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The island of misfit buildings: Detecting mixed-use or primary-use-type outliers using load shape clustering

A project focused on clustering different buildings and identifying outliner within them from raw whole building data.


Data collection and pre-processing

  • Data sources: Currently two datasets are being used, the Building Genome Dataset (BDG) and the Washington Dataset (DGS)
  • Data Collection: Read temporal dataset and make sure readings are resambpled by the hour.
  • Context Extraction: Generate resampled datasets filtering observations based on the specify context.
  • Load Curve Generation: Based on the given aggregation function, generate one load curve for each building.

The naming format for all generated files is DatasetName_Context_LoadCurveFunction_Algorithm_TypeOfFile.extension. Each section is decribed as follows:

  • DatasetName: BDG for the Building Genome Dataset, DGS for the Washington Dataset, and BDG-DGS for the combination of both
  • Context: Currently implemented weekday, weekend, fullweek
  • LoadCurveFunction: Aggregation function used. Currently implemented average, median
  • Algorithm: Clustering algorithm used. Currently implemented kshape
  • TypeOfFile: The type of data that is stored in this file, most of the times is dataset
  • Extension: Usually .csv but also .png or pkl

Clustering and Validation

  • Clustering: Generate building clusters based on daily profiles (formed from hourley read data) using the specified algorithm
  • Clustering Validation Metrics: Calculate validation metrics for the clustering results for different choice of k and different algorithms

Experiments

  • Experiments: Sandbox to run all possible combinations of datasets, contexts, and load curve functions as experiments for clustering
  • Experiments Utils: Notebook where the main functions used in ExperimentPlayground.ipynb are. Also, it serves as a middle layer between the playground and the rest of notebooks.

After an experiment is ran, a scores .csv is generated (following the naming conventions from above) that looks as following:

dataset context function algorithm parameter DB RMSSTD RS XB calinski_harabaz_score cohesion separation

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Detecting mixed-use or primary-space-use outliers using load shape clustering

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