Skip to main content
SpringerLink
Log in

Hybrid teaching—learning artificial neural network for city-level electrical load prediction

  • Letter
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Wang Z, Srinivasan R S. A review of artificial intelligence based building energy use prediction: contrasting the capabilities of single and ensemble prediction models. Renew Sustain Energy Rev, 2016, 75: 796–808

    Article  Google Scholar 

  2. Li K, Hu C, Liu G, et al. Building’s electricity consumption prediction using optimized artificial neural networks and principal component analysis. Energy Buildings, 2015, 108: 106–113

    Article  Google Scholar 

  3. Rao R V, Savsani V J, Vakharia D P. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput-Aided Des, 2011, 43: 303–315

    Article  Google Scholar 

  4. Chen X, Yu K, Du W, et al. Parameters identification of solar cell models using generalized oppositional teaching learning based optimization. Energy, 2016, 99: 170–180

    Article  Google Scholar 

  5. Pang B, Liu M, Zhang X, et al. A novel approach framework based on statistics for reconstruction and heartrate estimation from PPG with heavy motion artifacts. Sci China Inf Sci, 2018, 61: 022312

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61873114, 51705206), China Postdoctoral Science Foundation (Grant Nos. 2018T110457, 2016M601741), and Project Foundation for Priority Academic Program Development of Jiangsu Higher Education Institutions.

Author information

Authors and Affiliations

  1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China

    Kangji Li, Xianming Xie, Wenping Xue & Xu Chen

Authors
  1. Kangji Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xianming Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenping Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kangji Li.

Supplementary File

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, K., Xie, X., Xue, W. et al. Hybrid teaching—learning artificial neural network for city-level electrical load prediction. Sci. China Inf. Sci. 63, 159204 (2020). https://doi.org/10.1007/s11432-018-9594-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-018-9594-9

Search

Navigation