AMI Analytics 1.0 – Automating, Exploring and Mining
From the time the first “automated meter” was conceived in the early 1970s, to the advent of deregulation in the mid-to-late 1990s, companies pursued smart meter investments primarily to reduce operations and maintenance (O&M) costs.
For many, automated meter reading was a relatively quick and certain capex solution to reduce burdensome O&M costs that were otherwise difficult to displace. AMI deployments initially focused on utilities with high meter reading and field servicing costs, incurred from covering remote customers spread over large service areas and hard-to-access meters, where paybacks were clearly visible and immediate. But before long, other sources of value became apparent. For example, reducing the cost of collections through automated disconnect and reconnect sweetened the value proposition greatly.
“Creating a regulatory climate that creates enough reward for utilities to replace poorly performing processes and technology clearly needs to become a priority”
The first 30 years of the smart meter could be summed up as a 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 began tapping into the value their AMI data streams could produce.
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. 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 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 that involved creating energy efficiency rebates and other incentives as a lower-cost alternative to 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
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 percent of the customer’s energy bill. That is…until now.
Innowatts, a Houston-based energy technology company, has been busy developing a holistic solution for harnessing the value of smart meter intelligence geared to the needs of the business and its shareholders. For the past four years, Innowatts has been combining big data analytics with machine learning technology to transform large pieces of the energy value chain from the inside out-creating improvements to energy supply and trading functions and reinventing the way customers interact with their utility.
The key to Innowatts’ value is not only the sophistication of its smart meter analytics, but also how its platform makes these analytics actionable and useful upstream of the customer, where value can be much more strategic and have far greater impact on cost, reliability and operating margins.
“The body of current intelligence we possess on our customers as an industry is still unharvested and underleveraged”, says Innowatts CEO Sid Sachdeva. “By only focusing on customer demand, other companies miss 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
At the core of Innowatts’ solution suite is its scalable technology platform that helps utilities and energy providers unleash the power of smart meter data into every aspect of their business, creating enormous amounts of new value for customers and shareholders.
“Innowatts’ platform is becoming the “nerve center” at leading utilities for many of their critical business functions.”
The Innowatts’ platform combines data from smart meters with machine learning technology to construct predictive load profiles and dynamic forecasts for each customer, profiles that continuously self-adjust in response to changes in climate, environmental and infrastructure dynamics, as well as the changing operating characteristics of the customer.
But it doesn’t stop there. Innowatts 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 a modular suite of functional SaaS-based applications, customers are able to quickly integrate the value of smart meter analytics directly into the core operating processes of their business.
Innowatts 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 specialized applications.
For example, Innowatts’ approach of isolating weather sensitivity at an individual customer level has enabled clients to completely re-define the industry’s standard for accuracy and granularity. The result is forecasts that are on average 30-40 percent more accurate than those 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 platform provides analytics for over 12 million customers residing in a wide variety of geographic locations and local energy markets, all delivered through its scalable SaaS solution suite.
Applications For Competitive Energy Markets
For companies in competitive markets, the results have been 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 competitive global 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. “Wherever 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 $30-50 million paybacks within the first year of implementation, and that’s just the tip of the Iceberg”.
Experiences And Challenges In Regulated Markets
In highly regulated markets, the challenges will be greater. Still, Innowatts believes the paybacks are well worth the investment in their predictive technology.
The challenge 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 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, deployment of this technology will depend on leaders’ willingness to make the shareholder investment risk to lower rates and increase efficiency in the long term.
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 is 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.
Jaša Žižek Fuis, Product Manager, Wastewater Treatment & Andreja Peternelj, Wastewater Treatment Development Manager, Treatment Plant & Tomaž Ružič, Product Manager, DISNet WS - Water systems, Petrol d.d., Ljubljana, Petrol Group