Challenge

Explore buildings in NYC on an interactive map and compare each building’s energy usage, by drilling down the energy consumption level on the basis of 4 dimensions: AC, plug equipment, home gas and heating.

Approach

Used K-means clustering to group buildings into efficient and inefficient, and built a predictive model whose output identifies if a new building is consuming energy inefficiently. Trained a linear regression model to predict the building’s annual energy bill.

Industry

  • Energy

Tools Used

  • Data Science Experience (DSX)

Data Science Techniques

  • Supervised ML (regression and classification), unsupervised ML (Kmeans)

Benefits:

  • Predict a building’s annual energy bill and help customers identify potential savings
  • Group buildings into efficient and inefficient
  • Identifies if a new building is consuming energy inefficiently

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