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1 .pp. A Study on Wind Forecast using Self-Organizing Map FUJIMATSU Seiichiro, SUMI Yasuaki, UETA Takuya, KOBAYASHI Asuka, TSUKUTANI Takao, FUKUI Yutaka SOM SOM Elman SOM SOM Elman SOM Abstract : Now a small wind generator with the power assistance is developed. In order to aim at the power assistance with good efficiency or more, we have studied wind forecast based on the weather observation data in the narrow region using Self- Organizing map SOM. In this paper, we have examined about the wind forecast to aim at the forecast rate improvement by using both Bi-directional SOM and Elman-type feedback SOM. In our experiment, we have forecasted and evaluated by changing the weather observation data and the learning data. As a result, there is very little difference in both bi-directional SOM and Elman-type feedback SOM. However, It has been understood that the forecast rate improves though it is a little, by using the difference value of the wind velocity for the learning data. Moreover, it has been understood that the forecast rate improves greatly by narrowing the average time width of weather observation data. Keywordswind generator, wind condition, forecast, Self-Organizing Map
2 N.N. : Neural networkgp : Genetic Programming - SOM : Self-Organizing Map SOM SOM SOM Elman SOM - SOM SOM SOM SOMElman SOM SOM SOM T. SOM T. i t mt i xt xt n mt i n h ci t αt t =,,... SOM - SOM n xt mt i n Fig. - Composition chart of Fundamental SOM SOM SOM SOM xt x m i i c
3 h ci tαt h ci t t αt SOM SOM - SOM SOM SOM SOM X Y X,Y SOM ij * X W ij Dij SOM ij * ε W ij X Y Y V ij Bij ij ij * R ε T Fig. - Composition chart of Bi-directional SOM
4 Elman SOM Elman SOM - SOM x y h z L MM M N Elman Elman SOM Elman SOM Elman SOM Elman SOM j y j w j k z k w k t x h Fig. - Composition chart of Elman-type Feedback SOM j * β γ Elman SOM γ γ SOM Elman SOM SOM SOM SOM n SOM SOM Elman SOM
5 SOM SOM SOM SOM SOM X Y X Y - X Y - RMSE : Root Mean Square Error RMSE - RMSE RMSE.... Fig. - Input data
6 a RMSE armse in spring Fig. - RMSE each season b RMSE brmse in autumn a RMSE:.// amin RMSE in spring:.// b RMSE:.// bmax RMSE in spring:.// Fig. - Wind forecast value in spring a RMSE:.// b RMSE:.// amin RMSE in autumn:.// bmax RMSE in autumn:.// Fig. - Wind forecast value in autumn
7 -a RMSE RMSE RMSE... -b RMSE RMSE RMSE RMSE - RMSE m/s m/s - SOM SOM RMSE SOM RMSE - SOM SOM SOM Fig. - Difference value distribution of wind velocity Fig. - Comparison between Bidirectional SOM and Fundamental SOM SOM SOM SOM SOM SOM
8 . Fig. - Forecast every one hour RMSE : Root Mean Square Error RMSE ME : Mean Error ME ME ME Fig. - Forecast every ten minutes Fig. - Forecast every five minutes
9 SOM RMSE - ME ME Elman SOM Elman SOM SOM SOM Elman SOM Elman SOM SOM Fig. - Comparison of ME - RMSE SOM xk xk xk Fig. - Comparison of RMSE RMSE Elman SOM. β. γ.
10 - Fig. - Forecast // SOM SOM Elman SOM ME:.RMSE:. SOM ME.RMSE.. SOM SOM RMSE.... SOM SOM SOM SOM ME RMSE Elman SOM Elman β γ Vol., No., pp.-, Jul.,. Vol., No., pp.-,. / pp.-, Nov.,. / pp.-, Oct.,. -,Jun.,. T.. Feature Map Vol. J-D-II, No., pp.-, Apr.,. D-IIVol.J- D-IINo. pp.-,. Seiichiro FUJIMATSU, Yasuaki SUMI, Takuya UETA, Takao TSUKUTANI, Yutaka FUKUI,The study of the wind synopsis prediction system by using
11 Self-Organization Map,ITC-CSCC Proceedings, Vol., pp.-, Jul.,. SOM G, Nov.,. SOM pp.-, Apr.,.
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