Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China

Zhou, Kaile; Yang, Cheng‐Hong; Shen, Jianxin

doi:10.1016/j.jup.2017.01.004

Cited by 52 publications

(18 citation statements)

References 36 publications

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“…Massive Online Analysis (MOA) is an open source framework for large data streams analysis, project that is the complement of WEKA for Big Data analysis. [3], [40], [44], [49], [64], [67], [84], [94], [129], [153], [160], [204], [212], [214], [245]), fuzzy c-means clustering (7) ( [49], [64], [173], [204], [245], [265], [266]), Hierarchical Clustering (HAC) (7) ( [44], [56], [64], [94], [204], [212], [232]), Support Vector Machine (SVM) (6) [3], [112], [150], [204], [239], [250], Self-Organising Map (SOM) (4) [2], [64], [167], [212], Multi Layer Perceptron (MLP) ANN (3) [40], [150], [232], t-means clustering [183], k-Nearest Neighbour (kNN) [112], [204], Random Forest…”

Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

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Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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“…Smart energy systems were first mentioned as a term in 2009 that combines the series of management objectives, strategies, concepts, tasks, models, processes, mechanism, measures based on big data analytics and advanced information and communication technologies (ICTs), cloud computing, the internet of things to deal with the challenge of traditional energy systems and to supply progressively demand high quality and personalized energy products and services (Zhou, Yang, and Shen, 2017).…”

Section: Smart Energy Systems and Related Conceptsmentioning

confidence: 99%

“…The capacity of energy big data offers real value to energy consumers using smart meter and smart grid technologies. The smart grid is the primary phase, and the basic form of smart energy systems (Zhou, Yang, and Shen, 2017) and smart grid focuses on the electricity sector while smart energy systems cover more sectors (Lund et al, 2017). As a modern infrastructure smart grid can integrate information and energy flow, power generation and operation can be AJIT-e: Online Academic Journal of Information Technology 2020 Spring/Bahar -Cilt/Vol: 11 -Sayı/Issue: 41 DOI: 10.5824/ajite.2020.02.001.x optimized in real-time, electricity demand can be accurately predicted, and comprehensive information can be extracted from big data (Zhou et al, 2014).…”

Section: Smart Energy Systems and Related Conceptsmentioning

confidence: 99%

“…The 'smart' element of these energy systems refers to integrating energy flow, information flow, and business process flow (Lammers and Hoppe, 2019). Therefore, energy big data is composed of user description data, user behavior data, energy system data, and business systems related data (Zhou, Yang, and Shen, 2017). User description data contains the household data, demographic data, and residential characteristic data when user behavior data includes the marketing systems data, social media data, as well as others.…”

Section: Smart Energy Systems and Related Conceptsmentioning

confidence: 99%

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Big Data in Smart Energy Systems: A Critical Review

Codal

Ari

Ilter

2020

AJIT-e

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Climate change is an undeniable fact. Considering that two-thirds of greenhouse gas emissions originate from the energy sector, it is expected that the world's energy system will be transformed with renewable energy sources. Energy efficiency will be continuously increased. Reducing energy-related carbon dioxide emissions is the heart of the energy transition. Big data in energy systems play a crucial role in evaluating the adaptive capacity and investing more smartly to manage energy demand and supply. Indeed, the impact of the smart energy grid and meters on smart energy systems provide and assist decision-makers in transforming energy production, consumption, and communities. This study reviews the literature for aligning big data and smart energy systems and criticized according to regional perspective, period, disciplines, big data characteristics, and used data analytics. The critical review has been categorized into present themes. The results address issues, including scientific studies using data analysis techniques that take into account the characteristics of big data in the smart energy literature and the future of smart energy approaches. The manuscripts on big data in smart energy systems are a promising issue, albeit it is essential to expand subjects through comprehensive interdisciplinary studies

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“…En el año 2012 se utilizó el reconocimiento de patrones de consumo de electricidad para identificar el comportamiento de los residentes en viviendas, en este caso los patrones de consumo son representados por curvas horarias [45]. Por otro lado, en [46] se utiliza el método de agrupamiento fuzzy c-means para descubrir patrones de consumo en usuarios residenciales (2017). Actualmente, estas aplicaciones pueden ser útiles para fines de márquetin, ya que es posible detectar requerimientos o hábitos de los potenciales clientes.…”

Section: Imagen 212 Documentos En La Base De Datos Scopus De Patroneunclassified

Caracterización de la demanda de energía mediante patrones estocásticos en las Redes Eléctricas Inteligentes

Guerrero¹,

Xavier²

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The energy demand is increasing considerably every year worldwide. Moreover, the electrification of different sectors has caused the growth of electricity demand at an even higher rate. Thus, the electricity grids have technologically evolved in several aspects, one of them is the availability of demand data at different points and levels of the grid, such as in transmission and distribution systems, and large and small consumers. The data provided by the new smart grids are essential baseline information for the management and planning of electricity systems.

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Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China

Cited by 52 publications

References 36 publications

Smart Grids Data Analysis: A Systematic Mapping Study

Smart Grids Data Analysis: A Systematic Mapping Study

Big Data in Smart Energy Systems: A Critical Review

Caracterización de la demanda de energía mediante patrones estocásticos en las Redes Eléctricas Inteligentes

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