Wednesday January 23, 2019 0 comments
BOULDER -- Tendril, a leading provider of Home Energy Management solutions to the utility industry, announced it 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, the company said.
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, Tendril COO.
“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 VP, Energy and Manufacturing Insights, IDC.
“Only with advanced analytics, like those inherent in of 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.”
“We’ve long monitored the state of NILM but as stand-alone technologies, they often drive customer complaints due to false positives,” said Black.
“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.”