ASSESSING LONG-TERM WIND CONDITIONS BY COMBINING DIFFERENT MEASURE-CORRELATE-PREDICT ALGORITHMS

This paper significantly advanced the hybrid measure-correlate-predict (MCP) methodology, enabling it to account for the variations of both wind speed and direction. The advanced hybrid MCP method used the recorded data of multiple reference stations to estimate the long-term wind condition at the target wind plant site with greater accuracy than possible with data from a single reference station. The wind data was divided into different sectors according to the wind direction, and the MCP strategy was implemented for each wind sector separately. The applicability of the proposed hybrid strategy was investigated using four different MCP methods: (i) linear regression; (ii) variance ratio; (iii) artificial neural networks; and (iv) support vector regression. To implement the advanced hybrid MCP methodology, we used the hourly averaged wind data recorded at six stations in North Dakota between the years 2008 and 2010. The station Pillsbury was selected as the target plant site. The recorded data at the other five stations (Dazey, Galesbury