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In today’s rapidly evolving energy landscape, utilities face unprecedented challenges with increasing DERs, electrification and changing customer behaviors. In response, forward-thinking energy providers are increasingly turning to Non-Wires Alternatives (NWAs) as compelling solutions that go beyond traditional infrastructure investments.

“Historically, we have looked closely at how our traditional wires solutions have helped our customers to maintain reliability,” explains Dhaval Patel, Senior Network Manager at Hydro One. “But because of the changing nature of the electrification load and customer behavior, we have started looking for non-wires solution alternatives, and data is a big help in identifying the best use cases for NWAs.”

This shift in approach isn’t just about managing current challenges — it’s about positioning utilities for future success. Over the next decade, utilities that effectively leverage NWAs and data-driven capital investment strategies will enjoy significant advantages: stronger ROI, reduced operational costs and more resilient business structures.

Three Key Opportunities for Forward-Thinking Utilities

1. Enhanced Forecasting Capabilities

The foundation of effective grid management starts with accurate forecasting. By developing a behind-the-meter understanding of appliance ownership and usage patterns, utilities can create comprehensive territory-wide load profiles with unprecedented detail.

By combining these detailed load profiles with broader housing and appliance ownership trends, utilities are able to forecast peak load 5-15 years into the future with reliable accuracy. This granular approach also facilitates scenario analysis for various electrification pathways — revealing exactly how and when heat pumps, EVs and other DERs will impact the grid.

Patel confirms that Hydro One is pursuing just such a strategy: “We have been collecting a lot more customer data,” Patel explains. “However, primary monitoring and data collection points were at the feeder or station levels, but we never looked behind the meters. Now, by analyzing behind-the-meter customer behaviors, we try to sense how many electrical vehicles customers are adopting.”

2. Targeted Non-Wires Alternatives Programs

With AI-powered analytics, utilities can examine load curves across their grid assets to identify maximum, minimum, and average demand patterns. This precision reveals exactly which substations are approaching capacity constraints and which specific appliances are driving peak demand.

Armed with this intelligence, utilities can design highly effective NWA programs that:

  • Target the right customers with strategic load shifting initiatives
  • Identify grid assets with spare capacity for beneficial electrification
  • Enable personalized customer communications that drive meaningful behavioral changes

“Now we are looking more at customer behaviors and customer engagement,” Patel notes. “Looking at the behind-the-meter behaviors helps us understand how it integrates into the load profile, how it impacts our stations, and how we can better manage our stations and load profiles at a micro level.”

Consider the practical application: By analyzing household consumption patterns, utilities can identify customers who charge electric vehicles or run pool pumps during peak hours and target them specifically for load shifting programs — creating immediate benefits for both the customer and the grid.

"Looking at the behind-the-meter behaviors helps us understand how it integrates into the load profile, how it impacts our stations, and how we can better manage our stations and load profiles at a micro level...

3. Optimized Infrastructure Investment

While NWAs offer powerful alternatives to traditional infrastructure, strategic grid investments remain essential. The challenge is knowing exactly where and when to deploy capital for maximum impact.

“We are trying to understand the heating electrification impact on our grid as heat pumps are becoming really popular in Ontario. This data helps us to accurately forecast load, which helps identify the future grid investments we’ll need to make and the constrained areas that we need to alleviate.”

Using a comprehensive data approach, utilities are able to:

  • Accurately identify emerging grid constraints
  • Strategically plan infrastructure upgrades with precise timing
  • Appropriately size new assets to match actual needs
  • Deploy capital with optimal prioritization

The benefits extend beyond traditional grid assets. With insights into current and future EV adoption patterns, utilities can strategically place public charging infrastructure in locations that will deliver the greatest value.

Building a Resilient Energy Future

The energy landscape is transforming rapidly, driven by electrification, customer behavior shifts, and climate imperatives. Utilities that embrace data-driven approaches to grid management will be best positioned to navigate these changes successfully.

By developing comprehensive visibility into both grid-level and behind-the-meter dynamics, energy providers can balance reliability with cost-effectiveness through a strategic mix of NWAs and targeted infrastructure investments.

As Patel emphasizes, “Data serves as a basic foundation.” That foundation enables utilities to build a more resilient, flexible, and customer-centered grid that will meet the challenges of today while preparing for the demands of tomorrow.

The future of energy is already here—and it’s being shaped by those who understand that in an increasingly complex grid environment, data-driven decision making is the key to success.

For more information about Bidgely’s DER grid planning capabilities, download this guide to Bottom-Up Grid Planning Through Behind-the-Meter DER Intelligence.

As electric vehicles (EVs) reach mainstream customers, utilities are facing challenges in serving demand growth from EV charging, particularly on the distribution level. According to recent projections, EV adoption is expected to grow from 4.8 million EVs on U.S. roads today to 78.5 million by 2035 — representing more than 26% of all cars and light trucks. While recent market shifts due to tariffs and potential changes to EV tax credits may affect this projection, utilities are already experiencing substantial impacts directly attributed to EVs, which means that identifying and understanding EV-driving customers has never been more important for utilities to mitigate grid constraints.

In fact, unmanaged EV load has the potential to require billions* of dollars in secondary transformer and service upgrades, all while supply chain constraints on transformers complicate infrastructure expansion. At the same time, additional pressures from building electrification and other load growth are compounding the challenge.

Being proactive is key to reduce or delay the need for such investments, including EV time-of-use rates, active managed charging programs, and other load shifting and shaping interventions. In addition, when machine-learning (ML) and artificial intelligence are applied to AMI data to reveal territory-wide insights, project managers are able to develop strategies to use EVs for load flexibility and virtual power plants (VPPs). AI also empowers utilities to identify areas where EVs are contributing to non-peak time congestion on the distribution grid.

“The rapid growth in EV adoption creates both challenges and opportunities for utilities,” says Brittany Blair, Manager, Research & Industry at the Smart Electric Power Alliance (SEPA) and one of the authors of SEPA’s recent Insight Brief: AI for Transportation Electrification. “Without proactive management, grid impacts from unmanaged charging could be costly, but with the right data-driven approach, utilities can turn EV charging into a grid asset. And the time to prepare for that future is now.”

*California Public Advocates Office (2023); Kevala (2023); NYSERDA (2022)

With the right data-driven approach, utilities can turn EV charging into a grid asset. And the time to prepare for that future is now.

The Power of AI for Transportation Electrification

U.S. electric utilities have already begun preparing for transportation electrification. As of 2022, 59% of electric utilities in SEPA’s network had established strategic plans for managing this new load, and a key part of those plans is increasing situational awareness of the grid edge and creating data-driven approaches to managing EV charging. What has changed more

recently, is the ability of artificial intelligence (AI) and machine learning to capitalize on existing data flows from the grid and provide the insights needed to enhance utility strategies. At its core, the benefit of using AI for EV detection is precisely that: more sophisticated, data-driven situational awareness. 

