fe-ds-case

Predicting the energy consumption of houses

F&E Case Study - Data Science

This repository contains the code for the F&E Data Science case study. The goal of this project is to preprocess building data and energy consumption data, and build predictive models to estimate energy consumption.

Table of Contents

• Setup
• Usage
• Files
• Results

Setup

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)

Installation

1. Clone the repository:

   git clone https://github.com/BERHEY/fe-ds-case.git
   cd fe-ds-case

2. Create a virtual environment (optional but recommended):

   python3 -m venv venv
   source venv/bin/activate

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Prepare the data:

   • Place your building component data CSV files in the ./data/bauteile directory.
   • Place your energy consumption data CSV file as ./data/verbrauch/verbrauch.csv.

2. Run the analysis:

   • Open and run Analysis1.ipynb in a Jupyter Notebook environment. This notebook contains the complete workflow from data preprocessing to model training and evaluation.

Files

• Analysis1.ipynb: Jupyter Notebook containing the data preprocessing, feature extraction, model training, and evaluation code.
• requirements.txt: List of Python packages required to run the notebook.
• README.md: This file.

Results

The results of the model training, including the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for different models, are displayed in the final sections of the Analysis1.ipynb notebook.

License

This project is licensed under the MIT License. See the LICENSE file for details.