A Clustering Framework for Residential Electric Demand Profiles

Jain, Mayank; AlSkaif, Tarek; Dev, Soumyabrata

doi:10.1109/sest48500.2020.9203534

Cited by 13 publications

(5 citation statements)

References 19 publications

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“…In other words, there is no target labelled dataset in this case to learn the features from. Hence, unsupervised algorithms essentially train themselves by identifying the possible underlying patterns [29]. Commonly used clustering algorithms include k-means and the fuzzy c-means algorithm [30].…”

Section: Clusteringmentioning

confidence: 99%

Using U-Net network for efficient brain tumor segmentation in MRI images

Walsh¹,

Othmani²,

Jain³

et al. 2022

Healthcare Analytics

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Section: Clusteringmentioning

confidence: 99%

Using U-Net network for efficient brain tumor segmentation in MRI images

Walsh¹,

Othmani²,

Jain³

et al. 2022

Healthcare Analytics

View full text Add to dashboard Cite

“…The cluster analysis involves the grouping of data points into two (or more) different clusters. Given a set of data points, a clustering algorithm is used to classify each data point into a specific group/cluster so that the data points in the same group/cluster have similar properties and data points in different groups have highly dissimilar properties (Jain et al, 2020). These groups can then be analysed in details to gain further knowledge about common features in each group of climate sub-regions.…”

Section: Related Workmentioning

confidence: 99%

Graph‐based local climate classification in Iran

Akrami

Ziarati

Dev

2021

Intl Journal of Climatology

Self Cite

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In this paper, we introduce a novel graph-based method to classify the regions with similar climate in a local area. We refer our proposed method as graph partition based method (GPBM). Our proposed method attempts to overcome the shortcomings of the current state-of-the-art methods in the literature. It has no limit on the number of variables that can be used and also preserves the nature of climate data. To illustrate the capability of our proposed algorithm, we benchmark its performance with other state-of-the-art climate classification techniques. The climate data are collected from 24 synoptic stations in Fars province in southern Iran. The data include seven climate variables stored as time series from 1951 to 2017. Our results exhibit that our proposed method performs a more realistic climate classification with less computational time. It can save more information during the climate classification process and is therefore efficient in further data analysis. Furthermore, using our method, we can introduce seasonal graphs to better investigate seasonal climate changes.To the best of our knowledge, our proposed method is the first graph-based climate classification system.

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“…The cluster analysis involves the grouping of data points into two (or more) different clusters. Given a set of data points, a clustering algorithm is used to classify each data point into a specific group/cluster so that the data points in the same group/cluster have similar properties and data points in different groups have highly dissimilar properties (Jain et al, 2020). These groups can then be analyzed in details to gain further knowledge about common features in each group of climate sub-regions.…”

Section: Related Workmentioning

confidence: 99%

Graph-based Local Climate Classification in Iran

Akrami¹,

Ziarati²,

Dev³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper, we introduce a novel graph-based method to classify the regions with similar climate in a local area. We refer our proposed method as Graph Partition Based Method (GPBM). Our proposed method attempts to overcome the shortcomings of the current state-of-the-art methods in the literature. It has no limit on the number of variables that can be used and also preserves the nature of climate data. To illustrate the capability of our proposed algorithm, we benchmark its performance with other state-ofthe-art climate classification techniques. The climate data is collected from 24 synoptic stations in Fars province in southern Iran. The data includes seven climate variables stored as time series from 1951 to 2017. Our results exhibit that our proposed method performs a more realistic climate classification with less computational time. It can save more information during the climate classification process and is therefore efficient in further data analysis. Furthermore, using our method, we can introduce seasonal graphs to better investigate seasonal climate changes.To the best of our knowledge, our proposed method is the first graph-based climate classification system.

show abstract

A Clustering Framework for Residential Electric Demand Profiles

Cited by 13 publications

References 19 publications

Using U-Net network for efficient brain tumor segmentation in MRI images

Using U-Net network for efficient brain tumor segmentation in MRI images

Graph‐based local climate classification in Iran

Graph-based Local Climate Classification in Iran

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