Generator Machine Learning Emission Modeling

Created by: Daniel Sitompul

UC Berkeley - Renewable and Appropriate Energy Laboratory (RAEL)

This model is created to assess the use of machine learning to predict generator ussage within a time period. The data that we have is voltage and frequency (within a 2 minute period) from 2 locations in the healthcare facilities.

In this repository, I use 3 different machine learning model:

1. Logistic Regression
2. Random Forest
3. XG-Boost

More information about the methodology and results can be found in the presentation file:
Presentation - Climate Impact Assessment of Generator Usage using Machine Learning: A Case Study from DR Congo.pdf

---

Prepare Environment

To prepare the environment, follow these steps:

1. Create a Virtual Environment

git clone https://github.com/Danielstevends/Generator_ML_Emission_Modeling
python -m venv venv
source venv/bin/activate # for mac
venv\Scripts\activate # for windows

2. Install Dependencies

# For the modeling
pip install -r requirements.txt

# For the functions
cd ModelingFunctions
pip install -r requirements.txt

3. Verify installation

pip freeze