brick-model-summarizer 0.1.0

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.
• Ready-to-Use Outputs: Generates text summaries suitable for validation, reporting, or further analysis.

Installation

Local Installation

1. Clone the repository:

   git clone https://github.com/yourusername/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  # On Windows: .\env\Scripts\activate
   

3. Install the package locally:

   pip install .
   

Usage

The package includes functions for summarizing BRICK models and generating detailed outputs. Below is a simple example of how to use the tool in Python:

Example: Processing a BRICK Model

from brick_model_summarizer.main import process_brick_file

# Path to the BRICK schema TTL file
brick_model_file = "sample_brick_models/bldg6.ttl"

# Generate a summary
building_data = process_brick_file(brick_model_file)

# Optionally, save the output as a text file
output_file = "bldg6_summary.txt"
with open(output_file, 'w') as file:
    for key, value in building_data.items():
        file.write(f"{key}:\n")
        if isinstance(value, dict):
            for sub_key, sub_value in value.items():
                file.write(f"  - {sub_key}: {sub_value}\n")
        else:
            file.write(f"  {value}\n")

Example Output

AHU Information:
  - Total AHUs: 16
  - Constant Volume AHUs: 11
  - Variable Air Volume AHUs: 0
  - AHUs with Cooling Coil: 10
  - AHUs with Heating Coil: 7
  ...

Zone Information:
  - Total VAV Boxes: 132
  - Cooling Only VAV Boxes: 132
  ...

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.

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.