Access to energy meter readings from individual households has proven to give further advantages. A simple sketch of the change of the setting is shown in Figure 2. Obviously, this calls for using advanced aggregation techniques to ensure that the aggregated temperature is representative, and to respect pri- vacy and GDPR. The use of meter data implies that it is rather easy to operate with zonal temperatures. This new solution for using meter data and the methods for zonal temperature con- trol leads to further savings and better options for integrating local heat pumps. Today electricity prices are high from time to time, so the con- trollers have a built-in balance between reduction in heat loss- es and the pumping costs. Finally, it is crucial to notice that the use of data and AI methods implies that the tools are auto-cal- ibrated continuously. This means that the system is much eas- ier to operate and maintain. Production and bidding optimization for DH systems Data-driven forecasting and temperature control methods are now also used in new tools developed at DTU for production
data-driven methods, the forecasting tool can automatically consider complex shading and the time-varying dirtiness of the panels. Some of the new forecasting methods are imple- mented in, e.g., HeatFor and SolarFor. Temperature optimization (TO) Historically, methods for temperature optimization have been based on simulations using theoretical models and detailed knowledge about the network. A prerequisite for using such approaches is that the model is carefully updated with infor- mation about the physics (pipes, ground temperature, the hu- midity of the soil, properties of the insulation of the pipes, etc.). First of all, this is a very time-consuming procedure, and sec- ondly, such methods lead to suboptimal descriptions of the dy- namical characteristics needed for control of the temperatures. Exactly like for the buildings mentioned above, data-driven methods can provide significant improvements in tempera- ture optimization, such as in zonal control of the network tem- perature. Again the AI technologies implemented, for instance, in HeatTO, give a sort of x-ray vision of the thermal properties of the pipes and their surroundings. The resulting data-driven digital twin models describe the time delay, heat losses, and dynamics. According to the experiences with HeatTO, heat loss is reduced by 10 to 20 pct (see, e.g., https://enfor.dk/services/ heatto/).
Figure 2: Use of meter data in temperature optimization (HeatTO).
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