Smart meters continue to transform the global utility landscape, offering cutting-edge features for energy providers and consumers alike from outage detection to real-time consumption feedback.
In the US alone, the number of installed smart meters has approximately quadrupled over the past 5 years. These meters are collectively generating more than 1 billion usage data points every day enabling vital data insights for utilities and their customers.
The smart grid is growing fast in other regions, too. European Union member states have collectively invested around 4 billion USD across hundreds of smart grid projects over the past decade. The UK is targeting nationwide smart meter coverage by 2020. And Japans largest electric company is working to equip all 27 million of its customers with advanced meters.
As projects like these unfold, smart meters are becoming the industry norm. But some regions are farther ahead than others. The Edison Electric Institute predicts that by 2015, only around half of US states will have smart meter penetration rates higher than 50 percent.
But the energy industry cant afford to wait until everyone has a smart meter. The value of advanced data insights is just too high. Utility customers around the world with or without smart meters want personalized energy analysis today, and utilities have never been more able and interested in delivering it to them.
Thats why Opowers analytics team developed an algorithm that helps unlock the power of the smart grid not just for customers who have a smart meter, but also for those who dont.
In particular, the algorithm provides a personalized breakdown of a customers energy usage into distinct end-use categories air conditioning, appliances, hot water heating, and so on. That gives them a better understanding of whats driving their energy costs.
Whats especially cool is that this usage disaggregation feature also works reliably for customers with traditional energy meters. Customers whose meters are read once a month can get a level of insight thats very similar to what theyd get if their meter logged data every 15 minutes.
So how exactly does the algorithm work, especially for customers whose old-fashioned meters provide relatively sporadic and unspecific energy usage measurements? As you can imagine, the calculations require some clever data analytics involving a strategic combination of data on historical energy consumption, weather patterns, household characteristics, user input, and other key variables.
More importantly: how do we know the algorithm actually produces accurate results?
The answer hinges on the fact that a smart meter is also capable of being a dumb meter. That is, you can do razor-sharp disaggregation analytics on a smart meter (say, one that takes hourly electric reads), and compare those results to what youd get if that same meter were actually a dumb meter. To simulate this, one can simply mash together a smart meters 720 hourly reads (over the course of a month) into one single monthly usage read which is exactly what a dumb meter would give you.
By following this approach and incorporating a sufficient amount of related data (e.g. historical statistics, weather, household characteristics, etc.), our algorithm is able to produce consistent estimates across the two meter scenarios. This outcome indicates that, at least for certain applications, you can take a dumb meter and turn it into a smarter one.
The larger lesson here has important data science implications for the smart grid and beyond: when existing hardware is lacking in intelligence, you can often compensate for it by applying software intelligence.
And software intelligence can open up many other doors. For example, by building an integrated software platform that can quickly turn back-end calculations like those above into personalized advice for utility customers, you can deliver data insights at a minutes notice to millions of people, specifically at the moments that matter most.
Consider the personalized email alert that Opower sent last month to 85,000 utility customers in the Northeast immediately before a string of hot days. The communication allowed all customers even though the vast majority of them did not have smart meters to see a personalized and disaggregated view of their seasonal electricity consumption, in effect educating them on how savvy use of their thermostat could make a big impact in controlling energy costs during the imminent heat wave.
Source: Opower Labs
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