EPRI's 2015 NILM workshop

The Electric Power Research Institute (EPRI) has been a long term research player in the field of NILM since beginning development of NIALM systems in the 1980s. In conjunction with its efforts of laboratory and field trials, EPRI has been attempting to facilitate an industry effort to develop “consensus based” performance metrics, create protocols to test metric impacts and demonstrate the need for product labels. To this end, EPRI hosted a NILM workshop on 13 November 2015 in Orlando, FL, to follow on from the previous 2013 EPRI NILM workshop in Palo Alto, CA. The event was attended by disaggregation vendors, utilities, universities, a U.S. Department of Energy National Lab and research organisations and covered topics such as current EPRI research, use cases, utility and consultant experiences with NILM and product labelling (see full slide deck). One of the key outcomes of workshop was the recognition of gaps related to NILM metrics and how to address them as an industry through collaborative efforts. Specifically the need to define an analytical framework to understand metric characterisation, their impact on the representation of NILM device performance and ways to assess these impacts.

The meeting was conducted for the following two-fold objectives:

• Facilitate a collaborative dialogue between product manufacturers, utilities and other stakeholders such as national labs and researchers for identifying gaps and new opportunities that enable adoption of NILM technologies.
• Propose a set of “straw man” metrics to stimulate discussion and focus efforts to create working groups and follow-on activities to address identified gaps.

The discussions covered four key areas:

• Value of end use load data to utilities and new use cases
• Current research
• Practitioner experiences
• Quest for metrics and product labels

The collaborative industry group arrived at the following conclusions and expressed interest in the following activities for the future:

• Non-intrusiveness is a significant attribute of the technology that makes it appealing for both utilities and customers.
• Customer interfaces such as mobile apps and web dashboards play a decisive role in persuading customers to use the technology and benefit from the information reported
• Metrics and product labels can improve the credibility, visibility and confidence for use of these products both in utility and customer applications.
• Automatic load labelling is a must for high-value utility applications, and this characteristic may well be the “deal breaker” for some utilities.
• Metrics need to be simple and articulate so that utility customers can derive tangible benefit. EPRI’s set of metrics is a good start and lays the ground work for future work to assess metric characteristics and impacts on performance representation
• Other industry stakeholders such as PNNL’s NILM user group effort should coordinate their efforts to represent industry interest and requirements. (more on this soon!)

The following next steps are proposed:

• Identify use cases that can lend themselves well to the use of AMI data and demonstrate customer integration case studies.
• Start engaging with NILM vendors and interested utilities for pilots targeting the commercial sector by building type.
• AMI meter manufacturers are interested to develop embedded NILM solutions which can be included as part of the meter hardware and software. Partner with AMI meter manufacturers to define requirements for such apps for various use cases.
• Continue to track the NILM market space and understand product performance and features.
• Engage utilities and vendors through laboratory trials and field assessments as newer technologies become available 
• Continue to assess NILM metrics and test protocols. By virtue of the metrics proposed at the meeting, EPRI should work to create analytical frameworks to assess impact of metrics on performance representation.
• Exploring utility use cases, map NILM characteristics and performance levels to each use case
• Informing of future utility and research projects and release the data to vendors for algorithm development and refinement.

Many thanks to Chris Holmes and Krish Gomatom for contributing most of the material for this post!