An accurate, light-weight wind speed predictor for renewable energy management systems

Saira Al-Zadjali, Ahmed Al Maashri*, Amer Al-Hinai, Sultan Al-Yahyai, Mostafa Bakhtvar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


This paper proposes an approach for accurate wind speed forecasting. While previous works have proposed approaches that have either underperformed in accuracy or were too computationally intensive, the work described in this paper was implemented using a computationally efficient model. This model provides wind speed nowcasting using a combination of perturbed observation ensemble networks and artificial neural networks. The model was validated and evaluated via simulation using data that were measured from wind masts. The simulation results show that the proposed model improved the normalized root mean square error by 20.9% compared to other contending approaches. In terms of prediction interval coverage probability, our proposed model shows a 17.8% improvement, all while using a smaller number of neural networks. Furthermore, the proposed model has an execution time that is one order of magnitude faster than other contenders.

Original languageEnglish
Article number4355
Issue number22
Publication statusPublished - Nov 15 2019


  • Ensemble artificial neural networks
  • Renewable energy
  • Wind speed nowcasting

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering


Dive into the research topics of 'An accurate, light-weight wind speed predictor for renewable energy management systems'. Together they form a unique fingerprint.

Cite this