Comparative Analysis of Estimated Small Wind Energy Using Different Probability Distributions in a Desert City in Northwestern México

Juan A. Burgos-Peñaloza, Alejandro A. Lambert-Arista (), O. Rafael García-Cueto, Néstor Santillán-Soto, Edgar Valenzuela and David E. Flores-Jiménez
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Juan A. Burgos-Peñaloza: Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico
Alejandro A. Lambert-Arista: Facultad de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico
O. Rafael García-Cueto: Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico
Néstor Santillán-Soto: Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico
Edgar Valenzuela: Facultad de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico
David E. Flores-Jiménez: Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, Mexico

Energies, 2024, vol. 17, issue 13, 1-18

Abstract: In this paper, four probability functions are compared with the purpose of establishing a methodology to improve the accuracy of wind energy estimations in a desert city in Northwestern Mexico. Three time series of wind speed data corresponding to 2017, 2018, and 2019 were used for statistical modeling. These series were recorded with a sonic anemometer at a sampling frequency of 10 Hz. Analyses based on these data were performed at different stationarity periods (5, 30, 60, and 600 s). The estimation of the parameters characterizing the probability density functions (PDFs) was carried out using different methods; the statistical models were evaluated by the coefficient of determination and Nash–Sutcliffe efficiency coefficient, and their accuracy was estimated by the measured quadratic error, mean square error, mean absolute error, and mean absolute percentage error. Weibull, using the energy pattern factor method, and Gamma, using the Method of Moments, were the probability density functions that best described the statistical behavior of wind speed and were better at estimating the generated energy. We conclude that the proposed methodology will increase the confidence of both wind speed estimation and the energy supplied by small-scale wind installations.

Keywords: short-term wind variability; probability density function; small wind energy estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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