Artificial intelligence and machine learning provide utilities with an advanced understanding of EV impacts and allow them to better plan for EV demand growth. Using AI, forward-thinking utilities are accelerating their efforts to identify EV-driving customers, create targeted marketing and EV engagement programs, and account for EV charging within their broader distribution system management strategies.

According to SEPA’s research, effectively managing transportation electrification is made easier with a four-step, AI-enabled process:

  1. Identify Customers with EVs and Their Charging Behaviors: AI enables a more complete understanding of customer EV adoption by identifying EV customers within the utility service territory and parsing data on charging behavior, both of which support utility assessment of system impact and opportunities for managed charging.
  2. Create Managed EV Charging Programs: With granular information about customer EV adoption and charging patterns, utilities can strengthen programs designed to meet grid needs, avoid grid strain, and deliver potential customer savings.
  3. Incorporate EVs into Load Forecasts: An AI-based understanding of EV adoption, charging behavior, and grid impact by distribution grid segment (transformer, circuit, substation) allows utilities to better incorporate EVs into load forecasts.
  4. Map EV Load Growth by Geography: Analyzing EV adoption by geography provides an opportunity to detect local reliability risks that may appear before system-level issues arise, particularly due to high EV penetrations on certain levels of the distribution grid. Early identification can help utilities refine their distribution plans and management strategies.

For transportation electrification specifically, AI solutions that detect EVs from premise-level meter data give utilities visibility into charging behaviors without requiring additional hardware investments. Traditional methods of gathering EV ownership information like vehicle registration records, telematics and customer surveys can leave a data gap that limits utilities’ ability to plan distribution systems efficiently and implement effective EV load management programs. AI can reveal what traditional methods miss.

EV Myths for Utilities

WEBINAR: Debunking the Myths Around EV Detection

AI-Powered EV Strategies in Practice

The Insight Brief: AI for Transportation Electrification features two case stories from utilities who are leading the way when it comes to AI-informed EV strategies: Hydro One in Ontario, Canada, and NV Energy in the state of Nevada.

With EV adoption growing, particularly in areas already facing load growth from new construction, Hydro One was seeking better ways to identify EV drivers beyond customer surveys. By implementing Bidgely’s AI-powered analytics, Hydro One was able to identify 20,000 customers with EV charging activity — approximately 10 times more than they had identified via customer surveys. The utility also realized what it calls its “highest click through rate” in recent history via targeted email recruitment campaigns and is able to create territory-specific EV load shapes to inform grid planning.

NV Energy turned to AI to better understand customer preferences and charging trends while identifying and testing technology that could help improve distribution and resource planning processes to prepare for future grid constraints from additional EV load. Using Bidgely’s patented AI, NV Energy was able to detect 50 customers with high-value baseline charging behavior and achieve a load-shift potential of 2-4 kW/vehicle per event – far above the typical 0.2-0.8 kW/vehicle. The utility’s learnings helped inform its 2025-2027 Transportation Electrification Plan.

Looking Ahead

As utilities advance their distribution system planning capabilities for the future grid, improved EV insights can inform a cascade of other investments. Utilities can deploy AI’s capabilities in classification, assessment, automation, prediction, and customer engagement benefitting their teams at every stage from strategic planning to system investments to operations.

“Utilities that invest in managed charging strategies now will have an advantage in navigating the transportation electrification transition before EV adoption reaches a critical point in their territories,” emphasizes Blair. “As EV adoption continues to accelerate, the value of having a utility strategy based on precise, granular charging distribution and load patterns will aid in having a more flexible grid, more targeted infrastructure investments, and overall better customer experience. Data and AI capabilities are one piece of that strategy.”

To read the full utility case stories and learn more about how you can leverage AI to prepare for the growing adoption of electric vehicles, maintain grid reliability and grow customer satisfaction download the full Insight Brief: AI for Transportation Electrification today. And, reach out to our team to schedule a live demo of Bidgely’s EV Solution.

According to the U.S. Energy Information Administration (EIA) the average residential customer used 899 kilowatt-hours (kWh) per month, while the average commerical customer used 6,019 kWh per month. These figures indicate that, on average, commercial customers consume nearly 7 times more electricity per month than residential customers, making small and medium business (SMB) customers an essential partner in achieving decarbonization goals and long-term grid resiliency.

In an era of rising energy costs and evolving customer expectations, utilities face the challenge of maintaining strong relationships with their business customers. 

The good news is, just as Home Energy Reports (HERs) play a foundational role in residential customer engagement, so too do Business Energy Reports (BERs) foster greater participation and satisfaction among SMB customers.

Rocky Mountain Power (RMP) credits its BER program as instrumental in boosting its J.D. Power Business ranking by 45 points between 2020 and 2021. The consistency of BER communications and the personalized insights they provide has strengthened SMB customer relationships across the board.

Building Trust Through Regular Communication

“Business customers are feeling a little bit disenchanted with rate increases, and they want to know that they have an energy partner in the utility,” explains Barb Modey, Customer Satisfaction Market Research Manager at PacifiCorp (parent company for RMP). “They are always looking for insights into their usage. What the utility can do is present them with those insights, and that helps them understand what’s going on with their energy use, maybe showing them things they didn’t know before.”

This focus on partnership represents a shift from traditional utility-customer dynamics to a more collaborative relationship. For RMP, establishing a regular cadence of communication has been transformative.

“Before we had the Bidgely reports, our communications with business customers was a little more sporadic. We would do maybe short campaigns or newsletters, and then we’d lose our momentum,” Modey notes. “The beauty of these reports is that they go out on a monthly basis. So, it’s a regular cadence and ongoing.”

Data-Driven Insights That Empower Businesses

The foundation of RMP’s engagement strategy lies in sophisticated data analysis. Using Bidgely’s patented disaggregation technology, the utility creates detailed energy profiles for each business customer, breaking down usage by equipment category and time of day.

Monthly summaries provide commercial customers with a snapshot of their energy use that includes equipment-level spending. This granular view enables businesses to make informed decisions about their energy consumption.

“Knowledge is power,” Modey emphasizes. “And if you can leverage that knowledge, then you can make better choices.”

Knowledge is power, and if you can leverage that knowledge, then you can make better choices.

This precise analysis allows SMBs to identify efficiency and cost-saving opportunities, both behavioral and through equipment retrofits. It also offers a more useful means to compare current bills against historic ones, with the usage detail needed to pinpoint exactly why a bill has gone up or down.

Beyond Energy Efficiency: Demand Side Marketing

Rather than focusing solely on energy savings, RMP approached their BERs as a marketing and communication tool.

“We decided to not calculate savings from the programs,” Modey explains. “It was difficult to calculate savings because with business customers—there are fewer of them, for one. And then they’re so different. You might have hair salons or tire shops or restaurants, and how do you compare them in a savings model?”

This marketing-focused approach allowed the utility to be more flexible with messaging and expand its reach (no need for a control group!).

“We use the reports to get out other messages to customers, maybe about our wildfire mitigation plans, or to encourage them to update their contact information so we can reach them during emergencies,” says Modey. “And also to cross-promote other programs that might not be related, like we have EV charger incentives for business customers and renewable energy programs that they might be interested in.”

