ASHRAE Journal - August 2024 - 32
TECHNICAL FEATURE
Leveraging Machine
Learning to Optimize
Chiller Plant Controls
BY MICHAEL BERGER, MEMBER ASHRAE
Machine learning (ML) is one of the defining technologies of our era, yet its
application to HVAC controls is still in its infancy. At the same time, human-induced
climate change is well underway, and it is becoming increasingly important to
optimize HVAC operations to reduce energy consumption. In this article, we will
explore how ML and optimal control methods can be used for this purpose and
leverage the expert knowledge of engineers to deliver tangible value.
Terms such as artificial intelligence (AI) or ML have
become a mainstay in the daily news. However, these
technologies are still seldom applied in practice within
buildings and HVAC systems-and in particular when it
comes to improve efficiency by optimizing chiller plant
controls.1,2 Published research has not yet translated
into widespread acceptance into the building stock. Are
these technologies simply not a good fit in our field? Or
are there challenges to be identified and addressed?
One common question when considering applying AI
algorithms to HVAC controls systems is whether they
will improve the operation beyond simply replicating
the strategies of a well-designed conventional building
automation system (BAS). There has been significant
work in the industry recently to design and document
best-practice sequences of operations, most notably
with ASHRAE Guideline 36-2021.3 In some cases,
the sequence may already be at or near optimality.
An AI system that would take a global approach and
treat the whole system as a black box, with limited
expert knowledge of mechanical systems, may not
necessarily deliver energy savings, while potentially
decreasing transparency for facility managers. These
considerations may discourage stakeholders from
deploying AI systems in their buildings.
A more nuanced approach may be to attempt to
identify specific challenges that are not straightforward
to solve without predictive techniques and apply ML
methods in a more targeted manner. A number of these
challenges have been identified by collaborating with
experienced mechanical engineers and HVAC controls
specialists. First, in the chiller plant, there are variables
that result in trade-offs between equipment power
use. For example, varying the condenser pump speed
Michael Berger is the is the head of research & development at Conserve It in Melbourne, Australia.
32
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ASHRAE Journal - August 2024
Table of Contents for the Digital Edition of ASHRAE Journal - August 2024
Contents
ASHRAE Journal - August 2024 - Intro
ASHRAE Journal - August 2024 - Cover1
ASHRAE Journal - August 2024 - Cover2
ASHRAE Journal - August 2024 - 1
ASHRAE Journal - August 2024 - Contents
ASHRAE Journal - August 2024 - 3
ASHRAE Journal - August 2024 - 4
ASHRAE Journal - August 2024 - 5
ASHRAE Journal - August 2024 - 6
ASHRAE Journal - August 2024 - 7
ASHRAE Journal - August 2024 - 8
ASHRAE Journal - August 2024 - 9
ASHRAE Journal - August 2024 - 10
ASHRAE Journal - August 2024 - 11
ASHRAE Journal - August 2024 - 12
ASHRAE Journal - August 2024 - 13
ASHRAE Journal - August 2024 - 14
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ASHRAE Journal - August 2024 - 20
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ASHRAE Journal - August 2024 - 32
ASHRAE Journal - August 2024 - 33
ASHRAE Journal - August 2024 - 34
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ASHRAE Journal - August 2024 - Cover3
ASHRAE Journal - August 2024 - Cover4
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