Next Article in Journal
Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers
Previous Article in Journal
Detailed Modelling of the Deep Decarbonisation Scenarios with Demand Response Technologies in the Heating and Cooling Sector: A Case Study for Italy
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Energies 2017, 10(10), 1522; https://doi.org/10.3390/en10101522

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

1
School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China
2
The Second High School Attached to Beijing Normal University, Xi Cheng District, Beijing 100088, China
*
Author to whom correspondence should be addressed.
Received: 21 August 2017 / Revised: 22 September 2017 / Accepted: 22 September 2017 / Published: 4 October 2017
View Full-Text   |   Download PDF [3466 KB, uploaded 17 October 2017]   |  

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. View Full-Text
Keywords: random forests (RF); feature selection; input variables selection; kernel-based extreme learning machine; short-term wind speed forecasting random forests (RF); feature selection; input variables selection; kernel-based extreme learning machine; short-term wind speed forecasting
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Wang, H.; Sun, J.; Sun, J.; Wang, J. Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models. Energies 2017, 10, 1522.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top