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Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast

  • Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a realPredictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a real single-family house with a photovoltaic rooftop system. The predictive controls make use of a novel cloud camera-based short-term solar energy prediction and an intraday prediction system that includes additional data sources. In addition, machine learning methods were used to model the dynamics of the heating system and predict loads using extensive measured data. The results of the real and simulated operation will be presented.show moreshow less

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Metadaten
Document Type:Article (reviewed)
Zitierlink: https://opus.hs-offenburg.de/8213
Bibliografische Angaben
Title (English):Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast
Author:Oscar Villegas MierStaff MemberORCiDGND, Anna Dittmann, Wiebke Herzberg, Holger Ruf, Elke Lorenz, Michael SchmidtStaff MemberORCiDGND, Rainer GasperStaff MemberGND
Year of Publication:2023
Place of publication:Basel
Publisher:MDPI
First Page:1
Last Page:19
Parent Title (English):Energies
Volume:16
ISSN:1996-1073
DOI:https://doi.org/10.3390/en16196980
URN:https://urn:nbn:de:bsz:ofb1-opus4-82135
Language:English
Inhaltliche Informationen
Institutes:Forschung / INES - Institut für nachhaltige Energiesysteme
Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Fakultät Maschinenbau und Verfahrenstechnik (M+V)
Institutes:Bibliografie
Tag:PV power forecast; heat pump; model predictive control; neural networks; predictive control; short-term solar forecast
Funded by (selection):Ministerium für Umwelt, Klima und Energiewirtschaft Baden-Württemberg
Funding number:BWSGD 18001-18002
Formale Angaben
Relevance:Wiss. Zeitschriftenartikel reviewed: Listung in Master Journal List
Open Access: Open Access 
 Gold 
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International