In this special guest feature, Mark Gately, Senior Manager, Decision Science at Tendril, describes a future when homeowners can use data analytics to accurately model energy consumption and corresponding costs. Mark leads Tendril’s Decision Science team. He holds degrees in computer science and economics, and has more than a decade of experience working across the energy sector. Now in his fifth year at Tendril, Mark and his team combine physics-based simulation modeling, machine learning, and optimization to create highly personalized energy management applications.
I live with my family in a 3,000 square-foot, single family home that was built in 1993 just outside of Boulder, Colorado. Like millions of homeowners across the U.S. and the world, my home is among my most valued assets.
So I like to know how it works: I want to know how my home consumes energy across different end uses like cooling, heating, and hot water. I wonder how efficient it is compared with nearby homes of similar size and vintage. I’m curious where I should invest my money to reduce its operating costs.
To answer my own questions, it would be great if I could access unique data about my home’s energy consumption, along with personalized recommendations on how to become more efficient. It would be great if all residents could do the same, whether they’re homeowners or renters. It would be even better if our energy providers, solar companies, and real estate professionals could provide this information to us.
Thankfully, the above scenario is no longer wishful thinking. But it requires an investment in data analytics on the part of utilities, renewables providers, device makers, real estate professionals—anyone looking to sell energy products and services into the home, promote energy efficiency, help consumers manage their energy use, or enable them to anticipate their energy expenses in a particular property.
The most cutting edge, valuable technology will incorporate a home energy simulation model, a tool that generates critical data across multiple markets. By simulating the operation of a home under various hypothetical “what if” scenarios (one HVAC configuration over another, the installation of LED lights, solar generation, etc.) such a predictive model can provide all of the insights organizations need to help their customers optimize energy use.
Because these models require detailed physical descriptions of homes—their geometry, mechanical equipment, thermal properties, local weather conditions and occupancy—it has taken a while to scale home simulation beyond one-off analyses of single houses. But today, the capacity exists to know the physical characteristics of every home in North America, with Western Europe and Australia not far behind, and the remainder of the world to follow in time.
Here’s how it works. For each home address, public records, county deeds and local tax assessor databases provide the information necessary to fill out the most important inputs, including location, size, vintage and occupancy. Weather variables such as temperature, humidity, solar irradiance and wind speed populate based on location, while additional details of the home’s shape and orientation are estimated from satellite imagery and web mapping services.
From there, the technology applies heuristics derived from existing research and large-scale assessments of residential building stocks. Insulation R-values and HVAC systems are inferred from location and vintage. Lighting scales with home size. Cooking, laundry, and hot water use correlate with occupancy and family composition.
This global data set scales home energy simulation across new geographies and new markets—anywhere there is a value proposition for the home. So, in my case, my local utility can help me become more efficient, rooftop PV vendors will know that my south-facing roof is perfect for solar, and device companies can manage my thermostat’s set-point schedule to minimize my cooling cost.
And when it’s time to sell my house and buy a new one, a real estate search engine can tell me exactly how the properties I’m considering will differ in energy consumption. I’ll be able to estimate my energy costs in a potential new home, and I can decide upfront what upgrades I want to make to increase the home’s value.
Like all homeowners, I’ll have all the information I need to optimize my energy use and improve my residence. And I’ll have energy providers, retailers and real estate experts armed with cutting edge data analytics to guide me on my way.
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Thanks for the great share! I also like the idea of Home Energy. The best part I like is this: The reliability and availability of modern energy sources cause people to tend to assume that it will always be accessible. And as for the case of non-renewable energy sources, most people do not know or maybe even refuse to accept that it will eventually run out.