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BlocPower Demo

This demo involves modeling energy usage for buildings in NYC.

Highlights

Data Import and Exploration

Create Spark DataFrames from imported CSV files
Run some data exploration commands to inspect the data
Define functions to clean and prepare the data for modeling

Model Creation

Create a model using sklearn
Visualize the model accuracy
Detect buildings that consume energy inefficiently

Project-Specific Information

Tools Used

Python, through Jupyter Notebooks
Spark DataFrames
Scikit Learn for model creation

Model Information

Linear Regression

Response variable is energy usage in kWh
Features include age of the building, square feet, number of stories, total plugged equipment, etc.

Unsupervised Learning

Perform PCA and Clustering
Metrics used: plugged-in equipment, air conditioning, domestic gas, heating gas
Visualize clusters using two out of four dimensions and K-means to determine center locations

Classification using Logistic Regression

Model used to identify inefficient buildings
Visualize accuracy using a confusion matrix

Relevant Files

Notebooks

BlocPower.ipynb

Data Assets

BlocPower_T.csv
CDD-HDD-Features.csv
HDD-Features.csv