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AUTOTUNE E+ BUILDING ENERGY MODELS
Joshua New, Jibonananda Sanyal, Mahabir Bhandari, and Som
ShresthaOak Ridge National Laboratory, Oak Ridge, TN
ABSTRACTThis paper introduces a novel “Autotune” methodol-
ogy under development for calibrating building energymodels
(BEM). It is aimed at developing an automatedBEM tuning methodology
that enables models to repro-duce measured data such as utility
bills, sub-meter, and/orsensor data accurately and robustly by
selecting best-match E+ input parameters in a systematic,
automated,and repeatable fashion. The approach is applicable to
abuilding retrofit scenario and aims to quantify the trade-offs
between tuning accuracy and the minimal amount of“ground truth”
data required to calibrate the model. Au-totune will use a suite of
machine-learning algorithms de-veloped and run on supercomputers to
generate calibra-tion functions. Specifically, the project will
begin witha de-tuned model and then perform Monte Carlo
simula-tions on the model by perturbing the “uncertain” parame-ters
within permitted ranges. Machine learning algorithmswill then
extract minimal perturbation combinations thatresult in modeled
results that most closely track sensordata. A large database of
parametric EnergyPlus (E+)simulations has been made publicly
available. Autotune iscurrently being applied to a heavily
instrumented residen-tial building as well as three light
commercial buildings inwhich a “de-tuned” model is autotuned using
faux sensordata from the corresponding target E+ model.
INTRODUCTIONIn 2006, the US consumed $220 billion in annual
en-
ergy costs with 39% of primary energy (73% of total elec-trical
energy) being consumed by buildings; with less than2% of this
energy demand being met by renewable re-sources, the US constituted
21% of worldwide CO2 emis-sions in 2005 with an annual growth rate
of 1.2% from1990-2005 (U.S. Dept. of Energy, 2010) (Figure 1).
Forreasons financial, environmental, and social, the UnitedStates
Department of Energy (DOE) has set aggressivegoals for energy
efficiency, which constitutes the low-hanging fruit for slight to
moderate energy savings in theUS buildings sector.
A central challenge in the domain of energy efficiencyis being
able to realistically model specific building typesand scaling
those to the entire US building stock (Deru
Figure 1: Summary of US Primary Energy Consumption(U.S. Dept. of
Energy, 2010) and Production (U.S. EIA,2009).
et al., 2011) across ASHRAE climate zones (IECC 2009and ASHRAE
90.1-2007; Briggs et al., 2003a,b), thenprojecting how specific
policies or retrofit packages wouldmaximize return-on-investment
with subsidies throughfederal, state, local, and utility tax
incentives, rebates, andloan programs. Nearly all energy efficiency
projectionsare reliant upon accurate models as the central
primitiveby which to integrate the national impact with meaning-ful
measures of uncertainty, error, variance, and risk. Thischallenge
is compounded by the fact that retrofits and con-struction of
buildings happen one at a time and an individ-ual building is
unlikely to closely resemble its prototypi-cal building. Unlike
vehicles and aircraft, buildings aregenerally manufactured in the
field based on one-off de-
-
signs and have operational lifetimes of 50-100 years;
eachbuilding would need to be modeled uniquely and moreprecisely to
determine optimal energy efficiency practices.
This challenge has been partially addressed throughthe many
software packages developed for energy mod-eling and software tools
which leverage them. Thereare over 20 major software tools with
various strengthsand weaknesses in their capability of
realistically model-ing the whole-building physics involved in
building en-ergy usage (Crawley et al., 2008). The major
softwaresupported by DOE is EnergyPlus (E+), constituting
ap-proximately 600,000 lines of FORTRAN code. There aremany tools
which use similar simulation engines, suchas the National Renewable
Energy Laboratory’s (NREL)BEopt (Christensen et al., 2006) and
Lawrence Berke-ley National Laboratory’s (LBNL) Home Energy
Saver(HES) (Mills, 2008), in order to determine a set of opti-mal
retrofit measures. There are many other use cases forenergy
simulation engines and tools, some of which arebecoming required by
law such as the progressive Cal-ifornia Legislature Assembly Bills
AB1103 (CaliforniaEnergy Commission, 2010a) and AB758 (California
En-ergy Commission, 2010b) which require energy modelinganytime
commercial property changes owners. The in-creasing application of
energy software and the accuracyof projected performance is
entirely contingent upon thevalidity of input data a sufficiently
accurate input modelof an individual building and its use is
required.
