Jupyter notebooks for the Energy and Buildings Publication
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01_LoadShape Models Creation.ipynb
01_Loadshape Visualizations.ipynb
02_Normalized Consumption Visualizations.ipynb
03_LoadRatio Visualizations.ipynb
04_Spearman Rank Order Correlation.ipynb
05_DayFilter Feature Creation and Visualization.ipynb
06_BreakoutDetection Visualization.ipynb
07_EEMeter Change Point Models Visualizations.ipynb
07_EEMeter Models Creation.ipynb
07_EEMeter Models Heating and Cooling Visualization.ipynb
08_STL Model - Weather Normalized Preprocessing.ipynb
08_STL Model Creation.ipynb
09_VISDOM Feature Creation.ipynb
10_ jMotif - Hourly Model to get Daily Specificity Metrics a=12, p=12 w=24.ipynb Initial commit Aug 2, 2017
10_ jMotif - Hourly Model to get Daily Specificity Metrics a=6, p=6, w=24.ipynb
10_ jMotif - Hourly Model to get Daily Specificity Metrics a=8, p=8, w=24.ipynb
11_ jMotif - Hourly Model to get Weekly Specificity Metrics a=6, p=14, w=168.ipynb
11_ jMotif - Hourly Model to get Weekly Specificity Metrics a=8, p=21, w=168.ipynb
12_HCTSA_Test.ipynb Initial commit Aug 2, 2017
40_Feature Aggregation and Preparation.ipynb
50_RandomForrestModels.ipynb
60_hctsa input file creation.ipynb
LICENSE
README.md
buildingdatagenome_featuresforvisdom.csv
buildingdatagenome_featuresforvisdom_cols.csv
jMotif Examples.ipynb

README.md

Applying temporal data mining to the Building Data Genome

This repository is a collection of temporal feature mining techniques implemented in the following publication:

Miller, C., & Meggers, F. (2017). Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings. Energy and Buildings, 156(Supplement C), 360–373. https://doi.org/10.1016/j.enbuild.2017.09.056

These notebooks use the Building Data Genome Project data set:

Miller, C., & Meggers, F. (2017). The Building Data Genome Project: An open, public data set from non-residential building electrical meters. Energy Procedia, 122, 439–444. https://doi.org/10.1016/j.egypro.2017.07.400

Using the notebooks

We recommend you download the Anaconda Python Distribution and use Jupyter to get an understanding of the data.

  • Raw temporal and meta data are found in /data/raw/ in the Building Data Genome project and can be copied and pasted into the data folder in this project to begin

This project is based upon work completed part of Clayton Miller's Ph.D. dissertation: Miller, C., 2017. Screening Meter Data: Characterization of Temporal Energy Data from Large Groups of Non-Residential Buildings. ETH Zurich, Zurich, Switzerland.