Inside the walls of high-density residential energy savings
Previously I have noted a few key challenges in relation to achieving energy efficiency in a high-density residential context. If developers aren’t prioritising sustainability due to a perceived lack of market demand, and owners corporations’ focus is elsewhere, where else can efficiencies be gained? What about what happens “within the walls” of the apartments themselves?
While the individual savings might be small, the cumulative benefits might be significant. Just how significant is unclear, however. So it’s hard to judge just what sort of impact energy efficiency measures across a medium- or high-density residential complex would be. I did a bit of digging but couldn’t find readily available stats. Are savings in this context just going to be a “band-aid” solution? Or can it make a significant contribution?
Let’s assume, for a moment, that the cumulative effect is significant enough to warrant attention.
For residents, sometimes it can be difficult to even get information about usage at all — not all apartments have sub-metering (that is where each individual dwelling has its own meter), and fewer still have smart meters. Where they do, information about usage is still often only available in the form of the monthly or quarterly bill — long after it can be actioned or used to “troubleshoot” and identify which devices are consuming the most energy.
This is also potentially exacerbated by the owner/resident divide (noted in my previous post). Those that rent have even less opportunity to gain access to this information if it’s not already provided.
Thinking firstly about this fundamental requirement — data collection.
An owner might be able to place a real-time monitor at the meter level, but even for owners there are a number of potential barriers to doing so (including security/access to meters, the need for a qualified electrician, access to wifi or other mechanisms to get the information out of the device in utility rooms etc.).
These devices report the aggregate usage across all appliances etc. associated with the property. This in itself is useful, but can make it difficult to determine which appliances (incl. lighting etc.) are the “high energy consumers” in the home. It’s also difficult to compare whether an appliance that has a high current draw for short periods of time (say, for a kettle or microwave) compares with low energy consumers that are on for extended periods (e.g. a home entertainment unit). When in a “troubleshooting” or “analysis” mode of thinking (which is less prevalent than we might first think — more on that in a future post), this sort of information is critical for making informed judgements about where savings might be achieved.
There are some promising approaches that engage machine learning to determine individual appliance usage from an aggregate power source. Wattcost is a great (Australian-designed) example. Ecoisme is another. However, to my understanding, these too require access to the sub-meter, which is not always possible.
Residents could spend money, time and effort on devices like the Wemo, WattWatchers, or WattsClever. These devices allow monitoring at an individual power point and are not cost-effective for monitoring a variety of points/locations/appliances around the home. So this requires planning and a high degree of motivation to implement and use — moving the device from one power point to another, logging, analysing etc. And they are expensive (at one point the Wemo was retailing for around AUD$90 for a single point/unit.)
Not all such devices are “smart” or connected, either (that is, they only display information on the device itself, not transmitting/storing it separately), limiting opportunities for “real time” engagement and behaviour response. Lastly, some are also very bulky and therefore impractical in general use.
So, there are a number of barriers just to collecting the data. What about what happens if this data is collected?
A quick review of the systems available show a very strong bias towards “charts and graphs” as a means of displaying information. And outside of a few exciting concepts (like Artefacts’ Serenity “home OS with a heart”), they aren’t necessarily the most engaging tools to use. There doesn’t seem to be a huge amount of investment in user experience. At least there seems to be a strong move towards smartphone-based apps, or in-home displays, which in principle is much better than being tethered to a desktop or laptop computer.
I suspect this means that many household energy users are poorly motivated to modify their behaviour on the basis of the outputs of the data collected. There’s limited social engagement (e.g. with peers/neighbours etc.) which has been demonstrated as a key driver of sustainable behaviours in other contexts. Similarly, game dynamics don’t seem to play a significant role in what’s being provided.
Once armed with that information, the savings can be fairly minimal on an individual apartment level making it hard to justify the expense, time and effort on a pure “return on investment” (ROI) basis. And that’s just looking at the cost of the devices you need to troubleshoot and get visibility on consumption, let alone the costs of making changes, such as major appliance upgrades, on the basis of those findings.
So, how might we reduce the cost of collecting data “within the walls” of high-density residential? What alternate form factors — other than “charts and graphs” — might prove valuable in driving behaviour change? How can we make it as magnetising as browsing Pinterest or checking in on Strava or Fitbit? How can we encourage sharing of data from across different systems to be able to (collectively) evaluate the scale and scope of savings (and therefore impact) of individual systems? What mechanisms might be employed to reduce the cost of data collection devices? Could the new rules currently under consideration by the AEMC drive this type of efficiency measures, through quantified savings contributing to “infrastructure” investment?
Originally published at Zumio.