oaslananka / energy-consumption-prediction

A practice project for predicting building energy consumption using machine learning models. The project involves data preparation, model training, evaluation, Docker containerization, and Kubernetes deployment. It also includes a CI/CD pipeline setup using GitHub Actions.

Building Energy Consumption Prediction with MLOps Sample

Project Overview

This project aims to predict the energy consumption of buildings in terms of heating load and cooling load. The objective is to utilize machine learning models to estimate energy efficiency based on various building parameters.

Dataset

• Source: UCI Machine Learning Repository - Energy Efficiency
• Instances: 768
• Features: 8
• Target Variables: Heating Load (Y1), Cooling Load (Y2)

Project Structure

energy-consumption-prediction/
├── .github/
│   └── workflows/
│       └── ci_cd_pipeline.yml
├── data/
│   ├── raw/
│   │   └── ENB2012_data.xlsx
│   ├── processed/
│       └── ENB2012_data_processed.csv
├── kubernetes/
│   ├── deployment.yaml
│   └── service.yaml
├── notebooks/
│   ├── 1_data_preparation.ipynb
│   ├── 2_model_training.ipynb
│   └── 3_model_evaluation.ipynb
├── src/
│   ├── data_preparation.py
│   ├── model_training.py
│   └── model_evaluation.py
├── Dockerfile
├── README.md
└── requirements.txt

Setup and Usage

Prerequisites

• Python 3.8 or later
• Docker
• Kubernetes (kubectl)
• GitHub account (for CI/CD)

Installation

1. Clone the repository:

   git clone https://github.com/oaslananka/energy-consumption-prediction.git
   cd energy-consumption-prediction

2. Install Python dependencies:

   pip install -r requirements.txt

3. Prepare the data:

   • Download the dataset and place it in the data/raw/ directory.
   • Run the 1_data_preparation.ipynb notebook to preprocess the data.

4. Train the model:

   • Run the 2_model_training.ipynb notebook to train the models.

5. Evaluate the model:

   • Run the 3_model_evaluation.ipynb notebook to evaluate and visualize the model performance.

Docker Usage

1. Build the Docker image:

   docker build -t energy-consumption-prediction .

2. Run the Docker container:

   docker run --rm energy-consumption-prediction

Kubernetes Deployment

1. Apply the deployment and service:

   kubectl apply -f kubernetes/deployment.yaml
   kubectl apply -f kubernetes/service.yaml

2. Check the status:

   kubectl get deployments
   kubectl get services

CI/CD Pipeline

This project uses GitHub Actions for continuous integration and continuous deployment. The pipeline is defined in .github/workflows/ci_cd_pipeline.yml.

1. Set up GitHub Secrets:

   • DOCKER_USERNAME: Your Docker Hub username
   • DOCKER_PASSWORD: Your Docker Hub password

2. Push the changes to GitHub:

   git add .
   git commit -m "Set up CI/CD pipeline"
   git push origin main

Results

• The model performance metrics and visualizations are available in the 3_model_evaluation.ipynb notebook.
• Heating Load MSE: value
• Cooling Load MSE: value

License

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

Acknowledgements

• UCI Machine Learning Repository for the dataset.
• Contributors to the open-source libraries used in this project.

Note

This project is a practice exercise and is not intended for production use. It serves as a learning tool for implementing machine learning models, Docker, and Kubernetes.