Several times a month, some breathless enterprise software sales rep calls to acquaint us with the compelling advantage of the newer, greater enterprise software suite. Algorithms! AI! Machine Learning! Massive productivity gains! Just connect it to your existing data streams and…
"Meters measure, and measurements mean accountability. Accountability has outcomes. If those outcomes are the ones we want, then we must start with how we measure, and we must care"
And do what exactly?
That’s where these conversations end. What data streams? Facility meters vary by commodity measured, collection frequency, how data is transferred, and so forth; but they work. Many utilities offer web pages to display date and trends. Often, utilities allow downloading. Customers can act, or not, based on that. “THAT” is the problem. “THAT” is all there is. Customers can project, interpolate, crosstab, analyze and introduce error in all manner of creative ways… and that assumes the meter measures what they think it does. The same applies to energy management systems and the rest.
Take pulse factors; … PLEASE!
Meters measure, and measurements mean accountability. Accountability has outcomes. If those outcomes are the ones we want, then we must start with how we measure, and we must care. Overall, electricity is very inexpensive compared to the value it adds. If there’s a little slop in the process, repairing that doesn’t justify the cost unless other needs are not met.
The recent New York City carbon tax for large facilities brings this into focus. The premise is simple. Building owners (and by extension everyone) will pay a tax based on the fossil fuel mix on the grid at the time of energy use. That comes on top of requirements for comfort, convenience, and safety, frequently enshrined in local codes.
Add a carbon tax, and suddenly slop matters. Taxes are different. History teaches they will increase. As they do, taxpayers will be much more interested in ways to cut that bill. Those taxes may become a greater motive for action by energy users than the cost of the electricity they use.
None of the AI, machine learning, and algorithms available will work with data they can’t access. That data comes from meters, and those meters are not where they need to be. Consider this recent headline from the Popular Science web site: “New York City passed a carbon tax for large structures. ‘Mass retrofitting’ is sure to follow.” Really? Based on what? That service meter may show a post-retrofit energy reduction. It won’t reveal whether codes are violated, or why tenants aren’t renewing.
Without better metering, here is what will happen: the landlord will pass along the carbon tax to the tenant. Like energy efficiency, the tenant won’t make a major capital investment in a building he does not own. The tenant moves to a lower cost location or closes for economic reasons.
The owner must decide if improvements are justified by a lower tax. What about data centers? Often perfect tenants, data centers use a lot if energy, but they can move with a change in router instructions. Space demand drops, then rents. The building becomes uneconomic. Maybe a foreclosure, maybe a rehab result. It’s hard to know.
In a different future, metering is cheap. There are cheap metering chips in everything. Algorithms use new and granular data, turning it into information. Developers offer apps that convert information to knowledge which building managers convert to strategies. Those strategies have options, and each option has a value. The building owner knows what to do based on best practices. The conflict between energy reductions and building code requirements is averted. There is a new scenario based on a system that meters everything. Blockchain is very promising, and there are such services available commercially today. Also available:
Thermostats that manage “stack effect” (wind currents resulting from temperature differences) to reduce convection losses. Buildings with multiple chillers coordinate them so all run in the “sweet spot”, not overloading one while others sit idle. Onsite power sources are routed to their best uses. For example, rooftop solar powers a DC circuit for native DC equipment (LEDs, computer processors) for better efficiency.
Tokenized data feeds a blockchain system that alerts the manager to issues or counters an overzealous carbon tax assessment. Tenants have access to that system which generates confidence. Efficiency has and prudent investments allow new styles of leases be based specs like temperature, lumens, operating hours, etc. Exceptions would result in upcharges. The responsibility for capital investments reverts to the building owner.
The key pre-requisite is meters that are cheap, reliable, and everywhere. That is the missing link.
Everything else exists and is in use today. Perhaps the transformed utility can become the driver, changing the definition of “behind the meter” from the building to the device. That is what a trusted energy advisor means.