Inproceedings3224: Unterschied zwischen den Versionen
Kaba (Diskussion | Beiträge) (Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorNachname=Bao |ErsterAutorVorname=Kaibin }} {{Publikation Author |Rank=2 |Author=Florian Allerding }} {{Publikation Author |…“) |
Qh7462 (Diskussion | Beiträge) |
||
(2 dazwischenliegende Versionen von 2 Benutzern werden nicht angezeigt) | |||
Zeile 12: | Zeile 12: | ||
}} | }} | ||
{{Inproceedings | {{Inproceedings | ||
− | |Referiert= | + | |Referiert=True |
|Title=User behavior prediction for energy management in smart homes | |Title=User behavior prediction for energy management in smart homes | ||
|Year=2011 | |Year=2011 | ||
Zeile 22: | Zeile 22: | ||
|Address=Shanghai, China | |Address=Shanghai, China | ||
|Volume=2 | |Volume=2 | ||
+ | }} | ||
+ | {{Publikation Tool | ||
+ | |Tool=Energy Smart Home Lab | ||
}} | }} | ||
{{Publikation Details | {{Publikation Details | ||
|Abstract=In this paper, we focus on the prediction of user interactions within a real world scenario of energy management for a smart home. External signals, reflecting the low voltage grid's state, are used to address the challenge of balancing energy demand and generation. An autonomous system to aim at this challenge is proposed, in particular to coordinate decentralized power plants with the electrical load of the smart home. For that two prediction algorithms to estimate the future behavior of the smart home are presented: The Day Type Model and a probabilistic approach based on a first order Semi Markov Model. Some experimental results with real world data of the KIT smart home are presented. | |Abstract=In this paper, we focus on the prediction of user interactions within a real world scenario of energy management for a smart home. External signals, reflecting the low voltage grid's state, are used to address the challenge of balancing energy demand and generation. An autonomous system to aim at this challenge is proposed, in particular to coordinate decentralized power plants with the electrical load of the smart home. For that two prediction algorithms to estimate the future behavior of the smart home are presented: The Day Type Model and a probabilistic approach based on a first order Semi Markov Model. Some experimental results with real world data of the KIT smart home are presented. | ||
− | |ISBN=978-1-61284-180-9 | + | |ISBN=978-1-61284-180-9 |
|DOI Name=10.1109/FSKD.2011.6019758 | |DOI Name=10.1109/FSKD.2011.6019758 | ||
|Projekt=MEREGIOmobil | |Projekt=MEREGIOmobil | ||
|Forschungsgruppe=Effiziente Algorithmen | |Forschungsgruppe=Effiziente Algorithmen | ||
+ | }} | ||
+ | {{Forschungsgebiet Auswahl | ||
+ | |Forschungsgebiet=Energieinformatik | ||
}} | }} |
Aktuelle Version vom 3. Juli 2015, 13:14 Uhr
User behavior prediction for energy management in smart homes
User behavior prediction for energy management in smart homes
Published: 2011
Juli
Buchtitel: Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Ausgabe: 2
Seiten: 1335 - 1339
Verlag: IEEE
Erscheinungsort: Shanghai, China
Organisation: Fuzzy Systems and Knowledge Discovery (FSKD)
Referierte Veröffentlichung
Kurzfassung
In this paper, we focus on the prediction of user interactions within a real world scenario of energy management for a smart home. External signals, reflecting the low voltage grid's state, are used to address the challenge of balancing energy demand and generation. An autonomous system to aim at this challenge is proposed, in particular to coordinate decentralized power plants with the electrical load of the smart home. For that two prediction algorithms to estimate the future behavior of the smart home are presented: The Day Type Model and a probabilistic approach based on a first order Semi Markov Model. Some experimental results with real world data of the KIT smart home are presented.
ISBN: 978-1-61284-180-9
DOI Link: 10.1109/FSKD.2011.6019758