top of page
01.jpg

Building Performance Simulation

Introduction to Data Science

Course: J-term Introduction to Data Science for Building Performance Simulation and Architectural Design
Instructor: Jung Min Han
Date: Jan., 2021

The modeling of net zero energy buildings is an increasing concern in both the building design and sustainable consulting industries. The objectives that have been raised and recognized will change how buildings are designed, constructed, and maintained. The building design industry will soon be galvanized by regulations and standards designed to encourage net zero energy buildings while still providing comfortable built environments.

 

Early adoption of performance simulation software in the design decision-making process is imperative to realizing such goals. Passive building design can be achieved in the early design stage. Buiding designers to pursue sustainability in built environments will bring favorable outcomes and require only low-cost changes. 

KEYWORDS:
Building Simulation, Data Science, Decision-making, Sustainable Design

OBJECTIVES

Machine learning and data science are promising approaches to shaping the design process, offering instant performance feedback. This class introduces several methods of environmental analysis and a number of building performance simulation tools, including daylighting, airflow, and energy. The required programming skills and analysis techniques are incorporated by importing generic weather information to predict energy use in response to design changes. This course also introduces data management skills including Python scripting, machine learning, and 3D data visualization. 

02.jpg

Day 1

Theme: Introduction to building performance simulation and data science
Skillset: Installation of the Python and ML packages and weather data manipulation
Tools: Anaconda, Python, Jupyter notebook

 

Python Basic - Basic Syntax & Data Structure
 

View script in Jupyter Notebook
 

Pandas Data Process - Visualize & Manipulation

View script in Jupyter Notebook
 

微信截图_20220118211232.png
微信截图_20220118211252.png
微信截图_20220118211420.png
微信截图_20220118211444.png

Day 2

Theme: Daylighting simulation and data processing
Skillset: Data processing (imputing missing values, cleaning data)
Tools: Anaconda, Python, Jupyter notebook, DIVA, Rhino, and Grasshopper

Pandas Data Process - Visualize & Sampling

View script in Jupyter Notebook
 

Viz_6_21_15.00_Overcast.jpg
微信截图_20220118214342.png
Viz_6_21_15.00_Overcast_fc.jpg
微信截图_20220118214405.png

Day 3

Theme: Energy simulation and parametric study
Skillset: Parametric simulation and optimization using Rhino and Grasshopper
Tools: ArchSim, GH-Python, Rhino, and Grasshopper

Pandas Data Process - Visualize & Multiple Files

View script in Jupyter Notebook
 

微信截图_20220119145535.png
图片1.png
微信截图_20220119145544.png
微信截图_20220119145553.png

Day 4

Theme: Airflow simulation and visualization
Skillset: Data visualization: 2D (energy) and 3D (airflow)
Tools: Python, Jupyter notebook, Butterfly, Rhino, and Grasshopper

Pandas Data Process - Natural Ventilation & Result Values

View script in Jupyter Notebook
 

微信截图_20220119151256.png
微信截图_20220119151322.png
微信截图_20220119151351.png
微信截图_20220119151407.png
Outdoor_airflow.jpg
Indoor_airflow.jpg

Day 5

Theme: Machine learning and advanced simulation techniques
Skillset: Introduction to ML using simulated data
Tools: Python, Sk-Learn, and Jupyter notebook

Pandas Data Process - Machine Learning Models

View script in Jupyter Notebook
 

3D CNN - Radiation Intensity Estimation

View full research article
 

图片2.png
图片3.png
图片11.png

Urban Modeling Interface - Energy Flows for Sustainable Neighborhoods

View MIT Sustainable Design Lab
 

5-1.jpg
5-2.jpg
5-3.jpg
5-4.jpg
bottom of page