Rather than fund the BER program through a traditional demand side management budget, RMP used its demand side marketing budget – diverting a portion of the money that they traditionally spend on television and radio ads.

“We had to get buy-in from the corporate communications teams and the program managers because they were used to using the money in a certain way with the type of marketing they had been doing in the past,” explains Modey. “Redirecting some of those funds from their budgets into another channel was a shift. But after the initial set up, the reports become very affordable — you can’t do traditional direct mail for the cost of these email reports. And, then when we started to see customer satisfaction results improve, then they were totally on board.”

SMB customer disaggregation and insights

TECHNOLOGY BRIEF: SMB DIsaggregation and Insights

Creating Personalized SMB Customer Journeys

Beyond monthly energy use summaries, the SMB customer profiles enable RMP to identify personalized next-best insights and interactions for every business, and communicate recommendations via proactive alerts, such as bill breakdowns paired with product offerings like demand response for batteries, HVAC rebates, and more.

The resulting engagement program is consistent and relevant, establishing the utility as a partner that SMB customers can rely on.

“We’re offering them something  other than a bill, but rather information about how to control their bills. With that information, they’re empowered to manage any rate increases and make smarter decisions about either the time of day that they’re using electricity or what equipment they’re using,” Modey says.

Measuring Success: Beyond Expectations

The results of the program have exceeded Rocky Mountain Power’s expectations. The utility has seen significant improvements in customer satisfaction scores across multiple dimensions, not just energy efficiency.

“The customers who recalled the reports gave us high marks across many categories within that survey. Everything from being more involved in the local community to helping the environment, which have nothing really to do with energy efficiency,” Modey states. “We saw a lift across most of the categories within the survey, not just energy efficiency.”

More remarkably, she says, “Even the customers who didn’t say they liked the Reports still gave us higher scores than those customers who did not recall receiving the reports.”

Advice for Other Utilities

For utilities considering a similar approach, Modey offers practical advice.

“I would advise other utilities to consider whether they really need savings from this type of program. It’s not that the program won’t deliver savings — because they might come through indirectly if you link to your incentive programs — but you don’t necessarily need to attribute it to these reports to make them valuable.”

She also emphasizes that impeccable data shouldn’t be a prerequisite for getting a BER program started.

“Even if your data is not perfect — and I don’t know any utility that has perfect data — we don’t have email addresses for all of our customers. We don’t even know if all of those email addresses are going to the right customer or the decision maker… If we waited until our data was perfect, we would never do anything with business customers. So, you have to start somewhere. Your data quality will improve as you go.”

The Future of Utility-Business Relationships

As J.D. Power has emphasized in their survey report, far too many business customers are not receiving proactive outreach from their utility. It’s incumbent upon utilities to change that reality and establish a stronger collaboration with their SMB customers.

“If we hadn’t done the reports, then I don’t think our customer satisfaction would be as high with our business customers. In fact, I know it wouldn’t be as high,” Modey concludes.

For RMP, the consistent engagement, personalized insights, and proactive communication have transformed their relationship with SMB customers. Since 2020, RMP’s SMB customers have been receiving monthly BERs. Ongoing survey measurement continues to reveal strong engagement with the Reports and a lift for customer satisfaction.

“Our goal is to use BERs to advance our SMB customers from awareness to participation. And with that evolution, the by-product is a more satisfied customer.“

Learn More

If you would like to learn more about RMP’s Business Energy Reports program and Bidgely’s SMB engagement capabilities, download the Bidgely SMB Solution Brief or visit bidgely.com/solutions/small-medium-business.

Hear more from PacifiCorp’s Barb Modey by watching the on-demand webinar: “Secrets of SMB Success: Rocky Mountain Power’s Award-Winning CX.”

The flywheel effect represents a powerful business model where multiple interconnected actions create a positive feedback loop, driving continuous, accelerating growth.

Amazon’s meteoric rise to a $2.5 trillion market cap exemplifies this concept perfectly. As customer numbers grew on their platform, more sellers and products were attracted to join, which in turn drew even more customers — creating an ever-accelerating cycle of value and growth.

Not unlike the pre-Amazon retail industry, legacy utility operations for power generation, distribution, and customer engagement have traditionally followed a linear value chain. Grid operations and customer interactions have seldom functioned as an integrated system. Today, however, this conventional paradigm faces unprecedented challenges as increasing adoption of electric vehicles, renewable energy sources, and battery storage systems enable consumers to actively participate in grid management.

The energy landscape is evolving as billions of decisions made by hundreds of millions of stakeholders — utilities, customers, regulators, and others — reshape how energy is produced, stored, managed, and consumed. Utility operations are developing into connected energy platforms that mirror Amazon’s integrated ecosystem.

And just as the retail sector was disrupted by the flywheel effect, the utility industry’s flywheel has begun to turn and is gaining momentum. Let’s explore how this new paradigm is taking shape to create the resilient grid of the future.

The energy landscape is evolving as billions of decisions made by hundreds of millions of stakeholders reshape how energy is produced, stored, managed and consumed...

Customer Engagement: Setting the Flywheel in Motion

The utility flywheel begins with meaningful customer engagement. 

As EVs, renewables, and other distributed energy resources (DERs) gain widespread adoption, we’re seeing significant variations in both their geographical distribution and DER types. Data indicates that even neighboring zipcodes may see as high as a tenfold difference in EV ownership. Likewise, customers may choose to install an L1 or L2 charger depending on their charging needs. 

These variations make it clear that treating customers region-wide as a single uniform cohort is no longer effective. To truly resonate with customers, utilities must employ messaging and engagement strategies tailored to each customer’s unique appliance usage, EV/solar DER ownership, and lifestyle preferences. 

Fortunately, by leveraging Bidgely’s UtilityAI™ Platform, utilities can now detect which customers have EVs, solar installations, or inefficient appliances, while also defining each customer’s energy use patterns across these devices. This allows utilities to hyper-personalize messaging with relevant insights and calls to action. For EV owners, for instance, messaging can be customized based on charger type, charging patterns, and whether they’re enrolled in time-of-use (TOU) rate plans.

This is how the flywheel is set in motion: by delivering exceptional, personalized customer experiences that foster positive sentiment toward the utility and motivate active participation in supporting the grid.

WEBINAR: Driving Success: Crafting Resilient EV Strategies for a Changing Grid

Grid Planning: Leveraging Data for Intelligent Growth

The second part of the flywheel focuses on intelligence-driven grid planning. 

Behind-the-meter (BTM) customer energy use data provides utility grid analysts with critical insights into where grid assets face constraints — identifying whether distribution transformers, substations or feeders are experiencing peak-time overload and understanding the root causes. As mentioned earlier, these BTM insights are especially valuable at both the home and circuit level due to the significant geographical and device-type variations in DER adoption patterns. 

