The comfort of tenants and customers in shopping centres is a critical factor in their success.
When a person enters the centre, the temperature needs to be just right, allowing for the external conditions and thermal load created by people and equipment in the building. With the cost of energy high, both in terms of dollars and carbon footprint, better managing the operation of chiller units – large machines that create cold water that is pumped around the building to cool the air – is a critical factor in better managing costs and emissions.
Chillers are large and expensive. As well as the initial capital cost, operations and maintenance consume a significant portion of a centre’s operating budget. So, it makes sense to use data to optimise everything from the acquisition of a chiller through to its operation and maintenance. Any inefficiency has a large energy and carbon impact.
The use of real-time data to make decisions about minute-by-minute adjustments to system operations isn’t new. HVAC (heating, ventilation and air-conditioning) systems and chillers use a lot of data in real-time to decide what to do. But once the data is used it’s usually discarded.
The challenge is to collect that data and use it, with analytics tools, to make longitudinal observations and decisions.
As the data is rarely in a standardised form that’s easily used there’s no real way to understand how a device is working across time or even how two identical devices are operating in different environments. For example, if the same chiller unit is installed in a building in Darwin and another in Hobart, there’s no way to tell whether either unit is operating optimally. Or even how either is performing over a long period of time.
That lack of data means purchasers are relying on manufacturer specification sheets to make major capital investment decisions. Chiller specifications use an efficiency curve to represent how that chiller will work under certain conditions. Those numbers were created almost three decades ago using weather data from the USA, gathered from the late 1960s to the 1990s and relatively simplistic usage models.
What we’ve found is that real-world usage doesn’t match the efficiency models that manufacturers publish. So, buying decisions were based on a limited view of how chillers are used. But now, the data from these large machines can be accessed and used to give a clearer picture of their operation.
When a purchaser chooses a chiller for their building, they can make a better choice based on their specific use-case because the data from a broad number of different chillers operating in different places is accessible. The data tells us which chillers work more efficiently for long periods under a low load or which are better in humid environments or which are optimal for operation under a sustained heavy load.
As well as helping people make better purchasing decisions, the data can also help manufacturers design chillers that are better suited to specific applications.
This is why the development of systems for collecting, standardising and analysing data is so important. Until now, that data was only available to the chiller manufacturers, giving owners limited visibility. As a result, they’ve been unable to understand how the chiller’s operation is impacting power bills and carbon footprint.
Once a chiller is in operation, being able to access its operational data allows you to better understand if the equipment is meeting the expectations set by the manufacturer. For example, one of the key metrics that’s considered when buying a chiller is its capacity to keep the building in a comfortable thermal band during a period of peak load. Having real-world data that confirms that and gives you information about the unit’s efficiency at that load, as well as when it operates at a lower intensity, is important. With building operators concerned about the cost of energy and their environmental impact, these are extremely important considerations.
Better access to data for the owners of chiller units is also critical for transparency of how units are running. Although building owners are responsible for paying the energy bill, they have not, historically, had access to the data coming from their chillers. By accessing that data, they can see clearly when their chillers are costing them the most to operate.
As well as seeing what is happening, access to data allows the owners of chiller units to detect potential faults. In many cases, there’s no single data point that can be used to diagnose a fault easily. But by collecting, aggregating and analysing data from different parts of the HVAC system, it’s possible to detect hardware faults or opportunities fix bad control strategies in real-time to make the system operate more efficiently, and therefore, more cost-effectively and with lower carbon emissions.
With faults and potential optimisations easier to detect, costly maintenance contracts can be reduced as well. If technicians can see faults easily through a management dashboard accessing thousands of data points rather than physically having to inspect and trace an issue, they can resolve problems faster.
The cheapest energy you can buy is the energy you don’t use. The cost of operating chiller units is shopping centres and large buildings is significant but the intelligent use of data, from the initial purchasing decision through to operations and management can make a major difference to initial and ongoing expenditure of chiller units.
Written by Dr. Troy Wilson, Chief Data Scientist.