The Power of AI to Democratize Energy Savings - Better data visibility can help ensure utility programs make an impact across the economic spectrumPosted to Bidgely
- Nov 1, 2019 11:30 am GMT
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A recent study by the Environmental Defense Fund (EDF) put hard numbers to a conclusion many utilities and regulators have already reached: the potential savings offered by utility-funded energy efficiency programs are not reaching the customers who could benefit the most.
Indeed, although the grid as a whole is becoming increasingly efficient, the ever-expanding array of energy efficiency programs and improvements available to utility customers simply aren’t reaching those who spend a disproportionate percentage of their income to pay their monthly bills.
According to the study’s authors, the 36 million-plus American families with incomes below twice the federal government’s poverty level – just under $50,000 for a family of four – use over 30% of U.S. residential electricity. Despite that, only 6% of all energy efficiency spending is dedicated to low-income programs.
This is a huge missed opportunity for utilities, low-income families and society. EDF calculates that the right menu of policies and approaches could slice the energy use of low-income families by 20%, delivering hundreds of dollars of savings each year per family and a total reduction in electricity expenditures of $7.4 billion. That also equates to the elimination of 48 million tons of carbon dioxide emissions.
Though many obstacles have to be overcome to achieve these benefits, one important challenge the authors highlight is the lack of comprehensive data – historically, at least – on everything from utility customer demographics and energy usage to program statistics.
The Value of a Data-driven Approach
This dynamic is beginning to change for two very powerful reasons: an uptick in regulators considering rules or implementing programs designed to ensure that all utility customers benefit from programs promoting efficiency and distributed energy resources (DER), and the power of data to help utilities achieve those compliance targets producing more compelling results. For example, California has long promoted the availability of solar through its Single-family Affordable Solar Homes (SASH) program, which provides generous incentives to encourage the installation of photovoltaic (PV) panels on low-income homes. New York has followed suit as part of its ambitious Reforming the Energy Vision (REV), which has made expanding access to renewable energy and energy efficiency to all New Yorkers a cornerstone of its efforts. Federally, the Low Income Housing Energy Assistance Program (LIHEAP) helps fund weatherization efforts and solar installations that reduce energy costs.
Research released last year by the Smart Energy Consumer Collaborative (SECC) underscores why incentives and programs are not enough by themselves to expand the benefits of energy efficiency and renewable energy to all utility customers. SECC researchers found that low-income households tended to know less about energy efficiency measures than the general population and were less likely to respond to utility outreach efforts. There is a significant need for personalized education and outreach, though utilities have not traditionally leveraged the kind of data that can make those efforts a success.
“Data has not been a focus or in the forefront of energy efficiency program planning, design, and implementation,” said Jordana Temlock, director of regulatory affairs for Bidgely. “It hasn’t been a priority because its value has been misunderstood within the traditional program paradigm, where utilities and their partners have been content with marketing the same rebate programs to everyone to be able to hit their program goals.”
While this approach can be effective for achieving program goals, it tends to favor higher-income households with large energy bills that could produce greater savings. It also helps explain why Home Energy Reports (HERs) from utilities have typically only been sent to homes with the highest bills. The return on the HERs could be much higher if targeted based on data that shows which homes have the highest inefficient usages instead of targeting just based on consumption.
Connecting Utility Programs with Customers
The combination of data and the emergence of sophisticated artificial intelligence (AI) algorithms can provide granular, appliance level insights making it far easier for utilities to democratize the participation in and benefits of customer funded utility programs. A host of companies have recognized the value of using data to make these connections. Bidgely’s energy disaggregation is the most sophisticated available today. Here’s how it works:
Through patented AI products and services, utilities are able to disaggregate the energy usage in a home appliance-by-appliance and load-by-load. The insights produced from this data provides utilities with information on who to target for a given program but also how to engage with customers in ways more likely to resonate with them. For example, this kind of data could help utilities identify a low- or moderate-income home with an extremely inefficient HVAC system; the customer could then be approached with a tailored offer to replace the system through a utility program that would fund the swap.
“As an AI company partnering with utilities, Bidgely produces disaggregated appliance-level insights so that utilities can personalize and target efficiency programs to really help with program uptake,” said Temlock. “It helps the utility understand the consumers and how they're using energy. From that point, you figure out which program might be the most useful for them and craft the engagement going forward.”
In other words, it’s all about utilities using hyper-specific information about household energy usage to guide their outreach and messaging to customers, ensuring that all customers can take advantage of utility incentives and programs that would be most appropriate for them. This is already possible when utilities use data and AI to examine how customers have used energy in the past, helping the utility to formulate a plan for customers to use it more efficiently in the future.
Another example are so-called seasonal alerts that enable utilities to tell a customer who had exceptionally high energy usage in the winter or summer about programs to upgrade their HVAC system. “Sending out seasonal alerts is a great way to get people to be thankful that their utility is looking out for them,” said Temlock. “It’s a really great way of marrying proactive, insightful and personalized messages to influence customer behavior.”
Potential Impact on Ratemaking
When data leads to improved uptake of programs across customer segments, utilities are able to comply with their regulatory obligations. The importance of granular data about household energy usage becomes even more important as regulators ponder the use of performance-based regulations.
In recent years, the traditional cost-of-service ratemaking approach, in which utilities earn profits based largely on capital expenditures, has come under greater scrutiny for disadvantaging DERs. By contrast, regulators in states such as Hawaii, Minnesota and Rhode Island have been analyzing performance-based regulations that incentivize utilities to meet customer demands for lower-cost, more sustainable and more efficient energy.
Insights delivered by AI and data could become a critical tool for achieving compliance of performance based metrics as customer engagement and satisfaction become included in this approach to ratemaking. “It’s going to vary state-by-state, but data and AI should be part of the solution and strategy for helping utilities meet some of these evolving performance-based ratemaking efforts,” said Temlock. “It can be difficult to meet these targets and to prove you’re meeting these targets to regulators. But if you have information and visibility, it makes it that much easier to comply.”