Bidgely’s AI-enabled platform helps identify which customers own solar, EVs, and/or batteries, the type of EV chargers they use, and their charging patterns. UtilityAI™ provides a granular understanding of appliance-level energy consumption patterns for every individual customer on an hourly, daily and monthly basis to help utilities pinpoint those customers who have the greatest load shaping or shifting potential to alleviate grid constraints. 

Bottom-up load aggregation of these “disaggregation-based BTM insights” further empowers utility teams to proactively identify where grid assets — transformers, feeders or substations — are likely to become stressed in the future. 

Load Management: Optimizing for Resilience

This brings us to the last part of the flywheel: load management. 

Efforts to manage peak load through demand response can take multiple forms: shimmying, shedding, shifting, and shaping. Among these, load shifting and load shaping prove most effective for addressing long-term grid stability as DERs scale. 

With Bidgely’s AI-based disaggregation of BTM data, utilities not only gain access to energy usage by appliance, they can also identify specific appliance types — including EVs, and gas vs. electric appliances — as well as consumption patterns that reveal whether an individual home consistently uses energy during peak hours or exhibits off-peak, low-load tendencies.  Thus, utilities are able to better target program recruitment to the ideal customers for load shaping or shifting. 

The synergistic benefit here is clear: because utilities have already leveraged disaggregation to deliver exceptional, hyper-personalized customer experiences, these target customers are primed to become willing participants in demand response programs.

Participating in such programs reduces overall energy footprint and costs for customers, thereby further enhancing customer experience and bringing us  full circle to the beginning of the flywheel, where it continues to turn with increasing momentum.

Utility Flywheel Diagram

Unleash the Power of the Energy Flywheel

Just as Amazon’s flywheel inspired a retail evolution, the energy space is now poised for its own transformative flywheel transition. 

If you would like to learn more about how an integrated approach to customer engagement, grid planning and load management can accelerate growth and resilience in your organization, reach out to our team to schedule a live demo.

I’m thrilled to share some exciting news: Bidgely has acquired Grid4C, a leader in AI-based predictive analytics for the energy industry. 

This acquisition marks a significant moment for Bidgely, solidifying our leadership position in AI-powered energy intelligence. Combining our industry-leading AI with strategic acquisitions like Grid4C allows us to deliver unprecedented innovation and value to the energy sector.  

Grid4C is a recognized leader in the predictive energy analytics space, with close to 10 patents to its name, and has developed novel solutions around fault detection & diagnostics of home appliances and grid assets, as well as load and DER forecasting. 

The company, backed by strategic investors such as Alectra Utilities and Engie, has gained traction among North American, APAC, and European utilities through these very relevant offerings that help solve the industry’s big challenges.

Grid4C Acquisition

I’m also pleased to welcome Dr. Noa Rimini, Founder and CEO of Grid4C and a PhD in artificial intelligence and machine learning as well as the Grid4C team to Bidgely.  We look forward to their contributions to our UtilityAI platform of customer- and grid-facing solutions.  Dr. Rimini is an engineering and data science leader, which will only further supercharge our data science leadership team

and the value outcomes we deliver to our utility partners as an Energy Intelligence leader.

Enhancing the UtilityAI™ Platform

Grid4C’s solutions are an excellent complement to our existing platform. This acquisition will strengthen the core capabilities of Bidgely’s UtilityAI™ Platform and expand support for new use cases. 

Specifically, Grid4C’s fault detection and short-term load forecasting solutions bring powerful enhancements:  

  • Fault Detection and Diagnostics: Grid4C’s fault detection algorithms, once integrated into our customer engagement solution, will enhance Bidgley’s ability to provide insights into when a utility customer’s appliances, such as heating and air conditioning (HVAC) systems, are likely to fail, benefiting multiple stakeholders. Utilities will be able to offer appliance maintenance programs to consumers, enhancing customer experience and creating new revenue streams. Home service partners of utilities will benefit from targeting the most promising customers. Consumers will benefit from proactively maintaining their appliances, preventing expensive repairs down the line, and gaining insights into potential malfunctions, inefficiencies, and behavioral anomalies for many home appliances and grid assets. 

  • DER-Ready Load Forecasting: Integrating Grid4C’s AI-powered granular load & DER forecasting into Bidgely’s customer engagement platform will assist customers to better budget their energy costs, with accurate seven-days- ahead hourly usage forecasts and 30-days-ahead bill predictions preventing bill shocks. When merged with Bidgely’s grid analytics offering, this load & DER forecasting feature will also predict faults and overload of grid assets, and identify when solar production or electric vehicle consumption will peak, allowing utilities to intelligently plan demand response programs and optimize DERs.  

Bidgely + Grid4C: A Win for Bidgely, A Win for our Utility Partners

This acquisition signals a new growth era for Bidgely. For over a decade, we have been focused on developing our own data science and solutions, and have built organically built out a client portfolio encompassing dozens of utilities around the world and tens of millions of utility customers served. 

Now, with this first strategic acquisition, we’re accelerating that trajectory. We are well-positioned as a growing partner for the future, to help utilities innovate and tap into the power of AI to solve their biggest grid and customer engagement challenges and to help drive the clean energy transition.

I had the pleasure of sharing the mic with NV Energy’s Director of Integrated Energy Solutions Adam Grant on The Edison Electric Institute’s Electric Perspectives podcast. During the broadcast, Adam and I shared our thoughts about how AI-powered technology is advancing electric transportation programs.

Adam kicked off the discussion by describing what led NV Energy to look to AI-driven EV solutions.

“We’ve been using Bidgely’s UtilityAI platform as our overall tool for our customer solutions since 2017, starting with customer experience and energy efficiency and now moving into EVs,” Adam explained. “We used Bidgely’s behind-the-meter intelligence—technology that analyzes energy usage patterns at the household level—to identify customers who had high usage load and were also charging their electric vehicles on peak. With Bidgely, we were able to detect not only the type of charger being used, but also when it was being used – which empowered us to precisely target EV owners with peak charging behaviors.”

Bidgely’s EV Solution not only detects electric vehicles and helps utilities understand their load patterns, but we also provide a data-driven approach to better engage those customers to help them optimize their charging habits and reduce their bill.

“As utilities, we need to recognize that every customer has their own unique charging habits,” he went on to say. “Some charge as soon as they get home. Others charge overnight. And some charge at different times based on convenience. And when the customer charges is just as important to grid management as the charging itself. Our goal was to use this deeper understanding of EV behaviors to inform new EV managed charging programs we’ve proposed to our commission, and really put forth a program for customers that effectively shifts the charging load.”

EV Load Management

VIDEO: EV Load Management

EV Solutions at Scale

The deeper understanding of EV behaviors that Adam mentioned is the foundation of Bidgely’s EV Solution. We believe identifying electric vehicles on the grid and their charging patterns is not just AI for the sake of AI. This intelligence has the potential to make a tangible impact on the system itself. When a utility is able to really understand who’s charging and how they’re charging, it becomes possible to take informed action to support grid modernization and grid stability and scale it rapidly.

