GreEn-ER–Electricity consumption data of a tertiary building

IF 2.4 Q3 ENVIRONMENTAL SCIENCES Frontiers in Sustainable Cities Pub Date : 2023-08-09 DOI:10.3389/frsc.2023.1043657
Gustavo Felipe Martin Nascimento, Frédéric Wurtz, Patrick Kuo-Peng, Benoit Delinchant, Nelson Jhoe Batistela, Tiansi Laranjeira
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Abstract

The increased use of intermittent renewable energy sources makes the use of machine learning methods combined with demand-side management more and more frequent. Machine learning algorithms rely on data to identify patterns and learn insights. Hence, data availability is of utmost importance, and the more, the merrier. Therefore, this data report aims to present a dataset concerning the electricity consumption of a tertiary building located in the French Alps region (Grenoble) in 2017 and 2018. It is a massively monitored and controlled building with about 330 electricity meters, whose measurement data constitute the dataset. The data were collected directly from the building management system and correspond to raw data, without any pre-treatment. The dataset also includes Python notebooks that allow for understanding the system design, navigating the data, and performing some simple analyses. This is a publicly available dataset that tries to fill the gap of the availability of electricity consumption data, especially regarding tertiary buildings.
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绿色-三级建筑用电数据
间歇性可再生能源的使用越来越多,使得机器学习方法与需求侧管理相结合的使用越来越频繁。机器学习算法依赖于数据来识别模式和学习见解。因此,数据的可用性是至关重要的,而且越多越好。因此,本数据报告旨在提供一个关于2017年和2018年法国阿尔卑斯山地区(格勒诺布尔)三级建筑用电量的数据集。它是一个大规模监控的建筑,大约有330个电表,其测量数据构成了数据集。数据直接从建筑管理系统中收集,与原始数据相对应,没有任何预处理。该数据集还包括Python笔记本,用于理解系统设计、导航数据和执行一些简单的分析。这是一个公开可用的数据集,试图填补电力消耗数据可用性的空白,特别是关于三级建筑。
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来源期刊
CiteScore
4.00
自引率
7.10%
发文量
176
审稿时长
13 weeks
期刊最新文献
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