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Combine Clustering and Machine Learning for Enhancing the Efficiency of Energy Baseline of Chiller System

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  • Chun-Wei Chen

    (Intelligent Machining Division, Taiwan Instrument Research Institute, NARL, Hsinchu City 300, Taiwan)

  • Chun-Chang Li

    (Intelligent Machining Division, Taiwan Instrument Research Institute, NARL, Hsinchu City 300, Taiwan)

  • Chen-Yu Lin

    (Intelligent Machining Division, Taiwan Instrument Research Institute, NARL, Hsinchu City 300, Taiwan)

Abstract

Energy baseline is an important method for measuring the energy-saving benefits of chiller system, and the benefits can be calculated by comparing prediction models and actual results. Currently, machine learning is often adopted as a prediction model for energy baselines. Common models include regression, ensemble learning, and deep learning models. In this study, we first reviewed several machine learning algorithms, which were used to establish prediction models. Then, the concept of clustering to preprocess chiller data was adopted. Data mining, K-means clustering, and gap statistic were used to successfully identify the critical variables to cluster chiller modes. Applying these key variables effectively enhanced the quality of the chiller data, and combining the clustering results and the machine learning model effectively improved the prediction accuracy of the model and the reliability of the energy baselines.

Suggested Citation

  • Chun-Wei Chen & Chun-Chang Li & Chen-Yu Lin, 2020. "Combine Clustering and Machine Learning for Enhancing the Efficiency of Energy Baseline of Chiller System," Energies, MDPI, vol. 13(17), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4368-:d:403420
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    References listed on IDEAS

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    1. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    2. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm," Energies, MDPI, vol. 12(15), pages 1-13, July.
    3. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2020. "Building thermal load prediction through shallow machine learning and deep learning," Applied Energy, Elsevier, vol. 263(C).
    4. Sungwoo Park & Jihoon Moon & Seungwon Jung & Seungmin Rho & Sung Wook Baik & Eenjun Hwang, 2020. "A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling," Energies, MDPI, vol. 13(2), pages 1-23, January.
    5. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
    6. Melnykov, Igor & Melnykov, Volodymyr, 2014. "On K-means algorithm with the use of Mahalanobis distances," Statistics & Probability Letters, Elsevier, vol. 84(C), pages 88-95.
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    Cited by:

    1. Dirk Deschrijver, 2021. "Special Issue: “Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization”," Energies, MDPI, vol. 14(6), pages 1-3, March.
    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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