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Comprehensive energy demand and usage data for building automation.

Philipp Heer1, Curdin Derungs2, Benjamin Huber2

  • 1Swiss Federal Laboratories for Materials Science and Technology, Urban Energy System Laboratories, UESL, Dübendorf, Switzerland. philipp.heer@empa.ch.

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|May 8, 2024
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Summary

This study releases a four-year dataset from the NEST platform, detailing building energy consumption and occupant behavior. This data enables data-driven approaches for optimizing building functions and energy efficiency.

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Area of Science:

  • Building Science
  • Energy Efficiency
  • Data Science

Background:

  • Buildings account for nearly half of global energy consumption, highlighting the need for research in comfort and energy efficiency.
  • A lack of publicly available data hinders the optimization of building functions and widespread adoption of data-driven building automation.
  • Data-driven approaches are crucial for making building automation financially viable and accessible.

Purpose of the Study:

  • To address the gap in publicly available data for building performance optimization.
  • To make detailed measurement data from the NEST platform accessible to the research community.
  • To facilitate data-driven learning for different building types (office and residential).

Main Methods:

  • Collected and curated a comprehensive dataset from three buildings within the NEST platform.
  • Recorded data over four years with a 1-minute temporal resolution.
  • Included detailed information on energy consumption, building operation, and occupant practices.

Main Results:

  • A publicly available dataset containing energy consumption (electricity, heating, cooling, domestic hot water), building operation (set points, valve openings, windows), and occupant behavior (presence, blinds, kitchen use, showering).
  • The dataset spans four years with high temporal resolution (1-minute).
  • Enables the study of different building types, including office and residential environments.

Conclusions:

  • The released dataset provides a valuable resource for advancing research in building energy efficiency and automation.
  • Facilitates data-driven modeling to understand and optimize building functions.
  • Supports research gaps related to the interplay of building design, operation, and occupant behavior.