One of the major barriers to DOE’s Building Tech-nology Program
(BTP) goals and the adoption of build-ing energy modeling software
is the user expertise, time,and associated costs required to
develop a software modelthat accurately reflects reality (codified
via measureddata). The sheer cost of energy modeling makes it
some-thing that is primarily done by researchers and for
largeprojects. It is not a cost that the retrofit market or most
usecases would absorb in the foreseeable future without dras-tic
reductions in the cost of having cheaper and more ac-curate model
generation. This weak business case, alongwith concerns regarding
the cost for upkeep, maintenance,and support of the very capable E+
simulation engine,has driven DOE sponsors to investigate
facilitating tech-nologies that would enable the energy modeler and
retrofitpractitioner in the field.
The business-as-usual approach for modeling whole-building
energy consumption involves a building modelerusing the software
tool they have most experience with tocreate the geometry of a
building, layer it with detailedmetrics encoding material
properties, adding equipmentcurrently or expected to be in the
building, with antici-pated operational schedules. An E+ building
model has∼ 3,000 inputs for a normal residential building with
veryspecific details that most energy modelers do not have
Figure 2: Autotune workflow for E+ building energy mod-els as a
cost-effective solution for generating accurate in-put models.
the sources of data for. Experimentation has establishedthat
even the ASHRAE handbook and manufacturer’s la-bel data are not
reliable due to substantial product vari-ability for some materials
(DeWit, 2001). This is com-pounded by the fact that there is always
a gap between theas-designed and as-built structure (e.g.,
contractors mayneglect to fill one of the corner wall cavities with
insula-tion). Due to the sources of variance involved in the
inputprocess, it should come as no surprise that building mod-els
must often be painstakingly tuned manually to matchmeasured data.
This tuning process is highly subjectiveand repeatable across
neither modelers nor software pack-ages. An automated
self-calibration mechanism capableof handling intense sub-metering
data is called for.
The development of an autotuning capability (Figure2) to
intelligently adapt building models or templates tobuilding
performance data would significantly facilitatemarket adoption of
energy modeling software, aid in ac-curate use cases such as the
effective retrofit strategies forexisting buildings, and promote
BTP’s goals of increasedmarket penetration for energy modeling
capabilities. Theidea of self-calibrating energy models has been
around fordecades and expertly consolidated in an ASHRAE reporton
the subject (Reddy et al., 2006), but is generally lack-ing in its
employ of machine learning algorithms or simi-lar autonomous
application of modern technology. In thisinitial paper, we discuss
the general methodology behindthe Autotune project, specific
technologies enabling itsimplementation, and preliminary data
generation resultsincluding a large database of parametric E+
simulationsavailable for general use.
SIMULATION/EXPERIMENTThe goal of the Autotune project is to save
building
modelers time spent tweaking building input parametersto match
ground-truth data by providing an ”autotune”
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Figure 3: A virtual building model (software space) and a real
building (sensor space), when viewed as vectors ofnumbers, allows a
mathematical mapping between vector spaces for direct comparison
between simulation state andsensed world state.
easy button for their computer which intelligently ad-justs
model inputs. In order to achieve this, the Autotuneproject entails
running millions of parametric E+ simula-tions on supercomputers,
multi-objective optimization ofE+ variables via sensitivity
analysis, using machine learn-ing systems to characterize the
effect of individual vari-able perturbations on E+ simulations, and
adapting an ex-isting E+ model to approximate sensor data. The
systemwill be demonstrated using an E+ building model
auto-matically matched to a subset of the 250+ sensors in aheavily
instrumented residential research building as wellas to DOE’s
commercial reference buildings (Field et al.,2010) for a medium
office, stand-alone retail, and ware-house in which 3 customized
buildings will provide fauxsensor data for tuning the original
models. This paper willsummarize the Autotune methodology focusing
primarilyon the definition of parametric simulations and
accessibil-ity of the public database.
