From historical weather records and future forecasts, to bushels and tons of each agricultural harvest, to phone books, to sports, to insurance actuaries, and the list goes on… people have been gathering, tracking, and using data for centuries. All that data is getting big. Really big. In 2012, the amount of data stored globally was greater than 2.5 zettabytes. (A zettabyte is 1.1 trillion gigabytes—a number with 21 zeros—or over three times the amount of all the grains of sand on all the beaches in the world).
Three converging trends have led to this rise of big data:
- Granularity—we can collect finer and finer levels of detail than ever before
- Speed—we can collect that data faster than ever before
- Cost—we can rapidly collect that data more cheaply than ever before
This speeding avalanche of data—and all the information in it—can lead to great opportunities, especially in the areas we work in at RMI—electricity, buildings, and transportation. The big questions are:
- What value can we get from all that data?
- What are the obstacles that prevent us from extracting that value?
- How can we overcome those obstacles to lead us towards a carbon-free energy future?
The Value of Big Data
Big data is important because it allows us to measure and monitor a system to a level of granularity and with a volume of information that was unimaginable just a decade ago. “The most powerful component of big data,” says Greg Rucks, a manager in RMI’s transportation practice, “is that it’s both smarter than us and not biased, showing us correlations that we would never have normally seen.”
For example, AutoGrid collects data from smart meters, grid sensors, and building management systems, and crunches it to predict and analyze what is happening on the grid in real time. Companies can use this data to predict demand days, minutes, and even seconds in advance, radically expanding how demand response programs are used. AutoGrid has helped the City of Palo Alto Utilities (CPAU) shed an average of 1.2 megawatts of peak demand through its Demand Response Optimization and Management System (DROMS). Seven of CPAU’s commercial customers participated in the demand response program. DROMS allows a utility to organize its entire demand response portfolio, from picking out the programs and customers it’s going to work with, to actually executing the real-time shutdown of power loads, to verifying and accounting for the results.
Big data can also make huge differences in the transportation sector. For example, UPS uses big data to increase fuel efficiency, saving 1.5 million gallons of fuel in 2012. Sensors capture over 200 data points on 80,000 vehicles every day. Data such as engine performance, driver behavior, emissions, fuel consumption, number of stops, speed, and mileage are all analyzed to enhance the efficiency of package delivery.
The Obstacles to Creating Value from Big Data
While big data can offer us myriad opportunities to improve how we do things, there remain obstacles to letting us extract the large quantity of value we can out of that data. These include:
- Transparency and access
- Extracting insights
“Big data means nothing if the people who need it don’t get access to it, and not useful if everyone is storing it in their own structure,” says Roy Torbert, a manager in RMI’s buildings practice. In regards to access, the value that we can get out of big data usually cuts across different businesses. For example, a manufacturer of HVAC equipment might collect data on how an air conditioner is being used. But then there is the thermostat that controls that air conditioner, and the maintenance person that has access to the thermostat. As Hervé Touati, a managing director in RMI’s electricity practice, explains, “All three of these entities need to be talking to one another for the end customer to get the value.”
The structure of the data is important as well. “There are big challenges around standardization,” according to Rucks. “There is an incredible amount of fragmentation when it comes to collecting big data. The quality and thoroughness varies across the board. We need everyone reporting to the same place in the same format.” This can be tricky, as people who set up data systems really have no idea of what it could be used for in the future. “You can be stuck with inconsistent and subpar structures with your data,” adds Torbert. “We inherit that, and have to use the data we’re given to get what we need.”
Extracting value is also challenging because big data doesn’t tell you anything by itself. It gives you correlations, but you must deduce the cause of those correlations. “You need to know something about the system you’re digging into,” according to Rucks, “to hypothesize as to what the causes might be.”
Extracting Data to Make a Difference
One way to make sure the value of big data is used for a common good is to share that data. Utility companies collect data on the electricity use of their customers, but it can be difficult for residential and especially commercial customers to get access to that data. Enter the admittedly imperfect but still noteworthy Green Button Initiative. Green Button is an industry-led effort that responds to a federal government call-to-action to provide utility customers with easy and secure access to their energy use information. Currently 35 utilities and electricity suppliers have signed on to the initiative, representing 36 million homes and businesses. The data gives consumers the ability to manage energy use and save on bills, provides a feedback loop to engage consumers in energy efficiency programs, allows managers of commercial properties to do virtual energy audits, and makes it easier to project the value of new services such as residential solar.
Making big data available to the consumer is not the only way to extract value. Making the data available to the public at large could have huge benefits. “There is an army of software developers chomping at the bit to get their hands on data and compete to make use of that data,” according to Rucks. Making the data open source, so that other companies and organizations have access to it, is a great way to find new value out of that data.
Parking is another sector that could benefit from consolidated and publicly available data. There are thousands of owners of parking spots in any given city, each having their own way of keeping track of open parking spaces, often only found out by driving by a parking lot and seeing the “Lot Full” sign. If all these parking owners had a way to collaboratively, in the same format, make their parking availability known, it opens up a huge pricing opportunity. Transportation is currently a non-market-driven service sector. Yet this would unleash the power of the market on transportation, by allowing parking owners to implement demand-responsive pricing among other things that we may not even be thinking of right now.
Which brings us to monetization. Big data is not useful unless it’s turned into action, and one of the best ways to do this it to monetize it. This is a great opportunity in buildings. “Reduction in energy use will occur very quickly when you figure out how to make money off it,” says Torbert. For example, using big data, utility companies can pay people a flat fee for intermittent control of certain appliances, such as a residential hot water heater. Then the utility company can turn it off for short periods of time—not enough time to change the temperature noticeably—during peak periods when electricity may cost more. This can save both the utility company and the consumer money.
Perhaps one of the most important things we can do to make big data useful is to use that data collaboratively across sectors. Oracle Utilities, in its 2013 big data study, found that although electric utilities are using more data today, many are not using that data as efficiently as possible. “Historic industry silos need to be pulled down, allowing a more open, holistic, and collaborative environment in which data, in particular, is owned and used by the entire enterprise, rather than by specific utility departments,” noted Oracle. A great example of data sharing is New York’s Reforming the Energy Vision (REV) proceeding to create a new retail marketplace for electricity to help distributed energy resources (DERs) compete on a level playing field with traditional grid investments. REV’s “distributed system platform” (DSP) will be the integrator of distributed generation and other DERs, including energy efficiency, demand response, energy storage, and electric vehicles. For this to function properly, REV is recommending policies for a high degree of transparency and open data sharing among customers and service providers.
From building energy use, to the electric grid, to our transportation infrastructure, the amount of data we are generating is staggering. And once we figure out how to extract the data from the zettabytes of data at our disposal, we can use it in a meaningful way to move towards a sustainable future.
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