The secret sauce of an IoT deployment? Trilliant shares its top tips

A desire for change, understanding customer energy behaviour and watertight security - three pointers for a successful digital rollout.

In an Engerati webinar in June, Dave Kranzler, Trilliant’s SVP of Product Development, shared key lessons that the company has gained from global smart meter rollouts with utilities numbering 85 million customers.

Diving into the characteristics of a successful technology deployment, Kranzler believes a key factor is a desire for change.

Energy companies should not only want the latest technology, but be excited, motivated and curious about it. And they must care deeply about their customers’ experience, he maintains.

Kranzler considers Tenaga Nasional Berhad (TNB), the Malaysian energy utility, as an example of this kind of company.

“TNB started with smart metering to learn about the technology and how they could improve customers’ experience, and now they have confidence in the technology and are deploying it at an extensive scale.”

In partnership with Trilliant, TNB is rolling out the first smart metering module in the city of Melaka (900,000 inhabitants), and the next goal is to expand the programme to the rest of the country.

One communication network, many devices

TNB and Trilliant are also exploring opportunities to solve other challenges using the same utility communications network.

“One of the key reasons Tenaga chose to work with us is because we showed them a backbone architecture using our wireless network that can scale to solve the different problems that they envision to solve,” explains Kranzler in the Engerati on-demand webinar.

Given the acute air and water problems that some South-East Asian cities are facing, the ability of smart meter technologies to scale and address several challenges with one network is a good starting point for utilities when thinking about how to implement smart meter rollouts, he maintains.

IoT lessons Trilliant webinar

The different applications of a smart network. To learn more about Trilliant’s lessons from smart meter rollouts, watch the Engerati on-demand webinar 'Essential IoT readiness: Communications secrets from the AMI smart grid era'


Understanding energy consumers through data analytics

Another key ingredient of successful deployments is to have clarity on consumer behaviour, advises Kranzler.

“Once you have a good understanding of a customer’s energy usage data, there is potential to offer them services to reduce their energy usage (such as better glazing of windows, better boilers, personalised energy advice); which are services that a utility can offer to offset revenues lost by lower energy usage.”

Kranzler identifies one of the UK’s big six as a company that has thought hard about this and started to deploy data analytics to understand consumer behaviour.

By doing this, the gas and electricity supplier can design more tailored products and services for their customers, as well as gain an advantage over its competitors.

Kranzler considers analytics and big data to be one of the highest growth opportunities for utilities seeking new revenue channels. “Smart grid launched the data tsunami and the IoT promises to accelerate it,” he says.

What is a utility frontrunner's approach to better understanding its customers’ needs for energy services? Check other lessons from smart meter rollouts by watching the Engerati webinar. View the full webinar

Energy communications network security

The third pillar of a successful rollout is ensuring that communication platform technology is robust and secure. “You can imagine what would happen if street lighting is hacked,” says Kranzler.

Even with world-class security and world-class encryption and authentication, it is hard to deter an attack. Hackers have become more creative as well.”

And, since attacks change all the time, an energy company needs to conduct deep contextual analysis to identify benign from harmful traffic.

This is hard for a human to do, but it is easier for a machine,” explains Kranzler. “Machines can learn about how to recognise what is normal and what is not, and they can do it very quickly.”