A 2.5-year campus-level smart meter database with equipment data for energy analytics

In response to the increasing necessity for accurate campus electricity management, understanding load patterns is essential for enhancing energy efficiency and optimizing usage. Yet, comprehensive electricity load data for campus buildings and their internal systems is often insufficient, posing challenges for research. This paper presents an energy consumption monitoring dataset from the Hong Kong University of Science and Technology (HKUST) campus, featuring data from over 1,400 meters across more than 20 buildings, collected over two and a half years. Utilizing the Brick Schema curation strategy, raw data was refined into a research-ready format. This dataset facilitates a variety of research applications, including load pattern recognition, fault detection, demand response strategies, and load forecasting.