E-Sketch: Gathering large-scale energy consumption data based on consumption patterns

Huang, Zhichuan; Luo, Hongyao; Škoda, David; Zhu, Ting; Gü, Yü

doi:10.1109/bigdata.2014.7004289

Cited by 15 publications

(3 citation statements)

References 19 publications

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“…Therefore, the more data, the better. Research into data storage and processing capabilities seeks to address this disparity through some methods of paring down storage requirements, either by distributed data storage systems that use digital networks to spread the load [3] or by processing collected information using data compression [14]. Regardless of the storage methods, the sheer quantity of data available from power system measurements, consumption patterns, and even weather forecasts contribute greatly to the applications of results drawn from BDA.…”

Section: Volumementioning

confidence: 99%

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Review of Big Data Analytics for Smart Electrical Energy Systems

2023

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Energy systems around the world are going through tremendous transformations, mainly driven by carbon footprint reductions and related policy imperatives and low-carbon technological development. These transformations pose unprecedented technical challenges to the energy sector, but they also bring opportunities for energy systems to develop, adapt, and evolve. With rising complexity and increased digitalization, there has been significant growth in the amount of data in the power/energy sector (data ranging from power grid to household levels). Utilization of this large data (or “big data”), along with the use of proper data analytics, will allow for useful insights to be drawn that will help energy systems to deliver an increased amount of technical, operational, economic, and environmental benefits. This paper reviews various categories of data available in the current and future energy systems and the potential benefits of utilizing those data categories in energy system planning and operation. This paper also discusses the Big Data Analytics (BDA) that can be used to process/analyze the data and extract useful information that can be integrated and used in energy systems. More specifically, this paper discusses typical applications of BDA in energy systems, including how BDA can be used to resolve the critical issues faced by the current and future energy network operations and how BDA contributes to the development of smarter and more flexible energy systems. Combining data characterization and analysis methods, this review paper presents BDA as a powerful tool for making electrical energy systems smarter, more responsive, and more resilient to changes in operations.

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Section: Volumementioning

confidence: 99%

“…Big Data Characteristics [11][12][13][14][15][16][17][18][19][20][21][22] BDA techniques ANN [5,8,9,27]; Deep Learning [28,[30][31][32] BDA applications…”

Section: Topics Referencesmentioning

confidence: 99%

Review of Big Data Analytics for Smart Electrical Energy Systems

2023

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“…Since sensor devices need to continuously collect data, energy management of sensor devices [36][37][38] is critical. On the other hand, utilizing sensors for building energy management [39][40][41] is an emergent application in sensor network community. As one of the most important research fields in the world of sensing, building energy management investigates energy consumption information in both space and time domains, by utilizing smart meters.…”

Section: Building Energy Managementmentioning

confidence: 99%

Emergent Technologies in Big Data Sensing: A Survey

Zhu

Xiao

Zhang

et al. 2015

International Journal of Distributed Sensor Networks

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When the number of data generating sensors increases and the amount of sensing data grows to a scale that traditional methods cannot handle, big data methods are needed for sensing applications. However, big data is a fuzzy data science concept and there is no existing research architecture for it nor a generic application structure in the field of sensing. In this survey, we explore many scattered results that have been achieved by combining big data techniques with sensing and present our vision of big data in sensing. Firstly, we outline the application categories to generally summarize existing research achievements. Then we discuss the techniques proposed in these studies to demonstrate challenges and opportunities in this field. Finally, we present research trends and list some directions of big data in future sensing. Overall, mobile sensing and its related studies are hot topics, but other large-scale sensing researches are flourishing too. Although there are no “big data” techniques acting as research platforms or infrastructures to support various applications, multiple data science technologies, such as data mining, crowd sensing, and cloud computing, serve as foundations and bases of big data in the world of sensing.

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Cyber-Physical Anomaly Detection for Power Grid with Machine Learning

Wang

Manimaran

2019

Advances in Information Security

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E-Sketch: Gathering large-scale energy consumption data based on consumption patterns

Cited by 15 publications

References 19 publications

Review of Big Data Analytics for Smart Electrical Energy Systems

Review of Big Data Analytics for Smart Electrical Energy Systems

Emergent Technologies in Big Data Sensing: A Survey

Cyber-Physical Anomaly Detection for Power Grid with Machine Learning

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