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一个多年的校园级智能电表数据库。

A multi-year campus-level smart meter database.

作者信息

Li Mingchen, Wang Zhe, Qu Yao, Chui Kin Ming, Leung-Shea Marcus

机构信息

Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China.

HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, China.

出版信息

Sci Data. 2024 Nov 26;11(1):1284. doi: 10.1038/s41597-024-04106-1.

DOI:10.1038/s41597-024-04106-1
PMID:39592622
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11599614/
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.

摘要

随着校园电力精确管理的需求不断增长，了解负荷模式对于提高能源效率和优化能源使用至关重要。然而，校园建筑及其内部设备的详细电力负荷数据往往缺失，这阻碍了相关研究。本文介绍了香港科技大学校园的一个能耗监测数据集，该数据集包含20多栋建筑中1400多个电表的数据，历时两年半收集而成。采用Brick Schema管理策略，将原始数据整理成可供研究使用的格式。该数据集支持各种研究任务，包括负荷模式识别、故障检测、需求响应策略和负荷预测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8af/11599614/c5a8f9f8675c/41597_2024_4106_Fig10_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8af/11599614/c5a8f9f8675c/41597_2024_4106_Fig10_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8af/11599614/bf413cfcabd1/41597_2024_4106_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8af/11599614/246f3650eff4/41597_2024_4106_Fig3_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8af/11599614/3c9340b69da3/41597_2024_4106_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8af/11599614/aaf6db36700b/41597_2024_4106_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8af/11599614/15f04b973b24/41597_2024_4106_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8af/11599614/a33573276ce6/41597_2024_4106_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8af/11599614/8f0a06c70f57/41597_2024_4106_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8af/11599614/c5a8f9f8675c/41597_2024_4106_Fig10_HTML.jpg

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本文引用的文献

1
An electricity smart meter dataset of Spanish households: insights into consumption patterns.西班牙家庭智能电表数据集：洞察用电模式。
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2
High-resolution electric power load data of an industrial park with multiple types of buildings in China.中国某工业园区多种类型建筑的高分辨率电力负荷数据。
Sci Data. 2023 Dec 6;10(1):870. doi: 10.1038/s41597-023-02786-9.
3
High resolution synthetic residential energy use profiles for the United States.美国高分辨率综合居民能源使用情况分析。
Sci Data. 2023 Feb 6;10(1):76. doi: 10.1038/s41597-022-01914-1.
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A three-year dataset supporting research on building energy management and occupancy analytics.支持建筑能源管理和占用分析研究的三年数据集。
Sci Data. 2022 Apr 5;9(1):156. doi: 10.1038/s41597-022-01257-x.
5
A residential labeled dataset for smart meter data analytics.智能电表数据分析的住宅标记数据集。
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The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes.理想家庭能源数据集，包含英国 255 户家庭的电力、燃气、环境传感器数据和调查数据。
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MFRED, 10 second interval real and reactive power for groups of 390 US apartments of varying size and vintage.MFRED，390 套不同规模和年代的美国公寓的 10 秒间隔有功和无功功率。
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