Today there are many sensors in buildings and district heating/cooling systems with a high temporal resolution. Data from such sensors opens up for new AI and IoT-based solutions. Here we will describe the potential of some of such solutions for district heat- ing systems. However, we will also touch upon how such solutions potentially make the system more vulnerable and challenging with respect to privacy and GDPR. ARTIFICIAL INTELLIGENCE IN DISTRICT HEATING
By Henrik Madsen, Professor and Section Head on DTU Compute
Buildings and occupants The demand for smartness, trust, transparency, and versatility in managing heating, cooling, ventilation, lighting, and access control systems for a family home, public buildings like schools, and office buildings is growing. People want a comfortable, sustainable, cost-efficient, and safe place to live and work, and that's where sensors, AI, IoT, and automation jump in. GDPR and privacy Personal data are any information related to an identified or identifiable person. Only if the processing of data concerns personal data, the GDPR (General Data Protection Regulation) applies. This implies that the problem typically does not exist e.g., for a section of a district heating network, as well as for public buildings like schools and some office buildings. Today we are able to obtain electricity consumption data with a very high temporal resolution (e.g., every 15 seconds) also for single-family buildings. Given such data, it is often relative- ly easy to conclude which appliance has been used at which time. Likewise, most of us have hourly readings of water con- sumption online. We can see, for instance, that 2 liters have been used at 2 am, 3 am, and 5 am during a single night. This indicates frequent bathroom visits, which could lead to some privacy issues. Frequent and real-time data for district heating consumption can be used to better control heating and ven- tilation systems, but also to detect absence (e.g., holiday peri-
It will be argued that energy meter data and the use of AI in district heating provide the foundation for efficiency improve- ments in buildings and district heating systems. In addition, such data-driven methods give possibilities for CO2 and cost savings, better integration of wind and solar power, efficient in- tegration of the energy systems, and more satisfied end-users due to lower costs and a better indoor climate. One of the major problems today is that data and solutions often are linked to proprietary platforms. Consequently, it is challenging to implement cross-system solutions and harvest synergies from systems integration. Sadly, this often hinders the possibility of obtaining large savings and efficient imple- mentations. However, this cross-system functionality can be obtained using a non-profit data hub, like the national hub for smart energy and water systems at Center Denmark. For instance, Center Denmark is successfully used for cross-sys- tem optimization in the HEAT 4.0 project (see HOTCOOL no. 8, 2022). In the following we will start considering AI tools for individual buildings. Then we will consider the district heating networks, the plants, and conclude with remarks on district heating in re- lation to the energy system, the electricity/energy markets, and the society. The findings mentioned here are based on several district heating-related projects (CITIES, HEAT 4.0, FED, IDASC, ARV - please see the reference list).
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