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Application of Robust Regression on Wind Speed Data
Noor Fatihah Binti Zolkefli
Supervisor: Assoc. Prof Dr. Norizan Binti Mohamed
Bachelor of Science (Financial Mathematics)
School of Informatics and Applied Mathematics
The most critical thing during the site choice for offshore wind farm development is wind
climate analysis. For this case, accurate wind data are needed. Buoy measurement is
considered as a reference source of data obtained from less accurate sources. The
regression techniques developed by using the principle of ordinary least squares (OLS).
But wind speed data normally containing several outliers that cause the validity of the
regression analysis may be questioned if it is not accurately checked. This research
concentrated on the implementation of the most important robust regression method,
which can detect and reveal outliers and maintain their efficiency at the same time. The
data used for this research is taken from National Data Buoy Center. The used of robust
regression is vital in wind speed application as outliers are present in the available data
sample. The estimators used in this study are S-estimator, LTS-estimator, M-estimator,
and MM-estimator.
839 | UMT UNDERGRADUATE RESEARCH DAY 2018