EMAIL INTERVIEW – Mike Donovan and Ken Sinclair

Sinclair:  What is Automatic Diagnostics?

Donovan:  Automatic 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 real-time.

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 efficiently.

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 informed decision-making.

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 energy consumption.

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?

Donovan:  Leveraging 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.