Demand side management using artificial neural networks in a smart grid environment

Macedo, Maria N.Q.; Galo, Joaquim J.M.; Almeida, L.A.L. de; Lima, Carla Caroline Valença de

doi:10.1016/j.rser.2014.08.035

Cited by 135 publications

(75 citation statements)

References 23 publications

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“…Weights of the inputs are accomplished and revolutionize the inputs by relevant function with subsequent neurons in Sets. In [46], a successful classification is done using ANN by the selection of suitable DSM approaches. Kohonen neural network is also used for self-arranging of mapping that it also a unsupervised neural network method, used mostly for Load Classification [9], [47].…”

Section: Classifications In Loadmentioning

confidence: 99%

Generation of dynamic energy management using data mining techniques basing on big data analytics isssues in smart grids

Lydia¹,

Kumar²,

Ramya³

2018

IJET

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The Optimal bidirectional flow of the electric power and the communicational data between suppliers and consumers are greatly enabled by the Smart Electricity in Grid. Reliable and Feasible micro energy generated due to Dynamic Energy Management (DEM) and the electricity market by consumers and suppliers. The smart grid features ICCM, aims to bring out the power at reduced cost. Powerful and practical DEM relies on load and sustainable production. Smart meters attain the huge data quantity through practical methods and solutions in this real world working. Smart Grids are enhanced by the operations such as data analytics, giving out high performance estimation, Adequate data network management and cloud computing. This paper aims focusthe issuesin big data and challenges experienced by the Dynamic Energy Management signed in Smart Grid. A detail explanation of data processing techniques that are mostly implemented and It also provides a brief description of the most commonly used data processing methods and recommended proposes a upcoming future directional research in thefield.

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Section: Classifications In Loadmentioning

confidence: 99%

Generation of dynamic energy management using data mining techniques basing on big data analytics isssues in smart grids

Lydia¹,

Kumar²,

Ramya³

2018

IJET

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“…In deploying a smart grid, all parts of the electrical system (generation, transmission and distribution of energy) are to be integrated, they must share digital communications, infrastructure and data processing while working as Internet devices [5][6][7].…”

Section: Features Functionalities and Major Challenges Of Deploying mentioning

confidence: 99%

“…The network architecture is divided into three layers: energy, communication and information technology. The last two layers, which form an intelligent network, create the infrastructure for the power layer [4][5][6].…”

Section: Features Functionalities and Major Challenges Of Deploying mentioning

confidence: 99%

Method for deployment of smart grids through the creation of a priority index

Galo

Macedo

Almeida

et al. 2015

Renewable and Sustainable Energy Reviews

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“…The emerging smart grid requires distributed intelligence, as well as the development of models based on artificial intelligence, e.g., [7][8][9]. Several studies have been published in the electrical energy field, mostly facilitated by the availability of suitable databases [10].…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Residential Gas Consumers Using Clustering Analysis

et al. 2017

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Abstract:The growing environmental concerns and liberalization of energy markets have resulted in an increased competition between utilities and a strong focus on efficiency. To develop new energy efficiency measures and optimize operations, utilities seek new market-related insights and customer engagement strategies. This paper proposes a clustering-based methodology to define the segmentation of residential gas consumers. The segments of gas consumers are obtained through a detailed clustering analysis using smart metering data. Insights are derived from the segmentation, where the segments result from the clustering process and are characterized based on the consumption profiles, as well as according to information regarding consumers' socio-economic and household key features. The study is based on a sample of approximately one thousand households over one year. The representative load profiles of consumers are essentially characterized by two evident consumption peaks, one in the morning and the other in the evening, and an off-peak consumption. Significant insights can be derived from this methodology regarding typical consumption curves of the different segments of consumers in the population. This knowledge can assist energy utilities and policy makers in the development of consumer engagement strategies, demand forecasting tools and in the design of more sophisticated tariff systems.

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Demand side management using artificial neural networks in a smart grid environment

Cited by 135 publications

References 23 publications

Generation of dynamic energy management using data mining techniques basing on big data analytics isssues in smart grids

Generation of dynamic energy management using data mining techniques basing on big data analytics isssues in smart grids

Method for deployment of smart grids through the creation of a priority index

Segmentation of Residential Gas Consumers Using Clustering Analysis

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