Open AccessShort term wind power interval prediction based on VMD and improved BLS
Author(s) -
Yang Zhao,
Chuanbo Wen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2108/1/012071
Subject(s) - interval (graph theory) , term (time) , wind power , prediction interval , power (physics) , electric power system , control theory (sociology) , component (thermodynamics) , mean squared prediction error , algorithm , interval arithmetic , mathematics , computer science , statistics , artificial intelligence , engineering , physics , control (management) , quantum mechanics , combinatorics , electrical engineering , thermodynamics , mathematical analysis , bounded function
Aiming at the problem of wind power interval prediction, a short-term wind power interval prediction model based on VMD and improved BLS is proposed. Firstly, the complex wind power time series are decomposed by variational mode decomposition to reduce the non stationarity of wind power. Then an improved broad learning system (BLS) is established to predict the power and error of each component, and a weight is given to the prediction error of each component. The sparrow search algorithm (SSA) is used to optimize the weight, and the width of the prediction interval is obtained by adding the power and error prediction values. The experimental data show that the proposed model improves the accuracy of prediction interval.