Scalability is crucial. There is no silver bullet in the realm of EV management or EV customer engagement. That’s why Bidgely’s EV solution is designed to engage the full range of customers with different ways they can participate — serving the full EV owner customer base both today, as well as when that population grows to hundreds of thousands of customers.

According to the International Energy Association, ​​new electric car registrations totalled 1.4 million in 2023, increasing by more than 40% compared to 2022. JD Power expects EV sales to reach 36% of the total U.S. retail market by 2030 and 58% by 2035.  For a mid-sized utility, this could mean managing thousands of new distributed energy resources within just a few years.

As Adam said, “When it’s time to make the leap from our trial program to the thousands of customers who will be participating in the future, we recognize that it’s going to take significantly more EV detection, targeting and personalized customer outreach to engage the greatest number of customers who are best-positioned to realize benefit for themselves and the grid.”

We recognize that it's going to take significantly more EV detection, targeting and personalized customer outreach to engage the greatest number of customers...

AI-Informed Rate Design

NV Energy is leading the way in deploying AI and other technology solutions to hyper-personalize and target its programs, including its approach to rate design.

Given Nevada’s primary industries, granular household appliance-level data is incredibly important to informing new tariffs. As Adam described, Las Vegas is different from other major metropolitan areas.

“Beyond the electrification challenges which every large city is beginning to face, the casino industry makes us a 24-hour town,” he said. “Everyone in our community doesn’t work 9 to 5, or even, 6 to 2. A large number of our customers work graveyard and overnight. So there are people who are home during the day when the time of use doesn’t necessarily work for them. So that’s another challenge here in Nevada, is to have the behind-the-meter insights to create a mix of opportunities for our customers that are unique to the schedules they have.”

NV Energy’s strategy of layering different types of offerings encourages participation of the broadest possible range of customers, whether it is through active managed charging, or a time of use rate that provides a way to save money on their own schedule. Not every customer is going to want the full automation of managed charging, but they may be enticed by a program that allows them to save money by scheduling their car’s charging during a reduced TOU rate period. 

It’s important to recognize that there is not one optimal end-all solution. Instead, we have to be responsive to the fact that the solutions we offer need to align with customers’ varied charging habits. 

“Our approach is multifaceted,” Adam emphasized. “It has behavioral components. It’s got the tariff. It’s got the technology components. So it really is a full strategy to make sure that we have the ability to serve as many customers as possible.” 

WEBINAR: Debunking the Myths Around EV Detection

You Can’t Afford to Wait

NV Energy’s approach to EV management will serve them well. They’re leveraging their behind-the-meter intelligence to implement a comprehensive approach to manage their EV load. Even if you’re not yet seeing the stress of EV adoption in your territory, you will soon, and it will be exponential in its growth. Utilities can’t fail to take steps now, assuming it will be possible to deploy an effective large scale EV program after adoption reaches scale.

Across the country, we are seeing a number of exciting EV program trials. But we can’t stay in trial mode much longer. We have to expand quickly to bigger and better programs and learn from these experiences. 

“We know that electric vehicle load is coming. We know potential building electrification load is coming. And we know that, especially in our territory in the desert southwest, extreme heat is a reality. I think we had 32 straight days over 110 degrees last summer. So having that extra load from EV chargers and buildings on top of cooling load is significant,” Adam said. 

“We need to find the most effective ways to shift EV load to periods when we have excess renewable energy in our system so that we don’t have to build that next power plant. Sure, we’re  going to have to expand our generation capacity over the long haul. But we’re designing our EV program to allow us to delay that step. It’s much less expensive to put these programs in place – which of course also allows us to provide more price stability for customers.”

Measuring Success

At the end of the day, commissions will play a very active role in determining whether to continue, expand or refine EV program investments. Across regulated territories, we’re seeing three key aspects to success. 

Customer Engagement and Satisfaction – High performing EV programs will enhance, rather than detract from, the utility-customer relationship. Measuring customer satisfaction with the program and their understanding of how it relates to their energy bill will be a key metric as the utility company becomes the new gas station. 

Load Shifting – We need to measure the ability of each electric vehicle to contribute to a kW shift off-of-peak. Doing so requires measuring the before and after – i.e. normally this customer would have contributed Y kW on an average demand day, but now we’re able to reduce that, or shift that to X kW. 

This is particularly important when bringing incentive dollars into the mix. We need to begin to equate incentive ROI to the relative value a customer provides to the system. In other words, if a customer is already charging their vehicle at midnight, it does not make sense to offer them an incentive.  Rather, we have to intelligently target the right customers -– those who are on constrained feeders or charging on-peak — and shift the metric for success from the number of vehicles enrolled in an incentive-driven program to kW reduction per participant, and begin compensating customers not for their participation but rather for their kWs.

Potential Savings – If you’re filing an IRP or a transportation electrification plan, what can you actually count on in terms of economic benefit and lower infrastructure impact? What can a grid operator rely on? It’s important to quantify the amount of grid support an EV program has the potential to deliver. 

Highlighting that customer engagement is the foundational metric, Adam said, “I think that the main thing that we are learning is that education is the most important thing up front — really defining what the parameters are for the customer, so they understand the benefits that not only they’re getting for themselves, but also to the grid and price stability, because we don’t have to either buy power during the highest usage times, or build that next power plant or that peaker unit. Because, as we all know, customers are going to do what benefits them individually for the most part. So showing them their personal benefit is a really good start, and then layering onto that the benefits it has for the community and for everyone else could be really beneficial overall.” 

The next big step is integrating that customer engagement into grid side planning and building full non wires alternatives programs. 

Looking Ahead

Managing EV load is an essential part of an AI-driven, holistic approach to creating the resilient grid of the future.

Learn more by listening to the full EEI Electric Perspectives Podcast: AI-Powered Solutions for Transportation Electrification podcast.

For more than a decade, Bidgely’s advanced AMI analytics have empowered utilities with AI-powered Customer Experience (CX) solutions. Leveraging meter data and our patented appliance-level load disaggregation algorithms, our utility customers have achieved multi-quartile improvements in JD Power Scores, verified customer satisfaction ratings typically ranging from 80–90+ percent, and more than 1 TWh in energy efficiency savings.

In this legacy “AMI 1.0” world, home energy use data has been measured on the meter and transferred to the cloud for analysis, providing utilities with insights about what appliances were used the previous day. This actionable intelligence has allowed energy providers to guide customers to make smarter energy decisions, to more successfully engage them in behavioral programs and personalize, and to improve their overall energy experience.

However, as grid stress, customer expectations, and regulatory pressures steadily increase, the AMI 1.0 world is giving way to utilities investing in next-generation AMI 2.0 meters with more sophisticated capabilities for these new operational demands. 

AMI 2.0 meters let utilities and technology innovators like Bidgely move the intelligence closer to the data source, enabling real-time insights and engagement at the grid edge. 

For example, through a unique collaboration with Itron, Bidgely’s patented AI-powered applications are embedded within the physical meter. 