Parametric AnalysisSensitivity analysis is a standard
statistical technique
(Bradley et al., 1977) in which a large parametric sweepof
possible values for each input variable in a simulationis altered
and then mathematically classified as contribut-ing the variance in
the final simulation result. This tech-nique has been the hallmark
mathematical technique forseveral analyses regarding energy
efficiency. In fact, theoft-referenced Building Energy Data Book
(U.S. Dept. ofEnergy, 2010) does not use direct measurements of
thereported data, but relies upon ratios developed in
earlierreports (Huang et al., 1987), some of which can be
tracedback to reports from the Energy Crisis in the late 1970s.
In
Huang et al. (1987), the authors used thousands of
DOE-2simulations to establish sensitivities and develop
look-uptables for practitioners in the field since energy
modeling,particularly in a mobile fashion, was inaccessible at
thattime. As a potential use case, DOE sponsors have consid-ered
forming a new basis consisting of hundreds of mil-lions of E+
simulations, rather than thousands of DOE-2runs, to develop more
modern and robust data for use ina reconstruction project. As such,
we are using the latestversion of E+ and OpenStudio to run millions
of simu-lations, store those in a database, and make that
databasepublicly accessible for anyone to mine for relevant
knowl-edge.
The computational space for this search problem is onecrucial
aspect of the project. While a database of mil-lions of simulations
would be a boon to the energy anal-ysis community, it would not be
sufficient for the successof this project. Domain experts have
defined a set of pa-rameters for a building model that it would be
preferen-tial to vary; however, all combinations of these
variableswould require 5×1052 E+ simulations. There are
manytechniques to be utilized in an effort to effectively pruneand
intelligently sample the search space. First, domainexperts have
identified ∼ 156 parameters typically usedby energy modelers that
need to be varied and rankedthem in several importance categories.
Second, buildingexperts have realistic (minimum, maximum, and step
size)ranges for those variables. Third, researchers have
definedmeta-parameters that allow several individual parametersto
be varied as a function of a single variable. Fourth,low-order
Markov simulations are being conducted to de-
-
Figure 4: Sensitivity analysis of E+ simulations mapped to their
effect in sensor space.
termine variables with a monotonic effect on sensor datathat
could reliably be interpolated to estimate impact ofa given
variable. Fifth, sources of variance for individ-ual variables in
the initial results will be used to guidehigher sampling rates for
more sensitive variables. Sixth,an expert in multi-parameter
optimization will be investi-gating computational steering
algorithms to determine theoptimal sampling strategy for the
remaining space beyondthe brute-force sampling of higher order
Markov chains ofMonte Carlo simulations.
Mapping MechanismIn order for autotuning to work, there must be
a map-
ping from the measured data to the corresponding statevariables
within the simulation (Figure 3). By defining amathematical mapping
between measurements in sensorspace and simulation variables in
software space, a Eu-clidean or similar vector-distance approach
can be usedto identify “how close” the software simulation is to
themeasured performance.
This mapping must be performed by domain expertsinitially, but
the expert-defined mapping will be minedto discover labeling
patterns used by the domain experts.The final result will be a data
dictionary in which otherfield experiments can easily have their
sensor data mappedto internal software state using labels (i.e.
Temperature °F,north wall, 3′ above grade). We also plan to
investigate au-tomating the mapping for new sensor data using
machinelearning techniques. This general mapping mechanism
isnecessary for widespread use of the autotune technology.
