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Prediction study and application of wind power development based on filtering error threshold
Author(s) -
Zhang Ziqi,
Zhang Rui,
Fang Da,
Wang Jianzhou
Publication year - 2015
Publication title -
environmental progress and sustainable energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.495
H-Index - 66
eISSN - 1944-7450
pISSN - 1944-7442
DOI - 10.1002/ep.12138
Subject(s) - wind speed , wind power , kalman filter , renewable energy , wind power forecasting , computer science , data pre processing , meteorology , power (physics) , data mining , engineering , electric power system , artificial intelligence , geography , physics , quantum mechanics , electrical engineering
As a clean and renewable energy source, wind energy has been increasingly gaining global attention. Wind speed forecasting is of great significance for wind energy domain: planning and design of wind farms, wind farm operation control, wind power prediction, power grid operation scheduling, and more. Many wind speed forecasting algorithms have been proposed to improve forecast accuracy. In terms of such factors, in this article, a novel hybrid wind speed forecasting method was proposed based on Kalman filter and Generalized regression neural network as well as the idea of filtering error threshold in data preprocessing. The proposed models can implement long‐term wind speed forecasting with higher precision and reliability compared with single method and conventional approach, as demonstrated by several cases study using daily average wind speed samples collected in western China in a given year. © 2015 American Institute of Chemical Engineers Environ Prog, 34: 1536–1546, 2015