  Refrigeration and chiller optimization is an important and well studied topic
in mechanical engineering, mostly taking advantage of physical models, designed
on top of over-simplified 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
flowing 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.
