Data Disaggregation Helps Reveal Energy Waste, and Target Solutions

This article is republished from the October 2020 issue of Strategies, AESP’s exclusive magazine for members. 

By Jeff Adams, Brad Cain and Deb Dynako

•    New energy-savings opportunities were sought out by nearly one-third of survey respondents, and 57% said their opinion of Alliant Energy had improved since participating in the pilot. These findings were consistent with Phase 1 results.
•    Prior to participating, 81% of respondents said they were very or somewhat knowledgeable about energy-savings strategies for their home. After participating, 95% of respondents said they were very or somewhat knowledgeable about energy-savings strategies for their home.
•    27% of respondents said they had sought out additional energy-saving opportunities since participating in the Sense program.
•    More than half (58%) of respondents said they had purchased at least one new energy-efficient product since participating in the program.

Appliance-Level Data Disaggregation via Sense

Electric Dryers

Electric dryer energy consumption of 33% of participants were above baseline levels.
Another 7% of participants exceeded the efficient consumption level.

Table 1 contains estimates of the potential savings from device upgrades for two scenarios. For customers in the inefficient category, a dryer upgrade to the baseline or efficient level would save an average of 319 kWh or 480 kWh per year, respectively. These savings correspond to a 2.8% or 4.2% average reduction relative to average pilot participant total annual consumption.

Refrigerators

The savings analysis for refrigerator consumption data showed 168 devices that registered non-zero consumption on at least 95% of analysis days.

Table 2 illustrates the distribution of refrigerator consumption relative to TRM-defined levels. Ten percent of refrigerators exceeded the baseline consumption level. Another 15% of refrigerators exceeded the efficient consumption level. Four refrigerators were identified as having unusually large energy consumption (greater than 1,000 kWh per year).

Air Conditioners – TOU+D Opportunity.

Air conditioners present an average potential peak demand savings opportunity of 0.56 kW and 0.47 kW per device, during high peak and mid peak hours, respectively, as shown in Table 3.

Residential Pilot Summary

Home disaggregation technology delivers opportunities in several areas of interest
•    Customer engagement
•    Behavioral and resource acquisition energy savings
•    Demand response initiatives

Challenges to overcome
•    Initial cost
•    Accurate device identification
•    Installation and connection complications

From the customer survey – positive outcomes
•    More than half sought out new energy saving opportunities
•    57% had improved opinion of Alliant Energy

Significant savings potential from:
•    “Always on” load
•    Incandescent lighting
•    Inefficient equipment (dryers, water heaters)
•    Behavioral savings (6%)
•    Identification of devices contributing to peak coincident load (e.g., dryers)

Business Customers Are Different

Like their residential counterparts, non-residential consumers are hungry for new energy insights, analysis, and behavioral suggestions derived from their meter data. These consumers are far more varied in the ways they use energy and determine their energy goals and constraints. A plastics manufacturer, hair salon, and grocery store, for example, each have vastly different operating hours, equipment, and appetites for savings techniques such as load shifting. Yet each has a large savings potential. So, how can utilities connect with them all?

Segmentation is critical. Disaggregation and business type modeling are a good starting point, but research has shown that inaccurate assumptions about a customer’s load breakdown hurt the utilities’ credibility. In the non-residential space, remote disaggregation alone simply isn't accurate enough for reliable segmentation and load breakdown. Instead, utilities should supplement these baseline models with firmographic data like NAICS codes, geographic location, and rate class.

To further improve accuracy, utilities need to collect additional facility and equipment information. For example, key account managers should record data during on-site visits or regular check-ins with their clients. Likewise, call-center reps should leverage a dynamic two- to three-question survey about the facility every time they engage with a customer. Finally, the utility needs to actively encourage customers to fill out a “business profile” on their own. Non-residential customers are surprisingly responsive to frank, honest communications about the accuracy of their load breakdown and will take the time to complete business surveys if the utility makes the value of those actions apparent.

What Do Non-Residential Accounts Want from Their Data?

Savings, options, and a sense of greater control. Businesses are constantly evaluating ways to improve their bottom line and reducing energy costs is often a high priority. However, the tactics that each individual account is willing to use to achieve those goals will vary greatly from segment to segment. Any data analysis provided by the utility needs to guide customers toward savings options relevant to that specific customer's needs. For example, demand or usage shift analysis are much more likely to be relevant to a large manufacturer, while no-cost behavioral tips are more likely to appeal to a neighborhood hair salon. Meanwhile, a chain of grocery stores would be most receptive to promotions for on-site energy audits at their worst performing locations.

