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Transformation of Raw Electrical Meter Data for Temporal Data Mining of Building Performance
A repository of whole building electrical meters from non-residential buildings
==============================
This project is a supplement to [Clayton Miller's](https://www.researchgate.net/profile/Clayton_Miller2) dissertation titled [Screening Meter Data: Unsupervised Characterization of Temporal Energy Data from Large Groups of Non-Residential Buildings](https://github.com/architecture-building-systems/clayton-thesis-screening-meter-data)
Citation
Citation of Data-Set
------------
[Please cite this work if you use the data set](https://www.researchgate.net/publication/314081046_The_Building_Data_Genome_Project_An_open_public_data_set_from_non-residential_buildings_electrical_meters)
Goals of Project
[Miller, C., Meggers, F., 2017. The Building Data Genome Project: An open public data set from non-residential building electrical meters. Presented at the CISBAT 2017, Lausanne, Switzerland.](https://www.researchgate.net/publication/314081046_The_Building_Data_Genome_Project_An_open_public_data_set_from_non-residential_buildings_electrical_meters)
Details of Project
------------
1. Give an overview of 507 non-residential buildings that have hourly data for one year.
2. Engineer various temporal features to characterize building use type
3. Build prediction models to classify use-type and performance level
1. A temporal data set of hourly whole building electrical data for one year (8760 points per building for 507 buildings)
2. Meta data about each building including floor area, primary use type, weather file, and other various characteristics
This project was used in the following publications:
[Miller, C., 2017. Screening Meter Data: Characterization of Temporal Energy Data from Large Groups of Non-Residential Buildings. ETH Zurich, Zurich, Switzerland.
](https://www.research-collection.ethz.ch/handle/20.500.11850/125778)
Data Overview
------------
@@ -28,38 +31,11 @@ Project Organization
│   ├── interim <- Intermediate data that has been transformed.
│   ├── processed <- The final, canonical data sets for modeling.
│   └── raw <- The original, immutable data dump.
├── docs <- A default Sphinx project; see sphinx-doc.org for details
├── 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, e.g.
`1.0-jqp-initial-data-exploration`.
├── references <- Data dictionaries, manuals, and all other explanatory materials.
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures <- Generated graphics and figures to be used in reporting
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
├── src <- Source code for use in this project.
│   ├── __init__.py <- Makes src a Python module
│ │
│   ├── data <- Scripts to download or generate data
│   │   └── make_dataset.py
│ │
│   ├── features <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│ │
│   ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│ │
│   └── visualization <- Scripts to create exploratory and results oriented visualizations
│   └── visualize.py
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
generated with `pip freeze > requirements.txt`
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