Real-time Usage Insights Drive New Use Cases

“We see that the change on the grid for our utility customers is tremendous. And it really is driven by new behaviors behind the meter on the customer premises as consumers buy electric vehicles, install solar and adopt other new equipment. Of course, at the same time, the climate is changing and infrastructure is aging,” explains Itron Vice President Of Product Management, Stefan Zschiegner. “So now the opportunity with our latest generation of meters is to bring the computing power to the meter with a distributed intelligence or edge intelligence that processes data and enables communication. We’re offering utilities new ways to better connect the consumer to the grid, extend visibility and manage this influx of new requirements.”

“With our Grid Edge Intelligence solutions, we’re able to push apps similar to an iPhone,” adds Nipesh Patel, Itron’s Area Vice President, Strategic Sales. “We’ve collaborated with Bidgely to push some applications to the meter to identify, for example, when a new EV is connected to the grid or when PV is connected to the grid, and we’re already working with our existing customer base to bring that value to them. Having real-time data and making use of that data is going to drive a lot more value and enable the sort of hyper-scaling that the industry is now facing.” 

As Patel mentioned, EV charging provides a compelling use case for the potential of AMI 2.0 technology. While Bidgely can already reveal on-peak charging that has occurred through analysis of AMI 1.0 data, with our intelligence at the grid edge, utilities can engage and coach their EV-owning customers in real-time about the impacts and costs of their charging choices.

ON DEMAND WEBINAR: CRAFTING RESILIENT EV STRATEGIES FOR A CHANGING GRID

Next Generation CX

The opportunity that Distributed Intelligence enables utilities to connect with consumers in new ways, opens up countless new use cases that were previously impossible.

“For example, you can not only detect whether there is a level-two charger on a customer premises, you can also engage with the consumer in real time to talk about their charging pattern, the time of the charging and the duration of the charging patterns – making targeted recommendations on what to do,” explains Zschiegner. “This makes it easier to engage customers in time of use programs, behavioral programs that are specific to electric vehicles, and other opt-in programs that give the power to the consumer while at the same time enabling the utility to provide a more reliable and better customer experience.”

Bidgely is pursuing a wide-range of AMI 2.0 use cases, including:

Demand Response 

  • Pre-event coaching: Providing reminders to reduce load before an event, based on appliance usage
  • Real-time event monitoring: Track load spikes and reductions during DR events in real time, improving the customer’s event performance
  • Post-event feedback: Delivering appliance-specific feedback as soon as an event concludes

TOU Optimization

  • Dynamic TOU alerts: Generating alerts if specific high-consumption appliances are operating during peak periods, encouraging load shifting to off-peak times
  • Pre-peak alerts: Generating alerts on specific appliance actions (such as AC pre-cooling) before peak hours
  • Daily/weekly coaching: Suggesting behavior changes, such as running dishwashers or laundry at night
  • Real-time EV peak charge alerts: Sending alerts immediately and not the next day

VPP Participation 

  • DER monitoring for VPP participation events: Generating active DER signals to support VPP scenarios such as solar curtailment or vehicle-to-grid, and communicating these events to customers
There are endless opportunities to engage the consumer with our joint technologies that allow the utilities to truly become the trusted advisor and partner...

“Imagine, if you have some issue or an unusual energy use pattern in your house, you could get a notification real time, not the next day,” adds Zschiegner. “There are endless opportunities to engage the consumer with our joint technologies that allow the utilities to truly become the trusted advisor and partner and source of information for the consumer. It’s an opportunity to really change the consumer utility relationship. And why is this so critical? Because it will allow utilities to more effectively engage with consumers who now play a critical role in extending the life of the grid in order to avoid costly grid upgrades and make aging infrastructure more reliable. We have a great opportunity to take customer engagement to a whole new level.

Better Together: AMI 1.0 and 2.0

The evolution from AMI 1.0 and 2.0 will take time, and utilities must consider ways to leverage the benefits of each type of meter through the transition. 

Bidgely’s CX platform delivers valuable insights through both traditional cloud-based AMI analytics and innovative DI applications, covering all your customers, regardless of meter deployment timelines to maximize a utility’s AMI investment. As new meters are deployed, customers and utilities see value from both sides of the solution.

These combined capabilities work in unison to engage customers through a combination of real-time and monthly insights.

AMI 1.0 and 2.0

Working Together to Drive Value Across the Utility Industry

“The collaboration between Itron and Bidgely goes way back. In fact, we have a long term relationship that started off with the fundamental idea that ‘meter data that is highly accurate, highly precise and more frequent’ is the foundation to better insights,” says Zschiegner. “The right data streams make insights easily accessible and provide the context that is going to be critical in delivering advanced analytics. So we look at our partnership, at this foundation, as very strategic and long term.”

In my role at Bidgely, I have the chance to speak with energy industry analysts on a fairly regular basis. Their insights about how the market is evolving serve as an integral part of Bidgely’s strategic planning process.

Bidgley’s recent conversation with Guidehouse Insights Associate Director Mike Kelly highlighted what he calls a “changing of the guard” in terms of industry motivations around software purchasing decisions that is putting customer engagement front and center when it comes to grid optimization.

“Over the past 3 to 5 years I’ve seen a shift from the traditional emphasis on ADMS, DERMS and other operations-oriented software solutions toward more customer-centric technologies,” he said. “There is a growing realization of the relevance of the consumer in facilitating operational and business efficiencies, and so ADMS and DERMS are now giving ways to terms like DER and DR engagement, which increasingly fall under the umbrella of what we call customer engagement.”

More specifically, Kelly said two customer-oriented operational trends demonstrating real impact are real-time intelligence and complex rate structures.

"Two customer-oriented operational trends demonstrating real impact are real-time intelligence and complex rate structures.”

Real-Time insights Accelerate Load-Shift Innovation 

“When I say real time intelligence, I’m referring to behind-the-meter insights, he explained. “In terms of facilitating greater customer program adoption, engagement and enrollment, we’ve seen through multiple studies that customers respond better to real-time intelligence and real-time interactions.”

EV pricing provides a compelling use case. 

“If you’re charging your EV during a peak pricing period, the ability to get that insight in real time versus maybe a 15-minute interval can have a tangible impact on the economic incentive for that customer to actually act,” he said. “And that’s just one example. There are a plethora of use cases that are either enabled or enhanced by the provision of real time insights.”

This evolution is made possible in part by hardware technology as manufacturers continue to introduce more sophisticated meters capable of providing real-time data streams across voltage, energy, current and out waveform. But sophisticated analytics are also a driver.

“This transition is being powered not only by the Itrons and Landis+Gyrs of the world, but also by open ecosystems of analytic partners such as Bidgely that are effectively leveraging their disaggregation technologies to maximize the value of those enhanced data streams,” Kelly explained. 

Recognizing that the evolution to real-time-insight-based-solutions requires both advanced hardware and analytics, Bidgely is partnering with Itron to provide DI (distributed intelligence) “agents” for its next-generation meters that can detect and monitor EV charging and Solar PV. The solution is already being rolled out at several utilities, to provide

  • Real-time awareness of DERs;
  • Real-time DER load to grid infrastructure insights;
  • Real-time engagement with DER-owning customers; and
  • Increased ROI on AMI Investment.

