To learn more about how Bidgely and Analytics Workbench can help you turn your AMI investment and its data volume into data value, see Analytics Workbench in action. To hear more from PSEG, West Monroe, and other industry leaders, check out all the session video-on-demand at

According to a 2021 Forrester Consulting/Dell Technologies survey entitled Unveiling Data Challenges Afflicting Businesses Around The World, despite being an organization’s greatest asset, data can paradoxically become its biggest barrier to transformation when it’s allowed to overwhelm existing systems rather than being harnessed to inform better business outcomes across the enterprise. 

Utilities are facing the same data paradox.

The primary objective that drove initial AMI deployments was the desire for automated, timely, and accurate billing by eliminating weather and property access challenges. While it’s true that utilities have realized millions in savings thanks to remote billing and metering capability, that bottom line impact pales in comparison to the utility-wide budget boost potential of AMI-data-driven savings. In fact, the millions of mission-critical insights generated by smart meters every day should be regarded as a daily infusion of a new form of institutional capital for progressive utilities. 

Realizing that incredible economic potential, however, requires ensuring that utility data teams are equipped to analyze this greater-than-ever-before volume of data, and that utility leadership seizes every opportunity to leverage AMI insights to generate universal organizational value.

Load Research

When load research was introduced in the late 1970s, early 1980s, utilities began by identifying a subset of homes, monitoring their usage, and assuming the observed energy use patterns were representative of the entire service population. AMI meters now enable customer-by-customer monitoring capability, but many utilities are still relying on the legacy approach of sampling fewer than 1 percent of the customer base to make plans for the full service territory. With Analytics Workbench, Bidgely empowers utilities to leverage their AMI investment to easily and efficiently make the transition from sampling to full population load analysis.

Grid Planning

Historically, grid loads have been understood at an aggregate level. End user analysis has only been conducted sparingly, making it difficult to inform short-term grid balancing efforts. 

Now, by applying our patented disaggregation technology to AMI data, Bidgely is able to break down grid loads by individual service point end use consumption, enabling utilities to develop load curves for individual substations, feeders and other assets. This, in turn, reveals granular opportunities to reduce or shift demand or implement targeted infrastructure changes. 

Because AMI data is collected at regular intervals on a continuous basis, Bidgely makes it possible for utilities to spot changes sooner, such as new EVs coming online, and take action immediately.

Industry experts estimate that it costs ~$2 million to build a new substation. Using AMI data to inform load management has the potential to defer or even eliminate this cost.

Segmentation and Targeting

Customer-specific AMI energy use data reveals appliance ownership, time of use and efficiency. These insights enable hyper-personalization and strong segmentation, delivering better marketing results and improving customer recruitment for energy efficiency, demand response, distributed energy resources and EV programs, rate plan switching and non-energy related campaigns. More targeted and effective marketing in turn reduces cost to serve and improves customer engagement.

“We strongly believe in leveraging AMI and learning from the AMI data in order to provide more information and insight to the customers — helping them understand how they use energy and so they can control and manage their energy use. I would say it’s one of our core areas of focus as a customer services organization,” said Nayan Parikh, Senior Manager, Customer Technology, PSEG-LI.

“Metrics are key to engaging customers and in ensuring utilities are meeting their customer satisfaction goals,” agreed Jennifer Popkin, Senior Consultant for Energy and Utilities at West Monroe. “What we’ve seen with AMI deployments is that it’s really easy to get focused on the physical deployment of meters and lose track of all of the other value-driven AMI benefits that don’t necessarily show up in a P&L or the financials. The metrics that help drive customer engagement aren’t easily measured by a number on an Excel sheet. But having metrics centered around customer engagement, and focusing on engaging customers in EV, DER and other programs helps ensure that utilities are able to realize the full benefits of AMI.”

EV Detection and Estimation 

Electric vehicles aren’t your standard DER — they are far more complex, and becoming more complex by the day. 

First, EVs are owned by many different customers including those in the residential, small and medium business, and large C&I and government categories. These customers also don’t always charge their vehicles in the same location — alternatively charging at home, a business or a public charging station — and do so at different times of day and for varying lengths of time. Plus, with the introduction of vehicle-to-grid integration, EVs are beginning to not only consume capacity on the grid, but also create capacity with battery storage.

That’s why AI-powered AMI analytics are essential in informing understanding of current and future impacts, and driving EV-related resource planning. 

Neither DMV data nor car API/hardware telematics sampling provides sufficient location and behavioral data on which to build EV forecasts. Only AMI data provides an accurate understanding of  EV adoption at a granular, location-by-location and time-specific basis to ensure utilities are able to identify EVs, EV charging patterns, and growth trends to drive investment and strategy in grid infrastructure and EV programs.

Overcoming Data Imperfections

Utilities are often concerned that relying on AMI data can pose problems due to gaps and errors that often exist within the data set. Bidgely applies machine learning to identify perfect and imperfect data, and then leverages clustering technology to extrapolate perfect data from imperfect data.

Realizing Maximum Value 

Load research, grid planning, segmentation and targeting, and EV forecasting are only a few of the more than 70 smart meter data applications Bidgely has identified to help utilities optimize their operations, fast-track progress toward net zero goals, reduce costs and grow revenue. 

Bidgely created Analytics Workbench to enable utilities to use their AMI data flexibly and holistically — providing a single source of truth across DSM, marketing, M&V, planning, load research and all other departments. 

With this approach, Analytics Workbench allows utilities to leverage all the AMI investment they’ve made so far while also providing a robust platform upon which utility data analysts and data scientists are able to further develop custom applications to derive even more value from every AMI data point.

To learn more about how Bidgely and Analytics Workbench can help you turn your AMI investment and its data volume into data value, see Analytics Workbench in action. To hear more from PSEG, West Monroe, and other industry leaders, check out all the session video-on-demand at