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Utilities face tremendous pressure to reduce electricity demand and encourage residential energy efficiency. The residential sector accounts for 38% of total electric usage - more than the commercial or industrial sectors.
Unfortunately, 83% of residential customers have very limited knowledge of how they use electricity. Compounding this general lack of energy education, consumers lack the motivation to take action because it has historically been difficult to show consumers how their individual energy choices can make a difference.
"The utilities that succeed in educating their residential customers may be the best positioned - not only to survive but also to thrive in the new customer-centric electricity landscape that is now taking shape”, says Utility Dive in their residential customer survey.
To realize the savings associated with increased energy efficiency, utilities need to find new ways to engage and motivate residential customers to save energy. While 76% of utilities agree that consumer education is a higher priority for their business today, only 2% believe they’re doing a good job at it.
Disaggregation breaks the entire house electrical usage into appliance-specific usage. For example, instead of only knowing that your home used 27 kilowatt-hours on Tuesday, disaggregation breaks that 27 kWh down into usage by specific appliances. With access to appliance-level energy usage information, consumers can see exactly how they are using energy.
However, as we will see below, our ability and desire to act on this information is based on the accuracy of the information itself. Without accuracy, consumers will distrust the data and disengage.
Of the companies employing disaggregation methods in the market, most use a software-based approach, also known as Non-Intrusive Load Monitoring (NILM). Using machine learning algorithms, estimates are developed for appliance-specific usage by using only whole house consumption as an input. This appears to be an appealing option on the surface given that no plugs, sensors, or additional hardware are required to generate usage estimates.
In practice, software-based disaggregation has not been successfully deployed on a wide scale despite the fact that research began in 1982. Many companies offer energy management solutions that integrate software-based algorithms because additional hardware is not required. However, no utility has yet to deploy accurate software-based disaggregation to a large group of residential customers. This may be because systematic reviews show that providing information alone, as with the software-based approaches, fails to decrease energy usage.
While the absence of hardware and the associated costs would make software-based methods seem like an attractive option for disaggregation, but the following characteristics limit its viability as a large-scale energy saving tool:
Powerley has developed a novel, scalable approach, called Load-ID, that surpasses the accuracy of software-based disaggregation methods. The key component to elevate accuracy is hardware. Through this hardware-assisted approach, the solution delivers accuracy in real-time and with finer granularity. Load-ID utilizes machine learning algorithms that leverage data from smart thermostats as well as connected appliances and devices.
Why is this important? Powerley's machine learning algorithms achieve substantially lower error rates and can guide users to use less energy through personalized, data-driven coaching. It has the potential to drive consumer actions to increase energy efficiency because, unlike software-based disaggregation methods, the approach is:
Powerley’s hardware-assisted Load-ID algorithm goes beyond pure software disaggregation methods to elevate accuracy which, in turn, is immensely more effective at impacting consumer behavior to increase energy efficiency.
|Software-based Disagg||Hardware-Assisted Load-Id|
In the next article, we’ll explore how both utilities and consumers can achieve energy efficiency goals thrugh a hardware-assisted Load-ID methodology. We will also show how a real-time connection to energy usage data can redefine the consumer relationship with energy and the utility.