Nonlinear wind prediction using a fuzzy modular temporal neural network
- GeoControl Systems, Inc., Houston, TX (United States)
- West Texas A&M Univ., Canyon, TX (United States)
This paper introduces a new approach utilizing a fuzzy classifier and a modular temporal neural network to predict wind speed and direction for advanced wind turbine control systems. The fuzzy classifier estimates wind patterns and then assigns weights accordingly to each module of the temporal neural network. A temporal network with the finite-duration impulse response and multiple-layer structure is used to represent the underlying dynamics of physical phenomena. Using previous wind measurements and information given by the classifier, the modular network trained by a standard back-propagation algorithm predicts wind speed and direction effectively. Meanwhile, the feedback from the network helps auto-tuning the classifier.
- Research Organization:
- American Wind Energy Association, Washington, DC (United States)
- OSTI ID:
- 269399
- Report Number(s):
- CONF-950309--; ON: DE96011159
- Country of Publication:
- United States
- Language:
- English
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