Date of Award
5-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
College/School
College of Science and Mathematics
Department/Program
School of Computing
Thesis Sponsor/Dissertation Chair/Project Chair
Aparna Varde
Committee Member
Pankaj Lal
Committee Member
Stefan Robila
Abstract
This study investigates using satellite-based wind data and machine learning models to support offshore wind energy planning and prediction. Sentinel-1 Level 2 data was utilized to analyze wind patterns. K-means clustering reveals that 64.6% of the wind originates from the southwest, while higher speeds are more frequently recorded from the northwest during winter. Principal Component Analysis (PCA) was employed to reduce dimensionality and interpret key patterns in wind speed and power output at the Jersey-Atlantic onshore wind farm. Artificial Neural Networks (ANN), a temporal ANN, and a Convolutional Neural Network (CNN) were developed to forecast turbine power output. The CNN, which captured spatial dependencies across turbines, achieved the best overall performance, with R2 scores exceeding 0.93. Results indicate that model performance is influenced by turbine location and wind direction, reflecting the role of wake effects. This research illustrates how spatial data and machine learning can enhance wind energy forecasting and inform more effective wind farm design.
File Format
Recommended Citation
Noriega Monsalve, Cristian Camilo, "Enhancement of Offshore Wind Farms Using Geospatial Wind Data and Machine Learning in the Northeastern United States" (2025). Theses, Dissertations and Culminating Projects. 1560.
https://digitalcommons.montclair.edu/etd/1560
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Data Science Commons, Natural Resources Management and Policy Commons, Oil, Gas, and Energy Commons