LBNL Example Building 3 Model 1

LBNL Example Building 3 Model 1#

Example Building 3 is a real-world medium-sized office building. It is approximately 67,000 square feet in size, contains 2 floors, 27 space types, and 469 rooms. Labels have been anonymized, and are not interpretable. It uses an underfloor air distribution system with fan-powered terminal reheat coils for perimeter zones. Four roof-top units with VAV are located on the roof. The lighting system primariliy uses fluorescent luminaires and zone-based lighting controllers that communicate with user interface devices and wireless gatetways over DALI networks. The gateways collect occupancy and light sensor data over an ISM-band wireless network.

Example Building 3 Model 1 is provided by Lawrence Berkeley National Laboratory. See PNNL Example Building 3 model 2 for a different modeling approach for the same building.

Contents#

This model contains a representation of the HVAC system.

Source#

Downloads#

What are these files?

Turtle file (original): This is the original source Turtle file that was provided to models.open223.info, usually as the output of some model creation tool.
Turtle file (compiled): This is the original Turtle file with all inferred relationships and values added through SHACL inference against the 223P ontology and other dependencies. You should use this file for any further processing. It does not contain any of the ontologies.
Turtle file (with all imports): This is the compiled Turtle file with all imports included in the file (223P ontology, QUDT ontology, and others). This is helpful when you do not want to deal with downloading and managing ontology dependencies. It is also much larger than the compiled file.
JSON-LD file (original): This is the original Turtle file converted to the JSON-LD format.

Turtle is a syntax for RDF (Resource Description Framework) that is easy to read and write. It is a popular format for representing linked data. Parsers and serializers are available in many programming languages. JSON-LD is a JSON-based format for linked data that is easy to use with JavaScript and other web technologies.

Queries#

Description	Query URL
Zone/room temperature sensors	Query Link

Model Components#

Parent Class	Class	Instances
Equipment	Fan	116
Equipment	Coil	58
Equipment	HeatingCoil	54
Equipment	Valve	54
Equipment	TwoWayValve	54
Equipment	Sensor	48
Equipment	Damper	20
Equipment	TemperatureSensor	20
Equipment	Filter	8
Equipment	DifferentialSensor	8
Equipment	AirHandlingUnit	4
Equipment	CoolingCoil	4
ConnectionPoint	InletConnectionPoint	616
ConnectionPoint	OutletConnectionPoint	500
ConnectionPoint	BidirectionalConnectionPoint	28
DomainSpace		58
Zone		2
Property	QuantifiableProperty	506
Property	ObservableProperty	368
Property	QuantifiableObservableProperty	368
Property	ActuatableProperty	140
Property	QuantifiableActuatableProperty	132
Property	EnumerableProperty	8
Property	EnumeratedActuatableProperty	8

Load and Validate Model#

This code uses the BuildingMOTIF library to load the 223P ontology and the model file into a temporary in-memory instance. It then validates the model against the ontology. If the model is invalid, it will print the validation report.

To run this code, you need to have Java installed on your system. If you do not have Java installed, you can remove the shacl_engine='topquadrant' parameter from the BuildingMOTIF constructor. Be warned that without the shacl_engine='topquadrant' parameter, the validation process will be slower.

Note

BuildingMOTIF installation To install the buildingmotif library, you can use the following command:

pip install 'buildingmotif[topquadrant] @ git+https://github.com/NREL/buildingmotif.git@develop'

If you do not have Java installed, you can use the following command to install the library:

pip install 'buildingmotif @ git+https://github.com/NREL/buildingmotif.git@develop'

from buildingmotif import BuildingMOTIF
from buildingmotif.dataclasses import Library, Model
import logging

# Create a BuildingMOTIF object. If you do not have Java installed, remove the "shacl_engine" parameter
bm = BuildingMOTIF('sqlite://', shacl_engine='topquadrant', log_level=logging.ERROR)

# load 223P library. We will load a recent copy from the models.open223.info
# git repository; later, we will load this from the location of the actual standard
s223 = Library.load(ontology_graph="https://open223.info/223p.ttl")

# load the model into the BuildingMOTIF instance
model = Model.create("urn:lbnl-bdg3-1")
model.graph.parse("https://models.open223.info/lbnl-bdg3-1.ttl")

# validate the model against 223P ontology
ctx = model.validate([s223.get_shape_collection()], error_on_missing_imports=False)

# print the validation result
print(f"Model is valid: {ctx.valid}")

# if the model is invalid, print the validation report
if not ctx.valid:
    print(ctx.report_string[:1000]) # first 1000 characters of the report

# BuildingMOTIF can also interpret the report to provide recommendations on fixes
for focus_node, diffs in ctx.get_reasons_with_severity("Violation").items():
    if len(diffs) == 0:
        continue
    print(focus_node)
    for diff in diffs:
        print("  - " + diff.reason())

Model is valid: True