BTL Mark: Resolve interoperability issues & increase buyer confidence
Donovan is the vice president of product at Aquicore, a leading
commercial real estate
software company with an all-in-one
operations and management platform. In this role,
Donovan drives the development,
maintenance and implementation of new features and
enhancements to the Aquicore
platform. As an accomplished software engineer, Donovan
specializes in creating and
implementing web and mobile applications, building cloud-native
architectures as well as optimizing
enterprise systems and IoT software.
Sinclair: What is
Diagnostics is a machine-learning feature that detects usage anomalies
from a building’s energy curve and alerts building staff of potential
building operations problems, and their estimated financial impact, in
Sinclair: Why did Aquicore decide to develop and launch this technology?
Donovan: Today, everyone is expected to do more
with less and property teams don’t have time to conduct massive data
analysis. Therefore, Aquicore decided to use machine learning to
synthesize years of engineering expertise and apply it to real-time
sensor data, creating a model that predicts and suggests optimal ways
for users to operate their facilities. The industry has been yearning
for more intuitive tools that save time and don’t require data
expertise to help them better manage and operate their buildings
Sinclair: Who was this tool designed for?
Donovan: The tool was
developed for both building engineers and property owners/managers. For
building engineers, the tool identifies possible issues that could
impact building operations, such as late shutdowns or equipment cycling
overnight, which are often overlooked without quarterly or weekly
human-intensive energy reviews. For property managers, Automatic
Diagnostics calculates the potential costs associated with a buildings’
operations and produces a financial impact report for faster, more
Sinclair: What type of data does Automatic Diagnostics use?
Donovan: Aquicore uses
decades of crowdsourced knowledge from building engineers and its own
sensor data to calculate an expected baseline of ‘normal behavior’ for
each building. Specifically, Aquicore’s algorithm combines historical
energy and weather data with operational data to detect anomalies in
It’s important to note that Aquicore’s Automatic Diagnostics tool learns from each individual building and provides insights based on everyday performance. User feedback is critical in training the algorithm to make more informed, accurate predictions in the future. After a reasonable number of new data points have been sourced and analyzed, predictions are then modified based on its findings.
Sinclair: Is automatic diagnostics customized to each building?
Donovan: No two
buildings are the same, and therefore, Aquicore’s automatic diagnostics
algorithm is not a one size fits all equation. Every building has its
own unique set of building performance data and patterns of “normal
behavior.” Aquicore believes it is critical to understand each
buildings’ unique patterns and not simply rely on industry assumptions
for how a building should be performing. For this reason, Automatic
Diagnostics comes equipped with machine learning capabilities.
Sinclair: What are the benefits of using Automatic Diagnostics?
Aquicore’s depository of building data, information can be translated
into something truly concrete: dollars and cents. Automatic Diagnostics
presents potential building operations issues in a meaningful way such
that time-crunched building engineers and property managers can
instantly take action, resulting in lower operational costs, increased
energy efficiency and a boost in overall asset value and performance
across a portfolio. Aquicore’s Automatic Diagnostics tool offers an
enhanced user engagement portal and activity feed with actionable data
and real-time reporting for commercial real estate stakeholders.
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