TY  - CHAP
A1  - Grund, Raphael M.
A1  - Altherr, Lena
ED  - Reiff-Stephan, Jörg
ED  - Jäkel, Jens
ED  - Schwarz, André
T1  - Development of an open source energy disaggregation tool for the home automation platform Home Assistant
T2  - Tagungsband AALE 2023 : mit Automatisierung gegen den Klimawandel
N2  - In order to reduce energy consumption of homes, it is important to make transparent which devices consume how much energy. However, power consumption is often only monitored aggregated at the house energy meter. Disaggregating this power consumption into the contributions of individual devices can be achieved using Machine Learning. Our work aims at making state of the art disaggregation algorithms accessibe for users of the open source home automation platform Home Assistant.
KW  - Home Automation Platform
KW  - Home Assistant
KW  - Open Source
KW  - Machine Learning
KW  - Energy Disaggregation
Y1  - 2023
SN  - 978-3-910103-01-6
U6  - http://dx.doi.org/10.33968/2023.02
N1  - 19. AALE-Konferenz. Luxemburg, 08.03.-10.03.2023.
BTS Connected Buildings & Cities Luxemburg
(Tagungsband unter https://doi.org/10.33968/2023.01)
SP  - 11
EP  - 20
PB  - le-tex publishing services GmbH
CY  - Leipzig
ER  - 
TY  - CHAP
A1  - Amir, Malik
A1  - Bauckhage, Christian
A1  - Chircu, Alina
A1  - Czarnecki, Christian
A1  - Knopf, Christian
A1  - Piatkowski, Nico
A1  - Sultanow, Eldar
T1  - What can we expect from quantum (digital) twins?
N2  - Digital twins enable the modeling and simulation of real-world entities
(objects, processes or systems), resulting in improvements in the associated value
chains. The emerging field of quantum computing holds tremendous promise for
evolving this virtualization towards Quantum (Digital) Twins (QDT) and
ultimately Quantum Twins (QT). The quantum (digital) twin concept is not a
contradiction in terms - but instead describes a hybrid approach that can be
implemented using the technologies available today by combining classical
computing and digital twin concepts with quantum processing. This paper
presents the status quo of research and practice on quantum (digital) twins. It also
discuses their potential to create competitive advantage through real-time
simulation of highly complex, interconnected entities that helps companies better
address changes in their environment and differentiate their products and
services.
KW  - Artificial Intelligence
KW  - Digital Twin Evolution
KW  - Machine Learning
KW  - Quantum Computing
KW  - Quantum Machine Learning
Y1  - 2022
N1  - 17. Internationale Tagung Wirtschaftsinformatik, 21. – 23. Februar 2022, Nürnberg (online)
SP  - 1
EP  - 14
PB  - AIS Electronic Library (AISeL)
ER  -