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

Chun-Wei Chen, Chun-Chang Li and Chen-Yu Lin
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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

Energies, 2020, vol. 13, issue 17, 1-20

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.

Keywords: energy baselines; machine learning; clustering (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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