TY - GEN
T1 - Dissecting the Problem of Individual Home Power Consumption Prediction using Machine Learning
AU - Casella, Enrico
AU - Sudduth, Eleanor
AU - Silvestri, Simone
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The growth and widespread diffusion of Internet-of-Things devices and advanced metering infrastructure allows to closely monitor appliances in a user home. Although only few works have focused on the issue of individual home power consumption predictions, recent efforts have unveiled the complexity of this task. As opposed to building-level power predictions, the finer granularity of single home predictions is characterized by the high impact that individual user actions have on the power consumption. As a matter of fact, the current state of the art shows inadequate prediction performance. In this work, we investigate the issue of single home power prediction by analyzing a recent dataset of real power consumption data. We carry out a profound analysis of several processing parameters and environmental parameters that make this task so challenging, thus providing meaningful insights that can guide future research on individual home power consumption predictions. Results show an overall low daily error, and very accurate hourly predictions when less variable usage patterns occur.
AB - The growth and widespread diffusion of Internet-of-Things devices and advanced metering infrastructure allows to closely monitor appliances in a user home. Although only few works have focused on the issue of individual home power consumption predictions, recent efforts have unveiled the complexity of this task. As opposed to building-level power predictions, the finer granularity of single home predictions is characterized by the high impact that individual user actions have on the power consumption. As a matter of fact, the current state of the art shows inadequate prediction performance. In this work, we investigate the issue of single home power prediction by analyzing a recent dataset of real power consumption data. We carry out a profound analysis of several processing parameters and environmental parameters that make this task so challenging, thus providing meaningful insights that can guide future research on individual home power consumption predictions. Results show an overall low daily error, and very accurate hourly predictions when less variable usage patterns occur.
UR - http://www.scopus.com/inward/record.url?scp=85136113538&partnerID=8YFLogxK
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U2 - 10.1109/SMARTCOMP55677.2022.00037
DO - 10.1109/SMARTCOMP55677.2022.00037
M3 - Conference contribution
AN - SCOPUS:85136113538
T3 - Proceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022
SP - 156
EP - 158
BT - Proceedings - 2022 IEEE International Conference on Smart Computing, SMARTCOMP 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Smart Computing, SMARTCOMP 2022
Y2 - 20 June 2022 through 24 June 2022
ER -