Imagine opening an app on your phone and being able to check if you left the oven on or see if your electric vehicle is charging. Or maybe you’re more interested in viewing how much energy your solar panels are producing and the impact that has on your electric bill.

For some consumers, that scenario is easy to envision. Affordable smart home technology is rapidly reaching the mainstream. The global market for smart thermostats alone is expected to reach $4 billion by 2024. Smart doorbells and security systems, voice-controlled lighting and other appliances add connectivity and convenience to a growing smart home movement.

Until recently, very little of this technology provided the real-time insights and automation needed to strengthen the interaction between consumers and energy providers.

The need for insight and control into energy usage is increasing. The promotion of electrification for combating climate change is one reason. Another is the ongoing decentralization of power generation. A grid powered by distributed energy resources requires more automation and intelligence to maintain reliability and keep electricity costs within reasonable limits.

Consumers also agree that smarter use of power is a benefit to themselves and society, and many are turning to their local utility for help. A 2018 study of Australian consumers commissioned by Landis+Gyr found that 66 percent of respondents believe energy companies should supply more information on energy use. Another 88 percent of consumers expect to receive an alert when their energy usage suggests they have a faulty appliance in need of repair or replacement.

Many utilities already offer access to some form of historical data on energy use to customers in online portals and bill statements. While this level of detail helps consumers understand what contributed to their energy bill, the information is a rearview mirror perspective of what has happened, leaving the customer with little ability to control their energy spending. The next generation of smart energy technologies will serve as a platform for real-time energy management and allow customers to engage proactively with their utility.

Moving intelligence to the grid edge

Smart metering is nothing new. But what began three decades ago as a way to remotely read electric, gas and water meters, is today the foundation of what’s termed grid edge intelligence. Instead of relying solely on information from centralized locations on the grid, utilities can now see what is happening at every meter in real-time.

Digital meters were designed to measure not only how many kilowatts of electricity a consumer uses, but also the voltage and variations in voltage at each meter. Utilities use this information not only to bill customers but also analyze power quality and system capacity, respond to outages, and prevent future outages by identifying areas for preventative maintenance.

Equipped with microcomputers and communications devices, these meters are already being used to manage load and measure two-way power flows from residential solar generation.

Like every computing device, the smart meter continues to get smarter. What makes these new meters unique is the capability to sample waveform data, not just current and voltage. Previously only available in high-end grid meters, waveform data can be used for pattern recognition, which has far-reaching implications for energy management.

The rise of machine learning

With pattern recognition capabilities, a meter can use machine learning algorithms to identify anomalies on both the consumer and utility sides of the meter. For example, malfunctioning appliances in the home that are using more energy than necessary or tree limbs touching the power line and causing intermittent faults.

Machine learning opens the door to remote decision making at the grid edge based on data that previously required a variety of sensors, switches, and devices or perhaps wasn’t even possible before. For instance, a meter that can identify the operation of a healthy service transformer or sense the cause of a voltage dip can begin to learn the cause of an anomaly, not just report that an anomaly occurred. It can also know instantaneously that clouds caused a sudden voltage drop from a consumer’s solar panels.

Waveform sampling techniques allow analysis of data to happen in milliseconds, and that level of information can drive an immediate response to anomalies as they happen.

For utilities, this next step not only provides cost savings and convenience. It’s a necessary part of managing a cleaner, safer, and more useful power grid. For consumers, it provides the kind of “set and forget” control for energy management they already experience from other important and essential services.

Consumers expecting more options for efficiency, convenience, and security from their energy provider won’t need to wait.  Want real-time control of your energy use? There’s an app for that, and it’s coming soon to your electric meter.