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October 13, 2015

Getting a Handle on Smart Meter Data

Public utilities all over the globe are installing so-called “smart meters” that can take fine-grained snapshots of how resources like electricity, water, and natural gas are consumed in a given house or bulding. While the overall goals of these programs are noble—you can’t conserve what you don’t know you’re consuming–the glut of usage data presents a major challenge to those tasked with analyzing it.

There are many reasons to implement a smart meter, but some are better than others. For example, in drought-plagued California, homeowners recently discovered that smart water meters were helping water districts identify customers who were not complying with a statewide mandate to reduce water use by about 30 percent. That set off a firestorm of controversy that has yet to abate.

But there are more honorable uses of smart meters, including encouraging customers to make informed decisions on their consumption of resources. When implemented as part of a conservation initiative, smart meters can help large population groups have a big and positive impact.

One group that’s exploring the ramifications of smart meters in the electric industry is Sweco, a large sustainable construction consultancy based in Stockholm, Sweden. Magnus Lindén is leading a team of engineers at Sweco that’s investigating how billing data gathered from smart meters can be more effectively used to help shape public policies, with the overall goal of meeting the European Union’s “20-20-20” goal of reducing energy use, curbing energy waste, and using more renewable energy.

Smart Meter, Big Data

EU law calls for smart gas and electric meters to be nearly ubiquitous across Europe by 2020, which gives policy makers about four years to find out how to make the best use of the data. The problem is, a lot of the billing data from existing smart meters is going to waste.sweco_logo

“I’ve been working within deregulated Nordic energy markets for 17 years… and in all these years, the metering has been an issue,” Lindén tells Datanami. “In all these discussions, it’s always been that if you have it, you can use it for more analytics, you can use this to get a better understanding” of energy usage patterns.

Sweco finds itself at the junction of the energy suppliers and consumers. The grid operators must make sure they are maximizing the use of renewable energy sources, such as hydro, wind, and solar, against more traditional energy sources, such as coal, nuclear, and natural gas. Wind power makes up the bulk of renewable mix in the region, but if the wind isn’t blowing, grid operators typically replace it with hydro, which is more expensive.

But the bigger problem is how to shape the energy policy to encourage conservation, or at least greater awareness among customers. Changing customer behavior is not going to be easy, but it’s the best way to meet conversation and energy reduction goals, and data from smart meters will play a big role.

Changing Consumer Behavior

Lindén uses himself as a good example of the current state of energy conservation, and how it ought to be transformed by intelligent policies. He’s been driving a plug-in hybrid car for the past year, and typically he charges it when he gets home each weeknight. The problem is, that’s when all the major appliances at his home are turned on—and also when his neighbors are likely to be consuming lots of electricity, too. “Everything is on at 6 o’clock at night, but the car could start charging at 2 in the morning just as well,” he says.shutterstock_electric_tower

“But there are absolutely no price incentives for me to deal with this,” he continues. “In the beginning, I used to go out later and plug in my car, just to be a good citizen, but one night I forgot, and went out the next morning and the car wasn’t charged. But if there was a price signal in the system, I probably would invest in some kind of technology so I can deal with this.”

Identifying that “price signal” amid the electric noise is exactly what Lindén’s group is tasked with doing. “If you want to have a price incentive in the rate so people care when they use the energy, then you need to change the rate plans,” he says. “It’s a very big step to take…And you need to go to the hourly values to figure this out, and that’s exactly what we’re into now. That’s the driver.”

Data Analysis

The problem that Sweco faced is that the sheer size of the data collected from existing smart meters has proven to be daunting. The variability in the time-series data also thwarted earlier attempts at accurate analysis.

lavastormOne day Lindén discovered a data analysis tool from Lavastorm that could handle the volume and variability of the data, and he and his team dove into the data. Today his team is using Lavastorm to process over five billion rows of data, representing data from 200,000 customers over a period of three years. That total is slated to double to nearly 10 billion rows of data, he says.

Lindén has used Lavastorm to build models that allow him to analyze how various changes would impact energy usage, as well as utility revenues. For example, what happens if retailers are incented to conserve energy during particularly hot or cold weather, and what happens if the grid operator adds more energy storage to the mix.

“We discovered that a portfolio of different rate plans is what society would benefit the most from,” Lindén says. “Different kinds of customer can help the system at different times during the day and year to balance the load and optimize the capacity use of the grid.”

The Lavastorm analytics run on a server at Sweco’s office in Stockholm, where it’s accessed by four members of Lindén’s team. Two other utilities in the Nordic region have also licensed Lavatorm to run their own analyses as well.

Lindén credits Lavastorm with making the analyses possible. “If we haven’t discovered the tool we probably wouldn’t be where we are,” he says. “To me we’re at the very front line of analytics in these areas due to the Lavastorm tool.”

Without the analytics done in Lavastorm’s tool,  Sweco would still be struggling to make sense of the data. But  Lindén’s team has made lots of progress is shaping the discussion going forward. “The peak capacity is the problem,” he says. “That is getting clearer and clearer in the analytics we do.  Everybody says they know this, but we are actually able to put figures to it and to perform analytics that say, ‘what you think and what you believe–this we can prove to you. ‘”

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