energy consumption architecture

The project involves an energy consumption prediction system for non-residential buildings, utilizing clustering techniques and machine learning models. The goal is to improve prediction accuracy by grouping buildings with similar consumption patterns. Representative time series for each cluster (average and the one closest to the centroid) are evaluated. Several machine learning models are compared, and the best ones are selected based on the root mean square error (RMSE) to predict total energy consumption.

Installation guide

Please read install.md for details on how to set up this project.

Project Organization

├── LICENSE
├── README.md          <- The top-level README for developers using this project.
├── install.md         <- Detailed instructions to set up this project.
├── data
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── models             <- Trained and serialized models, model predictions, or model summaries.
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, eg.
│                         `1.0-jqp-initial-data-exploration`.
│
├── environment.yml    <- The requirements file for reproducing the analysis environment.
├── requirements.txt   <- The pip requirements file for reproducing the environment.
│
├── test               <- Unit and integration tests for the project.
│   ├── __init__.py
│   └── test_model.py  <- Example of a test script.
│
├── .here              <- File that will stop the search if none of the other criteria
│                         apply when searching head of project.
│
├── setup.py           <- Makes project pip installable (pip install -e .)
│                         so energy_consumption_architecture can be imported.
│
└── energy_consumption_architecture   <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes energy_consumption_architecture a Python module.
    │
    ├── config.py               <- Store useful variables and configuration.
    │
    ├── dataset.py              <- Scripts to download or generate data.
    │
    ├── features.py             <- Code to create features for modeling.
    │
    ├── modeling                
    │   ├── __init__.py 
    │   ├── predict.py          <- Code to run model inference with trained models.
    │   └── train.py            <- Code to train models.
    │
    ├── utils                   <- Scripts to help with common tasks.
    │   └── paths.py            <- Helper functions for relative file referencing across the project.        
    │
    └── plots.py                <- Code to create visualizations.

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Project based on the cookiecutter conda data science project template.