Demand and Usage Shift Analysis

In 2012, a large Midwestern utility was transitioning several hundred C&I accounts to more complex rate structures centered around time-of-use and demand charges. As a customer, rate changes can make you feel powerless, and the added complexity of the new tariffs increased the chances that these customers would perceive the new structure as a burden rather than an opportunity. To avoid that, Agentis worked with the utility to design digital energy solutions that graphically display the potential savings associated with shifting usage or demand to off-peak hours. Customers on TOU rates can access the analysis via their digital energy portal and based on their unique business profile and meter data, they can manipulate a slider to identify the savings potential of shifting their usage to off-peak times.

In one case, a large plastics manufacturer with a monthly electric spend of roughly $120,000 was able to reduce its costs by ~$24,000 a year. That customer used load shift analysis to:

•    Identify which manufacturing runs occurred during on-peak hours
•    Simulate the bill impact of shifting those runs to weekend off-peak times
•    Alter their production schedule to reduce their peak demand charge

Low-Cost and No-Cost Behavioral Suggestions

SMB engagement remains a top priority and a major challenge for many energy providers. This segment represents a large, and mostly untapped opportunity for DSM savings, but is notoriously difficult to engage. In many cases, however, data analysis is the missing factor needed to drive these accounts toward energy reductions. Utilities can combine meter data with behavioral techniques to achieve three key goals:

1.    Capture Interest - Research shows that SMBs are more likely to respond to insights as opposed to charts and graphs. A behavioral strategy we use is called Reference Dependent Preferences. Explained simply, if certain customers are poor performers they get benchmarked to average, an average performer gets compared to top performers, and top performers get a congratulatory message.

2.    Explain Value - SMBs want feedback. Research shows that monthly email alerts explaining cost trends improve the overall engagement among small accounts.

3.    Entice Action - SMBs respond to social norms. They are more likely to participate in a program if they have some sort of reference as to how many of their peers have benefited from similar activities.

The owner of a typical neighborhood hair salon, for example, likely doesn't care about load duration curves, but they would like to know how their costs compare to similar businesses, what days/times are the most expensive for them, and how many of their peers have taken steps to save.

Increased Program Participation

Another critical factor that differentiates the non-residential segment is the prevalence of multi-location accounts. Municipalities, school districts, grocery stores, etc. make up a large cross section of your customer base. Utilities need to provide data analysis that benefits the account-level user, while also adding value to the portfolio-level manager.

For example, Agentis recently heard feedback from the District Sustainability Supervisor for a large grocery chain. That individual used her utility's digital energy portal to compare the hot-weather energy usage and shut down efficiency between the dozen stores in her district. She then invited the individual store managers for each location into the shared account portal and walked through the findings with them. By using this analysis to collaborate with individual store managers, the District Supervisor was able to convince the worst performers to sign up for on-site energy audits from the utility.

Why Disaggregation Matters

Raw data is like a tangled fishing line — there’s not much you can do with it. Segmenting the data smooths out all the knots so you can make a quality cast to your target market or hook the results of new technology pre- and post-install. As more customer data becomes available and new tools and insights are more easily derived, energy efficiency measures can be implemented at greater savings per kWh. Data disaggregation ultimately energizes energy efficiency with potential new savings through increased adoption of tools and technology that improve grid flexibility, resiliency, and savings for all.

This article was contributed by the AESP Tools & Technology Topic Committee.

Deb Dynako, Business Development Manager at Slipstream, builds relationships with utilities, stakeholders, vendors, implementers, and various organizations to find gateways that accelerate market adoption of new technologies and carbon draw down opportunities. She currently serves as co-chair of AESP’s Innovation in Tools and Technology Committee.

Brad Cain is the Director of Marketing at Agentis where he is responsible for customer insights and thought leadership. Brad has spent the past ten years in complex software marketing and works to identify meaningful trends and opportunities for improved engagement within the non-residential segment.

Jeff Adams is the Lead Customer Product Manager at Alliant Energy, where he participates in and supports development of demand side management (DSM) strategy,consisting of energy efficiency and energy conservation programs and integration with company-based customer programs and Focus on Energy.

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