While vector-distance is used as an error metric, itshould be
pointed out that the search space is so large thatthere most likely
exists a large multitude of feasible so-
lutions (buildings which match the measured data withinsome
threshold). We anticipate eventually using clus-tering to present
unique/representative solutions. How-ever, as additional outputs
are added (e.g. room temper-atures), the problem becomes more
difficult to find a ro-bust match, thereby reducing the number of
potential solu-tions and allowing quantification of the tradeoffs
betweenvector size and tuning accuracy. While the
commercialbuildings discussed in the Commercial Building
Simula-tion section were selected to allow direct comparison
of“actual” building properties to the tuned models, it is
im-portant to realize that approaches employed by Autotuneoffer the
capability of compensating not only for input er-rors, but for the
unavoidable algorithmic approximationsrequired by software modeling
algorithms on computingdevices.
Suite of Machine Learning AlgorithmsMachine learning allows the
autonomous generation of
algorithms by iteratively processing empirical data in or-der to
allow repeatable detection of patterns (Figure 4).More importantly,
cross-validation techniques ensure thateach instance of a machine
learning technique (agent)learns only from a small portion of the
data and thenits classification accuracy is tested on data which it
hasnot seen before. This process of validation is crucial tothe
generalized learning necessary for properly capturingBEM dynamics
without over-fitting for a specific build-ing. This process is
rarely used by energy modelers inthe manual tuning process and is
the primary culprit forpost-retrofit measurements not matching a
model that wasexpertly tuned.
Each type of learning system has its own strengths and
-
weaknesses, making it particularly suited for solving
aparticular type of problem. Moreover, a given type oflearning
system can vary in its performance based upon itsown internal
variables (learning rate, etc.). We have pre-viously developed a
suite of machine learning algorithms,called MLSuite, that allows
general XML-file based def-inition of jobs to run on supercomputers
and was pub-lished previously for testing “sensor-based energy
mod-eling” (sBEM) in which whole building electrical us-age was
predicted as a function of sensor data (Edwardset al., 2012).
MLSuite currently allows various types ofparameter-settings for
multiple learning systems, input or-derings, cross-validation
techniques, and accuracy metricsto analyze the patterns in
simulation data. It includes thefollowing 8 machine learning
algorithms: linear regres-sion, genetic algorithms, feed forward
neural networks,non-linear support vector regression, hierarchical
linearregression experts, hierarchical least-squares support
vec-tor regression experts, hierarchical feed forward neuralnetwork
experts, and Fuzzy C-means with local modelsof feed forward neural
networks.
A massive amount of data will be generated during theparametric
sensitivity analysis, and mapped to the sensordata. This data
captures dynamics that can quickly in-form the role multiple
simulation input variables have onthe simulation output to inform
the Autotuning process.There are three primary learning tasks that
have been de-fined for MLSuite which constitute novel and
promisingdata mining use cases for the building community:
patterndetection, simulation approximation, and inverse model-ing
(Kissock et al., 2003).
Pattern detection of single-variable parametric simula-tions
(all other variables constant) can be used to deter-mine the
sensitivity and pattern changes evoked by that“knob” of the
simulation. By detecting the patterns forevery pre-computed
combination of parameters, a set of“knob turns” can be defined
which is expected to push thesimulation results into alignment with
sensor data.
The primary problem and focus of development effortin the latest
E+ 7.0 was to address the long simulation run-time. E+ simulations
vary with the amount of temporalresolution required in reporting,
algorithms used to modelcertain properties, the amount of equipment
included, andmany other properties. While an envelope-only
simula-tion takes 2 minutes, one with ground loops and
additionalequipment currently takes ∼ 9 minutes. The
parametricdatabase stores a compressed and vectorized version ofthe
E+ input file (*.idf) and 15-minute data for 82 E+ re-port
variables (*.csv). By applying MLSuite to processthe IDF as the
input feature vector to learn and reliablymatch the CSV output
feature vector, machine learningagents can be developed which
require kilobytes (KB) ofhard drive space to store and can give
approximate E+
Figure 5: EnergyPlus model of the ZEBRAlliance housewith
Structurally Insulated Panels (SIP).
simulation results for a given input file in seconds ratherthan
minutes. Tradeoffs between storage, runtime, andaccuracy are
currently undergoing study.
