Next Article in Journal
Spatial and Temporal Variations in the Ecological Footprints in Northwest China from 2005 to 2014
Next Article in Special Issue
Residential Fuel Choice in Rural Areas: Field Research of Two Counties of North China
Previous Article in Journal
Evaluation of a Semi-Intensive Aquaponics System, with and without Bacterial Biofilter in a Tropical Location
Previous Article in Special Issue
Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Sustainability 2017, 9(4), 596; doi:10.3390/su9040596

A Hybrid Method for Short-Term Wind Speed Forecasting

1
School of Economics and Management, North China Electric Power University, Beijing 102206, China
2
Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100181, China
3
School of Management and Economics, Beijing Institute of Technology, Beijing 102206, China
4
School of Management and Economics, Illinois Institute of Technology, Chicago, IL 60616, USA
*
Author to whom correspondence should be addressed.
Received: 23 January 2017 / Revised: 4 April 2017 / Accepted: 7 April 2017 / Published: 12 April 2017
View Full-Text   |   Download PDF [768 KB, uploaded 17 April 2017]   |  

Abstract

The accuracy of short-term wind speed prediction is very important for wind power generation. In this paper, a hybrid method combining ensemble empirical mode decomposition (EEMD), adaptive neural network based fuzzy inference system (ANFIS) and seasonal auto-regression integrated moving average (SARIMA) is presented for short-term wind speed forecasting. The original wind speed series is decomposed into both periodic and nonlinear series. Then, the ANFIS model is used to catch the nonlinear series and the SARIMA model is applied for the periodic series. Numerical testing results based on two wind sites in South Dakota show the efficiency of this hybrid method. View Full-Text
Keywords: short-term wind speed forecasting; ensemble empirical mode decomposition (EEMD); adaptive neural network based fuzzy inference system (ANFIS); seasonal auto-regression integrated moving average (SARIMA) short-term wind speed forecasting; ensemble empirical mode decomposition (EEMD); adaptive neural network based fuzzy inference system (ANFIS); seasonal auto-regression integrated moving average (SARIMA)
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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhang, J.; Wei, Y.; Tan, Z.-F.; Ke, W.; Tian, W. A Hybrid Method for Short-Term Wind Speed Forecasting. Sustainability 2017, 9, 596.

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]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top