An Analysis of Regional and Seasonal Wind Characteristics and Energy Output

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Sunday, 4 January 2015
Rachael N. Isphording, Embry-Riddle Aeronautical University, Daytona Beach, FL; and C. Herbster

As climate change impacts continue to threaten global economic, social, and political stability, scientists and policy makers are increasing efforts to mitigate these effects. The energy sector has seen an increase in research and the application of renewable energy sources that will reduce the amount of greenhouse gases that are currently released into the atmosphere from dated energy production practices. For this study, statistical analyses of regional and seasonal wind characteristics and corresponding energy output were performed using the Eastern Wind Dataset created by AWS Truewind with assistance from The National Renewable Energy Laboratory (NREL). This dataset contains 10-minute wind speed and energy output for 1,326 simulated wind farms over a three year time span, which provided a very robust dataset for this study. These simulated wind farms were first plotted in ArcMap using the sites' latitude and longitude coordinates. After visually displaying the location of each simulated wind farm, three separate regions of study were created: the Northern Midwest, the Appalachian Mountains, and the Great Plains. These regions were further sub-divided to form a total of eight regions.

At this point, the null hypothesis was identified, predicting that there would be no variations in regional or seasonal wind speed or energy outputs. The alternate hypothesis stated that variations would exist. To test these hypotheses, the wind speed and energy data for each farm were first analyzed using a combination NCAR's Coding Language (NCL) and Microsoft Excel. A statistical exploratory data analysis (EDA) was performed for each region and sub-region where a variety of statistical data types were calculated to examine the location, spread, and symmetry of the data. The data from these regions were then further separated into Cold and Warm seasons and similar EDA's were performed to evaluate the seasonal data. The hypotheses were then tested using the appropriate statistical two-sample T-Tests for unequal variances. This study found that regional, sub-regional, and seasonal variability do exist in both wind speed characteristics and corresponding energy output.