Enhancing Long-Term Wind Power Forecasting by Using an Intelligent Statistical Treatment for Wind Resource Data

Monica Borunda (), Adrián Ramírez, Raul Garduno, Carlos García-Beltrán and Rito Mijarez
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Monica Borunda: Centro Nacional de Investigación y Desarrollo Tecnológico, Tecnológico Nacional de México, Cuernavaca 62490, Morelos, Mexico
Adrián Ramírez: Faculty of Science, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
Raul Garduno: Instituto Nacional de Electricidad y Energias Limpias, Cuernavaca 62490, Morelos, Mexico
Carlos García-Beltrán: Centro Nacional de Investigación y Desarrollo Tecnológico, Tecnológico Nacional de México, Cuernavaca 62490, Morelos, Mexico
Rito Mijarez: Instituto Nacional de Electricidad y Energias Limpias, Cuernavaca 62490, Morelos, Mexico

Energies, 2023, vol. 16, issue 23, 1-34

Abstract: Wind power is an important energy source that can be used to supply clean energy and meet current energy needs. Despite its advantages in terms of zero emissions, its main drawback is its intermittency. Deterministic approaches to forecast wind power generation based on the annual average wind speed are usually used; however, statistical treatments are more appropriate. In this paper, an intelligent statistical methodology to forecast annual wind power is proposed. The seasonality of wind is determined via a clustering analysis of monthly wind speed probabilistic distribution functions (PDFs) throughout n years. Subsequently, a methodology to build the wind resource typical year (WRTY) for the n + 1 year is introduced to characterize the resource into the so-called statistical seasons (SSs). Then, the wind energy produced at each SS is calculated using its PDFs. Finally, the forecasted annual energy for the n + 1 year is given as the sum of the produced energies in the SSs. A wind farm in Mexico is chosen as a case study. The SSs, WRTY, and seasonal and annual generated energies are estimated and validated. Additionally, the forecasted annual wind energy for the n + 1 year is calculated deterministically from the n year. The results are compared with the measured data, and the former are more accurate.

Keywords: forecasting; wind power generation; machine learning; clustering; Weibull PDFs; statistical seasonality; wind resource typical year; energy yield (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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