ON DEMAND WEBINAR: Crafting Resilient EV Strategies for a Changing Grid

Targeted Incentives Influence Customer Behavior

On the topic of complex rate structures, Kelly pointed out Western Europe, Japan and other comparative markets are leading the US when it comes to new and advanced rate structures beyond basic time of use. 

“Have we seen some North American utilities effectively engage their customer bases in enrolling in these types of programs? Sure,” he said. “But to really engage customers with meaningful savings requires real time pricing, critical peak pricing and increasingly DER- and EV-oriented rate structures that are more hyper-personalized and really tailored to the individual and target a smaller subset of the population then a generic time of use. That’s where you can provide some of those more tangible economic incentives that I believe truly get customers to engage at a higher level.” 

What these trends point to, Kelly said, is that there is a growing recognition amongst utility decision makers and vendors alike that the consumer — and especially the prosumer — should have a seat at the table when developing strategies for peak demand management and electrification. 

Bidgley has worked with multiple utilities to identify and target EV owners for Time-of-Use rate enrollment and managed charging initiatives, resulting in high levels of peak load shift for EV charging. From these experiences, Bidgely has also found that, to effectively shift EV loads, a multi-faceted approach is most effective, including EV managed charging, EV time of use, and behavioral load shifting. In addition, engagement with EV owners must continue beyond enrollment through continued coaching touch points to make EV load-shift behaviors “sticky.”

Without Coaching Customers May Not Shift

In working with one utility to drive EV time-of-use (TOU) rate adoption, Bidgely found that after onboarding customers into the rate, their EV peak-time charging decreased by 70%. The ongoing coaching once they are on the rate helps reduce EV peak-time charging by an additional 26%.

Learn More

To learn more about how Bidgely can help you harness the power of behind-the-meter insights to advance your customer engagement and load shift goals, visit our demo portal. To hear more from Guidehouse Insights and other industry leaders, sign up to receive more information about EmPOWER AI, Bidgely’s annual event where technology innovation meets utility leadership in navigating the clean energy transition.

High bill resolution is a pressing concern for both utilities and customers, often causing dissatisfaction and reduced call center performance.

To make a bad situation better, customers need clear answers to questions like, “Why is my bill so high?” and “What can I do about it?” Responses such as “it’s the weather” don’t go far to resolve customer frustration.

But Avista has succeeded in turning negatives into positives by equipping call center agents with Bidgely’s AI-enabled energy analytics about each customer’s personal energy use.  

We sat down with Avista Customer Relationship Representative Connor Hennessey at Bidgely’s EmPOWER AI conference, and he shared how the Avista customer service team is leveraging Bidgely’s High Bill Analyzer to improve the customer experience for these challenging calls.

Connor shared how Bidgely’s energy insights and personalized customer recommendations are amplifying their customer support efforts across the board, including a remarkable 27% reduction in truck rolls as well as reduced call handling time.

Then vs. Now

“In the old ways, when a customer came to us with a high bill, all that we had to go on – both the customer and the CSR – was the bill itself,” recalled Hennessey. “We’d be able to see the amount of kilowatt hours or therms, depending on if it was gas or electricity, but would have to dig in further on the calls to try to figure out where that energy usage might be coming from. 

“We didn’t go into the conversation with any insights about whether the energy usage was linked to cooling or heating or entertainment or some other specific appliance. So what most often ended up happening is that we would send our field representatives out to those homes to do a meter test. 

“And even with that test, the cause of a high bill might not become apparent. The customer would be left in a state of vagueness, and any sort of impactful follow up conversation on the part of the CSRs would be severely limited.”

But in 2020, things began to change as Avista partnered with Bidgley to leverage UtilityAITM in order to extract appliance-level energy intelligence from its AMI data and then use those insights to enhance customer service and experience.

Avista leadership made the strategic decision to start with internal use cases first before rolling out energy insights to customers, beginning with the call centers and high bill call support.

Avista leadership made the strategic decision to start with internal use cases first before rolling out energy insights to customers, beginning with the call centers and high bill call support.”

Avista equipped its CSRs with Bidgely’s CSR Console, giving them visibility into disaggregated energy use for each customer across multiple appliance categories at five-minute data intervals. This detailed behind-the-meter visibility, combined with personalized AI-recommendations in the portal, equipped the CSRs to troubleshoot high bill calls and advise customers on savings improvements. 

“Now, Bidgely gives us a lot of data that we’re able to use on a daily basis to have more impactful conversations with customers that are outside what we were previously able to have,” he said. 

“We’ve seen a 27 percent decrease in the amount of high bill truck rolls. And, when a truck roll is necessary, we’re able to empower our field service teams before they go out so they have an ability to identify what type of usage is going on in that house and can have a meaningful conversation with the customer when they’re on site.”

Avista High Bill Analyzer

Making the Most of Your Eight Minutes

Hennessey said he heard a statistic that customers spend an average of only eight minutes interacting with their utility every year. 

“We have to make the most of those eight minutes and really capitalize on the time we spend with our customers,” he emphasized. “Bidgely allows us to more efficiently and effectively identify ways that we can create higher efficiency for what they want to use in their homes, and provide that guidance in a way that meets them where they are – whether that’s talking with a CSR or looking at the website or self-serving in other ways.”

CSR adoption and feedback was strong, as evidenced in one very cold winter, as Avista’s CSR Portal was used over 3,000 times in December alone to address high-bill and other customer inquiries. 

“High bill calls involve a lot of questions. Oftentimes when a customer gets a bill, they see the kilowatt hours, but they don’t know what a kilowatt hour is. It’s not really layman’s terms. But our ability to talk to them in terms they understand about what is actually going on in their home with their appliances and energy habits establishes a common language and allows us to take advantage of the full eight minutes we might have with a customer,” Hennessey explained. 

Bidgely's Appliance Breakdown

“By utilizing the Bidgely tools, we’re able to have the types of conversations customers want to have to increase their education and help them self-serve — which ultimately helps us avoid high bill calls in the future and is part of the reason our average handle times are going down.”

Hennessey added, “I’m really excited about the ability for us to utilize Bidgely in our organization. And I think the options are really limitless in regards to how we want to utilize it and how it will make the lives of CSRs easier every day.”

Learn More

To explore Bidgely’s UtilityAI™ High Bill Analyzer, visit our interactive demo portal, or reach out to our team to schedule a live demo.

If you look at the timeline of artificial intelligence, AI is actually a fairly old science. In fact, the first neural networks were built sometime in the 1950s.

But when it comes to AI’s application in the real world, you might say that the field was treading water for more than 60 years. It wasn’t until 2012 that Stanford developed deep learning algorithms, and the adoption of AI in the marketplace began to skyrocket.

