Villegas Mier, OscarOscarVillegas MierDittmann, AnnaAnnaDittmannHerzberg, WiebkeWiebkeHerzbergRuf, HolgerHolgerRufLorenz, ElkeElkeLorenzSchmidt, MichaelMichaelSchmidtGasper, RainerRainerGasper2023-10-252023-10-252023Note-ID: 000089CEhttps://publica.fraunhofer.de/handle/publica/45214710.3390/en16196980Predictive control has a great potential in the home energy management domain. However, 1 such controls need reliable predictions of the system dynamics as well as energy consumption and 2 generation, and the actual implementation in the real system is associated with many challenges. 3 This paper presents the implementation of predictive controls for a heat pump with thermal storage 4 in a real single family house with photovoltaic rooftop system. The predictive controls make use of 5 a novel cloud camera-based short-term solar energy prediction and an intraday prediction system 6 that includes additional data sources. In addition, machine learning methods were used to model the 7 dynamics of the heating system and predict loads using extensive measured data. The results of the 8 real and simulated operation will be presented.enpredictive controlPV power forecastshort-term solar forecastheat pumpmodel- 10 predictive controlneural networksPredictive Control of a Real Residential Heating System with Short-Term Solar Power Forecastjournal article