Simultaneous Forecasting of Meteorological Data Based on a Self-Organizing Incremental Neural Network
Wonjik Kim* and Osamu Hasegawa**,***
*Department of Systems and Control Engineering, Tokyo Institute of Technology
2-12-1-S5-22 Ookayama, Meguro, Tokyo 152-8550, Japan
**Department of Systems and Control Engineering, Tokyo Institute of Technology
4259 J3-13 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan
Cureindo-building 405, 8-4-30 Turuma, Machida, Tokyo 194-0004, Japan
In this study, we propose a simultaneous forecasting model for meteorological time-series data based on a self-organizing incremental neural network (SOINN). Meteorological parameters (i.e., temperature, wet bulb temperature, humidity, wind speed, atmospheric pressure, and total solar radiation on a horizontal surface) are considered as input data for the prediction of meteorological time-series information. Based on a SOINN within normalized-refined-meteorological data, proposed model succeeded forecasting temperature, humidity, wind speed and atmospheric pressure simultaneously. In addition, proposed model does not take more than 2 s in training half-year period and 15 s in testing half-year period. This paper also elucidates the SOINN and the algorithm of the learning process. The effectiveness of our model is established by comparison of our results with experimental results and with results obtained by another model. Three advantages of our model are also described. The obtained information can be effective in applications based on neural networks, and the proposed model for handling meteorological phenomena may be helpful for other studies worldwide including energy management system.
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