Smart Cities And Big Data
As Anthony Townsend argues in his SMART CITIES, even though the communications industry has changed beyond recognition since its inception, the way we consume power has remained stubbornly anachronistic. The rules of physics are, of course, partially to blame, for making grid networks harder to decentralize, as opposed to communication networks.
However, much can be and is being done to make the grid more efficient, potentially saving a chunk of the $119 to $188 billion (according to the Electric Power Research Institute) losses due to power disturbances, and allowing utilities to generate power more efficiently.
Big Data Brings Big Savings
There are myriad ways in which power suppliers can leverage data to optimize their power deliveries. With smart meters — meters that have two-way communication with the supplier — utilities have access to much more actionable data. They now have instant power blackout reports; if an outage happens at night, they don’t have to wait for the customer to wake up and report. Startups like Opower, which already serves 50+ million households in 9 countries, help reducing energy consumption with price incentives. Last but not least, abnormal readings alert suppliers about possible energy theft.
In 2012, there were 43 million smart meters installed in the US, most of them in residential properties. The number is set to multiply by the end of the decade. Elsewhere, the adoption of smart meters continues unevenly, with some countries like Italy having already done the bulk of work, but others, like the UK and Norway, lagging behind. It is, however, evident that the world utilities are to become documented (and hopefully used, too) much better, as smart meter usage extends to water and gas consumption as well.
And it goes beyond smart meters perpetually quantifying the grid. Vast quantities of data can be obtained from satellites, showing vegetation growth and brewing storms. With such data suppliers can pinpoint places where maintenance work will be required and potentially dispatch their crews in advance.
Predictive analysis can be achieved to a degree, allowing utilities to cut down on using “peak-hour” power plants and focus on generating power efficiently. The problem is that the variables involved are quite random. Past data may or might not be indicative of future performance (like stock ads tell us), but, coupled with the fact that human behavior, along with weather and other factors influencing power consumption, is hard to predict, creating a passable algorithm would take enormous investments and an awful lot of time.
Be that as it may, Big Data is bringing to utilities what it’s bringing to city management. Both are becoming smarter, due to real-time insights being available from previously scarce or non-existent data. There’s potential for the utilities to become better, cheaper, and of course generate higher profit margins.
(Image Source: Shutterstock)
By Lauris Veips