Data driven chiller plant energy optimization with domain knowledge

Hoang Dung Vu, Kok Soon Chai, Bryan Keating, Nurislam Tursynbek, Boyan Xu, Kaige Yang, Xiaoyan Yang, Zhenjie Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


Refrigeration and chiller optimization is an important and well studied topic in mechanical engineering, mostly taking advantage of physical models, designed on top of oversimplified assumptions, over the equipments. Conventional optimization techniques using physical models make decisions of online parameter tuning, based on very limited information of hardware specifications and external conditions, e.g., outdoor weather. In recent years, new generation of sensors is becoming essential part of new chiller plants, for the first time allowing the system administrators to continuously monitor the running status of all equipments in a timely and accurate way. The explosive growth of data owing to databases, driven by the increasing analytical power by machine learning and data mining, unveils new possibilities of data-driven approaches for real-time chiller plant optimization. This paper presents our research and industrial experience on the adoption of data models and optimizations on chiller plant and discusses the lessons learnt from our practice on real world plants. Instead of employing complex machine learning models, we emphasize the incorporation of appropriate domain knowledge into data analysis tools, which turns out to be the key performance improver over state-of-the-art deep learning techniques by a significant margin. Our empirical evaluation on a real world chiller plant achieves savings by more than 7% on daily power consumption.

Original languageEnglish
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages9
VolumePart F131841
ISBN (Electronic)9781450349185
Publication statusPublished - Nov 6 2017
Externally publishedYes
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore
Duration: Nov 6 2017Nov 10 2017


Conference26th ACM International Conference on Information and Knowledge Management, CIKM 2017

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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