Thursday, June 18, 2015

Taking the “Pulse” of Your Home or Office’s Electric Load/Part 1

How non-intrusive load monitoring can enhance smart meters, save energy, and save money

Great article from Rocky Mountain Institute, who we've talked with on the radio side, on today's technology and tools (see our earlier post) that can help you, and us, collectively, get efficient and manage energy.   This is the grunt work of the business side of green, with obvious and proved financial return.

This is a long piece.  We will post it in two parts.



There are now over 50 million smart meters deployed across the United States, with millions more planned for installation by ComEd in Illinois, by Consumers Energy in Michigan and many other utilities domestically and internationally. Smart meters measure and transmit customer load profiles at hourly or sub-hourly increments—measuring a customer’s apartment, single-family house, or small business—and represent a step forward from analog meters that were read manually once each month.
Smart meters importantly enable things like time-of-use pricing, but despite their name, smart meters have not yet proven intelligent enough to give customers sufficiently actionable information about the devices and systems they touch in their daily lives in a manner that interests them. As a result, many of the operational benefits of smart meters have largely accrued to utilities, not customers.

ENTER NON-INTRUSIVE LOAD MONITORING

A number of companies are developing increasingly more-accurate and transparent solutions to this problem to enhance the value of smart meter data for consumers, collectively known as non-intrusive load monitoring (NILM). NILM is analogous to an always-on electrocardiogram (EKG) for your home or business. It dissects electric meter load curves to “see” and diagnose the performance of the appliances and systems that use electricity that are embedded in the signal. In a sense, each major electricity-using appliance has a distinct “signature” that can be extracted from the whole-home load curve, like a recognizable heartbeat. By parsing out individual components such as air conditioners, hot water heaters, dishwashers, refrigerators, electric vehicles, and other loads, NILM disaggregates smart meter data (e.g., load) into its respective pieces.
NILM provides granular content customized for individual households to induce greater energy savings. Granular NILM content, if coupled with the right messaging channel and timing, could deliver on the promise of customer interfaces that motivate better customer responses from energy data. If we have learned anything from the brief history of Google PowerMeter,Microsoft Hohm, and dozens of still commercially available consumer portals that are based on the premise of just monitoring energy use (and who have failed to engage the mainstream consumer), it is that energy-efficiency stakeholders are not wired to think about what makes consumers tick. NILM expands customer benefits beyond energy use monitoring, and into appliance health notifications. In other words, it bridges from merely monitoring energy use passively to doing something about it actively.
For example, by identifying aberrations in appliance operation signatures, NILM enables virtual monitoring of potentially billions of devices behind customers’ smart meters that represent the majority of residential electric loads. The same systems could notify customers when their devices experience problems before they result in appliance or system failures. Because NILM leverages meter data, the customer does not have to buy a new Internet of Things (IoT)-enabled device that has an embedded communications chip and software package, offering similar notification capability.

NILM GOING MAINSTREAM

NILM is not new. However, it has evolved from an academic pursuit to recently launched commercial products now gaining traction in the market. Companies like EEmeBidgely, Intel, C3 Energy, and others are racing to develop and validate the most accurate algorithm for software solutions that interface between customers and other actors in the IoT value chain. A barrier to commercial launch, up to recently, had been the absence of validation studies that used a robust set of ground-truth data proving the algorithms worked. Previous validation studies were conducted by EPRI and other research organizations that were not publicly available, and generally limited to 10–15 mock homes and time spans of 1–2 months.
Historically, NILM’s low accuracy, combined with the lack of publically available ground-truth data, has been a barrier to commercialization. However, both the accuracy of NILM algorithms, along with the availability of ground-truth data, is proving the technology merits a fresh look by utilities and third-party technology companies. Academic studies have previously set the benchmark for NILM accuracy at 55 percent, not much better than a coin toss. The accuracy of NILM algorithms has improved, now hovering around a much more impressive (and useful) 70 percent.
And the process is improving further and becoming increasingly more transparent, based on a March 2015 public test from EEme and Pecan Street Inc., which employed previously unavailable ground-truth circuit-level data that encompassed 270 homes and 12 months making it the most comprehensive of its kind. That transparency and rigor is crucial, since many in the industry are skeptical of load disaggregation’s value.

UNLOCKING CUSTOMER ENERGY SAVINGS

The grid of the future will be centered on the customer, enabling customers to understand and manage their energy use more efficiently. Personalized, transparent, and actionable data availability to customers and to the marketplace is a key factor enabling that transition.
Yet to date, customer engagement in the electric industry has not exactly been captivating. Many customers autopay their utility bill and thus never see it. We’re billed for energy we already consumed last month. And traditional, simple meters mean that we “see” little more than a lump sum of kWh consumption month by month. The result is the “standard” utility bill as many of us have known it:


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