Data Mining Applications in Smart Grid System (SGS)

Mohammad Taghi Dehghan Nezhad () and Mohammad mahdi Sarbishegi ()
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Mohammad Taghi Dehghan Nezhad: Sharif University
Mohammad mahdi Sarbishegi: Tehran University

A chapter in Handbook of Smart Energy Systems, 2023, pp 1557-1573 from Springer

Abstract: Abstract Smart grid system (SGS) is a state-of-the-art technology that has been invented for the creation of a smart relation between consumers and producers then handled by a central center to control and manage the grid. Data mining is a useful tool, and it has been almost used in all industries and researches to including SGS and power industry. Data mining has profit methods such as classification, clustering regression, time series, and they are helping to SGS for performing the task to defined for its. This chapter introduces a comprehensive survey of applications of data mining in SGS. First, survey data mining applications in demand response, for example, load forecasting, generate forecasting, smoothing the consumption of power curve. Second, survey data mining applications in distribution and transmission of power in SGS such as detecting the power transmission line faults and islanding situation and power quality measurement. Third, survey data mining applications in the security of SGS such as finding anomaly, false data injection, and DoS (or DDoS), and then in fourth, survey data mining applications in predictive maintenance and fault management (PM &FM) such as an outage, equipment breakdown, electrical and arc faults. After this data mining applications, survey the best of data mining and visualization software and compare them with each other and introduce the strengths of every software in data mining methods. In the end, survey challenges of today’s data mining applications in SGS and then give some suggestion for future works.

Keywords: Smart grid system; Data mining; Machine learning; Demand response; Security; Distribution (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_142

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DOI: 10.1007/978-3-030-97940-9_142

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