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Energywise is an analytics tool to make commercial building more energy efficient.

This project of the 2013 Data Science for Social Good fellowship, in partnership with Lawrence Berkeley National Laboratory and Agentis Energy.

Everyone wants to be more energy efficient, but it's hard to know how buildings actually use energy. If you own a building, how much will you save by shutting off half your elevators on weekends? How much will you save due to AC costs by switching from sliding to revolving doors?

Typically these questions are answered by hiring experts to walk through your building and examine each system in depth, a time consuming and expensive process.

Our tool instead analyzes a building's energy data to answer these questions. It illuminates the relationship between a building's systems and its energy usage, allowing building owners to spot opportunities for retrofits and behavior change that can cut down on their energy consumption and save money.

• Problem: reducing commercial building's energy use
• Data: building energy interval data
• Methodology: how the tool works - exploratory data analysis, outlier detection, anomally detection, peak prediction
• Results
• Coding conventions: how we wrote our code