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Benchmarking Lab Performance:
Where Are We Today?
Craig Wray, P.Eng.
Building Technologies and Urban Systems
Lawrence Berkeley National Laboratory
21 September 2015
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Learning Objectives
1. Identify whole-building energy metrics currently
available in Labs21 benchmarking tool
2. Explain how to use tool & benefits/limitations of data
analyses involving filtering & regression of empirical
benchmark data
3. Discuss how simulation-based benchmarking can
complement empirical data
4. Describe how system-level data can be used for
“action-oriented” benchmarking
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Acknowledgements
Presentation based on 2010 ACEEE “Summer Study
on Energy Efficiency in Buildings” paper:
“Advanced Benchmarking for Complex Building Types:
Laboratories as an Exemplar”
– P. Mathew, R. Clear, K. Kircher
(Lawrence Berkeley National Laboratory)
– T. Webster, K.H. Lee, T. Hoyt
(University of California, Berkeley)
http://aceee.org/files/proceedings/2010/data/papers/2004.pdf#page=1
Support provided by Assistant Secretary for Energy Efficiency
and Renewable Energy, Office of Building Technologies of U.S.
Department of Energy, under Contract No. DE-AC02-
05CH11231
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Labs are Energy-IntensiveLabs21 dataset: chemical, biological, chem/bio laboratories (all climate zones)
Average
Office
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Labs are Different (Mostly)
Complex functional requirements
– Health & safety paramount
– Large outdoor airflows
HVAC & process loads significant
– Lighting & envelope loads relatively minor
Substantial efficiency opportunities
– Unique opportunities (e.g., high-performance hoods)
– 30% to 50% potential savings over standard practice
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Key Benchmarking
Considerations for Labs
Benchmarking allows stakeholders to compare facility
performance & thereby identify potential energy savings
Several parameters used to normalize/filter performance
– Gross building area vs. lab area with 100% outdoor air
– Lab type and use (e.g., chem, bio, combination)
– Occupancy schedule: std (≤ 80 hrs/wk) vs. high (> 80hrs/wk)
– Climate (15 primary US zones)
– Programmatic requirements (typical rather than design values)
Ventilation rates
Cooling, lighting, & process loads
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Benchmarking Metrics
Whole Building
BTU/gsf-yr (source) kWh/gsf-yr (elec)
BTU/gsf-yr (site) Peak W/gsf (elec)
$/gsf-yr (site)
Submetering (System Level)
VentilationkWh/gsf-yr Peak supply cfm/sf(lab)
Peak W/cfm Avg cfm/peak cfm
Cooling kWh/gsf-yr Peak gsf/ton
Peak W/gsf Installed gsf/ton
LightingkWh/gsf-yr Installed W/sf(lab)
Peak W/gsf
Process/PlugkWh/gsf-yr Peak W/sf(lab)
Peak W/gsf
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Labs21 Benchmarking Tool
Tool developed by LBNL (funded by DOE/EPA);
became public August 2002; no major changes
since first release
National database of academic, government, industry
lab energy use data; mostly whole-building level
Database best available. CBECS data more limited
(42 or 43 lab buildings, only 19 with measured data)
http://labs21benchmarking.lbl.gov/
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Labs21 Benchmarking Tool
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Labs21 Benchmarking Tool
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Regression Analysis: Approach
Multiple regression model relates annual source
kBTU/gsf to facility characteristics (i.e., normalizing
parameters: lab area, lab type, climate zone, year
built, occupancy hours)
Dataset issues at time of analyses:
– 174 buildings with measured building-level energy use
– Type of data collected evolved over many years
– “Early” data (92 buildings) did not break out fuel use
– “Later” data (82 buildings) – significantly lower energy use
– Subsequent analysis limited to only “later” data
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Regression Analysis: Dataset
Source kBtu/sf
Frequency Distribution of Annual Source kBtu/gsf (N=82)
Mean = 540 kBtu/gsf Std. Dev = 230 kBtu/gsf
