Advertisement

One-Day Building Cooling Load Prediction Based on Bidirectional Recurrent Neural Network

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

Short-term building cooling load prediction is very important for building energy management tasks. Traditional way relies on physical principles. Due to the nonlinearity of the features of the data, it is a challenge for prediction. This work applies the Bidirectional Recurrent Neural Network (BRNNs) in prediction of 24-h ahead building cooling load profiles. The results show that BRNNs have good performance in prediction on building cooling load prediction. The mode can predict the building cooling load profiles effectively.

Keywords

Building cooling load Short-term prediction BRNNs 

References

  1. 1.
    Ben-Nakhi, A.E., Mahmoud, M.A.: Cooling load prediction for buildings using general regression neural networks. Energy Convers. Manag. 45, 2127–2141 (2004)CrossRefGoogle Scholar
  2. 2.
    Hou, Z., Lian, Z., et al.: Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique. Appl. Energy 83, 1033–1046 (2006)CrossRefGoogle Scholar
  3. 3.
    Kwok, S.S.K., Yuen, R.K.K., et al.: An intelligent approach to assessing the effect of building occupancy on building cooling load prediction. Build. Environ. 46, 1681–1690 (2011)CrossRefGoogle Scholar
  4. 4.
    Fan, C., Xiao, F., et al.: A short-term building cooling load prediction method using deep learning algorithms. Appl. Energy 195, 222–233 (2017)CrossRefGoogle Scholar
  5. 5.
    Sun, Y., Wang, S., et al.: Development and validation of a simplified online cooling load prediction strategy for a super high-rise building in Hong Kong. Energy Convers. Manag. 68, 20–27 (2013)CrossRefGoogle Scholar
  6. 6.
    Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. Signal Process. 45, 2673–2681 (1997)Google Scholar
  7. 7.
    Graves, A., Mohamed, A., et al.: Speech Recognition with Deep Recurrent Neural Networks. https://arxiv.org/abs/1303.5778
  8. 8.
    Su, Y., Huang, Y., et al.: On Extended Long Short-term Memory and Dependent Bidirectional Recurrent Neural Network. https://arxiv.org/abs/1803.01686
  9. 9.
    Reddy, T.A., Maor, I., et al.: Calibrating detailed building energy simulation programs with measured data-Part II: application to three case study office buildings (RP-1051). HVAC&R 13, 221–241 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Fujian Province University Key Laboratory of New Energy and Energy-Saving in BuildingFujian University of TechnologyFuzhouChina

Personalised recommendations