TY - CHAP
T1 - Consumer segmentation through multi-instance clustering time-series energy data from smart meters
AU - Gómez-Boix, Alejandro
AU - Arco, Leticia
AU - Nowé, Ann
PY - 2018/1/1
Y1 - 2018/1/1
N2 - With the rollout of smart metering infrastructure at large scale, demand-response programs may now be tailored based on consumption and production patterns mined from sensed data. In previous works, groups of similar energy consumption profiles were obtained. But, discovering typical consumption profiles is not enough, it is also important to reveal various preferences, behaviors and characteristics of individual consumers. However, the current approaches cannot determine clusters of similar consumer or prosumer households. To tackle this issue, we propose to model the consumer clustering problem as a multi-instance clustering problem and we apply a multi-instance clustering algorithm to solve it. We model a consumer as a bag and each bag consists of instances, where each instance will represent a day or a month of consumption. Internal indices were used for evaluating our clustering process. The obtained results are general applicable, and will be useful in a general business analytics context.
AB - With the rollout of smart metering infrastructure at large scale, demand-response programs may now be tailored based on consumption and production patterns mined from sensed data. In previous works, groups of similar energy consumption profiles were obtained. But, discovering typical consumption profiles is not enough, it is also important to reveal various preferences, behaviors and characteristics of individual consumers. However, the current approaches cannot determine clusters of similar consumer or prosumer households. To tackle this issue, we propose to model the consumer clustering problem as a multi-instance clustering problem and we apply a multi-instance clustering algorithm to solve it. We model a consumer as a bag and each bag consists of instances, where each instance will represent a day or a month of consumption. Internal indices were used for evaluating our clustering process. The obtained results are general applicable, and will be useful in a general business analytics context.
UR - http://www.scopus.com/inward/record.url?scp=85024488399&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-62359-7_6
DO - 10.1007/978-3-319-62359-7_6
M3 - Chapter
AN - SCOPUS:85024488399
SN - 978-3-319-62359-7
T3 - Studies in Fuzziness and Soft Computing
SP - 117
EP - 136
BT - Studies in Fuzziness and Soft Computing
PB - Springer Verlag
ER -