Inverse modeling (Kissock et al., 2003) is a methodof working
backwards from observed sensor data to in-formation about a
physical object/parameter; this methodworks even if the physical
parameter is not directly ob-servable. In the context of BEM,
inverse modeling oftenworks backwards from utility bill data and
use mathemat-ical models (primarily statistics and model
assumptions)to identify more specific breakdown of energy use
withina building. By using CSV data as the input feature vec-tor
and IDF as the output feature vector, machine learningalgorithms
can be used to predict E+ input files as a func-tion of sensor data
and is the primary autotuning techniquecurrently being tested.
DISCUSSION AND RESULT ANALYSISResidential Building
Simulations
A three-level highly energy efficient research house,with a
conditioned floor area of 382 m2, was selectedfor the initial phase
of this project. This house isone of the four energy efficient
ZEBRAlliance houses(http://zebralliance.com) built using some of
the mostadvanced building technology, products, and
techniquesavailable at the time of construction. The main
reasonsfor this house selection was to eliminate the uncertain-ties
of input parameters and schedules (such as lighting,plug loads and
occupancy) through emulated occupancyand since it was very heavily
instrumented for valida-tion studies allowing investigations into
the tuning capa-bilities with intense submetering. In this
unoccupied re-search house, human impact on energy use is simulated
tomatch the national average according to Building
Americabenchmarks (Hendron and Engebrecht, 2010) with show-ers,
lights, ovens, washers and other energy-consumingequipment turned
on and off exactly according to sched-ule. This house uses a
structurally insulated panel (SIP)envelope with a thermal
resistance of 3.7 m2K/W, with
-
Figure 6: Large database of publicly available E+ para-metric
simulations.
very low air leakage (measured ACH50 = 0.74) and thushas very
low heat gain and loss through the building enve-lope. The details
of this house’s envelope and other char-acteristics are described
in Miller et al. (2010) (Figure 5).
This E+ model was created and carefully iterated andcompared to
sensor data by domain experts, but manydiscrepancies still exist.
This is compounded by the factthat there are many input parameters
for which a precisevalue cannot be attained; as examples: the
conductivity ofall materials used, radiant fraction of all lighting
fixtures,submetering of all individual plug loads, or heat
dissipatedby the dryer to the conditioned space. Various studies,
in-cluding Hopfe and Hensen (2011), highlight the danger
incombining multiple uncertainties in input parameters dueto their
different source of nature (climatic, structural, orserviceability
parameters), controllability, etc.; therefore,during the first part
of this project the main focus is on thebuilding envelope related
input parameter uncertainties. Aset of 156 parameters was selected
for the initial variation.Since many of the characteristics for
this house were iden-tified through lab tests, experts decided to
specify a real-istic range for the uncertain parameters manually
insteadof assigning a fixed percentage variation as used in
severalcalibration and uncertainty analyses (O’Neill et al.,
2011).A base, minimum and maximum value was assigned toeach of the
156 parameters. This approach allows greaterspecificity over the
parameter values while reducing thenumber of parameter
variations.
Commercial Building Simulations
While the residential application allows connection toreal-world
sensor data and tests for practical deploymentdecisions, the
commercial buildings were chosen to al-low a cleaner approach to
the testing of multiple autotun-ing methodologies. DOE’s reference
buildings for ware-house, medium office, and stand-alone retail
were selecteddue to their predominance in either number of
buildings orsquare footage in the US. In an approach similar to
signal-processing, we have made changes to the original mod-els to
create 3 “golden” models, added noise by permut-ing random
variables to create 3 “de-tuned” models, and
Figure 7: Illustration of the database on the server, theuser
views of the data, and remote client methods for ac-cessing the E+
simulation data.
then use internal E+ variables from simulation runs of thegolden
models as “sensor data” for tuning the “de-tuned”models back to the
golden” models.
