DSS5105-YYDS [Your Yielding partner in sustainable Development Solutions]

We are a company that provides GHG Emission Calculations and Estimations for Buildings in Singapore. This is an easy-to-use, interactive and customizable calculator and dashboard with all-in-one functionality:

• Automatic Data Validation and Cleaning, Data Analysis, Machine Learning Model and Indicator Combined Prediction, Data Visualizations
• Benchmarking & Ratings with Green Mark Incentive Schemes, Customizable GHG Reduction Recommendations
• AI-Powered GHG Emission Chatbot Assistant

    "Project Assessment: for the openai API Key please find attached the key in the Group B/assets/openaiAPIkey.pdf, passwd: 5105yyds, thank you!"

To install and use this Calculator:

1.Clone the repository:

git clone https://github.com/Lucas1213WZY/DSS5105-YYDS.git

2.After that use pip install or conda install to install all required libraries:

pip/conda install requirements.txt

3.Change directory from the root to Group B to run the yydsCalculator:

cd Group\ B
python/run yydsCalculator.py

4.Once it's running, please go to your browser and use the link below to access the application on your ease:

http://127.0.0.1:8050/

Tutorial Video

Please visit this link for more detailed tutorials: https://drive.google.com/file/d/1_aHJkNOm_8wIf8eiH5TUHi5JyQGWG6sc/view?usp=sharing

Directory / Path Structure

The following is the directory structure for the project:

DSS5105-YYDS/
├── Group A/
│   ├── Geospatial_Analysis/
│   ├── business travel & procurement factor/
│   ├── commuting factors/
│   ├── datasets/
│   ├── prediction models/
│   ├── scripts/
│   ├── validation_initial_analysis/
│   ├── validation_root/
│   ├── Business Travel & Procurement.xlsx
│   ├── Commuting Factor
│   └── README.md
├── Group B/
│   ├── assets/
│   ├── datasets/
│   ├── Dash/
│   ├── yydsCalculator.py
│   ├── src_historical_versions/
│   ├── structured_feedback_handler.py
│   ├── CompleteDataset.csv
│   └── README.md

Tutorial: Create Conda and Python Virtual Environments

This guide provides quick instructions for setting up virtual environments using Conda or Python's virtualenv/venv to manage dependencies and ensure isolated environments.

---

1. Conda Environment from myenv.yml

1. Install Conda: Download from Miniconda or Anaconda.
2. Create Environment:

   conda env create -f myenv.yml

3. Activate Environment:

   conda activate <your_env_name>

4. Verify Installation:

   conda list

5. Update Environment:

   conda env update --file myenv.yml --prune

6. Deactivate Environment:

   conda deactivate

---

2. Python Virtual Environment

1. Install virtualenv (if needed):

   pip install virtualenv

2. Create Environment:

   python -m venv myenv  # or virtualenv myenv

3. Activate Environment:
   • Windows: myenv\Scripts\activate
   • macOS/Linux: source myenv/bin/activate
4. Install Dependencies:

   pip install -r requirements.txt

5. Verify Installation:

   pip list

6. Deactivate Environment:

   deactivate

---

Command Summary

Conda

• Create: conda env create -f myenv.yml
• Activate: conda activate <your_env_name>
• Update: conda env update --file myenv.yml --prune
• Deactivate: conda deactivate

Python Virtual Environment

• Create: python -m venv myenv or virtualenv myenv
• Activate:
  • Windows: myenv\Scripts\activate
  • macOS/Linux: source myenv/bin/activate
• Install: pip install -r requirements.txt
• Deactivate: deactivate

Requirements

Ensure Python is installed on your system. Required libraries are all stored in the requirements.txt

Data Setup: For Demo pls upload the CSV file : merged_df1.csv, from the "datasets" folder in Group B folder. For your own calculations, please follow the below data dictionary. The file should include:

Column Name	Description	Data Type	Required
Building Name	Name of the building	String	Yes
Postcode	Postal code of the building location	Integer	Yes
Year	Year of data collection	Integer	Yes
Latitude	Latitude coordinate of the building	Integer	Yes
Longitude	Longitude coordinate of the building	Integer	Yes
Address	Full address of the building	String	Yes
Type	Type of building (e.g., only applicable to office buildings)	String	Yes
Function	Functionality of the building (e.g., office)	String	Yes
Size	Size of the building (e.g., area in square meters)	Float	Yes
Employee	Number of employees working in the building	Integer	Yes
Transportation	Transportation-related data (e.g., parking spaces, public transit proximity)	String	Yes
GFA	Gross Floor Area of the building (in m²)	Float	Yes
Energy	Energy consumption data (e.g., kWh/year)	Float	Yes
Waste	Waste production data (e.g., tons/year)	Float	Yes
Water	Water consumption data (e.g., m³/year)	Float	Yes
Scope1	Direct GHG emissions (Scope 1, t CO2e)	Float	No
Scope2	Indirect GHG emissions from electricity (Scope 2, t CO2e)	Float	No
Scope3	Other indirect GHG emissions (Scope 3, t CO2e)	Float	No
GHG_Total	Total GHG emissions (t CO2e) [to be predicted/calculated]	Float	No
GHG_Intensity	GHG emissions per unit area or employee (e.g., t CO2e/m²)[to be predicted/calculated]	Float	No
Award	Awards or certifications (i.e., Green Mark Incentive Scheme) [to be predicted]	String	No

For manual input options: Transportation breakdowns are also needed : (commuting: Bus, MRT, Taxi; Business Travel: Flight & Hotel(Cost); Business Procurement: Air Freight, Electric Truck, Diesel Truck.)

Troubleshooting

Encoding Errors: If the CSV file has encoding issues, the app attempts to resolve these by setting encoding="utf-8" or "latin-1" and encoding_errors="ignore". Graph Display Issues: Ensure the dataset has required columns (Year, Building Name) and values are correctly formatted.