Clustering methodology for smart metering data based on local and global features

Leticia Arco; Gladys Casas; Ann Nowe

doi:10.1145/3109761.3158398

Clustering methodology for smart metering data based on local and global features

Leticia Arco, Gladys Casas, Ann Nowe

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Research

3 Citations (Scopus)

Abstract

In order to develop real intelligent smart grids, understanding the patterns hidden in the smart grid data is crucial. More precisely, the detection of the preferences, behavior and characteristics of consumers and prosumers is crucial. In this work, we introduce a general methodology that groups energy consumption and production of households, based on global as well as local features, allowing the characterization of load and production profiles for consumers and prosumers. Our methodology is illustrated using a two-level clustering approach. The theoretical
results are applicable in other areas, and have utility in a general business analysis.

Original language	English
Title of host publication	IML '17 Proceedings of the 1st International Conference on Internet of Things and Machine Learning
Publisher	Association for Computing Machinery (ACM)
Number of pages	13
ISBN (Electronic)	978-1-4503-5243-7
DOIs	https://doi.org/10.1145/3109761.3158398
Publication status	Published - 17 Oct 2017
Event	1st International Conference on Internet of Things and Machine Learning - Liverpool, United Kingdom Duration: 17 Oct 2017 → 18 Oct 2017

Conference

Conference	1st International Conference on Internet of Things and Machine Learning
Abbreviated title	IML
Country/Territory	United Kingdom
City	Liverpool
Period	17/10/17 → 18/10/17

Access to Document

10.1145/3109761.3158398

EU429: SCANERGY: a SCAlable & modular system for eNERGY trading between prosumers
Nowe, A.
1/02/13 → 31/01/17
Project: Fundamental

Cite this

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title = "Clustering methodology for smart metering data based on local and global features",

abstract = "In order to develop real intelligent smart grids, understanding the patterns hidden in the smart grid data is crucial. More precisely, the detection of the preferences, behavior and characteristics of consumers and prosumers is crucial. In this work, we introduce a general methodology that groups energy consumption and production of households, based on global as well as local features, allowing the characterization of load and production profiles for consumers and prosumers. Our methodology is illustrated using a two-level clustering approach. The theoreticalresults are applicable in other areas, and have utility in a general business analysis.",

author = "Leticia Arco and Gladys Casas and Ann Nowe",

year = "2017",

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doi = "10.1145/3109761.3158398",

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booktitle = "IML '17 Proceedings of the 1st International Conference on Internet of Things and Machine Learning",

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note = "1st International Conference on Internet of Things and Machine Learning, IML ; Conference date: 17-10-2017 Through 18-10-2017",

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Arco, L, Casas, G & Nowe, A 2017, Clustering methodology for smart metering data based on local and global features. in IML '17 Proceedings of the 1st International Conference on Internet of Things and Machine Learning., 65, Association for Computing Machinery (ACM), 1st International Conference on Internet of Things and Machine Learning, Liverpool, United Kingdom, 17/10/17. https://doi.org/10.1145/3109761.3158398

Clustering methodology for smart metering data based on local and global features. / Arco, Leticia; Casas, Gladys; Nowe, Ann.

IML '17 Proceedings of the 1st International Conference on Internet of Things and Machine Learning. Association for Computing Machinery (ACM), 2017. 65.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Research

TY - GEN

T1 - Clustering methodology for smart metering data based on local and global features

AU - Arco, Leticia

AU - Casas, Gladys

AU - Nowe, Ann

PY - 2017/10/17

Y1 - 2017/10/17

N2 - In order to develop real intelligent smart grids, understanding the patterns hidden in the smart grid data is crucial. More precisely, the detection of the preferences, behavior and characteristics of consumers and prosumers is crucial. In this work, we introduce a general methodology that groups energy consumption and production of households, based on global as well as local features, allowing the characterization of load and production profiles for consumers and prosumers. Our methodology is illustrated using a two-level clustering approach. The theoreticalresults are applicable in other areas, and have utility in a general business analysis.

AB - In order to develop real intelligent smart grids, understanding the patterns hidden in the smart grid data is crucial. More precisely, the detection of the preferences, behavior and characteristics of consumers and prosumers is crucial. In this work, we introduce a general methodology that groups energy consumption and production of households, based on global as well as local features, allowing the characterization of load and production profiles for consumers and prosumers. Our methodology is illustrated using a two-level clustering approach. The theoreticalresults are applicable in other areas, and have utility in a general business analysis.

U2 - 10.1145/3109761.3158398

DO - 10.1145/3109761.3158398

M3 - Conference paper

BT - IML '17 Proceedings of the 1st International Conference on Internet of Things and Machine Learning

PB - Association for Computing Machinery (ACM)

T2 - 1st International Conference on Internet of Things and Machine Learning

Y2 - 17 October 2017 through 18 October 2017

ER -

Clustering methodology for smart metering data based on local and global features

Abstract

Conference

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Projects

EU429: SCANERGY: a SCAlable & modular system for eNERGY trading between prosumers

Cite this