Next Article in Journal
Review: Characterization and Modeling of the Mechanical Properties of Lithium-Ion Batteries
Previous Article in Journal
Stress Inversion of Coal with a Gas Drilling Borehole and the Law of Crack Propagation
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Energies 2017, 10(11), 1744; doi:10.3390/en10111744

Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network

Department of Electrical and Electronic Engineering, University Putra Malaysia, Selangor 43400, Malaysia
Faculty of Electronics Information Engineering, Huaiyin Institute of Technology, Huai’an 223003, China
Author to whom correspondence should be addressed.
Received: 20 September 2017 / Revised: 18 October 2017 / Accepted: 20 October 2017 / Published: 30 October 2017
View Full-Text   |   Download PDF [9411 KB, uploaded 31 October 2017]   |  


One of the most crucial prerequisites for effective wind power planning and operation in power systems is precise wind speed forecasting. Highly random fluctuations of wind influenced by the conditions of the atmosphere, weather and terrain result in difficulties of forecasting regardless of whether it is short-term or long-term. The current study has developed a method to model wind speed data predictions with dependence on seasonal wind variations over a particular time frame, usually a year, in the form of a Weibull distribution model with an artificial neural network (ANN). As a result, the essential dependencies between the wind speed and seasonal weather variation are exploited. The proposed model utilizes the ANN to predict the wind speed data, which has similar chronological and seasonal characteristics to the actual wind data. This model was applied to wind speed databases from selected sites in Malaysia, namely Mersing, Kudat, and Kuala Terengganu, to validate the proposed model. The results indicate that the proposed hybrid artificial neural network (HANN) model is capable of depicting the fluctuating wind speed during different seasons of the year at different locations. View Full-Text
Keywords: wind speed forecasting; artificial neural network; Weibull model; Malaysia wind speed forecasting; artificial neural network; Weibull model; Malaysia

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

Kadhem, A.A.; Wahab, N.I.A.; Aris, I.; Jasni, J.; Abdalla, A.N. Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network. Energies 2017, 10, 1744.

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



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