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Regression Analysis: Outlook
Fitted annual source EUI 280 to 870 kBtu/gsf, which
represents variation of -260 to +330 kBtu/gsf around
mean of 540 kBtu/gsf
Appears that regression better able to identify lab
buildings significantly worse than peers
Surprised that climate zone did not correlate
– Even when using HDD and CDD data for zip code
Further work needed to determine how much of
variance is due to efficiency vs. other parameters
Existing dataset may not represent high efficiency
– General limitation of using existing datasets
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Simulation-Based Approach
Model can normalize for many parameters that may not
be available in empirical datasets
Compare actual energy use with “best-practice”
benchmark generated by simulation model
EBR usually < 1; higher EBR implies higher efficiency
Energy Benchmark Ratio (EBR) =Benchmark energy use
Actual energy use
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Energy Modelling
Lab & non-lab space
– Separate air handling units
– Shared central plant
“Better practice” efficiency levels
for HVAC, lighting, envelope
Carry out parametric analyses
– Location, aspect ratio, gross area, lab area, occupancy
hours, plug loads, min. air change rates, heat recovery, …
Need to consider system interactions & coupled
phenomena
Various tools available now; continuous evolution
Lab
Space
Non-lab
Space
Central
Plant
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Simple Data Filtering vs.
Sim-Based Benchmarking
Case study of 15 facilities in Labs21 dataset:
– Climate zone: 4A “Mixed humid”
– Lab type: chemical, biological, chemical/biological
– Lab area ratio: 0.4 - 0.6
– Occupancy hours: “standard” (≤ 80 hours/week)
Simple data filtering: EUI for each facility compared
to mean EUI for group
Simulation-based: EBR for each facility compared to
mean EBR for group
– Based on location, lab ratio, & occ hours for each facility
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Simple Data Filtering vs.
Sim-Based Benchmarking
-150%
-100%
-50%
0%
50%
100%
150%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Devia
tio
n f
rom
Ave
rag
e (
%)
Facility ID (in order of EUI deviation)
EUI deviation EBR deviation
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Action-Oriented Benchmarking
Extends Whole-Building Benchmarking
Whole-Building
Energy Benchmarking
Action-Oriented
Energy Benchmarking
Investment-Grade
Energy Audit
Screen facilities for overall
potential
Minimal data requirements
(utility bills, building
features)
Identifies and prioritizes
specific energy-saving
opportunities
Requires sub-metered end-
use data; may require
additional data logging
Applicable for RCx and CCx
Estimates savings and cost
for specific opportunities
Requires detailed data
collection, cost estimation,
financial analysis
Necessary for retrofits with
capital investments
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Action-Oriented BenchmarkingHierarchy of Metrics Can Help Identify Potential Actions
Site kWh/ft2-yr
Ventilation
kWh/ft2-yr
Air change
ach
Vent. Efficiency
W/cfm
Fume hood
density
Sash closure
ratio
Fan Efficiency
%
Pressure drop
Pa
Cooling
kW/ton
Potential to improve fan efficiency
Potential to reduce energy use through
ventilation system efficiency improvements
Potential to reduce energy use through operational practices
e.g., by optimizing ventilation rates
Potential for energy efficiency in ventilation system
Overall potential for building-wide energy efficiency
Potential to reduce system pressure drop
Impact of fume hoods on ventilation energy use
Effectiveness of VAV fume hood sash management
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Benchmarking Issues: Summary
May need targeted efforts to collect granular data
– Data collected should address unique energy drivers
Simple data filtering a starting point
– Transparent and easily understandable
– Inadequate if more rigorous normalization required
Multiple regression viable only with large enough
dataset with relevant normalizing parameters
Simulation-based approach can be viable
complement to empirical benchmarking
Use key system-level benchmarks to identify
potential efficiency actions
