We’ve made major strides in medicine by mapping the human genome. Now a Boston-based data analytics company has set out to do the same for our buildings.
Like humans, buildings have a kind of DNA, markers that provide indicators about their energy health, such as age of construction or type of heating equipment.
“Think of a giant decision tree. If you know 10 or 15 data points about a building you can essentially infer the other 3,000 data points needed to create that energy model,” said Bennett Fisher, CEO & co-founder of Retroficiency, which specializes in energy analytics for buildings.
Retroficiency estimates that mapping buildings in this way could help the world save $370 billion annually in energy costs.
But it takes a long time to gather the data in the conventional way – by manually inspecting each building. Auditing every building in the US would take 22 years, according to Retroficiency. It’s like sequencing human DNA with an abacus.
Using data analytics is a lot quicker. In just a few days, Retroficiency modeled 30,000 New York City buildings. The company used publicly available data, such as tax records and zip code level energy consumption, along with privately sourced weather information. No one needed to enter any of the buildings.
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Here is some of what Retroficiency gleaned about NYC.
- If every building turned its temperature down one degree in winter and up one degree in summer, NYC would save $145 million.
- If every building with old windows installed new ones, the savings would be $227 million.
- If every building with a No. 4 or 6 grade oil boiler installed a natural gas replacement, the savings would be $10 million
The findings also show that we may be casting our net too wide when creating incentives and policies to spur green cities. What’s effective in one zip code may not be effective in another.
For example, Retroficiency looked at the impact of replacing old windows with new ones in New York City. Thirty-five zip codes showed the highest savings potential – and that savings was three times greater than what the 35 lowest potential zip codes could achieve. The high potential buildings tended to be in Manhattan, and they were tall, narrow and had lots of glass, the kind of structures that can realize substantial energy savings with better windows.
Retroficiency wants to bring its analysis to other major cities, so has launched the Building Genome Project. The company describes the self-funded initiative as the largest, most ambitious effort to mine, collect and organize publicly available building data for energy efficiency opportunities.
The company hopes the information will help utilities, policymakers, energy service companies and building owners better plan energy efficiency programs and projects.
“From a high level what we want them to realize is that data analytics today can be a game changer. We want to plant that seed and get them to think, ‘What if?’” Fisher said.
What’s next?
Retroficiency’s analysis was at the zip code, not building level. To achieve that finer granularity, the analysts need one important piece of information that is not in the public domain: actual energy consumption data for each building.
“Once you have the consumption, you open up that next level of granularity and it becomes much more powerful,” Fisher said. “Imagine being able to open up one of those zip codes. You could walk down the street in Manhattan and look left to right and say, ‘That building has a 10 percent opportunity in lighting and this building has a 20 percent opportunity in HVAC.”
But there is a stigma associated with releasing energy consumption data. It’s seen by utilities and owners as sensitive information to keep private. The stigma creates “a massive hurdle,” Fisher said.
Still, the Building Genome Project offers a first step, and a big one, in better understanding how our neighborhoods and cities use energy.
“We can give you back analysis and capabilities you never thought were possible, and it is only going to get better,” Fisher said.
Retroficiency is now taking recommendations on next cities to model. More information is here.
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