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

Oscar Villegas Mier (), Anna Dittmann, Wiebke Herzberg, Holger Ruf, Elke Lorenz, Michael Schmidt and Rainer Gasper
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Oscar Villegas Mier: Institut für Nachhaltige Energiesysteme, Hochschule Offenburg, 77652 Offenburg, Germany
Anna Dittmann: Fraunhofer Institut für Solare Energiesysteme ISE, 79110 Freiburg, Germany
Wiebke Herzberg: Fraunhofer Institut für Solare Energiesysteme ISE, 79110 Freiburg, Germany
Holger Ruf: P3R GmbH, 89077 Ulm, Germany
Elke Lorenz: Fraunhofer Institut für Solare Energiesysteme ISE, 79110 Freiburg, Germany
Michael Schmidt: Institut für Nachhaltige Energiesysteme, Hochschule Offenburg, 77652 Offenburg, Germany
Rainer Gasper: Institut für Nachhaltige Energiesysteme, Hochschule Offenburg, 77652 Offenburg, Germany

Energies, 2023, vol. 16, issue 19, 1-19

Abstract: 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 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.

Keywords: predictive control; PV power forecast; short-term solar forecast; heat pump; model predictive control; neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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