Wind Power Pattern Forecasting Based on Projected Clustering and Classification Methods

Title
Wind Power Pattern Forecasting Based on Projected Clustering and Classification Methods
Author(s)
신용호이헌규[이헌규]Minghao Piao[Minghao Piao]
Keywords
CURVE ESTIMATION; OPTIMIZATION; REGRESSION; MODELS
Issue Date
201504
Publisher
ELECTRONICS TELECOMMUNICATIONS RESEARCH INST
Citation
ETRI JOURNAL, v.37, no.2, pp.283 - 294
Abstract
A model that precisely forecasts how much wind power is generated is critical for making decisions on power generation and infrastructure updates. Existing studies have estimated wind power from wind speed using forecasting models such as ANFIS, SMO, k-NN, and ANN. This study applies a projected clustering technique to identify wind power patterns of wind turbines; profiles the resulting characteristics; and defines hourly and daily power patterns using wind power data collected over a year-long period. A wind power pattern prediction stage uses a time interval feature that is essential for producing representative patterns through a projected clustering technique along with the existing temperature and wind direction from the classifier input. During this stage, this feature is applied to the wind speed, which is the most significant input of a forecasting model. As the test results show, nine hourly power patterns and seven daily power patterns are produced with respect to the Korean wind turbines used in this study. As a result of forecasting the hourly and daily power patterns using the temperature, wind direction, and time interval features for the wind speed, the ANFIS and SMO models show an excellent performance.
URI
http://hdl.handle.net/YU.REPOSITORY/32698http://dx.doi.org/10.4218/etrij.15.2314.0070
ISSN
1225-6463
Appears in Collections:
경영대학 > 경영학과 > Articles
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