智能电网公共数据集:特点和相关应用

IF 2.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
IET Smart Grid Pub Date : 2024-05-02 DOI:10.1049/stg2.12161
Emran Altamimi, Abdulaziz Al-Ali, Qutaibah M. Malluhi, Abdulla K. Al-Ali
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引用次数: 0

摘要

智能电网、传统电网的发展以及物联网设备的集成,产生了大量对推进能源管理和效率至关重要的数据。然而,由于电网运营商和公司不愿公开专有信息,公开的数据集仍然有限。作者对 50 多个公开数据集进行了全面分析,主要分为三大类:微观和宏观消费数据、详细的家庭消费数据(通常称为非侵入式负荷监测数据集或建筑物数据)和电网数据。此外,该研究还强调了未来的研究重点,如推进合成数据生成、提高数据质量和标准化,以及加强智能电网中的大数据管理。作者的目的是为智能电网和电网领域的研究人员提供一个全面的参考点,以挑选合适的相关公共数据集来评估他们提出的方法。所提供的分析强调了在评估未来方法时遵循系统化和标准化方法的重要性,并为读者指引了智能电网分析领域未来潜在的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Smart grid public datasets: Characteristics and associated applications

Smart grid public datasets: Characteristics and associated applications

The development of smart grids, traditional power grids, and the integration of internet of things devices have resulted in a wealth of data crucial to advancing energy management and efficiency. Nevertheless, public datasets remain limited due to grid operators' and companies' reluctance to disclose proprietary information. The authors present a comprehensive analysis of more than 50 publicly available datasets, organised into three main categories: micro- and macro-consumption data, detailed in-home consumption data (often referred to as non-intrusive load monitoring datasets or building data) and grid data. Furthermore, the study underscores future research priorities, such as advancing synthetic data generation, improving data quality and standardisation, and enhancing big data management in smart grids. The aim of the authors is to enable researchers in the smart and power grid a comprehensive reference point to pick suitable and relevant public datasets to evaluate their proposed methods. The provided analysis highlights the importance of following a systematic and standardised approach in evaluating future methods and directs readers to future potential venues of research in the area of smart grid analytics.

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来源期刊
IET Smart Grid
IET Smart Grid Computer Science-Computer Networks and Communications
CiteScore
6.70
自引率
4.30%
发文量
41
审稿时长
29 weeks
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