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JRM Vol.10 No.4 pp. 301-304
doi: 10.20965/jrm.1998.p0301
(1998)

Paper:

Chaotic Short-Term Prediction to Water Flow into Hydroelectric Power Stations

Masaya Koyama and Tadashi lokibe

System Technology Division, Meidensha Corporation, 36-2 Nihonbashi Hakozaki-cho Chuo-ku, Tokyo 103-8515, Japan

Received:
April 13, 1998
Accepted:
June 5, 1998
Published:
August 20, 1998
Keywords:
Chaos, Nonlinear short-term prediction, Local fuzzy reconstruction, Water flow data into hydro-electric power station
Abstract

We applied local fuzzy reconstruction as deterministic nonlinear short-term prediction to data for water flow into hydroelectric power stations. Such prediction involves complex natural phenomena, and conventional hydraulics-based mathematical models do not produce satisfactory results. When a neural network is used, its construction cannot be easily determined, so extra neural networks must also be provided separately, based on experts’ opinions. To solve these problems, we held that if time-series data of the inflow rate for hydroelectric power stations exhibits deterministic chaos, the status in the near future is predicted. Typical outflow analysis using conventional mathematical models is described briefly, followed by local fuzzy reconstruction, then results are given from applying this to water flow prediction.

Cite this article as:
Masaya Koyama and Tadashi lokibe, “Chaotic Short-Term Prediction to Water Flow into Hydroelectric Power Stations,” J. Robot. Mechatron., Vol.10, No.4, pp. 301-304, 1998.
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