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Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms

Author

Listed:
  • Elsa Chaerun Nisa

    (Refrigeration, Air Conditioning and Energy Engineering Department, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

  • Yean-Der Kuan

    (Refrigeration, Air Conditioning and Energy Engineering Department, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

Abstract

Over the last few decades, total energy consumption has increased while energy resources remain limited. Energy demand management is crucial for this reason. To solve this problem, predicting and forecasting water-cooled chiller power consumption using machine learning and deep learning are presented. The prediction models adopted are thermodynamic model and multi-layer perceptron (MLP), while the time-series forecasting models adopted are MLP, one-dimensional convolutional neural network (1D-CNN), and long short-term memory (LSTM). Each group of models is compared. The best model in each group is then selected for implementation. The data were collected every minute from an academic building at one of the universities in Taiwan. The experimental result demonstrates that the best prediction model is the MLP with 0.971 of determination (R 2 ), 0.743 kW of mean absolute error (MAE), and 1.157 kW of root mean square error (RMSE). The time-series forecasting model trained every day for three consecutive days using new data to forecast the next minute of power consumption. The best time-series forecasting model is LSTM with 0.994 of R 2 , 0.233 kW of MAE, and 1.415 kW of RMSE. The models selected for both MLP and LSTM indicated very close predictive and forecasting values to the actual value.

Suggested Citation

  • Elsa Chaerun Nisa & Yean-Der Kuan, 2021. "Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:744-:d:480093
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    References listed on IDEAS

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    1. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    2. Jonghyuk Kim & Hyunwoo Hwangbo, 2019. "Real-Time Early Warning System for Sustainable and Intelligent Plastic Film Manufacturing," Sustainability, MDPI, vol. 11(5), pages 1-13, March.
    3. Wang, Jian Qi & Du, Yu & Wang, Jing, 2020. "LSTM based long-term energy consumption prediction with periodicity," Energy, Elsevier, vol. 197(C).
    4. Zhang, Guoqiang & Guo, Jifeng, 2020. "A novel ensemble method for hourly residential electricity consumption forecasting by imaging time series," Energy, Elsevier, vol. 203(C).
    5. Hu, Huanling & Wang, Lin & Peng, Lu & Zeng, Yu-Rong, 2020. "Effective energy consumption forecasting using enhanced bagged echo state network," Energy, Elsevier, vol. 193(C).
    6. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    7. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
    8. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
    9. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu, 2020. "Minutely Active Power Forecasting Models Using Neural Networks," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
    10. Mansu Kim & Sungwon Jung & Joo-won Kang, 2019. "Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea," Sustainability, MDPI, vol. 12(1), pages 1-28, December.
    11. Le Cam, M. & Zmeureanu, R. & Daoud, A., 2017. "Cascade-based short-term forecasting method of the electric demand of HVAC system," Energy, Elsevier, vol. 119(C), pages 1098-1107.
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    Cited by:

    1. Mario Pérez-Gomariz & Antonio López-Gómez & Fernando Cerdán-Cartagena, 2023. "Artificial Neural Networks as Artificial Intelligence Technique for Energy Saving in Refrigeration Systems—A Review," Clean Technol., MDPI, vol. 5(1), pages 1-21, January.
    2. Elsa Chaerun Nisa & Yean-Der Kuan & Chin-Chang Lai, 2021. "Chiller Optimization Using Data Mining Based on Prediction Model, Clustering and Association Rule Mining," Energies, MDPI, vol. 14(20), pages 1-14, October.

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