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Activity Number: 121 - SPEED: Environmental Statistics
Type: Contributed
Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #324891 View Presentation
Title: Green Power Statistics: Local Wind Speed Modeling as Basis for Wind Turbine Performance Prediction
Author(s): Marina Nechayeva* and Malgorzata Marciniak and Vladimir Przhebelskiy and Michael Wiley and Paul DeVries
Companies: LaGuardia Community College and LaGuardia Community College and LaGuardia Community College and LaGuardia Community College and LaGuardia Community College
Keywords: Wind Speed / Power ; Weibull Parameters ; Maximum Likelihood Method ; Parameter Estimation ; Convergence ; Wind Turbine Energy
Abstract:

Statistics plays a crucial part in renewable energy research, e.g. building an efficient wind turbine calls for detailed analyzes of the local wind speed distribution. We will present outcomes of a year-long research by our faculty/student team at LaGuardia Community College. We obtained and validated short term wind speed measurements on campus roof and produced a long term time series model using correlation method and wind speed records from the nearby airport. Weibul distribution did not provide a great fit for the airoport data. We improved the fit by simulating continuous data that agreed with the records. Maximum Goodness of Fit and Quantile methods surpassed Maximum Likelihood method in determining parameters of Weibull Model, while Gumbel and Logistic distributions provided a better fit. Airoport data taken at ground level yielded average wind speed insufficient to justify the installation of the turbine, however rescaling the data for the height at which we propose to install the turbine showed sufficient average wind speed. Using the probability model that provided the best fit for our local data, we predicted the average annual energy output for a particular turbine


Authors who are presenting talks have a * after their name.

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