Our final blog of #IUW17 features a session with Robert Sonderegger and Luke Scheidler from Itron discussing how Itron’s OpenWay® Riva distributed intelligence application for load disaggregation can provide new perspectives into energy usage data.
During this session, Robert and Luke discussed the possibility to deploy load disaggregation in real time with no special equipment at the customer premise. This enables utilities to gain insights to support more effective demand response projects, design better energy efficiency programs, improve customer service and build more accurate load profiles to guide the utility asset planning processes.
The quality of energy disaggregation is directly related to data availability, which includes frequency of interval data, the data elements used and the number of premises. As you increase each of these data elements, you achieve better results and your end use information becomes more accurate. Itron has an advantage in all of these areas, capturing and processing data at a one-second resolution without additional equipment in the circuit panel.
Furthermore, in order to deliver distribution deferral using distributed resources, utilities must succeed at targeting, marketing, program planning, measurement and verification, and forecasting. Demonstrating the need for these solutions, the world has become more complicated, creating a need for better predictability, access to data and location-specific information. Real-time energy disaggregation in every meter is a better starting point for these activities, as it is scalable to the population and location specific.
Therefore, the level of accuracy and data granularity needed is dependent on the specifics of each situation. Any use cases that have a distribution benefit or require temporal or locational analysis require highly granular data. With Itron you get a high degree of accuracy without additional hardware, and it enables the full range of use cases.
Understanding how customers produce and consume energy today is key to predicting how they will produce and consume energy in the future. This provides insight into how rates, tariffs and incentives can be used to benefit the grid. Robert took us through the technical aspects—explaining that Itron’s approach using metrology data, measured at high frequency. Since we are developing something that’s completely automated, which updates and evolves over time, we must deal with cases such as a new device being added to a home.
For reference, here is the based flow of the application, as developed to-date:
Thanks for reading our blog, this brings #IUW17 to a close! For more information, please visit our website. We look forward to seeing you next year in Scottsdale, Arizona!
I am wondering what it might take to be able to do something similar with water, i.e. usage disaggregation at one second intervals? Please let me kow your thoughts. Thanks!
Hi Roger!
Thanks for the comment. We have done some conceptual work regarding water disaggregation but have not explored it in any meaningful way. A few thoughts though: 1) one-second or better data resolution is absolutely critical for energy disaggregation. At this level of detail, you can see individual appliances turning on/off – and not only that but you can examine various electrical characteristics such as resistance and inductance to further distinguish between two loads that look similar from a kW perspective. Because there are relatively fewer types of water “loads” and only one parameter (flow), it seems that one-second data may not be as critical for this type of disaggregation. However, there are definitely some applications that 1-second data would make easier (like identifying low flow shower heads or leaky toilets). I’m interested in your thoughts on the data resolution required (especially if different). 2) Number 1 may have been a bit self-serving because most water meters/communications modules are not line powered. Because they are battery powered, we do not have the luxury of doing 1-second data analytics within the meter without draining the battery. 3) Combining high-resolution energy disaggregation data with lower resolution water data (15 min) could potentially result in more accurate water disaggregation.