brick-model-summarizer 0.4.1

A package for summarizing BRICK models

BRICK Model Summarizer

BRICK Model Summarizer is a Python tool designed to validate and benchmark AI-generated BRICK models against reference models. It transforms complex BRICK schema TTL files into concise, human-readable summaries of HVAC systems, zones, meters, and central plants. By leveraging reference BRICK models, this tool enables users to validate AI-created models for consistency, accuracy, and adherence to expected standards.

Purpose

The primary purpose of this repository is to provide a framework for summarizing BRICK models into HVAC-centric insights. This is especially useful for:

• Benchmarking AI-generated BRICK models against reference models.
• Validating BRICK schemas for completeness and alignment with building system expectations.
• Empowering building engineers, analysts, and AI developers with clear summaries of mechanical systems and operational data.

Key Features

• HVAC-Focused Summarization: Extracts key details about AHUs, VAVs, meters, and central plant equipment.
• Model Validation: Provides a framework for benchmarking AI-created BRICK models.
• Scalable Processing: Processes individual or multiple BRICK schema TTL files.

Installation

Tested in Linux and Windows Subsystem for Linux

pip install brick-model-summarizer

Local Installation for development purposes

1. Clone the repository:

   git clone https://github.com/bbartling/brick-model-summarizer.git
   cd brick-model-summarizer
   

2. Set up a virtual environment (optional but recommended):

   python -m venv env
   source env/bin/activate
   

3. Install the package locally:

   pip install .
   

---

Usage

The package includes functions for summarizing BRICK models and generating detailed outputs. Below is an example of how to use the tool in Python to generate JSON-style data.

Example: Processing a BRICK Model

import os
from brick_model_summarizer import (
    load_graph_once,
    get_class_tag_summary,
    get_ahu_information,
    get_zone_information,
    get_building_information,
    get_meter_information,
    get_central_plant_information,
    get_vav_boxes_per_ahu,
)

# Get the absolute path of the project root
script_dir = os.path.dirname(os.path.abspath(__file__))
project_root = os.path.dirname(script_dir)

# Construct the relative path to the BRICK model
brick_model_path = os.path.join(project_root, "sample_brick_models", "diggs.ttl")

print("Resolved Brick Model Path:", brick_model_path)

# Load the RDF graph once
graph = load_graph_once(brick_model_path)

# Get the individual data components
ahu_data = get_ahu_information(graph)
print("ahu_data \n", ahu_data)

zone_info = get_zone_information(graph)
print("zone_info \n", zone_info)

class_tag_sum = get_class_tag_summary(graph)
print("class_tag_sum \n", class_tag_sum)

building_data = get_building_information(graph)
print("building_data \n", building_data)

meter_data = get_meter_information(graph)
print("meter_data \n", meter_data)

central_plant_data = get_central_plant_information(graph)
print("central_plant_data \n", central_plant_data)

vav_boxes_per_ahu = get_vav_boxes_per_ahu(graph)
print("vav_boxes_per_ahu \n", vav_boxes_per_ahu)

Example Output

=== AHU DEBUG Summary ===
Processed AHU's: 0

ahu_data
 {'total_ahus': 0, 'constant_volume_ahus': 0, 'variable_air_volume_ahus': 0, 'ahus_with_cooling_coil': 0, 'ahus_with_heating_coil': 0, 'ahus_with_return_fans': 0, 'ahus_with_supply_fans': 0, 'ahus_with_return_air_temp_sensors': 0, 'ahus_with_mixing_air_temp_sensors': 0, 'ahus_with_supply_air_temp_sensors': 0, 'ahus_with_supply_air_temp_setpoints': 0, 'ahus_with_static_pressure_sensors': 0, 'ahus_with_static_pressure_setpoints': 0, 'ahus_with_air_flow_sensors': 0, 'ahus_with_air_flow_setpoints': 0, 'ahus_with_active_chilled_beams': 0, 'ahus_with_chilled_beams': 0, 'ahus_with_passive_chilled_beams': 0, 'ahus_with_heat_wheels': 0, 'ahus_with_heat_wheel_vfds': 0}
zone_info 
 {'zone_air_temperature_setpoints_found': False, 'total_variable_air_volume_boxes': 59, 'total_variable_air_volume_boxes_with_reheat': 0, 'number_of_vav_boxes_per_ahu': {}, 'vav_boxes_with_reheat_valve_command': 0, 'vav_boxes_with_air_flow_sensors': 0, 'vav_boxes_with_supply_air_temp_sensors': 0, 'vav_boxes_with_air_flow_setpoints': 0, 'co2_sensor_count': 0, 'co2_setpoint_count': 0, 'zone_air_conditioning_mode_status_count': 0, 'cooling_temp_setpoint_count': 0, 'dewpoint_sensor_count': 0, 'heating_temp_setpoint_count': 0, 'humidity_sensor_count': 0, 'humidity_setpoint_count': 0, 'temperature_sensor_count': 0, 'temperature_setpoint_count': 0, 'zone_count': 0, 'reheat_command_count': 0, 'reheat_hot_water_system_count': 0, 'reheat_valve_count': 0}

