BOULDER, Colo.--(BUSINESS WIRE)--Tendril, the leading provider of Home Energy Management (“HEM”) solutions to the utility industry, has acquired energy analytics provider EEme. The move delivers on Tendril’s mission to expand the already best-in-class capabilities of its data analytics platform through innovation and acquisition. EEme provides a Non-Intrusive Load Monitoring (NILM) technology that processes AMI data of any frequency to disaggregate home energy loads and deliver highly accurate and personalized recommendations with up to 90% accuracy. Combining EEme’s third-party validated technology with Tendril’s TrueHome simulation model creates the largest-scale NILM solution on the market, significantly improves reliability and accuracy, and moves the entire field of appliance detection forward.
“Appliance-level disaggregation holds great potential in helping people reduce their home energy consumption, but to be effective we must improve the accuracy and reliability of these solutions,” said Chris Black, COO, Tendril. “With the acquisition of EEme we are enhancing our device-usage detection capabilities with highly complex technology that has been rigorously tested by third parties and validated by us over the past 18 months with 10 terabytes of AMI data. Now when combined with the Tendril Platform, we will finally unlock the value of delivering appliance-level energy insights.”
Incorporating EEme’s technology within the Tendril Platform improves the accuracy of insights provided across the company’s High Usage Alerts, Home Energy Reports, Engagement Portals and other outbound communications. Potential use cases include:
- Identifying new EV owners, then communicating a personalized offer for a TOU rate and a recommendation to charge during lower cost off-peak hours.
- Identifying HVAC systems that are becoming less efficient over time, then presenting a Home Energy Report that focuses on AC load reduction, as well as personalized offers for HVAC repairs and rebates.
- Identifying cyclic loads such as pool pumps, then using Orchestrated Energy to schedule the AC unit so it doesn’t run at the same time as the pump, thus avoiding coincident peaks.
“The key to Non-Intrusive Load Monitoring is not just estimating energy usage but applying analytics to accurately parse the different loads - both large and small,” said Kevin Prouty, Group Vice President, Energy and Manufacturing Insights, IDC. “Only with advanced analytics, like those inherent in some of the energy management applications that utilities are currently deploying, can we deliver the insight needed to bring the power of rapid machine learning to utility engagement and efficiency programs.”
Continued Black, “We’ve long monitored the state of NILM but as stand-alone technologies, they often drive customer complaints due to false positives. This may sound like a small concern, but the moment a customer questions the veracity of their reported energy usage, they question everything and their trust has been lost. We’ve heard this over and over from our utility customers that have tested NILM solutions in the past so we really wanted to take our time and bring a unique approach to the market.”
Tendril is changing the way the world uses energy. Our data analytics on more than 123 million homes creates new business opportunities for any product or service provider connected to the home. Today, this includes electric and gas utilities, and energy retailers. Built over more than a decade, the Tendril Platform delivers real-time, ever-evolving data about the home and how people use energy in it. These rich insights help our customers improve customer acquisition, increase engagement and orchestrate home energy experiences. For more information, please visit www.tendrilinc.com and follow us on Twitter at @Tendril.
EEme provides a scalable machine learning platform that converts raw smart meter data into appliance-level and equipment-level insights using proprietary algorithms. EEme’s proven Disaggregation-as-a-Service™ (DaaS) technology provides demand-side management stakeholders with appliance-level insights leveraging existing smart meter data and without relying on new hardware investments or user intervention.