Optimization of In-House Energy Demand - Smart Information Systems: Computational Intelligence for Real-Life Applications - page 283

existing frameworks, such as the smart thermostat [ 14 ]. However, this would require
datasets that comprise information about actual occupancy states in the residential
homes and preferred temperature settings.
Our proposed approach to appliance state identification can furthermore be ben-
eficial for other applications. Recent studies [ 2 ] have shown that the availability of
smart meter data alone is often not sufficient to achieve high load disaggregation
accuracies. Future work could combine the knowledge of total energy consumption
with additional information about sequences of events, such as

states for each
individual appliance, to improve the accuracy of certain disaggregation algorithms
[ 2 ] that use such events along with smart meter data.

on / off

Acknowledgments This workwas funded by the Federal Ministry of Economic Affairs and Energy
(BMWi) under funding reference number KF2392312-KM2. The presented SOE application was
developed by Veit Schwartze, Stephen Prochnow, and Marie Schacht.

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