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.
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
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 Article 65 Association for Computing Machinery (ACM). https://doi.org/10.1145/3109761.3158398
@inproceedings{8615a725640b46949260e6d4aa037f92,
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",
month = oct,
day = "17",
doi = "10.1145/3109761.3158398",
language = "English",
booktitle = "IML '17 Proceedings of the 1st International Conference on Internet of Things and Machine Learning",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",
note = "1st International Conference on Internet of Things and Machine Learning, IML ; Conference date: 17-10-2017 Through 18-10-2017",
}