Learning User Activities from Energy Demand Profiles

DOI

10.2991/ifsa-eusflat-15.2015.123How to use a DOI?

Keywords

Ambient intelligence, modelling human activities, energy profiles, clustering

Abstract

In this paper, we propose the use of energy load profiles to learn human activities. An energy load profile determines the energy consumption of an appliance during a specific interval of time. We propose the use of clustering techniques to group the different profiles according to their temporal consumption. Both Hard and Soft clustering techniques are evaluated. We have tested the method with data from REMODECE (Residential Monitoring to Decrease Energy Use and Carbon Emissions in Europe) 1 database.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Maria Ros
AU  - Miguel Molina-Solana
AU  - M. José Martin-Bautista
AU  - Miguel Delgado
AU  - M. Amparo Vila
PY  - 2015/06
DA  - 2015/06
TI  - Learning User Activities from Energy Demand Profiles
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 873
EP  - 879
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.123
DO  - 10.2991/ifsa-eusflat-15.2015.123
ID  - Ros2015/06
ER  -