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

[https://doi.org/10.5061/dryad.k3j9kd5h6](https://doi.org/10.5061/dryad.k3j9kd5h6)

## General Information

1. Description: Accurate campus electricity management is crucial for energy efficiency and optimization. However, comprehensive load data is often lacking, hindering research efforts. This paper presents a dataset from HKUST, featuring over 1,400 meters across more than 20 buildings, collected over 2.5 years. The raw data was refined for research using the Brick Schema, enabling studies in load pattern recognition, fault detection, demand response strategies, and load forecasting.
2. Date of data collection: 2022-01-01 to 2024-05-27.
3. Geographic data collection location: Sai Kung District, Hong Kong, China(22.3363°N 114.2634°E).
4. Goal: This dataset is tailored for users engaged in energy management and analysis, featuring detailed internal equipment data. It supports applications in patte...