AMI Analytics 3.0: Unlocking the Predictive Power of Smart Meter Intel

How AMI-enabled SaaS solutions are helping leading utilities unlock insights and transform the energy value chain

AMI Analytics 1.0 – Automating, Exploring, and Mining

Between the time the first “automated meter” was conceived in the early 70’s, and the advent of deregulation in the mid to late 90’s, companies pursued smart meter investments primarily out of a desire to reduce operating costs for otherwise high-cost activities.

For many, automated meter reading was a relatively quick and certain CapEx solution for reducing burdensome O&M costs that were otherwise difficult to displace. Initially, AMI  deployments focused on utilities with high meter reading and field servicing costs (remote customers spread over large service areas and/or hard-to-access meters) where paybacks were clearly visible and immediate. But it wasn’t long before other sources of value became readily apparent. Reducing the cost of collections through automated disconnect and reconnect, for example, sweetened the value proposition greatly.

But for the most part, the first 30 years of the smart meter could be summed up as a pure financial story, as the vast majority of the first deployments were based on a simple operational value case. While there was a lot of talk and hype about the “value of the data”, it wasn’t until the turn of the millennium that companies were able to begin tapping into the value their AMI data streams were capable of producing.

Early-stage AMI analytics focused heavily on resolving data quality problems through advanced validation and data conditioning, as well as exploring and testing some of the basic and most obvious use cases for the available data. And most of these were centered on traditional “revenue cycle functions”: Strengthening billing accuracy, enhancing collection strategies, designing and deploying new rate options, and improving the billing and payment experience. The early going for AMI analytics was all about getting the data right, and proving that the data could in fact be used for more than automating operational activities.

Energy Analytics 2.0- Moving beyond the Revenue Cycle

As these low-hanging, meter-to-cash benefit streams are “tapped out,” utilities have been exploring new ways to harness the power of metering data for the broader benefit of customers and the environment. Energy technology companies have deployed applications to repurpose and deliver this same meter data to customers to help them make better energy decisions. The premise that customers equipped with better information are far more likely to make better energy decisions has changed the landscape of how the industry thinks about energy efficiency.

What was once a regulatory strategy to energy efficiency rebates and incentives as a lower-cost alternative to costly power plant investments, is quickly being replaced by an information-driven “behavioral” solution that is far more cost-effective and customer friendly; demonstrating that the value of meter intelligence can go well beyond automating the manual reading of meters.

Transformative Analytics (3.0)- Extending Insights Across the Energy Value Chain

Widening the scope of value

But as useful as these advances have been for the energy efficiency and customer engagement arenas, very little has been done with AMI intelligence to affect upstream supply and delivery functions – functions that can impact as much as 70% of the customers’ energy bill. That is…until now.

Innowatts, a Houston-based energy technology company has been busy developing a more holistic solution for harnessing the value of smart meter intelligence geared heavily to the needs of the business and its shareholders. Innowatts has become the industry leader in combining big data analytics with machine learning technology to transform large pieces of the energy value chain from the inside out, from improvements to energy supply and trading functions to reinventing the way customers interact with their utility.

But the key to Innowatts’ value is not only the sophistication of smart meter analytics, but also the way in which they make intelligence actionable and useful upstream of the customer, where value can be much more strategic and have a far greater impact on cost, reliability and operating margins.

“The body of current intelligence we possess on our customers as an industry is still grossly unharvested and underleveraged”, says Sid Sachdeva, CEO of Innowatts. “By only focusing on customer demand, we are missing opportunities to reduce large amounts of upstream cost and waste. These areas, which operate largely outside of the customer’s line of sight, are some of the most important parts of the cost and value equation. “

And it’s that upstream value that inspired Innowatts to build what has become the centerpiece of many utilities’ customer data and analytics initiatives.

Machine learning analytics that predict, prescribe, and automate

Innowatts’ Core Analytics is becoming the “nerve center” at leading utilities for many of their critical business functions

The foundation of Innowatts’ solution suite is Core Analytics, a scalable technology that helps utilities and energy providers unleash the power of smart meter data into literally every pocket of their business, creating enormous amounts of new value for customers and shareholders.

