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.…
Document Type: | Article (reviewed) |
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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): | Creative Commons - CC BY - Namensnennung 4.0 International |