Development and evaluation of digital twins for district-level heating energy demand simulation.

To achieve the aim of a CO2 neutral built environment in 2050, a large part of the existing housing stock will have to be energetically retrofitted. It has been noted that a neighbourhood-oriented approach will be necessary for the feasibility, affordability and timeliness of this aim. Considering that many different stakeholders are involved in renovations at the neighbourhood level, and that multiple neighbourhoods will have to be retrofitted at the same time, efficient working methods are imperative. To facilitate the design, construction and operation of the new energy infrastructure, a prototype for a digital environment (digital twin) is developed for four Dutch pilot neighbourhoods. In this contribution, the authors will describe a procedure to convert publicly available geo-information to a CityGML model, which is used to simulate the monthly and annual space heating energy demand using SimStadt. To assess model fidelity, the simulation results are compared with publicly available aggregated energy use data. A procedure will be described to split the measured natural gas use into gas usage for space heating, domestic hot water and cooking. It is found that the simulation tends to overestimate the energy demand for space heating by 4 - 125%. This difference is largely explained by the manner in which the thermal properties of the buildings are estimated. In addition, the homogeneity of the neighbourhood in terms of the different building functions present has an impact on the accuracy of the simulation. Finally, possible invalid assumptions concerning setpoint temperatures and internal heating loads are of interest. It is concluded that more accurate simulation results will be obtained through the use of current input data. Most importantly: (i) reliable information on the buildings’ current thermal properties through e.g. energy audits, and (ii) reliable information on the buildings’ setpoint temperatures and internal heating loads through on-board monitoring systems