In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Ruiz:2020:10.1016/j.eswa.2020.113731,
author = {Ruiz, LGB and Pegalajar, MC and Arcucci, R and Molina-Solana, M},
doi = {10.1016/j.eswa.2020.113731},
journal = {Expert Systems with Applications},
title = {A time-series clustering methodology for knowledge extraction in energy consumption data},
url = {http://dx.doi.org/10.1016/j.eswa.2020.113731},
volume = {160},
year = {2020}
}
TY - JOUR
AB - 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.
AU - Ruiz,LGB
AU - Pegalajar,MC
AU - Arcucci,R
AU - Molina-Solana,M
DO - 10.1016/j.eswa.2020.113731
PY - 2020///
SN - 0957-4174
TI - A time-series clustering methodology for knowledge extraction in energy consumption data
T2 - Expert Systems with Applications
UR - http://dx.doi.org/10.1016/j.eswa.2020.113731
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000573459900010&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
VL - 160
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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