Utilities are facing an increasingly complex energy landscape with growing energy demand from data centers and electrification, new distributed energy resources coming online, and an evolving regulatory environment. Among all of these things, utilities need to continue to supply safe and reliable energy using baseload, intermediate, and peak load supply-side resources.
Uplight programs can provide relief to the strain placed on utility energy supply by reducing, shifting, and shaping demand which collectively we call the demand stack. Since only a fraction of customers are enrolled in rates, demand response, and VPP programs, we also need to drive down demand without monetary incentives or direct control of devices by focusing on customer behaviors. In this installment of Meet the Models, we’ll reveal how Uplight’s Bill Analytics and Usage Disaggregation models work.
These models drive demand reduction by providing detailed energy insights to customers. Armed with this information, customers are empowered to make behavioral changes and improve the energy efficiency of their home. As customers engage with these tools, they will also develop greater awareness of other utility offerings such as rates, demand response, and virtual power plant programs creating more opportunities to move up the demand stack and deliver even more benefit to the utility.
In addition to reducing energy consumption, utilities using Bill Analytics and Usage Disaggregation can see increased customer satisfaction and reduced call center volumes as customers better understand their utility bills and energy usage patterns.
How Bill Analytics and Usage Disaggregation Work
Uplight’s Bill Analytics and Usage Disaggregation models leverage individual customer data to surface personalized insights about a customer’s home energy usage and associated cost. These models employ a mixture of machine learning, custom algorithms, and simulation methodologies to learn how external factors and customer behaviors are tied to a customer’s energy use. Uplight utilizes energy and billing data, in combination with weather, home characteristics, and other data sources to find patterns and generate insights surfaced to the utility customer.
Within this model suite, there are multiple ways a customer’s energy use and bills are leveraged to inform a customer about their usage.
- In our Bill Compare model, a customer’s current bill is compared to their previous bills and changes between bills are explained.
- In our Bill Forecast model, we project the amount of the customer’s next bill and can notify them if the cost is projected to be higher than expected.
- In our Usage Disaggregation model, a customer’s bill is broken down into end-use categories, like heating and kitchen, to explain the specific ways they are using energy in their home.
- In our Peer Compare model, a customer’s bill is compared to that of similar homes to inform a customer how their bill compares to those of their peers.
Together these four offerings make up our Bill Analytics and Usage Disaggregation model suite and provide personalized customer insights.
Value of Uplight’s Bill Analytics and Usage Disaggregation Models
Let’s dive deeper into the Bill Analytics and Usage Disaggregation models and highlight the value these models bring to the utility and the end customer. The Bill Compare, Bill Forecast, Usage Disaggregation and Peer Compare models each help utility customers understand a different aspect of their bill by answering common questions a customer may have about their bill.
Bill Compare
Customer Question: Why is my current bill different from my last bill? Was my current bill this high last year?
The Bill Compare model generates insights presented to utility customers to educate them about how their usage behavior impacts their energy bills and how both usage and cost have changed over time. These insights explain which factors led to changes in bill cost, including weather, usage and rate changes. This information can help energy consumers better understand which factors impacting their bill are under their control, such as usage, and can be changed compared to the factors that are out of their control, such as extreme weather events. As energy consumers better understand how their usage behavior impacts their bills, they are driven to take action to save money and reduce carbon impact.
In addition, by enabling a customer to self-serve these common bill questions, we can decrease utility call center volume. Call center representatives can also leverage these breakdowns to decrease call time. These surfaced bill insights result in improved customer satisfaction and reduced utility operational costs.
Bill Forecast
Customer Question: What is my next bill going to cost?
The Bill Forecast model projects a customer’s upcoming bill cost based on historical energy usage and weather. This model could indicate that the next bill will cost more or less than the previous bill. Customers facing potentially high bills can be motivated to take action to reduce energy consumption to avoid a potential high bill. By alerting customers to possible bill increases ahead of time, utilities can reduce both customer surprise and the associated spike in call center volume.
Usage Disaggregation
Customer Question: What appliances and activities are driving my energy bill cost? How much am I paying for heating my home?
The Usage Disaggregation model breaks down the total bill usage or cost to explain the specific ways a customer is using energy in their home. The model presents the following energy usage categories to a customer: cooling, heating, electronics, kitchen, laundry, lighting, water heating, pool/spa, other, and electric vehicle (if applicable). Seeing their usage broken down into these categories helps customers understand how their usage stems from different appliances and associated behaviors. High usage in a specific category can motivate the energy consumer to retrofit their home, upgrade an appliance, or change their behavior. For example, high usage in the cooling category could be mitigated by adding insulation, switching to a more efficient cooling system, or setting the thermostat to a higher temperature.
Peer Compare
Customer Question: My bill seems high, is this normal? How does my energy use compare to similar households?
The Peer Compare model shows energy consumers how their home energy usage compares to the usage of similar homes. Their usage is compared to both average and efficient homes in the same geographic region with similar characteristics, such as cooling type and home size. These personalized benchmarks educate energy consumers about whether they are using more or less energy than their peers, and what normal consumption is for a home like theirs. If they consume more than their peers, this knowledge motivates them to take actions to reduce energy consumption and save on their bills.
Combined these four offerings in the Bill Analytics and Usage Disaggregation suite increase customer engagement and reduce costs to the utility. Engaged customers are more likely to reduce energy consumption, enroll in rates, demand response, and virtual power plant programs – all of which lessen the strain on the electricity grid, helping utilities delay equipment upgrades or new build outs. Reduced call center volumes also leads to operational savings costs for utilities. Uplight’s Bill Analytics and Usage Disaggregation models prove that knowledge is power, especially for energy customers.