Class Similarities:
class_tag_sum
 {'class_mismatches': [], 'tag_mismatches': []}
building_data 
 {'building_area': 'not_available', 'number_of_floors': 'not_available', 'hvac_equipment_count': 9, 'hvac_zone_count': 0}
meter_data 
 {'chilled_water_meter_present': False, 'hot_water_meter_present': False, 'building_electrical_meter_present': False, 'building_gas_meter_present': False, 'building_water_meter_present': False, 'electric_energy_sensor_count': 0, 'electric_power_sensor_count': 0, 'active_power_sensor_count': 0, 'ev_charging_hub_count': 0, 'ev_charging_port_count': 0, 'ev_charging_station_count': 0, 'electrical_energy_usage_sensor_count': 0, 'pv_generation_system_count': 0, 'pv_panel_count': 0, 'photovoltaic_array_count': 0, 'photovoltaic_current_output_sensor_count': 0, 'photovoltaic_inverter_count': 0, 'peak_demand_sensor_count': 0, 'people_count_sensor_count': 0}
central_plant_data 
 {'chiller_count': 0, 'water_cooled_chiller_count': 0, 'air_cooled_chiller_count': 0, 'centrifugal_chiller_count': 0, 'absorption_chiller_count': 0, 'boiler_count': 0, 'natural_gas_boiler_count': 0, 'noncondensing_natural_gas_boiler_count': 0, 'condensing_natural_gas_boiler_count': 0, 'electric_boiler_count': 0, 'cooling_tower_count': 0, 'cooling_tower_fan_count': 0, 'heat_exchanger_count': 0, 'heat_exchanger_discharge_temp_sensor_count': 0, 'heat_exchanger_leaving_temp_sensor_count': 0, 'heat_exchanger_supply_temp_sensor_count': 0, 'heat_exchanger_system_enable_status_count': 0, 'heat_pump_air_source_condensing_unit_count': 0, 'heat_pump_condensing_unit_count': 0, 'heat_pump_ground_source_condensing_unit_count': 0, 'heat_pump_water_source_condensing_unit_count': 0, 'heat_recovery_air_source_condensing_unit_count': 0, 'heat_recovery_condensing_unit_count': 0, 'heat_recovery_hot_water_system_count': 0, 'heat_recovery_water_source_condensing_unit_count': 0, 'hot_water_system_count': 0, 'water_pump_count': 0, 'chilled_water_system_count': 0, 'condenser_water_loop_count': 0, 'condenser_water_pump_count': 0, 'condenser_water_system_count': 0, 'domestic_hot_water_system_count': 0, 'preheat_hot_water_system_count': 0, 'radiation_hot_water_system_count': 0, 'reheat_hot_water_system_count': 0, 'water_system_count': 0, 'water_system': 1, 'water_pump': 4, 'hot_water_system': 1, 'chiller_water_flow_count': 0, 'boiler_water_flow_count': 0, 'cooling_tower_temp_count': 0}
vav_boxes_per_ahu 
 {}

One note on the output of the Class Similarities is it finds mismatched BRICK classes and tags by comparing them to the most current standard. If a mismatch is found, it returns a dictionary like data in the format of ('custom_tag', 'standard_tag', 0.90):

{
    'class_mismatches': [('Air_Handler_Unit', 'Air_Handling_Unit', 0.85)],
    'tag_mismatches': [('custom_tag', 'standard_tag', 0.90)]
}

Here, 0.85 and 0.90 are similarity scores from SequenceMatcher, which measure how close the custom class or tag is to the standard one. These values provide a statistical similarity percentage from the Python difflib package, helping you assess how much a custom class deviates from the standard.

Validating AI-Generated Models

Use the outputs to compare AI-created models against reference BRICK models, checking for consistency in:

• Equipment classification (e.g., AHUs, VAVs).
• Sensor and control points.
• Central plant configurations.

Sample Data

Reference BRICK models from BRICK resources are included in the sample_brick_models directory. These files can be used for testing and validation.

Web App Demo

View a web app interface on Bens Pythonanywhere account for free!

• https://bensapi.pythonanywhere.com/
• Upload and process .ttl files to generate detailed BRICK model summaries.
• Compare your AI-generated models with official BRICK Reference Models.
• Easy-to-use web interface with support for .ttl file validation.

Contributing

We welcome contributions to improve the repository. Please submit issues or pull requests to discuss new features, bug fixes, or enhancements.

Roadmap

Planned Enhancements

• ECM and KPI Suggestions: Develop functionality to recommend energy conservation measures (ECMs) based on model summaries.
• Advanced Validation: Add checks for missing or inconsistent relationships in AI-generated models.
• PyPI Distribution: Prepare the package for publication on PyPI.

License

This project is licensed under the MIT License. See the LICENSE file for details.