Pecan Street, Inc.

To improve its Dataport software and data visualization, expand available energy data content, and increase academic researcher use of the database

  • Amount $450,000
  • City Austin, TX
  • Investigator Brewster McCracken
  • Year 2016
  • Program Research
  • Sub-program Energy and Environment

High-quality, easily searchable data on the transmission, distribution, and use of electricity are hard to come by. Existing data sources usually fall short in a number of ways. Many data sets report electricity usage statistics only at monthly or yearly intervals, making it impossible to measure how demand varies from day-to-day, hour-to-hour, or minute-to-minute. Usage data are often aggregated at the household level, not broken down by individual appliance, making it difficult to study consumer behavior. Often, data are only available in hard-to-use formats that are not amendable to manipulation, combination, or visualization. Pecan Street has created a data analytics tool, called Dataport, to provide timely, disaggregated electricity usage information to researchers. Data are collected from more than 1,000 homes outfitted with appliance-level sensors that report energy usage at fine-grained intervals. These data are also presented in a way that can be easily queried and visualized. Funds from this grant support three initiatives aimed at strengthening Dataport and increasing its usefulness to researchers. First, the Dataport team will implement several technical improvements to the platform, including better visualization tools, an improved user interface, and a new capacity that allows researchers to draw information from multiple data sources simultaneously. Second, Pecan Street will expand and diversify available data through importing and integrating electricity usage and pricing data from several government, utility, and regional transmission sources. Third, Pecan Street will extend its academic outreach and education activities to expand use of the platform, including on-campus training sessions, a research conference, and a paper competition for papers using Dataport data.

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