An ultra-short-term wind speed forecasting model based on time scale recognition and dynamic adaptive modeling

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022

Autores

Zhao Zhen (Autor) Outra; Gang Qiu (Autor) Outra; Shengwei Mei (Autor) Outra; Fei Wang (Autor) Outra; Xuemin Zhang (Autor) Outra; Rui Yin (Autor) Outra; Yu Li (Autor) Outra; Gerardo J. Osório (Autor) Outra; Miadreza Shafie khah (Autor) Outra; João P. S. Catalão (Autor) FEUP

Revista

Título: International Journal of Electrical Power and Energy Systems

Vol. 135

Páginas: 1-13

ISSN: 0142-0615

Editora: Elsevier

Indexação

ISI Web of Knowledge - 15 Citações

Scopus - 22 Citações

Outras Informações

ID Authenticus: P-00V-ARB

DOI: 10.1016/j.ijepes.2021.107502

Abstract (EN): The forecast of wind speed is prerequisite for wind power prediction, which is one of the most effective means of promoting wind power absorption. However, when modeling for wind speed sequences with different fluctuations, most existing researches ignore the influence of time scale of wind speed fluctuation period, let alone the low compatibility between training and testing samples that severely limit the training performance of forecasting model. To improve the accuracy of wind speed and wind power forecasting, an ultra-short-term wind speed forecasting model based on time scale recognition and dynamic adaptive modeling is proposed in this paper. First, a series of wind processes are divided from the historical wind speed sequence according to the natural variation characteristics of wind speed. Second, we divide all the wind processes into two patterns based on their time scale, and an SVC model with input features extracted from meteorological data is built to identify the time scale of the current wind process. Third, for a specifically identified wind process, the complex network algorithm is applied in data screening to select high compatible training samples to train the forecast model dynamically for current input. Simulation indicates that the proposed approach presents higher accuracy than benchmark models using the same forecasting algorithms but without considering the time scale and data screening.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13