The heat sub digital twin has been advanced through dynamic simulation of a heat pump coupled with thermal energy storage. Initial model calibration used synthetic data, followed by optimisation and machine learning based control strategies for both standalone and integrated heat pump operation. This work demonstrates how intelligent control can enhance operational flexibility and reduce peak electricity demand, key challenges for electrified heating.
Real time data from a lab scale heat pump system have now been incorporated, enabling the digital twin to better capture transient and dynamic behaviour. In parallel, a commercial heat pump has been commissioned in the Liverpool environmental chambers, providing additional high quality operational data. These data are being used to calibrate a Dymola based digital twin, improving fidelity and robustness.
The calibrated heat pump model has been integrated into WP2 network simulations, enabling more accurate short term, system level predictions and strengthening cross WP collaboration.
Finally, WP3 has translated its technical advances into a practical outcome through the development of a user friendly, web based Residential Heating Calculator. This tool estimates electricity consumption and emissions based on home characteristics and heating technology, demonstrating how the WP3 digital twin framework can support real world decision making—initially for SPEN, with wider applicability in future.
Over the past three months, WP3 has advanced the development of a physics-informed heat digital twin for residential buildings, aimed at improving heating demand prediction under sparse data conditions. Building on earlier work combining physics-based models, machine learning, and PINNs, recent efforts have moved beyond simplified resistance–capacitance (RC) representations toward a more complete whole-building physical description.
A new approach, Heat-Balance Physics-Informed Neural Networks (HB-PINNs), has been introduced, embedding full building heat balance equations within the learning framework. This enables inference of latent physical parameters and a more complete representation of thermal behaviour compared to traditional RC-based PINNs.
Systematic evaluations show HB-PINNs deliver superior accuracy and robustness across diverse scenarios, marking a significant step forward in whole-building heating demand modelling.
Over the next six months, WP3 plans to further explore the capability and practical relevance of physics-informed modelling within the Heat Digital Twin.
First, WP3 plans to explore new research directions in PINN-based modelling, for example through the use of real experimental and operational data, moving beyond simulation-only studies. This exploration aims to improve understanding of the applicability, robustness, and limitations of PINNs under realistic data conditions.
Second, WP3 is planning to develop a simple online heat prediction system focused on rapid estimation of heating demand. The initial concept involves breaking down gas consumption data to provide fast heat demand estimates, offering a lightweight and scalable approach for early-stage heat assessment. This activity is intended to serve as an initial demonstration of how digital twin methodologies could support near-real-time heat demand prediction and user-facing applications.
WP3 – Heat Digital Twin, is building a digital twin to enable real-time control and optimisation of residential heating. Accurate demand forecasting, led by Dr Meng Zhang, is central to its success.
Key Activities:
Simulation: Developed a detailed EnergyPlus model of a Stockport district to simulate thermal behaviour and validate forecasts.
Forecasting Models: Compared Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Transformer models; published findings on data partitioning impacts using London data.
Paper published in Energy and Buildings – Volume 344 – “Machine Learning-based regional cooling demand prediction with Optimized dataset partitioning“
Physics-Informed Neural Networks (PINNs): Integrated physical constraints into neural networks to improve accuracy and reduce data needs.
Benefit to SP Energy Networks:
This work equips SPEN with a high-resolution view of heating demand, essential for unlocking building flexibility. By combining simulation, forecasting, and PINNs, WP3 is creating a robust digital twin to support cost-effective demand-side management, peak reduction, and renewable integration — all while maintaining system security.
Next Steps
Refine PINNs, connect the twin to live pilot data, and explore flexibility services like peak shaving and frequency response. Assess network impacts of heat pump integration and develop control strategies.
