Laboratory and season-long field evaluation of AI-based predictive control for residential heat pumps in DHW and space-heating operation

Residential heat pumps are commonly controlled using static rule-based controllers such as fixed heating curves and hysteresis-based DHW control, which can lead to unnecessary temperature lifts and avoidable storage losses as building and occupancy conditions change. This paper presents two AI-enabled predictive control strategies for (i) DHW production and (ii) space-heating supply-temperature control. In both cases, forecasting/modelling and optimization are separated to facilitate implementation. For DHW, a learned demand forecast is combined with Modelica-based simulation-in-the-loop scheduling that minimizes electrical energy while enforcing service constraints. Laboratory experiments show reductions in electrical energy use of 5–6% for an individual-housing configuration and 38–43% for a collective-housing configuration, together with a substantial increase in the heat-pump coefficient of performance (COP). For space heating, an attention-augmented encoder-decoder LSTM temperature predictor is embedded in a fast surrogate-assisted receding-horizon controller. A field trial in one residential building during the 2024–2025 heating season showed an 8% reduction in daily electricity use, an increase in mean COP from 3.95 to 4.15, and a reduction in mean absolute comfort deviation from 0.3 to 0.1 K. The results demonstrate that forecast-informed optimization can improve heat-pump efficiency while maintaining comfort, although broader field validation across building types and climates remains necessary.