期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 160, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2020.113731
关键词
Time-series clustering; Energy efficiency; Knowledge extraction; Data mining
类别
资金
- Department of Computer Science and Artificial Intelligence of the University of Granada [TIC111, TIN201564776-C3-1-R]
- European Union [743623, 754446]
- Marie Curie Actions (MSCA) [743623] Funding Source: Marie Curie Actions (MSCA)
In the Energy Efficiency field, the incorporation of intelligent systems in cities and buildings is motivated by the energy savings and pollution reduction that can be attained. To achieve this goal, energy modelling and a better understanding of how energy is consumed are fundamental factors. As a result, this study proposes a methodology for knowledge acquisition in energy-related data through Time-Series Clustering (TSC) techniques. In our experimentation, we utilize data from the buildings at the University of Granada (Spain) and compare several clustering methods to get the optimum model, in particular, we tested k-Means, k-Medoids, Hierarchical clustering and Gaussian Mixtures; as well as several algorithms to obtain the best grouping, such as PAM, CLARA, and two variants of Lloyd's method, Small and Large. Thus, our methodology can provide non-trivial knowledge from raw energy data. In contrast to previous studies in this field, not only do we propose a clustering methodology to group time series straightforwardly, but we also present an automatic strategy to search and analyse energy periodicity in these series recursively so that we can deepen granularity and extract information at different levels of detail. The results show that k-Medoids with PAM is the best approach in virtually all cases, and the Squared Euclidean distance outperforms the rest of the metrics. (c) 2020 Elsevier Ltd. All rights reserved.
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