TY - GEN
T1 - A Swarm intelligence approach for statistical modeling of wind speed and direction
T2 - ASCE International Conference on Computing in Civil Engineering 2023: Data, Sensing, and Analytics, i3CE 2023
AU - Salami Pargoo, Navid
AU - Amini, Erfan
AU - Zadeh, Mahshid Mohammad
AU - Hajj, Muhammad
N1 - Publisher Copyright:
© International Conference on Computing in Civil Engineering 2023.All rights reserved.
PY - 2024
Y1 - 2024
N2 - We present statistical modeling of wind speed and direction in the New York Bight region using a Weibull distribution for wind speed and a mixture of von Mises distributions for wind direction. The historic wind data for the years 2019-2021 with a 6-min sampling frequency are divided into two half-yearly intervals based on wind power, representing different seasonal behaviors. The parameters of von Mises distributions are estimated using four different metaheuristic optimization algorithms. The suitability of the distributions is judged based on the Pearson correlation coefficient, which indicates good fits for the proposed models. The results demonstrate the flexibility of the proposed models in representing the probability density function of wind direction regimes in zones with several modes or prevailing wind directions. This research provides valuable insights into the evaluation process of wind energy resources of the New York/New Jersey Bight region, including wind farm layout optimization.
AB - We present statistical modeling of wind speed and direction in the New York Bight region using a Weibull distribution for wind speed and a mixture of von Mises distributions for wind direction. The historic wind data for the years 2019-2021 with a 6-min sampling frequency are divided into two half-yearly intervals based on wind power, representing different seasonal behaviors. The parameters of von Mises distributions are estimated using four different metaheuristic optimization algorithms. The suitability of the distributions is judged based on the Pearson correlation coefficient, which indicates good fits for the proposed models. The results demonstrate the flexibility of the proposed models in representing the probability density function of wind direction regimes in zones with several modes or prevailing wind directions. This research provides valuable insights into the evaluation process of wind energy resources of the New York/New Jersey Bight region, including wind farm layout optimization.
KW - Metaheuristic Optimization
KW - New York-New Jersey Bight
KW - Statistical Wind Modeling
KW - von Mises Distribution
KW - Weibull Distribution
KW - Wind Energy Farm
UR - http://www.scopus.com/inward/record.url?scp=85184282928&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85184282928&partnerID=8YFLogxK
U2 - 10.1061/9780784485224.022
DO - 10.1061/9780784485224.022
M3 - Conference contribution
AN - SCOPUS:85184282928
T3 - Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023
SP - 176
EP - 185
BT - Computing in Civil Engineering 2023
A2 - Turkan, Yelda
A2 - Louis, Joseph
A2 - Leite, Fernanda
A2 - Ergan, Semiha
Y2 - 25 June 2023 through 28 June 2023
ER -