Skip to main content
SpringerLink
Log in

Short-Term Forecasting and Application About Indoor Cooling Load Based on EDA-PSO-BP Algorithm

  • Conference paper
  • First Online:
Web Technologies and Applications (APWeb 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9865))

Included in the following conference series:

  • 966 Accesses

Abstract

In order to improve the precision of cooling load prediction, the authors of this essay proposes neural network model based on EDA-PSO-BP algorithm. We used PSO optimization algorithm combined with BP neural network to do cooling load prediction experiments of indoor sample data of a building. The results showed that compared with other three kinds of prediction algorithms, the error of this algorithm is minimum and its running speed is the fastest.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gibson, G.L., Kcraft, T.T.: Electric demand prediction using artificial neural network technology. ASHREA J. 3, 60–68 (1993)

    Google Scholar 

  2. Yalcintas, M., Akkurt, S.: Artificial neural networks applications in building energy predictions and a case study for tropical climates. Int. J. Energy Res. 29(10), 891–901 (2005)

    Article  Google Scholar 

  3. Kandel, E.R., Schwarts, J.: Principles of Neural Science, pp. 1–49. Elsevier, Amsterdam (1985)

    Google Scholar 

  4. Lippmann, R.P.: An introduction to computing with neural nets. IEEE ASSP Mag., 12–35 (1987)

    Google Scholar 

  5. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 34, issue 3, pp. 103–107 (1995)

    Google Scholar 

  6. Xiao, N., Lei, Z., Bo, Z., et al.: PSO grey model to power load forecasting. China Manag. Sci. 15(1), 69–73 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Architectural Design Institute, South China University of Technology, Guang Zhou, 510640, Guang Dong, China

    ZhiWei Huang

  2. School of Foreign Language, South China University of Technology, Guang Zhou, 510640, Guang Dong, China

    Li Yan

  3. School of Software, South China University of Technology, Guang Zhou, 510800, Guang Dong, China

    XinYi Peng

  4. School of Computing, South China University of Technology, Guang Zhou, 510800, Guang Dong, China

    Jia Tan

Authors
  1. ZhiWei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. XinYi Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jia Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to ZhiWei Huang .

Editor information

Editors and Affiliations

  1. University of Tsukuba , Tsukuba, Japan

    Atsuyuki Morishima

  2. East China Normal University , Shanghai, China

    Rong Zhang

  3. University of New South Wales , Sydney, New South Wales, Australia

    Wenjie Zhang

  4. The University of New South Wales, School of Computer Science and Engineering, Sydney, Australia

    Lijun Chang

  5. Advanced Digital Sciences Center, Illinois at Singapore Pte Ltd, Advanced Digital Sciences Center, Fusionopolies Way, Singapore

    Tom Z. J Fu

  6. Hyundai Motor Tower, Pivotal Inc 15/F, Hyundai Motor Tower, Chaoyang, China

    Kuien Liu

  7. Illinois at Singapore Pte Ltd, Advanced Digital Sciences Center , Singapore, Singapore

    Xiaoyan Yang

  8. South China Normal University, School of Computer Science, Guangzhou, China

    Jia Zhu

  9. Department of Computer Science, Hong Kong Baptist University Department of Computer Science, Kowloon Tong, Hong Kong

    Zhiwei Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Huang, Z., Yan, L., Peng, X., Tan, J. (2016). Short-Term Forecasting and Application About Indoor Cooling Load Based on EDA-PSO-BP Algorithm. In: Morishima, A., et al. Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9865. Springer, Cham. https://doi.org/10.1007/978-3-319-45835-9_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-45835-9_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45834-2

  • Online ISBN: 978-3-319-45835-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation