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Contract No. DE-AC36-08GO28308

Leveraging OpenStudio’s 

Application Programming 

Interfaces

Preprint

Nicholas Long, Brian Ball, David Goldwasser, 

and Andrew Parker 

National Renewable Energy Laboratory

Jennifer Elling

Xcel Energy

Oliver David

concept3D

Dale Krutchen

National Grid

Presented at the IBPSA Building Simulation Conference

Chambery, France

August 25-28, 2013

Conference Paper

NREL/CP-5500-58520 

November 2013
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LEVERAGING OPENSTUDIO’S APPLICATION PROGRAMMING INTERFACES

Nicholas Long1, Brian Ball1, Larry Brackney1, David Goldwasser1, Andrew Parker1, Jennifer Elling2, 

Oliver Davis3, and Dale Kruchten4

1

National Renewable Energy Laboratory, Golden, CO, USA

2

Xcel Energy, Denver, CO, USA

3

concept3D, Boulder, CO, USA

4

National Grid, Waltham, MA, USA

ABSTRACT

OpenStudio development efforts have focused on 

providing application programming interfaces (APIs) that 

enable users to extend OpenStudio without compiling the 

open source libraries. This paper discusses the basic 

purposes and functionalities of the core libraries that have 

been wrapped with APIs, including the building model, 

results processing, advanced analysis, uncertainty 

quantification, and data interoperability through 

translators.

Several building energy modeling applications have been 

produced using OpenStudio’s API and software 

development kits, including the U.S. Department of 

Energy’s Asset Score calculator, a mobile-based audit tool, 

an energy design assistance reporting protocol, and a 

portfolio-scale incentive optimization analysis application. 

This paper discusses each application briefly and describes

how the APIs were leveraged for various uses, including 

high-level modeling, data transformations from detailed 

building audits, error checking and quality assurance of 

models, and high-performance computing for mass 

simulations. 

INTRODUCTION

The last several years have seen a number of exciting 

developments that make simulation engines such as 

EnergyPlus (USDOE 2013) and Radiance (Ward 1994) 

accessible to a broader range of design professionals and 

researchers. Since OpenStudio’s initial release in 2010, the 

application programming interfaces (APIs) and software 

development kits (SDKs) have evolved to support a 

number of use cases beyond its initial goal of accelerating 

the development of practitioner-focused building energy 

modelling graphical user interfaces (Weaver et al., 2012).

OpenStudio abstraction of EnergyPlus objects makes it 

substantially easier to articulate new energy models and 

automate analyses (Hale et al., 2012). The Building 

Component Library (BCL) has also emerged as an integral 

web service that further simplifies model articulation and 

transformation through data exchange (Fleming et al., 

2012). This paper focuses on recent application examples 

of the OpenStudio API and SDK to create new web and 

mobile applications along with automated, large-scale 

portfolio analyses that support a variety of market needs 

for new commercial building construction and retrofit.

WEB APPLICATIONS

OpenStudio’s programmatic bindings enable developers to 

use their favourite languages. The Ruby bindings were 

initially produced out of necessity to support SketchUp’s 

Ruby-based API 

(http://www.sketchup.com/intl/en/developer/). Ruby has

proven to be extremely versatile, enabling quick 

prototyping of new modelling and analysis workflows 

using a simple and intuitive interpreted language. It

provides a convenient pathway for extending basic 

OpenStudio functionality, and can be used to produce web 

applications when used within the popular Ruby on Rails 

framework (http://rubyonrails.org).

Multiple organizations are actively working to apply 

OpenStudio to create web services that convert high-level 

user input into a complete energy model using supporting 

data and assumptions. Two early examples include the 

U.S. Department of Energy’s (DOE) Asset Score 

calculator web service (PNNL 2012), being led by 

researchers at Pacific Northwest National Laboratory.

Another example is the VirtualPULSE

(http://www.buildsci.us/virtual-pulse.html) project led by 

Penn State researchers associated with the Energy 

Efficient Buildings Hub (http://www.eebhub.org/). Figure 

1 contains the Ruby script required to fully articulate an 

energy model from high-level variables and run a 

simulation. The OpenStudio Plug-in for SketchUp and 

OpenStudio Application may also be used to inspect the 

resulting model’s geometry and systems (Figure 2) along 

with simulation results (Figure 3).

require 'openstudio'

require 'VirtualPULSEModel'

#create a new model

model = VirtualPULSEModel.new

#add geometry (simple multi-story core/perimeter building)

model.add_geometry({"length" => 100, 

“width" => 50,

"num_floors" => 3,

“floor_to_floor_height" => 4,

“plenum_height" => 1, 

"perimeter_zone_depth" => 3})

#add windows at a given window-to-wall ratio
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model.add_windows({"wwr" => 0.4,

"offset" => 1,

"application_type" => "Above Floor"}) 

#add HVAC - PVAV w/ Reheat - DX Cooling, Hot Water heat and reheat

model.add_hvac({"fan_eff" => 0.5,

"boiler_eff" => 0.66,

"boiler_fuel_type" => "Natural Gas",

"coil_cool_rated_high_speed_COP" => 5.5,

"coil_cool_rated_low_speed_COP" => 6.6,

"economizer_type" =>"Fixed Dry Bulb Temperature Limit",

"economizer_dry_bulb_temp_limit" => 30})

#add thermostats

model.add_thermostats({"heating_setpoint" => 20,

"cooling_setpoint" => 25}) 

#assign constructions from a local library to the walls/windows model

model.add_constructions({"construction_library_path" =>

"#{Dir.pwd}/VirtualPULSE_default_constructions.osm"})

#add space type from a remote library (BCL) to the model

model.add_space_type({"NREL_reference_building_vintage" =>

"ASHRAE_90.1-2004",

"Climate_zone" => "ClimateZone 1-8",

"NREL_reference_building_primary_space_type" =>

"SmallOffice",

"NREL_reference_building_secondary_space_type" =>

"WholeBuilding"})

#add design days to the model

model.add_design_days()

#save the OpenStudio model (.osm)

model.save_openstudio_osm({"osm_save_directory" => Dir.pwd,

"osm_name" => "exampleVirtualPULSEModel.osm"})

#translate the OpenStudio model (.osm) to an EnergyPlus model (.idf)

model.translate_to_energyplus_and_save_idf({"idf_save_directory" =>

Dir.pwd, "idf_name" => "exampleVirtualPULSEModel.idf"})

#run the EnergyPlus model (.idf)

VirtualPULSEModel::run_energyplus_simulation({"idf_directory" =>

Dir.pwd, "idf_name" => "exampleVirtualPULSEModel.idf"})

Figure 1 VirtualPULSE Ruby Script

Figure 2 VirtualPULSE Modelled Envelope as rendered 

with OpenStudio Plug-in for SketchUp

Figure 3 Example VirtualPULSE Simulation Results

This compact, easy-to-read script illustrates the types of 

high-level parameters that may be used to specify a 

building energy model with OpenStudio. By comparison, 

the resulting EnergyPlus input file is many thousands of 

lines long. In practice, a derivative of this example will be 

deployed on a website using Ruby on Rails along with a 

web form to collect the high-level inputs that are presently 

“hard coded” in the script.

The VirtualPULSE script accomplishes most of its 

modelling simplification by calling the BCL’s API to 

produce detailed space type definitions (building 

equipment loads, people loads, lighting loads, thermostats, 

etc.) used in the model. The BCL produces this content 

“on-demand” based on API arguments, using its own 

instance of OpenStudio-based scripts with Ruby on Rails 

bindings (Fleming et al., 2012). The theme of leveraging 

APIs and distributed services to easily create and simulate 

models illustrated in this example are further explored in 

subsequent sections for a range of purposes.

The DOE Asset Score calculator also uses OpenStudio to 

identify the Asset Score sensitivities associated with key 

input variables made available to the user via the web 

interface. To accomplish this, OpenStudio leveraged the 

R-project, a rich library of algorithms for sampling, 

uncertainty quantification, sensitivity analysis, and 

optimization (R-project). In this case, R’s Latin Hypercube 

Samples (LHS) package is used to sample the high-level 

input parameter space to identify critical sensitivities 

affecting the overall asset score produced by the 

calculator.

Input parameter spaces may be sampled according to 

specific probability density functions to perform quasiactuarial analyses. Figure 4 illustrates an example in which 

a nominal small office building (shown as a singular red 

solution) has been perturbed to simulate hypothetical 

deteriorated/derated performance (green lognormal 

distribution) and a potential retrofit (blue normal 

distribution). Both HVAC coefficient of performance 

(COP) and effective insulation R-values are sampled, and 

the resulting distributions in energy use intensity (EUI) are 

computed. The EUI distributions include interactive 

effects from varying these two input parameters 

simultaneously. This sample problem is available as a 

tutorial on the OpenStudio website 

(http://openstudio.nrel.gov/openstudio-tutorials/

openstudio-uncertainty-quantification-tutorial).

Figure 4 Uncertainty Quantification Example
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WEB-INTEGRATED WORKFLOWS

Like a number of USA gas and electric utilities, the 

Midwest’s Xcel Energy offers an energy design assistance 

(EDA) program that incentivizes energy efficiency for new 

construction. Pay-for-performance relationships with 

energy consultants are designed to influence customers 

who are considering energy conservation measures 

(ECMs) early in the design process. Increasingly stringent 

building codes and market conditions have placed 

significant pressure on EDA programs to reduce program

costs and simultaneously increase the quality of outcomes.

To maintain its EDA program’s economic viability, Xcel 

Energy has developed an OpenStudio-integrated web 

service to automate quality assurance and quality checking

(QA/QC), program reporting, and communication between 

all stakeholders.

EDAPT, the Energy Design Assistance Program Tracker, 

is a web service designed to interoperate with OpenStudio 

applications used by energy consultants. The site, shown 

in Figure 5, provides a structured workflow that helps 

energy program administrators, energy consultants, and 

customers track approvals and progress of individual 

projects throughout the EDA life cycle from application, 

introductory meeting, ECM assessment and selection, and 

through measurement and verification modelling.

Figure 5 Xcel Energy’s EDAPT Web Service

For this application, OpenStudio has specifically been 

extended to include Ruby scripts that perform a range of 

automated QA/QC checks that use model inputs and 

outputs to identify excessive unmet hours, out of range 

EUIs as shown in Figure 6, and more. These are combined 

with compliance checks against Xcel Energy protocol 

requirements such as a test for fuel switching compared to 

the baseline model and deviation from standard BCL input 

data prescribed by the protocol. Results are stored in an 

eXtensible Markup Language (XML) file along with 

baseline and design alternative model performance that is 

uploaded to the EDAPT web portal.

#checks the EUI for the whole building

def eui_check(model,sql)

 eui_check = Check.new

 building = model.getBuilding

 total_site_energy_GJ = OpenStudio::Quantity.new(

 sql.totalSiteEnergy.get, $GJ_unit)

 total_site_energy_kBtu = OpenStudio::convert(

 total_site_energy_GJ, $kBtu_unit).get

 floor_area_m2 = OpenStudio::Quantity.new(

 building.floorArea, $m2_unit)

 floor_area_ft2 = OpenStudio::convert(floor_area_m2, $ft2_unit).get

site_EUI = total_site_energy_kBtu / floor_area_ft2

 if site_EUI.value> 200

 eui_check.add_flag(Flag.new($gen_mod,"Site EUI of #{site_EUI} 

looks high. A hospital or lab (high energy buildings) are 

around 200 kBtu/ft^2"))

 end

 if site_EUI.value< 30

 eui_check.add_flag(Flag.new($gen_mod,"Site EUI of #{site_EUI} 

looks low. A high efficiency office building is around 50 

kBtu/ft^2"))

 end

 debug_puts eui_check.flags.inspect

 return eui_check

end

Figure 6 EUI Quality Control Ruby Script

EDAPT combines the OpenStudio XML output with 

project information extracted from the application and 

other process stages. The user interface uses this 

information to enable Xcel Energy to assess and track the 

performance of its entire EDA portfolio. It is also used to 

automatically generate template documents for energy 

consultants (e.g., construction documents that enumerate 

all of the ECMs specified in the model). These documents 

contain boilerplate information culled from all the 

project’s data, along with standardized end-use plots, 

comparison tables, etc., which consultants may tailor to 

support their own customer-focused narratives. In addition 

to providing a uniform and minimal level of content 

required by Xcel, the template helps consultants save 

significant time by avoiding transcription of key 

simulation results into reports.

MOBILE APPLICATIONS

New mobile workflows are also being enabled by 

OpenStudio’s ability to automate model generation and 

analysis via web interfaces. An emerging company, 

concept3D, is developing a new service called 

“simuwattTM”. simuwatt is an application for energy 

auditors that combines a mobile software-guided 

workflow, OpenStudio’s procedural modelling capability, 

and the BCL to dramatically reduce the cost of performing 

consistent, high-quality, investment-grade energy audits 

(Figure 7).
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Figure 7 simuwatt Approach to Auditing

Auditors using the mobile application use a tablet 

computer to define geometry, loads, mechanical systems, 

schedules, utility rates, and more. Specific pieces of 

equipment identified in the building are associated with 

corresponding BCL components. New BCL components 

may also be entered and uploaded from the tablet, enabling 

crowd-sourced content generation. Data from the tablet are 

pushed to the simuwatt server as an XML file. The server 

automatically translates the audit XML file to a baseline 

model for use by any OpenStudio-based application

(Figure 8).

Figure 8 simuwatt Distributed Architecture

The OpenStudio Parametric Analysis Tool (PAT) (NREL 

2013) is a user interface that has been works with Xcel’s 

EDA program, simuwatt, and for general use by modelling 

practitioners. Shown in Figure 9, it is designed to search, 

download, and apply BCL measures to the baseline model; 

manage multiple simulations; present tabular results; and 

generate the XML output file described in the previous 

section. This same XML output file is consumed by the 

simuwatt server, and is used to generate template audit 

reports. Near-term versions of PAT will support semiautomated model tuning, using calibration measures and 

cloud computing.

Figure 9 OpenStudio Parametric Analysis Tool

simuwatt, and any OpenStudio-based tool suite, is 

designed for extensibility as new BCL content is made 

available. As with model components, measure content 

creation is expected to be crowd-sourced. PAT provides 

functionality to author OpenStudio-compatible measure 

templates, and a measures authoring guide is available to 

assist authors (http://openstudio.nrel.gov/openstudiomeasure-writing-guide).

simuwatt is also intended to be a scalable solution that 

facilitates auditing of single buildings by multiple 

performers, or multiple building audits that are aggregated 

to define portfolios (see Figure 10). In addition to audit 

reports from single buildings, simuwatt is planned to 

produce portfolio assessment reports that compare baseline 

performance and identify opportunities to achieve 

economies of scale when purchasing ECMs for multiple 

buildings. Audit data, models, and analysis results persist 

on the server for future use; e.g., when continuing to 

comply with the U.S. Energy Independence and Security 

Act 2007 auditing mandate for federal buildings.

Figure 10 simuwatt Architecture Scalability

LARGE-SCALE ANALYSIS

National Grid, a North East USA utility, focuses on a 

different portfolio-scale application of OpenStudio 

designed to optimize incentive and marketing expenditures 

for their energy efficiency programs. National Grid is 
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developing automated model generation and tuning 

processes that leverage a combination of geographic 

information systems (GIS), public records, customer data, 

and content from the BCL to feed portfolio optimization 

algorithms (Figure 11).

Figure 11 Data Sources for Automated Modelling

High-level data required for baseline model articulation 

includes: physical address, square footage, number of 

floors, vintage, building type, and consumption data. A 

web application (Figure 12) uses the Google Styled Maps 

API to automatically identify the building footprint from 

aerial imagery, with options to establish a simple bounding 

box or user-directed geometry (https://developers.google. 

com/maps/documentation/javascript/styling). All this 

information is “mashed up” into a single JavaScript Object 

Notation (JSON) input file (Figure 13) for each customer 

building.

Figure 12 Web Application for Footprint Capture

{ "building": {

 "address": {

 "city": "Anytown",

 "state": "NY",

 "street": "1234 RedactedAve",

 "zip": "12345" },

 "geometry": {

 "lat_lng": [12,34],

 "length": 175,

 "rotation": -29.3,

 “sqft": 44414,

 "stories": null,

 "width": 60,

 "wwr": 0.5},

 "number_of_customers": 57,

 "number_of_non_customers": 59,

 "apparent_power": {

 "annual_kwh": 7910294,

 "fuel_type": "electric",

 "hourly_data": [{"time": 1354572784,"value": 0},

 {"time": 1354573084,"value": 0}],

 "max_kw": 1860,

 "monthly_data": [{"time": 1354572784,"value": 0},

 {"time": 1354573084,"value": 0}] },

 "reactive_power": {

 "annual_kwh": 7910294,

 "fuel_type": "electric",

 "hourly_data": [{"time": 1354572784,"value": 0},

 {"time": 1354573084, “value": 0}],

 "max_kw": 1860,

 "monthly_data": [{ “time": 1354572784,"value": 0},

 {"time": 1354573084,"value": 0}] },

 "real_power": {

 "annual_kwh": 7910294,

 "fuel_type": "electric",

 "hourly_data": [{ “time": 1354572784,"value": 0},

 {"time": 1354573084,"value": 0}],

 "max_kw": 1860,

 "monthly_data": [{"time": 1354572784,"value": 215},

 {"time": 1354573084,"value": 215}]},

"roof_construction": {"insulation": 0,"type": ""},

"schedule": {},

"type": "office",

"vintage": 1967,

"window_construction" : {"insulation": 0,"type": ""} },

"site": { "climate_zone": "5A",

"weather_uid": "0b0da8b1-ae8a-012d-eb00-00ff10a04904"},

"tenants": [{"id": 6767660100,

"name": "",

"sqft": null,

"type": "office"}]}

Figure 13 JSON Input File for Automated Modelling with 

Customer-Identifying Data Removed

The JSON file provides the necessary input to what is, in 

effect, an OpenStudio-based expert system for automatic 

model generation of the hundreds of thousands of National 

Grid customers with minimal human-in-the-loop 

interaction (Figure 14). A series of OpenStudio measures 

create a model envelope with core and perimeter zoning, 

prescribe multiple space types and associated loads based 

on building type from BCL content, infer construction and 

mechanical systems based on vintage, and define historical 

weather input data based on location.
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Figure 14 OpenStudio-Based Expert System for Automated 

Baseline Model Generation

This process has been used on more than a dozen buildings 

in National Grid’s portfolio. The first, an office building, 

produced the model shown in Figures 15 and 16, along 

with the simulated end uses shown in Figure 17.

Figure 15 Automated Baseline Model Envelope

Figure 16 Automated Baseline Model HVAC System

Figure 17 Automated Baseline Model End Uses

Figure 18 shows a comparison of the actual monthly 

electricity use against the building’s modelled data.

Figure 18 Comparison of Actual and Modelled Data

Comparisons of metered and modelled data in this initial 

population show a broad range of variation, the worst of 

which was associated with a building that included 

unmodelled photovoltaics and medical imaging 

equipment. Additional heuristics, including precalculated 

EUI metrics to refine space type allocations and 

correlation of electricity and gas loads with season to 

refine HVAC system selection, are planned for addition to 

the baseline expert system to reduce the initial error bands.

A next step in the project will involve the use of 

OpenStudio’s analysis and optimization framework to tune 

baseline models using National Grid’s metered data. As 

with the simuwatt tool, a constrained set of calibration 

measures will be applied to each baseline to minimize an 

objective function that includes monthly and annual 

electric and gas consumption. These measures will operate 

on the model at a high level, and will include parameters 

such as volumetric allocation of space types and wholesale 

replacement of HVAC systems. This approach relies 

heavily on OpenStudio’s abstraction of model inputs, and 

avoids manipulation of low-level EnergyPlus input 

parameters.

Historically, large-scale analyses such as the one 

previously described have required access to high 

performance computing resources. However, using cloudcomputing services can enable a large number of 

practitioners to perform such analyses. The ability to 

distribute OpenStudio simulations through the OpenStudio 

RunManager and PAT to various cloud services is 

currently under development.

CONCLUSION

This paper has presented several case studies that leverage 

the OpenStudio SDK and BCL to enable web and mobile 

applications along with portfolio-scale analysis capability. 

This ability to procedurally generate fully articulated 

building energy models is being used by multiple DOE 

National Laboratories, university researchers, and private 

sector interests to quickly build and analyse models for a 

range of applications.
7

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The impact of using a standardized model articulation 

layer from various sources of high-level inputs allows tool 

developers and end users to focus on how best to offer 

incentives and/or focus on the engineering and not the 

debugging of models. Use of a self-consistent modelling 

ecosystem creates collateral benefit for stakeholders 

involved in new construction and retrofit design at many 

scales. Each use case presented in this paper represents a 

significant decrease in time spent in modelling, removing 

barriers for utilities and design firms to use energy 

modelling to provide actionable guidance to clients.

Rapid and consistent approaches to building energy 

modelling are crucial to help drive down modelling and 

operational costs, improve the penetration of emerging 

technologies through more rigorous analyses and will help 

designers meet or exceed increasingly stringent codes and 

standards required for an energy efficient built 

environment.

ACKNOWLEDGMENT

This work was supported in part by the U.S. Department 

of Energy Building Technologies Program. The authors 

appreciate the support of our U.S. DOE sponsors, Amir 

Roth and Joan Glickman. The authors also thank the rest 

of the OpenStudio development team including: Kyle 

Benne, Katherine Fleming, Robert Guglielmetti, Elaine 

Hale, Daniel Macumber, Luigi Gentile Polese, Marjorie 

Schott, Alex Swindler, Jason Turner, and Evan Weaver.

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