Bidgely was founded that same year in 2012 –- well before real world applications for AI exploded as they have in the past decade. And in the years since, we’ve had the time to innovate the use of AI in the energy space and to perfect our UtilityAI™ platform.

In fact, Bidgely has built the energy industry’s only true disaggregation platform. Our patented disaggregation science has continuously served as the foundation for our industry-leading data science advances and AI innovation. 

Bidgely’s UtilityAI platform analyzes whole-home meter data and looks for fingerprints unique to every appliance to accurately identify which appliances are running in a home, at what time, and how many kWhs they consume. In the case of some appliances, UtilityAI dives even deeper to identify whether an appliance is inefficient or near end of life – such as an HVAC that is short cycling or having saturation problems. Or in the case of EVs, we can determine the type of charging equipment in use – i.e. level 1, 2 or 3.

Perfecting Applied AI for the Energy Sector

In the face of the tremendous pressures now facing the energy industry, true disaggregation provides the basis for a full spectrum of essential AI solutions — from customer engagement to infrastructure planning to grid resilience.

Grids are becoming more complex. Intermittent renewable energy is coming online at scale, including more behind-the-meter generation than ever before. Electric vehicles are introducing new loads, together with the near-term reality of bi-directional vehicle-to-grid energy flow. 

At the same time, the number of stakeholders playing a critical role in energy supply and demand is growing and diversifying. Who is going to install solar panels? Who is willing to sell power onto the grid right now? Who is going to buy an EV? Who is going to replace an HVAC with a heat pump? Billions of choices made by hundreds of millions of people across utilities, customers, regulators, and other stakeholders will shape how energy is produced, stored, managed, and consumed. Consumers and the grid are coming together in never-before imagined ways to form a new integrated whole in which customers play a critical load-balancing role. 

Billions of choices made by hundreds of millions of people across utilities, customers, regulators, and other stakeholders will shape how energy is produced, stored, managed, and consumed.”

In this new paradigm, Bidgely has empowered utilities with applied AI tools that have been perfected over more than a decade. Using our AI-based true disaggregation as a foundation, we enable utilities to enhance customer engagement, energy efficiency and load shifting.

For example, disaggregation-based insights make it possible to identify that Customer X on Transformer Y is responsible for significantly increasing demand for energy because they have multiple EVs at home or an inefficient HVAC or pool pump. The utility is then able to target that household with a personalized offer to enroll in an EV TOU or managed charging program. Then, Bidgely’s award-winning customer engagement platform empowers utilities to achieve the level of customer participation required for meaningful load shift.

Utilities are also able to take those same behind-the-meter home energy use insights and roll them up to the transformer or feeder level to pinpoint which of their infrastructure assets are likely to see the greatest strain. This bottom-up grid view allows utilities to avoid unnecessary capital expenditure through data-informed non-wires alternatives. 

In short, we have managed to bring together cutting-edge AI technology in a single platform that can target all the needs of the modern grid — the precise reason why we were recognized as a “Top 10 Applied AI Company” by Fast Company on their 2024 list of The World’s Most Innovative Companies, among other accolades.

Applied AI has been instrumental in equipping utilities to shift what has historically been a transactional, billing-based relationship with its customer base into an energy supply and demand optimization partnership.

GenAI Solution Brief

SOLUTION BRIEF: UtilityAI supercharged with GenAI to make energy intelligence more interactive, explainable & automated

UtilityAI, Now Amplified 

When I say the words “Gen AI,” you’re likely thinking, ‘why do we need another ChatGPT?’ And more specifically, ‘Why do I need it as a utility?’ 

Generative AI in the energy space is much more than ChatGPT. Gen AI can help improve customer experience by making it easier and more satisfying to interact with their energy data. 

Consider the impact of one of our first Gen AI tools: the Bidgely Energy Assistant.

We’ve established that load shifting now requires customers to be actively engaged in helping provide the solution. You might ask a customer to take steps to reduce stress on a particular transformer because they are one of the main contributors to the energy load on that asset. But how do you improve the likelihood that they will do their part?

Improve Customer Experience with Bidgely Energy Assistant

A customer might ask the Energy Assistant “what is happening with my energy use?” Rather than provide a generic answer, the Energy Assistant is able to leverage behind-the-meter data for that household to provide an easy-to-understand and immediate natural language answer that is specific to that customer. 

The Energy Assistant can also help customers evaluate the impact of energy decisions. For example, a residential customer might ask whether it makes sense to buy an EV, or upgrade an HVAC system or insulation. The Energy Assistant can run an analysis on that household’s energy data and quickly provide meaningful personalized recommendations, such as “your HVAC performance indicates it is nearing its end of life, you should consider an upgrade” or “your heating costs are higher than similar homes in your area, you should consider new insulation.” The tool goes even further: It can run the numbers, telling a customer how much they are likely to save in a year, and what their payback period is likely to be.

Our UtilityAI platform enhanced with generative AI meets customers where they are, and provides insights and recommendations that are immediate and accessible. The result is a better customer experience, higher CSAT and improved customer engagement, enhancing utility efforts to nurture the sort of energy partnerships with their customers that are essential to our future grid.

Simplify Grid Management

Bidgely’s Grid Assistant will provide powerful new interfaces that simplify grid management by empowering grid planners to analyze behind-the-meter data more quickly, conduct scenario planning more accurately, and determine where to upgrade infrastructure with even greater precision. 

Utility teams will be able to pose questions such as “how will EVs grow in our territory?” or “which of my infrastructure assets are most likely to be over capacity from EV load?” Asset managers can also extrapolate and create different scenarios to assess how particular transformers are going to respond as supply and demand evolves both geographically and over time –- all based on AI-driven behind-the-meter insights. 

Similarly, Bidgely’s Grid Assistant will equip program managers with new insights. To achieve a load shift goal, for example, requires engaging the right customers, for example asking a Grid  Assistant to generate a list of accounts meeting a certain set of criteria such as customers who have both EVs and solar or inefficient HVACs. The assistant then helps target every customer with best-suited programs, whether they be TOU, managed charging or an offer for a specific type of EV charger or other appliance purchase or upgrade.

In addition to improving infrastructure management and program outcomes and reducing spend, Gen AI can also positively impact utility employee experience by increasing team member efficiency and providing critical insights that enable better performance outcomes. 

A Platform You Can Trust for Gen AI Innovation

Bidgely is uniquely positioned to fully unlock the value of Gen AI on behalf of our utility partners.

The quality of a Gen AI user experience depends on the accuracy of the data and insights used to train the AI. If that data is not accurate, your Gen AI will not work properly. Over more than 12 years, Bidgely has perfected the AI that transforms meter data into actionable insights. Our AI models have been trained on energy data from more than 30 million homes and businesses worldwide. Now, our UtilityAI’s Gen AI tools sit on top of that tried-and-tested AI platform. 

I invite you to watch Empowering Utilities with GenAI:A Guide to Enhanced CX and Grid Management, our on-demand webinar that dives deeper into the potential use cases for Energy and Grid Assistants. Or, reach out to our team to schedule a demo and learn more about pilot opportunities.