CMAC Applied in Wind Power Prediction Based on Applied Mechanics

Yi Chuan Shao; Xing Jia Yao

doi:10.4028/www.scientific.net/AMR.496.75

Paper Titles

Simulation of Monomers Selection on Single-Molecular Copolymer Nano-Particles that Mimic Protein Folding
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N-Acetyl-L-Cysteine-Templated Formation of the Fluorescent Silver Nanocluster and its Application to Bioimaging
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Changes in Layer Properties and Physical Properties of Papers from Old Corrugated Container Fibres with PDADMAC/HEC Polyelectrolyte Multilayers
p.67

TEMPO-Mediated Oxidation of Unbleached Bagasse Pulp
p.71

CMAC Applied in Wind Power Prediction Based on Applied Mechanics
p.75

Influence of High Temperature Annealing in Nitrogen on Upconversion Luminescence of β-NaYF₄: Yb³⁺, Er³⁺ Hexagonal Sub-Microplates
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Synthesis of Ionic Liquids [BMIM]BF₄and [BMIM]PF₆ under Microwave Irradiation by One-Pot
p.84

The Effects of Temperature and Heating Rate on the Preparation of Stalk Char
p.88

Comparison between Microcosmic Characterizations of Propellants by SEM and AFM
p.94

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 496CMAC Applied in Wind Power Prediction Based on...

CMAC Applied in Wind Power Prediction Based on Applied Mechanics

Abstract:

Wind Power prediction is very important in the wind power grid management. This paper introduces how to use Cerebellar Model Articulation Controller(CMAC) to build a short-term wind power prediction model.CMAC and Back-propagation Artificial Neural Networks(BP) are used respectively to do the short-term prediction with the data from a wind farm in Inner Mongolia. After comparison of the results, CMAC is more stable, accurate and faster with less training data.. CMAC is considered to be more suitable to do the short-term prediction. All of the study are based on applied mechanics, which will be useful for energy engineering and mechanics study.