HOT|COOL NO. 2/2023 "AI & Digitalization"

ware and services under the umbrella of Leanheat for the con- trol and optimization of district energy systems, from the pro- ducer to the consumer. The Leanheat software suite includes four different solutions. • Leanheat Production: An advanced software for load fore- casting, planning, and optimizing district energy produc- tion and distribution. The cornerstone of Leanheat Produc- tion is a six-day AI-based demand forecast. The software calculates the cost-optimal production mix from available heat sources based on the estimates and energy spot prices. • Leanheat Network: A thermo-hydraulic modeling tool de- veloped specifically to support district energy system plan- ning, design, and operation. With the help of the Leanheat Network digital twin in the planning and design process, the cost of establishing new and modifying existing district energy systems can be minimized. Once in operation, the digital twin will support the district heating utilities by opti- mizing the operation, leading to lower operational expenses. • Leanheat Monitor: A dedicated software for efficient remote monitoring, optimizing, and managing substations and heating installations. Leanheat Monitor further simplifies collecting and visualizing data that the district heating util- ity can use to optimize its operation. By using the software, district heating utilities can remotely detect faults or wrong settings and perform tasks that before required on-site in- tervention – thereby resulting in time and cost savings. • Leanheat Building: A software solution for optimizing the operation of heating installations of buildings with a cen- tralized heating system. It utilizes the latest AI and machine learning developments to generate accurate thermody-

namic models of the buildings it controls. It combines in- door climate monitoring and weather forecast to achieve energy savings and decrease the volatility of indoor temper- ature associated with traditional heating control strategies, improving living conditions for occupants. Furthermore, the control algorithm can optimize consumption and shift load while maintaining indoor comfort. Case example: Flexumers project in Copenhagen with HOFOR and Copenhagen City Properties & Procurement. An example of a Danfoss software application is to be found in Copenhagen. Here HOFOR (district heating utility), Copen- hagen City Properties & Procurement (Municipality’s building department), and Danfoss are currently testing the potential of minimizing peak heating demand to increase CO2 neutral base-load heat production usage in Copenhagen by utilizing Leanheat Building AI-based heating control. The first part of the demonstration took place during the heat- ing season 2021/2022 and included 17 municipal buildings (mainly daycare centers). The buildings were already equipped with Danfoss communicative heating controllers, and they were connected to the Leanheat AI control via the Danfoss ECL portal. The main goal of the demonstration was to reduce the peak in heat demand that occurs in the mornings (6-10 am) by mak- ing the heat consumption more flexible. Thus, the project has been named district heating Flexumers since the buildings that previously were only seen as energy consumers have be- come an active part of the district heating system. Each build- ing acts as a virtual heat plant by increasing its consumption when heat production is cheap and ecological and decreasing during times of high demand by providing flexibility on the consumption side.

Figure 1. Average daily heating profile with and without Leanheat control

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