The warehouse, retail, and office golden models havebeen defined
to use approximately 5%, 10%, and 20%more electrical energy than
the original models, respec-tively. These changes were created
using overlapping sub-sets of input variables and show, in
agreement with previ-ous sensitivity analysis studies, that small
changes add upquickly.
Open Research Buildings DatabaseIn order to deploy Autotune as a
desktop program in
which a limited number of E+ simulations can be run,several
mechanisms are required to speed up the pro-cess. In addition to
the application of machine learn-ing, pre-computing E+ simulations
using supercomputersare necessary to explore the combinatorial
search spaceof E+ input parameters. Time on several supercomput-ers
have been competitively awarded or used to demon-strate the ability
to scale software and algorithms for theseresources. Systems
include the 1024-core shared mem-ory Nautilus, 2048-core Frost, and
224,256-core Jaguarwhich is currently the 3rd fastest supercomputer
in theworld at 2.3 petaflops and is in transition to become
the299,008-core Titan. Frost is being used as a staging areato
verify large computational parameter sweeps beforerunning on Jaguar
and both are used primarily for em-barrassingly parallel
compute-bound E+ simulation jobs.Nautilus unique shared-memory
architecture allows ev-ery core to access the 4TB (terabytes) of
Random AccessMemory (RAM) for processing of memory-bound jobscommon
in machine learning.
The parametric simulations run by desktops and super-computers
has been uploaded to a centralized database toallow public access
to this data (Figure 6). It is anticipatedthat this data would be
of interest to researchers at univer-
-
Figure 8: Screenshot of EplusCleaner showing desktopclient
upload of simulations.
sities, data-mining experts, entrepreneurs, industry, andseveral
other organizations for a myriad of purposes. Sev-eral tools have
been developed for easily defining and run-ning parametric E+
simulations, compressing data, andsending to a centralized server.
In addition, several meth-ods have been made available for the
general public tofreely access and use this data (Figure 7).
The data storage and access software has been ar-chitected as a
distributed, heterogeneous, client-serverframework. The
embarrassingly parallel nature of the in-dependent simulations
allows us to exploit computationalresources that are remote and
disparate leading to an ar-chitecture capable of collecting
simulation results fromindividual desktop systems as well as
supercomputing re-sources. Our experiments indicate that the system
func-tions efficiently and has been found to be bound primar-ily by
the network bandwidth connecting the resourcesor local hard disk
access.The database engine currentlyin use is the MyISAM relational
MySQL database, al-though tools have been designed in a general
manner soas to allow easy interchange as database storage
technolo-gies continue to evolve. The database has been createdin a
manner that allows data compression and efficient re-trieval. Data
access patterns are being studied to allow re-architecting the
database and load-balancing for higher ef-ficiency. The internal
data storage format is not tied to theformat of input or output E+
variables but instead uses itsown generic internal naming scheme.
Depending on thecurrent set of variables and preferences of the
users, a cus-
tom view of the data is provided that can be easily
queried,summarized, and analyzed, providing the full benefits ofa
relational database system. Figure 7 shows the varioussoftware
components of the Autotune database illustrat-ing the independence
of the data storage mechanism fromthe user view of the data, the
software components onthe server, and the remote web-based clients.
There areseveral methods for accessing the data: a web-based
IDFreconstructor, command-line access for MySQL queries,phpMyAdmin
for GUI-based data interaction, a webpagefor uploading simulation
data, and EplusCleaner.
One of the components of this framework is an appli-cation named
EplusCleaner (Figure 8) which has been de-veloped using the Qt
Software Development Kit (SDK)(Nokia) for platform-independent
support. It has been ar-chitected to provide a powerful and
intuitive interface ca-pable of cleaning up after E+ simulation
runs, compress-ing the input idf and the E+ output, and sending it
to theserver for database entry while waiting on the server
forsuccess/error status messages. It concurrently, continu-ally,
and remotely consolidates the parametric simulationdata. Options
for compression or deletion on the localmachine keep E+ from
flooding the local hard drive withstorage of simulation results.
