Power distribution network partitioning in big data environment using k-means and fuzzy logic

Νασιάκου, Αντωνία; Alamaniotis, Miltiadis; Tsoukala, L.H.

doi:10.1049/cp.2016.1078

Cited by 8 publications

(5 citation statements)

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“…Just as physical cities are divided into neighborhoods, a smart city can also be segmented into multiple zones (or partitions) that exhibit common characteristics [22]. Moreover, citizens within these zones can share common objectives and collaborate to optimize the utilization of available services, including energy-related services [23]. Additionally, dividing smart cities into zones enables the provision of specialized services tailored to the specific needs of each zone.…”

Section: Related Work On Smart City Zonesmentioning

confidence: 99%

Synergism of Fuzzy Leaky Bucket with Virtual Buffer for Large Scale Social Driven Energy Allocation in Emergencies in Smart City Zones

Alamaniotis,

Alexiou

2024

Electronics

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Smart cities can be viewed as expansive systems that optimize operational quality and deliver a range of services, particularly in the realm of energy management. Identifying energy zones within smart cities marks an initial step towards ensuring equitable energy distribution driven by factors beyond energy considerations. This study introduces a socially oriented methodology for energy allocation during emergencies, implemented at the zone level to address justice concerns. The proposed method integrates a fuzzy leaky bucket model with an energy virtual buffer, leveraging extensive data from diverse city zones to allocate energy resources during emergent situations. By employing fuzzy sets and rules, the leaky bucket mechanism distributes buffered energy to zones, aiming to maximize energy utilization while promoting social justice principles. Evaluation of the approach utilizes consumption data from simulated smart city zones during energy-constrained emergencies, comparing it against a uniform allocation method. Results demonstrate the socially equitable allocation facilitated by the proposed methodology.

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Section: Related Work On Smart City Zonesmentioning

confidence: 99%

Synergism of Fuzzy Leaky Bucket with Virtual Buffer for Large Scale Social Driven Energy Allocation in Emergencies in Smart City Zones

Alamaniotis,

Alexiou

2024

Electronics

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“…Recently, many methodologies and approaches have been studied in the literature to group the consumers of a power grid using clustering algorithms, including k-means [4], selforganized maps [5], hierarchical [6], and fuzzy c-means [7]. For assessing the validity of the clustering results of each algorithm, various validity indicators have been developed.…”

Section: A State Of the Artmentioning

confidence: 99%

“…A distance metric is used to calculate the distance between pairs of the cluster centers and these distances are used to calculate the SMI. In (4) and (5) the mathematical formula for the DBI and SMI are presented respectively.…”

Section: Distance Of the Items In A Clustermentioning

confidence: 99%

A three-stage scheme for consumers' partitioning using hierarchical clustering algorithm

Νασιάκου¹,

Alamaniotis²,

Tsoukalas³

et al. 2017

2017 8th International Conference on Information, Intelligence, Systems &Amp; Applications (IISA)

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The clustering of any type of consumers (residential, commercial, industrial) is of great importance in the operation of Smart Grids. In this paper, we propose a three-stage hierarchical scheme for residential consumers' partitioning using the Hierarchical clustering algorithm. The aim of this study is to cluster the consumers in well-separated and compact clusters using information from the near past (almost real time). The usage of electricity from a resident to another varies and this information can be used from the system operator for improving the efficiency of the distribution network. The first stage corresponds to the consumers' clustering of the distribution grid using data driven every three minutes (simulation time) from the meter of each residency. The procedure of the second stage takes part every a specific number of hours, called h, that is defined by the user. The average value of each of the k*20 clusters formed the last h hours is used as input for the hierarchical algorithm. In the third stage and in the end of each h hours, the average value of the data in each cluster is calculated and each consumer is reassigned to the cluster where the a distance metric is minimized. The results of the second stage provide deeper information about the load patterns existing each hour in the distribution grid. This information can be used from suppliers to design the energy tariffs for suiting better to the consumers' needs. This approach is tested using the IEEE-13 test feeder. The data are driven from 56 residencies.

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“…In this chapter, the goal is to introduce a new demand prediction methodology that is applicable to smart cities. The extensive use of information technologies in smart cities, as well as the heterogeneous behavior of consumers even in close geographic vicinity will further complicate the forecasting of the energy demand [27]. Furthermore, predicting the demand of a smart city partition (e.g.…”

Section: Introductionmentioning

confidence: 99%

Neuro-Kernel-Machine Network Utilizing Deep Learning and Its Application in Predictive Analytics in Smart City Energy Consumption

Alamaniotis

2020

Advances in Data Science: Methodologies and Applications

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In the smart cities of the future artificial intelligence (AI) will have a dominant role given that AI will accommodate the utilization of intelligent analytics for prediction of critical parameters pertaining to city operation. In this chapter, a new data analytics paradigm is presented and being applied for energy demand forecasting in smart cities. In particular, the presented paradigm integrates a group of kernel machines by utilizing a deep architecture. The goal of the deep architecture is to exploit the strong capabilities of deep learning utilizing various abstraction levels and subsequently identify patterns of interest in the data. In particular, a deep feedforward deep neural network is employed with every network node to implement a kernel machine. This deep architecture, named neuro-kernel machine network, is subsequently applied for predicting the energy consumption of groups of residents in smart cities. Obtained results exhibit the capability of the presented method to provide adequately accurate predictions despite the form of the energy consumption data.

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Power distribution network partitioning in big data environment using k-means and fuzzy logic

Cited by 8 publications

References 0 publications

Synergism of Fuzzy Leaky Bucket with Virtual Buffer for Large Scale Social Driven Energy Allocation in Emergencies in Smart City Zones

Synergism of Fuzzy Leaky Bucket with Virtual Buffer for Large Scale Social Driven Energy Allocation in Emergencies in Smart City Zones

A three-stage scheme for consumers' partitioning using hierarchical clustering algorithm

Neuro-Kernel-Machine Network Utilizing Deep Learning and Its Application in Predictive Analytics in Smart City Energy Consumption

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