EMIS ReportDesign-Prelim 2020sep11 - Page 13



Study
Type of EMIS
Number of Sites
Results
Building Energy Information Systems: Synthesis
of Costs, Savings, and Best-practice Uses
(Granderson and Lin, 2016) Study performed in
2013.
EIS
28 individual buildings
and 9 portfolios
Median of 17% for
individual buildings;
median of 8% for portfolios
Monitoring-Based Commissioning: Tracking the
Evolution and Adoption of a Paradigm-Shifting
Approach to Retro-Commissioning (Meiman et al.
2012)
EIS
17 campuses
(3.2 million sq ft)
8% energy savings;
4 year median simple
payback
Monitoring Based Commissioning: Benchmarking
Analysis of 24 UC/CSU/IOU Projects (Mills and
Mathew 2009)
EIS
24 buildings
(XX sq ft)
Energy cost savings were
$0.25/sq ft-year, for a
median simple payback time
of 2.5 years
Corporate Delivery of a Global Smart Buildings
Program (Fernandes et al. 2018)
FDD
116116 buildings
(6.7 million sq ft)
18.5% energy savings
EIS and
manual FDD
3 buildings
(681,982 sq ft)
13% energy savings
San Diego Gas & Electric M&V Report, Modelbased predictive HVAC control Enhancement
Software. (SDG&E 2015)
ASO
1
6.5 year payback,
11% HVAC savings
Field evaluation of performance of HVAC
optimization system in commercial buildings
(Granderson et al. 2018)
ASO
5
0-9% energy savings range
Real-Time Energy Management: A Case Study of
Three Large Commercial Buildings in Washington,
D.C. (Henderson and Waltner 2013)
isolating root causes. For example, the FDD
software might detect a problem with the outside air
economizer not bringing in enough air for free cooling
and recommend that the damper actuator be
checked, as well as temperature sensor calibration
and the air handler control sequence. As with all
enabling tools, the EMIS itself does not directly
produce savings, but requires action upon the analytic
results. There is a growing body of service providers
to help owners manage their data and analytics and
implement findings.
Smart Energy Analytics Campaign
In response to these challenges in implementing and
utilizing EMIS systems, a public-private partnership
between the Department of Energy and Lawrence
Berkeley National Laboratory, and industry was
initiated 2016 and this report is the fourth and final
annual summary of findings. Concluding in 2020,
the Smart Energy Analytics Campaign (Smart Energy
Analytics Campaign 2020) targeted the use of a wide
1. INTRODUCTION
INTRODUCTION &
& BACKGROUND
BACKGROUND
1.
TABLE 1: EMIS case studies that document energy savings results
variety of commercially available EMIS technologies
and ongoing monitoring practices to support data
collection and analysis that support energy savings.
This program provided expert technical support to
commercial building owners in implementing in-depth
analytics, and the program recognized owners with
exemplary deployments.
As a part of the program, participants were
offered technical assistance and engagement with a
peer network. Participants shared data about their
progress and the program team reported the latest
aggregated results fir EMIS savings, costs, and trends
in implementation. This research report expands and
builds upon previously published research based on an
earlier version of the dataset (Kramer et al. 2019). By
the end of the Campaign, there were 104 participating
commercial organizations across the United States,
totaling more than 567 million square feet of gross
floor area and 6,500 buildings, making this the
most comprehensive dataset available on analytics
installation and use.
Berkeley Lab | Proving the Business Case for Building Analytics
11





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