spatial projection will be the prediction of wind speed of target place. Furthermore, in this study, by segmenting the area as grid-style and learning it, it becomes possible to predict the wind speed more detail and more precise. It is believed, by the help of the proposed technique, a huge amount of time and cost will be saved for selection of off-shore installation point of off-shore wind power generation. Moreover, it will certainly contribute to the development and speed-up of off-shore wind power generation in the future.

Index Terms—Artificial neural network, modular network som, projection, wind speed.

Mitsuharu Hayashi and Ken Nagasaka are with the Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Japan (e-mail:,


Cite:Mitsuharu Hayashi and Ken Nagasaka, "Grid-Style Modular Network Self-Organization Map for Detail Projection of Unknown Off-Shore Wind Speed," Journal of Clean Energy Technologies vol. 6, no. 4, pp. 297-302, 2018.

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