A multi-year campus-level smart meter database

Area of Science:

• Energy Management
• Building Science
• Data Science

Background:

• Precise campus electricity management requires understanding building and equipment load patterns.
• Lack of detailed electricity load data hinders energy efficiency research.
• Optimizing energy use in academic environments is a growing concern.

Purpose of the Study:

• To introduce a novel, research-ready energy consumption monitoring dataset from a university campus.
• To facilitate research in campus-level energy management and efficiency.
• To provide a foundation for studies on load patterns, fault detection, and demand response.

Main Methods:

• Collected electricity load data from over 1400 meters across more than 20 buildings at HKUST over 2.5 years.
• Utilized the Brick Schema curation strategy to process raw data into a standardized format.
• Organized data to support diverse research applications in energy systems.

Main Results:

• Developed a comprehensive dataset covering extensive campus energy consumption.
• Successfully curated raw data into a research-ready format using a standardized schema.
• Dataset encompasses detailed information suitable for advanced energy analytics.

Conclusions:

• The HKUST energy consumption dataset is a valuable resource for campus energy management research.
• The Brick Schema approach provides a scalable method for curating building energy data.
• This dataset will advance research in energy efficiency, load forecasting, and smart grid technologies.