Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models

Hui Wang, Jingxuan Sun, Jianbo Sun and Jilong Wang
Additional contact information
Hui Wang: School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China
Jingxuan Sun: The Second High School Attached to Beijing Normal University, Xi Cheng District, Beijing 100088, China
Jianbo Sun: School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China
Jilong Wang: School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China

Energies, 2017, vol. 10, issue 10, 1-13

Abstract: Achieving relatively high-accuracy short-term wind speed forecasting estimates is a precondition for the construction and grid-connected operation of wind power forecasting systems for wind farms. Currently, most research is focused on the structure of forecasting models and does not consider the selection of input variables, which can have significant impacts on forecasting performance. This paper presents an input variable selection method for wind speed forecasting models. The candidate input variables for various leading periods are selected and random forests (RF) is employed to evaluate the importance of all variable as features. The feature subset with the best evaluation performance is selected as the optimal feature set. Then, kernel-based extreme learning machine is constructed to evaluate the performance of input variables selection based on RF. The results of the case study show that by removing the uncorrelated and redundant features, RF effectively extracts the most strongly correlated set of features from the candidate input variables. By finding the optimal feature combination to represent the original information, RF simplifies the structure of the wind speed forecasting model, shortens the training time required, and substantially improves the model’s accuracy and generalization ability, demonstrating that the input variables selected by RF are effective.

Keywords: random forests (RF); feature selection; input variables selection; kernel-based extreme learning machine; short-term wind speed forecasting (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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1996-1073/10/10/1522/pdf (application/pdf)
https://www.mdpi.com/1996-1073/10/10/1522/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:10:p:1522-:d:114051

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().