The EplusCleaner clientwas run simultaneously on several machines
and exhib-ited proper cleaning, compressing, and parsing with
noobservable slow-down in the simulation process indicat-ing a
bottleneck at the client machines access to the localhard drive.
The database server keeps track of submis-sions from clients and a
comprehensive log of the dataprovenance such that back-traces for
troubleshooting maybe performed if necessary. Upon receiving a
compressedunit of E+ data, the server decompresses the data,
createsa vector representative of the input, and commits the
en-tire unit to a database.
A web-based method for reconstructing an IDF filefrom the
database vector is provided which allows usersto retrieve an IDF
file from a stored vectorized set ofinput parameters. A
web-interface is also available foruploading external E+
simulations input and output filesto the database. External access
to this database canbe provided upon request using several user
validationand access methods including a command line
interface,password-protected phpMyAdmin for interactive
queries,drill-down, and analysis of the simulation database. Asof
the time of this writing, the server currently hosts tensof
thousands of parametric E+ simulations in 136GB frommultiple
distributed workstations and supercomputers, butmillions of
simulations (trillions of data points) are antic-ipated by time of
publication. The latest Autotune projectinformation, including
database size and access methods,can be found at
http://autotune.roofcalc.com.
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CONCLUSIONA heavily instrumented residential building has
been
selected to leverage intense submetering for the autotun-ing
process while eliminating variability due to occupantbehavior
through emulated occupancy. An E+ model ofthis house has been
iteratively refined by experts to modelthe house. Experts have
identified 156 input parametersto be varied with min, max, and
step-sizes for underminedproperties.
DOE’s reference buildings for warehouse, stand-aloneretail, and
medium office have been selected for creating“golden” models that
use 5%, 10%, and 20% more elec-trical energy, respectively.
“De-tuned” models have beencreated by permuting an undisclosed
number of overlap-ping subsets of E+ input parameters. E+ variables
fromruns of the “golden” models will be used for
autotuning“de-tuned” models back to “golden” models.
The database and submission/retrieval software toolsfor Autotune
have been developed with generalizabilityand scalability in mind.
Capabilities developed include aplatform-independent Qt application
named EplusCleanerfor continual curation, compression, and upload
of simu-lation data to a centralized MyISAM server storing tens
ofthousands of E+ parametric simulations with many mech-anisms
allowing public access. The software system isdistributed,
heterogenous, scalable and could potentiallyevolve into a
full-fledged simulation, curation, and dataassimilation
framework.
ACKNOWLEDGMENTThis work was funded by field work proposal
CEBT105
under the Department of Energy Building TechnologyActivity
Number BT0201000. We would like to thankAmir Roth for his support
and review of this project.We would like to thank our collaborators
which includeMr. Richard Edwards and Dr. Lynne Parker from
TheUniversity of Tennessee, Dr. Aaron Garrett from Jack-sonville
State University, and Mr. Buzz Karpay from Kar-pay Associates. This
research used resources of the OakRidge Leadership Computing
Facility at the Oak RidgeNational Laboratory, which is supported by
the Office ofScience of the U.S. Department of Energy under
ContractNo. DE-AC05-00OR22725. Our work has been enabledand
supported by data analysis and visualization experts,with special
thanks to Pragnesh Patel, at the NSF fundedRDAV (Remote Data
Analysis and Visualization) Centerof the University of Tennessee,
Knoxville (NSF grant no.ARRA-NSF-OCI-0906324 and
NSF-OCI-1136246).
Oak Ridge National Laboratory is managed by UT-Battelle, LLC,
for the U.S. Dept. of Energy under contractDE-AC05-00OR22725. This
manuscript has been au-thored by UT-Battelle, LLC, under Contract
Number DE-AC05-00OR22725 with the U.S. Department of Energy.
The United States Government retains and the publisher,by
accepting the article for publication, acknowledges thatthe United
States Government retains a non-exclusive,paid-up, irrevocable,
world-wide license to publish or re-produce the published form of
this manuscript, or allowothers to do so, for United States
Government purposes.
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