A Multi-component Chiller Status Prediction Method Using E-LSTM

  • Chenrui Xu
  • Kebin JiaEmail author
  • Zhuozheng Wang
  • Ye Yuan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1107)


With the development of intelligent information technology, chiller system composed by different interrelated components has been widely used in industry to cool products and machinery. Predicting the status of chiller system can effectively monitor energy consumption and reduce accident rate. In this paper, we propose an improved LSTM (E-LSTM) method to predict multi-component chiller status. Firstly, a mean filter method is used to preprocess the original multi-component time series data. Secondly, we adopt E-LSTM to extract hidden features from seven component-wise inputs, consisting of outdoor temperature, wet bulb temperature, outdoor enthalpy, L1 & L2 differential pressures, total power, and IT load. Finally, the learned hidden features are fed into a regression layer to predict three future chiller statuses, including PUE, cold source power, and refrigeration secondary pump power, respectively. Experimental results show that the proposed method outperforms the baselines, such as linear regression, SVR, RNN, GRU and LSTM, and hence demonstrate the effectiveness of our proposed method in the task of chiller status prediction.



This paper is supported by the Project for the National Natural Science Foundation of China under Grants No. 61672064, Advanced Information Network Beijing Laboratory (040000546618017).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Chenrui Xu
    • 1
    • 2
    • 3
  • Kebin Jia
    • 1
    • 2
    • 3
    Email author
  • Zhuozheng Wang
    • 1
    • 2
    • 3
  • Ye Yuan
    • 1
    • 2
    • 3
  1. 1.Faculty of Information TechnologyBeijing University of TechnologyBeijingChina
  2. 2.Beijing Laboratory of Advanced Information NetworksBeijingChina
  3. 3.Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing University of TechnologyBeijingChina

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