Deep data – less is more when analysing meter information


Just got to grips with big data? Well, the latest buzzword to bend your mind around is deep data, which could offer benefits to energy companies grappling with vast amounts of information from gas and electricity meters.

What is deep data?

The concept of deep data is to do “deep learning on relatively sparse data as opposed to analysing large volumes of data… and looking for the needle in the haystack”, reports InformationWeek in an interview with Badri Raghavan, CTO and chief data scientist of FirstFuel, a US-based analytics company for building energy efficiency.

Data collection is about efficiency, continues Mr Raghavan, or “leveraging the data asset you already have. The way to do that is to [determine] the technical or business problem you’re trying to solve. What is the single most important data stream you can leverage?”


Deep data in practice

In FirstFuel’s line of work – analysing the energy consumption of large buildings – Raghavan says that ‘single stream’ turns out to be meter data.

“We look at meter data as a scan of a building. Using our data science algorithms, we analyse the health of a building and pinpoint where it’s sick, and where it can be more efficient.”

And that, he noted, is one example of deep data at work. Meter data is “a relatively skinny data stream with so much content,” which allows FirstFuel to pinpoint the problem it’s interested in – identifying inefficiencies in energy consumption.

The trick for many organizations, of course, is knowing which data streams have the most value, and then figuring out how to combine them with other data to gain new insights.

FirstFuel has several data streams it finds particularly valuable, the trade magazine reports.

“Meter data tells us a lot about a building,” said Raghavan. “Then we start using high-resolution aerial imagery — you know, Google Earth, we use that a lot. In our domain, it’s very informative. It tells us what type of equipment is sitting on top of these buildings,” which tells FirstFuel a lot about the amount of energy a building should consume.

Choosing the right data

Where deep data differs with big data is you only collect the information you need to solve a specific business or technical problem.

Using the example again of FirstFuel, Raghavan says the company could collect additional data on traffic patterns and parking lots, as well as Twitter streams but hasn’t found a good reason to do so.

He says: “Instead of going down the big data path, where there’s a lot of data you could potentially analyse, but for relatively little incremental gain, we instead [focus on] the bare minimum that tells us the most about a building.

“And then we build on that, little by little by little. We pick up every new data stream if it adds to the information insight.”

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