Experiences / Energy Management
Energy & Demand Response
We were saving energy before we were saving lives.
Energy management is where Care Daily started. We have run residential demand response at scale, published the results, and built the tools that make a thermostat behave intelligently because the platform actually understands whether anyone is home.
Why most programs underperform
The constraint is not the hardware. It is whether anyone participates.
Utilities and operators have spent two decades installing better equipment and getting modest results. The reason is unglamorous: the best energy-saving measure saves nothing if the household ignores it, and most programs ask people to do work they will not do twice.
We studied this directly, with researchers at the University of Houston, across a twelve-week program in Oahu. Of 740 enrolled households, the analyzed cohort cut bills by about 2.83%, in line with comparable programs. The finding that mattered was different: designing for simplicity pushed the participation rate to 35%, several times what these programs usually achieve. Engagement, not equipment, is the lever.
What makes ours different
A thermostat that knows whether anyone is actually home.
Most energy platforms control devices on a schedule and hope the schedule matches the household. Care Daily already senses occupancy, sleep and daily routine for other reasons, which means the same signals can drive far better decisions about comfort and consumption.
Thermostats
We connect to a wide range of thermostats and make them considerably smarter, because setback decisions are driven by real occupancy rather than a guess about when people leave.
Hot water heaters
One of the largest loads in a home and one of the most shiftable, since nobody notices when the water was heated, only that it is hot.
Pool pumps
Large, entirely deferrable, and invisible to the household when moved off peak.
Lighting
Presence-aware lighting that reduces consumption while improving how a space feels, rather than trading one against the other.
Per-appliance visibility
Understand what each appliance is actually drawing, so a recommendation names the offender instead of asking the household to guess.
Appliances and plugs
Controllable loads across the home join the same picture, so shedding is targeted rather than blunt.
Electric vehicles
The largest new load on the residential grid, and one of the easiest to move, since most charging is time-insensitive overnight.
Unoccupied homes first
The cheapest kilowatt to save is one spent conditioning an empty house. Because we know occupancy, savings start where there is no comfort to trade away.
The part few platforms can do
Shift the load between neighbors, not just within a home.
Peak demand is a coordination problem across many homes, not an optimization problem inside one. If every air conditioner on a street backs off at once, everyone is uncomfortable and the saving is small. Stagger them, and the street draws far less at peak while each individual home stays comfortable.
Load shaping across a street
Two homes, the same total cooling delivered, coordinated so their peaks do not land together. The grid sees a flatter curve. Neither household notices anything.
The same logic scales from a street to an apartment building to a whole community, and it works because the platform already knows which homes have somebody in them.
Making an event land
Demand response people actually take part in.
Weather, ahead of the event
We read how conditions are trending and call automated events before the peak rather than during it, pre-cooling while power is cheap so the household coasts through the expensive hours.
A text, not a portal
Events and reminders reach people where they already are. Nobody logs into an energy dashboard, and a program that depends on them doing so has already failed.
Simple enough to repeat
The Oahu work showed that participation collapses the moment an activity feels like homework. Every prompt is one action, framed once, with the result visible afterwards.
The evidence
Published, peer-reviewed, and award-winning.
Our residential energy work with the University of Houston won Best Paper at SustainIT 2017. It is worth reading if you are designing a program of your own, because the participation finding generalizes well beyond Hawaii.
Effectiveness of a Task-based Residential Energy Efficiency Program in Oahu
Best Paper, SustainIT 2017. Mohammadmoradi and Gnawali, University of Houston, with Moss, Boelzle and Wang. Smart meters, a twelve-week engagement program, 740 enrolled households, and what simplicity did to the participation rate.
Download the paper → More · Research libraryThe rest of the evidence base
Our research on ambient sensing and caregiving, including work funded by the National Institute on Aging and randomized controlled trials with investigators at UC Berkeley.
Explore the research →Talk to us about a program.
Tell us what you operate and what you are trying to move, whether that is peak demand, operating cost, or participation in a program you already run.
- Demand response run at scale
- Occupancy-aware, so comfort is kept
- Published, peer-reviewed results
Thank you, that is with us.
Care Daily reads what comes in and routes it to whoever is best placed to answer.