Important

This repository has been archived and is no longer maintained. The code is provided for historical reference and may contain unpatched or unknown vulnerabilities. It should not be used in production systems.

urban-sim-flow

Urban Simulation Framework

WIP

src folder structure

file name	Usage
adaptors.py	adaptors for converting cbecs data to processor input
construction_meta_validator.py	validate construction meta json file (../resources/construction_meta.json)
constructions.py	construction processor
generate_hvac.rb	ruby osstd hvac generation call file
geometry.py	geometry processor
geometry_settings.json	geometry processor config file
hvac.py	hvac processor
hvac_settings.json	hvac processor config file
loads.py	loads processor
loads_settings.json	loads processor config file
new_hvac.py	temperary hvac testing file for new hvac systems, not used in production workflow
preprocessor.py	proprocessor used for organizing adaptors
processed_loads_sample.json	temporary file to show loads input format
processed_schedule_sample.json	temporary file to show schedule input format
rbcalls	temporary folder for ruby calls testing
recipes.py	utility functions imported and used in other places
reference-2020-04-30.bib	accidentally added ? lol ;D
schedule.py	schedule processor
schedule_database.json	used by schedule processor, probably should be put in resources folder
schedule_preparation.py	used by schedule processor
schedule_settings.json	schedule processor config file
standard_excel_processor.py	generate raw input json files from excel spreadsheet
workflow.py	python cells to execute the whole workflow

Parallel simulation workflow

• Insure you are using constance7a since normal constance doesn't have singularity.
• Currently, the source data comes from input/cbecs-standardized-200715.xlsx. Make any edits to input data here.
• cd into src/ subdirectory
• Run "sbatch run.sbatch"
• Track any output/error messages in the run.out and run.err text files

Usage

(Airflow adoption on the new implementation is on hold.)

• Running the code requires the following environments:

  • Python 3.5+ (The code is tested with 3.7.5)
  • Apache-Airflow (The code is tested with Airflow 1.10.7 and its defautl DB of SQLite3)
  • Linux (I had some issues setting up Airflow on Windows. Running Airflow using Docker or WSL on Windows 10 are tested and good)

• Follow the quick start guide to start the airflow service. Make sure to have environment variable AIRFLOW_HOME set up before running the the airflow db (airflow scheduler) and webserver(airflow webserver -p 8080). The database only needs to be initialized (airflow initdb) once, unless there are running errors. Before initializing the database, also make sure that AIRFLOW_HOME is set properly.

• If AIRFLOW_HOME is empty, then the airflow running folder is default to ~/airflow.

TO-DOs

• schedule change to non stochastic
• occupancy control: add occ based lighting control through schedule factor
• (code change) exterior fixtures: add one object (exterior lighting), with two inputs, schedule and design level.
• control electrical equipment schedule: similar to occupancy based lighting control
• (code change) improve electrical/gas equipment efficiency: add obe object for gas equipment
• (code change)shading overhang: add one object. one std input (overhang depth)
• (potential code change) vestibule / improve door: don't add door, but change infiltration rate and schedule. add std input: nodoor / door w/wo vestibule
• advanced control operation: change operation schedule
• HVAC setpoints
• (osstd code review before code change) economizer: check if economizer operation can be switched.
• (osstd code review before code change) DCV
• (osstd code review before code change) heat recovery: check ERV code in OSSTD
• (osstd code review before code change) SWH: check OSSTD. std input: swh efficiency, water temp setpoint.

Updates

• 05/04/2020:
  • modify geometry processor to accomodate no core zone cases. Tested with CBECS 2 (w/ core) & 3 (w/o core).
• 05/03/2020:
  • Schedule preprocessor in place.
• 04/29/2020:
  • Construction processor and preprocessor in place.
• 04/25/2020:
  • Loads preprocessor in place.
• 04/20/2020:
  • Add standard input loading
  • modify unit conversion to include post-conversion units and addtitional conversions
• 04/21/2020:
  • Add multi-storey capacity
  • Add schedule generator interface.
  • Add loads generator interface.

Dependencies

The following python pakcages need to be installed for using the framework

• eppy
• geomeppy
• esoreader
• apache-airflow
• notebook
• pandas

Developer tools utilized

We use black to format python code of this repository

Note

• Apache-airflow is used for workflow implementation and management. This modulize the framework and provides lots of features to run and manage the process. I also tried Metaflow and decided to go with Airflow because:

  1. Airflow has a feature-rich web-based UI, while Metaflow is purely CLI
  2. Metaflow has better documentation and community support, while Metaflow is just open-sourced
  3. Metaflow is AWS embeded, while Airflow are less oppinioned
  4. I tried to build AWS service for Metaflow and it is very non-trivial with bad documentation, while Airflow has AWS Batch operator already included (not tried yet)

• Sample I/O files are included in the repo at this moment for development purpose.