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

Abstract

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)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(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)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(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.

Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India

“…There was large wind potential in the direction of 210° to 290° with a maximum wind speed reaching to 22 m/s, so if you install the wind turbines in this direction there is a possibility to increase the power generation. In future, short-term wind forecasting [34,35] can be performed by using an artificial neural network approach in combination with results obtained from the Weibull distribution and parameter estimation though nature inspired algorithms. large wind potential in the direction of 210° to 290° with a maximum wind speed reaching to 22 m/s, so if you install the wind turbines in this direction there is a possibility to increase the power generation.…”

Wind Power Integration: An Experimental Investigation for Powering Local Communities

“…An appropriate method for analyzing the wind characteristics and wind energy potential is referred in [21]. Moreover, if the data for more than two years have been considered, then measure-correlate-predict (MCP) method is clearly described and implemented in [22]. Whereas, the presence of probable errors and accuracy of wind speed data is also discussed in the same article.…”