The brave new world of big data and analytics

Data analytics is relatively new to the HVAC industry, while companies strive to uptake these new technologies available, many opportunities still remain unfulfilled. Sean McGowan takes a deep dive into the relationship between HVAC and data analytics with Airmaster’s NSW state manager, Jason Harrison along with a number of other industry leaders.







Is the HVAC industry keeping up with advances in data analytics?


I believe that the HVAC industry was a first mover in the big data and data analytics space around six years ago. Fast forward to today and I believe we are now slightly behind the global trend of the use of big data.

The shift initially commenced when the HVAC industry was looking at ways to help gain visibility over the building/portfolio to help provide insight into what was occurring near real-time and provide actions that would help drive energy efficiency, improve tenant comfort and provide support to shake the way preventative maintenance was being performed.


Where are we keeping up, and where are we falling behind? Is it a technology or implementation issue?

The reason I believe that we are slightly behind the global trend is that data is everywhere and if we were only using it to analyse what was occurring within the building/portfolio then we would be ahead of the curve. However, within the HVAC industry, we need to combine the data with humans and many other stakeholders to provide outcomes to realise the value the data is providing.


How is traditional HVAC integrating with the data analytics field? Do demarcation lines exist between the two?


Data analytics is continuing to disrupt the way we are operating within the HVAC industry space. With skill shortages becoming ever more prominent and labour costs increasing, data is being used to assist with these two challenges.


I think there are two parts to data analytics – “just in time” maintenance and service and building performance and optimisation. I believe that these are separate functions when managing a building with the ultimate goal of operating in synergy. A building must be compliant, whereas the performance of a building can be relaxed at times, though I believe we should be striving for best in class performance at all times.

Is the Master Systems Integrator (MSI) a new role in the industry? What is their role and function?


We have always had a form of MSI within a building/portfolio in the HVAC industry - the enterprise Building Management System (BMS).

However, with that said the role description has changed dramatically as open platforms and technologies are becoming the norm within buildings. Clients want freedom of choice when it comes to choosing what equipment, product or tenant engagement system being included into a building design. The primary role of the MSI is to develop a single pane of glass with workflow management capability that feeds all the data points within the building into a normalised data lake.


The information from a building is rich and many stakeholders need the data to complete other responsibilities (i.e. tenant after hours billing, IEQ Rating, lease requirement confirmation etcetera).


Who performs this role – is it an individual or contracting firm? And where do they sit in the FM or project hierarchy?


In my experience the MSI typically sits at the top of the project hierarchy, due to the role it is playing within the building ecosystem. It is centralising all the downstream data to provide that single pane of glass and is the golden key to provide an experience for the building. The experience comes in many forms - tenant engagement, maintenance/service contractor insight, facility manager response time etcetera.


With ever-increasing sources (and amounts) of data, what considerations need to be given to the cloud environment’s role in storing data, and issues of data ownership and access?


Without smart consideration of how we should be storing and indexing the data we are capturing from each building, I fear we are going to end up back in the good old days of having a filing cabinet.


I remember using a filing cabinet and storing all files, documents etcetera within each drawer inside a folder with a nice label on it. The issue was when I wanted to find a particular document it often took some time. I had to remember how it was named and what folder it was placed under. Then it became difficult for someone else to locate that file if they didn’t understand my filing structure. So, with that said, I believe it is important to have a consistent tagging, naming and folder structure for all data that is captured so that when someone needs that historical data it is easily found.


To this end, what are the security issues surrounding the capture, storage and analysis of this data?


What type of device is being added on site? Is it secure? Is the data going to be mixed with other sources of data? It is easily extracted if need be? Is it going to be analysed to show holes in my portfolio’s operation, or embarrass the current state of the building?

These are the questions that need to be asked when considering the security of capture, stored and analysed data.


As we open up more channels into any given building, it does create challenges with securing and having control of where your data is going to. How often do you walk around a site and see multiple 4G routers, computers etcetera and no one seems to know what they are being used for?


How is machine learning impacting HVAC, and what progress is being made in this area?


With equipment and control systems becoming smarter, machine learning is becoming more prominent within the HVAC world. It requires a stronger processor to be able to perform the required functions to automatically learn from the operation and business-as-usual to work through ways to improve the performance.


I think this term relates to the HVAC world in regard to control loops, automatic tuning to reduce short cycling and overshooting etcetera. As the quantity of equipment being installed within a building increases, machine learning is able to assist with the tuning and ongoing optimising of the systems to trigger optimal control at all times without the need of engineers or technicians tuning individual control loops.


This is an excerpt from the article ‘Analyse this' that appeared in AIRAH’s Ecolibrium. To further explore the relationship between big data analytics and HVAC download the full article here.

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