Our WP3 focuses on creating a Heat sub-Digital Twin (sub-DT) which will be validated across operations and integrated with electrical distribution and the integrated energy systems digital twin (IES-DT) components. This digital twin will help predict electricity demand and optimize the operation of the heat pump and heat storage tank for energy demand management using fast machine learning (ML) and optimization approaches.
We will use housing data and ambient temperature to calculate heat demand for various homes using building energy software. This data will help us predict the heat demand for an entire region using ML. By modeling the heat pump, we can calculate the electricity demand of the buildings. Using ML, we will predict peak electricity usage times and, with optimization approaches, determine the best times to charge and discharge the heat storage assets, from short-term solutions to inter-seasonal options to manage the load variations.
This calculator provides an estimate of annual space-heating demand for a typical UK home and compares the expected annual running cost and CO₂ emissions for three options: a heat pump, an electric resistance heater, and a gas boiler. Users select their building type, SAP energy band, floor area and city, then enter electricity and gas prices. The tool uses Heating Degree Days and a heat pump performance model to calculate annual electricity use, annual costs, and annual CO₂ emissions.
Not sure which “Refrigerant” to choose? Most modern UK heat pumps use R32. Newer low-carbon systems increasingly use R290 (propane), while some older installations use R410A. Check your heat pump’s data plate or manual if unsure.
