This post is one in a series of feature stories on trends shaping advanced energy markets in the U.S. and around the world, drawn from Advanced Energy Now 2017 Market Report, which was prepared for AEE by Navigant Research.

Big data analytics – using software engines and algorithms to process and analyze large quantities of data to provide insights into how customers behave – is changing the way companies do business across the economy, and energy is no exception. For decades, utilities have offered energy efficiency and demand response (DR) programs as means of saving energy for customers and reducing the need for more generating capacity to meet demand. In recent years, utilities and energy efficiency providers have started using big data analytics to provide new insights into how this can be done.

Behavioral demand-side management (DSM) is one application of big data analytics in energy. Using big data analytics, utilities can offer Behavioral DSM platforms to customers through home energy reports, web portals, and mobile apps. Customers receive personalized energy consumption information; social and historical comparisons of energy use; targeted recommendations for decreasing consumption; and notifications or alerts for high bills, outages, or DR events. In this way, behavioral DSM programs encourage customers to reduce their energy consumption through changes in their behavior.

Another method to reduce energy use through big data analytics is called analytical DSM, a method that finds opportunities for savings through equipment monitoring, strategic energy management, operator training, and data analytics. While Behavioral DSM generally serves the residential sector, analytical DSM mostly serves commercial and industrial (C&I) and small and medium businesses.

Not only is big data unlocking opportunities to save energy, it is also changing the way that we monitor and evaluate energy savings more broadly. Utilities must typically demonstrate savings to state regulators through an evaluation, measurement, and verification (EM&V) process. Big data is driving this aspect of energy efficiency programs towards “EM&V 2.0” by allowing utilities to estimate the impact of energy efficiency projects in real time, rather than waiting for after-the-fact measurements. The ability to measure the efficacy of energy efficiency in this way enables utilities to target their programs to specific load pockets and avoid costly system upgrades.

The big data aspect of today’s DSM programs is evident in recent acquisitions. The dominant player in behavioral DSM, Opower, was acquired by Oracle in May 2016, where it has become part of the enterprise software and cloud computing giant’s offering to utility customers.

This year, CLEAResult acquired Green Team Energy, an Atlanta-based demand side management software company. Green Team’s trademarked DSMTracker, which is a software-as-a-service product, will now be added to CLEAResult’s energy efficiency offerings. Retroficiency, one of the leaders in the analytical DSM arena, was acquired by Ecova, in late 2015. This combination brings Retroficiency’s analytics into Ecova’s broader DSM implementation offering for utilities and businesses.

 Although in development for the last decade, behavioral and analytical DSM are relatively new methods for discovering and promoting energy savings. The big data approach they share has the potential to unlock energy savings beyond what traditional utility energy efficiency programs have been able to achieve.

Behind these data-driven DSM offerings are a variety of technical, policy, and economic factors:

• Higher energy savings targets: Utility and state-managed DSM programs in the United States have grown rapidly, from $1.1 billion in spending in 2000 to $7.7 billion in 2015. Twenty-four states now have Energy Efficiency Resource Standards (EERS) establishing specific targets that must be met by energy efficiency programs. Many of these states are also ramping up targets, which is driving program administrators to look for ways to identify more and deeper savings through data-driven programs.
• Cost-effectiveness: As utilities try to get more savings out of their DSM budgets, behavioral and analytical platforms can be a cost-effective addition to traditional programs. Behavioral platforms can reach more customers for less upfront cost than home-by-home energy audits, and analytical platforms can reduce costs by supplementing onsite energy assessments with analytical audits.
• Customer engagement and satisfaction: Communication between utilities and their customers has traditionally been limited to monthly bills and the occasional power outage. Customer surveys have shown that behavioral and analytical DSM services can improve customer satisfaction and engagement.
• Grid modernization: Modernizing the grid goes beyond utility infrastructure. It needs to involve a holistic structure for smart grid planning, reliability, and payment. This opens up new opportunities for utilities and third-party vendors to offer innovative customer solutions like behavioral and analytical DSM.

The market for behavioral and analytical DSM has been developing in North America for close to a decade, but it is fairly nascent in the rest of the world. Utilities in Europe and Asia Pacific have shown interest in recent years, and these regions appear poised for growth. Navigant Research estimates that spending on behavioral and analytical DSM will grow roughly 10 fold over the next eight years reaching an annual market of $2.1 billion globally by 2024. Nevertheless, the majority of spending is expected to take place in North America, primarily in the United States, even as other regions begin to implement data-driven DSM programs of their own.

Learn more about energy efficiency, and the size and scope of the advanced energy market, by downloading AEE's 2017 Market Report for free at the link below.