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  •   University of Thessaly Institutional Repository
  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ.
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  •   University of Thessaly Institutional Repository
  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ.
  • View Item
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A hybrid metaheuristics-based algorithm for electricity load curves profiling

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Author
Panapakidis I.P., Kechagias C.-A., Bargiotas D.
Date
2021
Language
en
DOI
10.1109/UPEC50034.2021.9548166
Keyword
Clustering algorithms
Curve fitting
Learning algorithms
Machine learning
Optimization
Clusterings
Electricity load
Hybrid metaheuristics
Load curves
Load profiles
Load profiling
Machine learning algorithms
Metaheuristic
Optimisations
Unsupervised machine learning
Heuristic algorithms
Institute of Electrical and Electronics Engineers Inc.
Metadata display
Abstract
Clustering-based load profiling utilizes unsupervised machine learning algorithms to form homogenous clusters composed by electricity load curves with similar characteristics. Due to the importance of load profiling in modern power systems, a variety of clustering algorithms of different type and complexity has been proposed in the technical literature. Nevertheless, no attention has been placed in metaheuristics-based clustering. The present paper proposes a novel hybrid algorithm that is composed by two clustering algorithms, namely a hierarchical algorithm and a meta-heuristic one. A comparison takes place with the most common algorithms of the literature. Experimental results indicate the robustness of the proposed approach to the electricity load curves profiling research problem. © 2021 IEEE.
URI
http://hdl.handle.net/11615/77477
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  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ. [19735]

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