A Survey of Deep Learning Techniques: Application in Wind and Solar Energy Resources

Shahab Shamshirband, Timon Rabczuk, Kwok Wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

194 Citations (Scopus)


Nowadays, learning-based modeling system is adopted to establish an accurate prediction model for renewable energy resources. Computational Intelligence (CI) methods have become significant tools in production and optimization of renewable energies. The complexity of this type of energy lies in its coverage of large volumes of data and variables which have to be analyzed carefully. The present study discusses different types of Deep Learning (DL) algorithms applied in the field of solar and wind energy resources and evaluates their performance through a novel taxonomy. It also presents a comprehensive state-of-the-art of the literature leading to an assessment and performance evaluation of DL techniques as well as a discussion about major challenges and opportunities for comprehensive research. Based on results, differences on accuracy, robustness, precision values as well as the generalization ability are the most common challenges for the employment of DL techniques. In case of big dataset, the performance of DL techniques is significantly higher than that for other CI techniques. However, using and developing hybrid DL techniques with other optimization techniques in order to improve and optimize the structure of the techniques is preferably emphasized. In all cases, hybrid networks have better performance compared with single networks, because hybrid techniques take the advantages of two or more methods for preparing an accurate prediction. It is recommended to use hybrid methods in DL techniques.

Original languageEnglish
Article number8894383
Pages (from-to)164650-164666
Number of pages17
JournalIEEE Access
Publication statusPublished - 1 Jan 2019


  • Big dataset
  • deep learning
  • modeling
  • optimizing
  • solar energy
  • wind energy

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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