Comprehensive energy demand and usage data for building automation

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.