Core Analytics combines data from smart meters with machine learning technology to construct predictive load profiles and dynamic forecasts for each and every customer. Those profiles can self-adjust based on real-time changes in climate, environmental and infrastructure dynamics, and the changing operating characteristics of the customer.

But it doesn’t stop there. Core Analytics translates these predictive profiles into a body of customized actionable intelligence for most of the functions in the energy value chain. Through easy access to our modular suite of products for Supply, Demand, and Customer Engagement business verticals, our clients are able to quickly integrate the value of smart meter analytics directly into the core operating processes of their business.

Core Analytics utilizes some of the most sophisticated data science and modeling techniques available. Working in partnership with its clients and academia, Innowatts has brought together a collection of relevant and time-tested techniques that until now had been restricted to highly specialized applications.

Diagram showing Core Analytics connects into Demand, Supply, and Customer products
Innowatts’ modular plug-and-play SaaS solutions utilizing insights from Core Analytics

For example, Innowatts’ approach of isolating weather sensitivity at an individual customer level has enabled clients to completely redefine the industry’s standard for accuracy and granularity. The result- forecasts that are on average 30-40% more accurate than produced by today’s existing technology. Similar results can be seen across the value chain, from upstream improvements in gross margin to reductions in customer acquisition cost and customer churn.

“By working together with our key clients, vendors, and academia, Innowatts has built a robust architecture that can ingest, analyze and manage large volumes of disparate data quickly, securely and seamlessly across the business through its family of functional applications, said Akhlak Ahmed, Innowatts’ Head of Product Engineering. “At the same time, the system is highly flexible, allowing many of our clients to seamlessly integrate into their existing operating systems and customer applications through a robust and growing library of APIs.”

Today, the Core Analytics platform provides the analytics for over 43 million customer accounts, in a wide variety of geographic locations and local energy markets, all delivered through its scalable SaaS solution suite.

Applications for competitive energy markets

North American Energy Markets utilizing Innowatts predictive analytics

For companies in competitive market climates, the results have been nothing short of dramatic. Innowatts’ suite of energy analytics is currently being used to forecast customer consumption in most of the competitive markets across North America and has captured the interest of other highly competitive markets across Europe, Australia, New Zealand and Japan. Three of the top five Retail Energy Companies in North America have all embraced Innowatts’ approach to bottom up energy intelligence and have each captured large tranches of value for their customers and their bottom lines.

In competitive markets, the answer is clear. “Anywhere there is a high penetration of smart meters and a thriving competitive energy market is a strong candidate for our value proposition”, says Bob Champagne, Head of Strategy and Business Development for Innowatts. “It’s not uncommon for moderately sized utilities in these markets to reap $30-50Million paybacks within the first year of implementation, and that’s just the tip of the Iceberg”.

Experiences and Challenges in Regulated Markets

In more highly regulated markets, Innowatts admits the challenges will be greater. Still, the company believes the paybacks are still well worth the investment in their predictive technology.

The challenge for us in these markets is not one of opportunity. In fact, the opportunity in many cases is significantly larger than that of competitive markets given the balancing risks associated with poorly forecasted load on transmission and distribution infrastructure.

In highly regulated climates today, price and volumetric risk have mostly been viewed as non-controllable- caused largely by changes in weather patterns and market price fluctuations. As a result, many utilities are still allowed to pass on the upstream errors or inefficiencies to their customers in the form of fuel adjustment clauses and other mechanisms common in today’s regulatory structures. As one Utility executive put it, as long as we can recover upstream inefficiencies through rates, it doesn’t make a lot of sense to invest a lot in eliminating them.

Creating a regulatory climate that creates enough reward for utilities to replace poorly performing processes and technology clearly needs to become a priority. For now, at least for regulated companies, it will need to depend on leaders taking the risk that the benefit of making a shareholder investment to lower rates will somehow work out in the wash, or find other ways to rationalize the investment.

Where to from here…

But even in the midst of those challenges, Innowatts believes that the power of the numbers will shift some of that embedded thinking, and hopes that continuing to show the magnitude of savings in both regulated and deregulated markets will be enough to drive the broader market and regulatory changes required.

In the meantime, the company remains committed to its bold vision of transforming the energy value chain, one business at a time. Regardless of the pace at which results are captured across these markets, Innowatts believes that this is truly an industry game-changer.

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