Three Ways Utilities Are Leveraging Data Analytics
The historical utility business model can best be summed up in three steps: 1) determine the correct demand for energy; 2) build power plants to meet that demand; and 3) collect a pre-determined rate of return on said power plant.
But many utilities are beginning to recognize a paradigm shift. Utilities face new realities across multiple fronts: customers who expect more timely and relevant service; regulators that seek alternatives to building new infrastructure to meet load growth; and even competitive threats such as on-site generation.
The result is that utilities are moving to a sales, marketing and service delivery approach that looks more like a competitive enterprise than a highly regulated, one-size-fits all entity. These efforts are designed to be more proactive and customer oriented.
CIOs must be a key part of this transition. A more nimble utility requires access to data and insights across the enterprise to make better decisions in real-time. This data could come from the grid, from customer interactions, enterprise applications or third-party sources.
This shift is already happening every day in energy efficiency, where data analytics and models are empowering utilities to move smarter and faster. Namely, utilities are using insights in three key areas: to tap new segments of customers for efficiency projects, improve their delivery processes and offer more comprehensive solutions.
Unlocking new segments
Historically, utilities knew very little about their customers — all they needed to know was how much energy they used and where to send the bill. But utilities now realize that different customers have different needs and ways they want to interact.
To better understand their customers and tailor interactions with them, utilities are focusing on data-driven segmentation efforts. For efficiency efforts, this involves determining their efficiency potential based on existing savings opportunities and other characteristics that may make them more or less likely to participate.
Segmentation is critical for small and medium sized business (SMB) customers, for example. Most utilities I speak with say that this is a class of customers that they struggle to penetrate. Large utilities often deal with hundreds of thousands of SMB customers, they must begin to target customers that have significant savings potential and that are likely to participate. Targeting the customers with the most significant savings potential, along with those most likely to participate in an efficiency program, is critical to an energy programs success. Typically about 30 percent of the buildings in a given customer segment account for 70 percent of the efficiency opportunity.
To do this, utilities are leveraging asset and consumption based analytics, as well as becoming more active users of third party business data to help categorize customers.This approach makes the problem manageable and scalable and is orders of magnitude more cost-effective than traditional methods.
Improving the delivery process
While utilities may offer different programs to their customers and face varying regulatory constructs, the basic steps required in the energy efficiency lifecycle are very consistent. Utilities must figure out which customers have savings potential, get those customers interested in their programs, convert projects with those interested customers to realize savings, and measure the resulting energy reduction.
Traditionally, that process has been largely manual and reactive.
Data analytics will play a key role as utilities seek to wring deeper savings out from their portfolio in less time and cost. Data analytics helps utilities remotely target buildings by their true potential, enables them to engage customers with specific opportunities that exist in their building, makes the onsite audit dramatically faster and more comprehensive, and provides the ability to easily scan for new opportunities over time.
Being more comprehensive
Mandates require utilities to reduce their load through energy efficiency, with the most aggressive states setting targets of ~2 percent load reduction each year. Most programs focused on lighting to achieve these goals — but you can only change so many light bulbs.
Analytics enable utilities to comprehensively valuate buildings, quickly and cost-effectively — identifying both lighting and non-lighting projects that can be converted into energy savings.
Retro-commissioning (RCx), which aims to reduce energy usage by optimizing buildings systems and operations, is a good example of this. In a recent utility portfolio Retroficiency analyzed, savings from HVAC operations accounted for approximately one-third of the efficiency potential.
Historically, retro-commissioning programs have been challenged by high up-front costs for a building RCx study (which can easily exceed $50,000) and uncertain energy savings (which typically range from 5-12 percent, according to the American Council for and Energy- Efficient Economy). Simply, RCx projects make great economic sense when the no/low cost savings are significant enough to justify the cost of the upfront study. Several utilities are beginning to use data analytics to ensure customers that participate in RCx programs will achieve significant savings.
Data as a strategic asset
Utilities are taking critical steps to more effectively leverage data throughout their business processes to drive greater efficiencies. Analytics transforms each of these processes from a one-off action to a strategic opportunity to learn and improve. By continuously capturing data and converting it into insights, utilities can position themselves for a better outcome over time – both for their business and with their customers.
CIOs must not only ensure that its IT infrastructure is capable of enabling this to happen, but perhaps more importantly, serve as an executive champion that educates the entire organization on the benefits of data analytics.
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