Year: 2024
Type: data-paper
Abstract: With the growing need for precise campus electricity management, understanding load patterns is crucial for improving energy efficiency and optimizing energy use. However, detailed electricity load data for campus buildings and their internal equipment is often lacking, hindering research. This paper introduces an energy consumption monitoring dataset from The Hong Kong University of Science and Technology (HKUST) campus in Hong Kong, comprising data from over 1400 meters across more than 20 buildings and collected over two and a half years. Using the Brick Schema curation strategy, raw data was curated into a research-ready format. This dataset supports various research tasks, including load pattern recognition, fault detection, demand response strategies, and load forecasting.
Source: Scientific Data
Authors: Mingchen Li, Zhe Wang, Yao Qu, Kin Ming Chui, Marcus Leung-Shea
Language: English
Topic: Smart Grid Energy Management
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,290
Awards: G3060098774, G322120470
OpenAlex ID: W4404726010