Next Article in Journal
City Green Economy Evaluation: Empirical Evidence from 15 Sub-Provincial Cities in China
Previous Article in Journal
Mining λ-Maximal Cliques from a Fuzzy Graph
Article Menu

Export Article

Open AccessArticle
Sustainability 2016, 8(6), 555; doi:10.3390/su8060555

A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China

1
School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
2
School of Law, Guangxi Normal University, Guilin 541004, China
3
Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Academic Editor: Andrew Kusiak
Received: 12 April 2016 / Revised: 1 June 2016 / Accepted: 7 June 2016 / Published: 14 June 2016
(This article belongs to the Section Energy Sustainability)
View Full-Text   |   Download PDF [3870 KB, uploaded 14 June 2016]   |  

Abstract

Wind speed forecasting plays a key role in wind-related engineering studies and is important in the management of wind farms. Current forecasting models based on different optimization algorithms can be adapted to various wind speed time series data. However, these methodologies cannot aggregate different hybrid forecasting methods and take advantage of the component models. To avoid these limitations, we propose a novel combined forecasting model called SSA-PSO-DWCM, i.e., particle swarm optimization (PSO) determined weight coefficients model. This model consisted of three main steps: one is the decomposition of the original wind speed signals to discard the noise, the second is the parameter optimization of the forecasting method, and the last is the combination of different models in a nonlinear way. The proposed combined model is examined by forecasting the wind speed (10-min intervals) of wind turbine 5 located in the Penglai region of China. The simulations reveal that the proposed combined model demonstrates a more reliable forecast than the component forecasting engines and the traditional combined method, which is based on a linear method. View Full-Text
Keywords: sustainable energy; wind speed forecasting; optimization algorithm; combined model; weight coefficient optimization; de-noising procedure sustainable energy; wind speed forecasting; optimization algorithm; combined model; weight coefficient optimization; de-noising procedure
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, F.; Dong, Y.; Zhang, K. A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China. Sustainability 2016, 8, 555.

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