From Theory to Practice: Lessons Learned from an Advanced M&V Commercial Pilot
Date Published |
08/2019
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Publication Type | Conference Paper
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Abstract |
Advanced measurement & verification (M&V), or M&V 2.0, is an energy data analysis method using advanced metering infrastructure (AMI) data in combination with analytics to quantify energy efficiency project savings. Advanced M&V is viewed as an enabler for underutilized behavioral, retrocommissioning, and holistic multi-measure efficiency efforts, as quantifying savings for these programs with existing methods has typically been very challenging. Despite its promise, there are still several unanswered questions about the use of advanced M&V such as: How accurately can these tools quantify energy savings? How can advanced M&V satisfy regulatory requirements for rigorous savings estimates? Where and how does advanced M&V intersectwith traditional impact evaluation and the principles of 'embedded' evaluation, measurement & verification (EM&V)? Pilots are being pursued in various regions and program contexts to address these research questions. However, results from these pilots are rarely made public, meaning that lessons may not be widely shared and the scope of analyses varies widely.The paper presents encouraging results from a recent advanced M&V pilot and discusses key process-related aspects such as managing AMI data and refining analysis methodologies. An approach to reviewing and classifying projects based on savings characteristics and magnitude is demonstrated, as a potential risk management tool. The authors also demonstrate how advanced M&V can identify underperforming projects and non-routine events in a timely manner. The paper also outlines lessons learned from this and other recent pilot activity and barriers to be overcome in moving toward the goal of widespread adoption of holistic meter-based programs. |
Conference Name |
International Energy Program Evaluation Conference
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Year of Publication |
2019
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URL | |
Refereed Designation |
Refereed
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Organizations | |
Research Areas | |
File(s) | |
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