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.

1. [1] J. T. Houghton, Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, X. Dai, K. Maskell, and C. A. Johnson (eds.), “Climate Change 2001: The Scientific Basis, Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change,” Cambridge University Press, ISBN 0-521-80767-0, 2001.
2. [2] J. B. Cardell et al., “Wind Power in New England: Modeling and Analysis of Non-Dispatchable Renewable Energy Technologies,” IEEE Trans. on Power Systems, Vol.13, No.2, pp. 710-715, 1998.
3. [3] R. Billinton et al., “Assessment of Risk-Based Capacity Benefit Factors Associated With Wind Energy Conversion Systems,” IEEE Trans. on Power Systems, Vol.13, No.3, pp. 1191-1196, 1998.
4. [4] New Energy and Industrial Technology Development Organization, “White Paper on Technology about Renewable Energy,” Morikita Publishing Co., Ltd., Chapter 3, 2013 (in Japanese).
5. [5] S. Tasaki, “Arrow of Time as seen from the Chaos,” Blue backs, 2000 (in Japanese).
6. [6] K. Aihara and R. Tokunaga, “Application Strategy of Chaos,” Ohmsha, Ltd., 1993 (in Japanese).
7. [7] B. Kermanshahi, “Design and Application of Artifical Neural Network,” Shokodo Co.,Ltd., 1996 (in Japanese).
8. [8] M. Hayashi and B. Kermanshahi, “Application of Artificial Neural Network for Wind Speed Prediction and Determination of Wind Power Output,” Proc. of the Int. Conf. on Engineering Education (ICEE 2001), 2001.
9. [9] M. Hayashi and K. Nagasaka, “Application of Intelligent Method for Wind Speed Prediction and Determination of Wind Power Output,” MS thesis, Tokyo University of Agreculture and Technology, 2003 (in Japanese).
10. [10] J. Príncipe and J. Kuo, “Dynamic Modeling of Chaotic Time Series with Neural Networks,” T. Cowan and A. Koufmann (eds.), Advances in Neural Information Processing Systems 6, pp. 311-318, 1994.
11. [11] K. Tokunaga, T. Furukawa, and S. Yasui, “Modular Network SOM Self organization of a group of function models,” Technical Report of IEICE NC2002-130, pp. 79-83, 2003.
12. [12] K. Tokunaga, T. Furukawa, and S. Yasui, “Modular Network SOM Self-Organizing Map of a Systems Group in Function Space,” Technical Report of IEICE NC2003-17, pp. 31-36, 2003.
13. [13] T. Kohonen, “Self-Organizing Maps,” Springer Publishing, 1995.
14. [14] K. Kimotsuki, K. Tokunaga, S. Yasui, and T. Furukawa, “Modular Network SOM Self-Organizing Maps Dealing with Dynamic Systems,” Technical Report of IEICE NC2003-141, pp. 35-40, 2004.
15. [15] K. Aihara, “Fundamental and Application of Chaos Time Seriese Analysis,” Sangyo tosho Publishing company, Chapter 5, 2000 (in Japanese).