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)


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.


Building cooling load Short-term prediction BRNNs 


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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

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