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Energies 2018, 11(6), 1541; https://doi.org/10.3390/en11061541

Forecasting the Long-Term Wind Data via Measure-Correlate-Predict (MCP) Methods

1
Smart City Construction Engineering, University of Science & Technology (UST), 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea
2
Environmental & Plant Engineering Research Division, Korea Institute of Civil Engineering and Building Technology (KICT), Daehwa-dong 283, Goyangdae-ro, Ilsanseo-Gu, Goyang-si 10223, Korea
*
Author to whom correspondence should be addressed.
Received: 4 May 2018 / Revised: 31 May 2018 / Accepted: 11 June 2018 / Published: 13 June 2018
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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Abstract

The current study aims to forecast and analyze wind data such as wind speed at a test site called “Urumsill” on Deokjeok Island, South Korea. The measured wind data available at the aforementioned test site are only for two years (2015 and 2016), making it impossible to analyze the long-term wind characteristics. In order to overcome this problem, two measure-correlate-predict (MCP) techniques were adopted using long-term wind data (2000–2016), measured by a meteorological mast (met-mast) installed at a distance of 3 km from the test site. The wind data measured at the test site in 2016 were selected as training data to build the MCP models, whereas wind data of 2015 were used to test the accuracy of MCP models (test data). The wind data at both sites were measured at a height of 10 m and showed a good agreement for the year 2016 (training period). Using the comparison results of the year 2016, wind speed predictions were made for the rest of the years (2000–2016) at the test site. The forecasted values of wind speed had maximum relative error in the range of ±0.8 m/s for the test year of 2105. The predicted wind data values were further analyzed by estimating the mean wind speed, the Weibull shape, and the scale parameters, on a seasonal and an annual basis, in order to understand the wind behavior in the region. The accuracy and presence of possible errors in the forecasted wind data are discussed and presented. View Full-Text
Keywords: long-term wind data; forecasting; Measure-Correlate-Predict (MCP) techniques; regression; variance ratio; Weibull plots long-term wind data; forecasting; Measure-Correlate-Predict (MCP) techniques; regression; variance ratio; Weibull plots
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Ali, S.; Lee, S.-M.; Jang, C.-M. Forecasting the Long-Term Wind Data via Measure-Correlate-Predict (MCP) Methods. Energies 2018, 11, 1541.

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