Photovoltaic Power Pattern Clustering - Munshi, Amr A.
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A method to cluster Photovoltaic power pattern data. Clustering algorithms, to determine the optimum number of clusters and to produce cluster representatives for Photovoltaic power pattern data, from different clustering categories are involved: K-means from partitional clustering, Hierarchical Ward's minimum variance (WMV) from agglomerative clustering, Fuzzy C-means (FCM) from fuzzy clustering, self-organizing maps (SOM) from neural network based algorithms, and Ant Colony and Bat from bio-inspired swarm optimization methods.

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Produktbeschreibung
A method to cluster Photovoltaic power pattern data. Clustering algorithms, to determine the optimum number of clusters and to produce cluster representatives for Photovoltaic power pattern data, from different clustering categories are involved: K-means from partitional clustering, Hierarchical Ward's minimum variance (WMV) from agglomerative clustering, Fuzzy C-means (FCM) from fuzzy clustering, self-organizing maps (SOM) from neural network based algorithms, and Ant Colony and Bat from bio-inspired swarm optimization methods.
Autorenporträt
Amr A. Munshi received his BSc in computer engineering from Umm Al-Qura University in 2008, and MSc in computer engineering from the University of Alberta in 2014. He is currently pursuing the PhD degree at the University of Alberta. He is a member of the Golden Key International Honour Society and serves as an Editor in the Alberta Academic Review