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In this chapter, data-driven methods for the efficient operation of DHSs are described. DHSs are inherently non-linear and time-varying systems as the heating demand is highly influenced by non-linear dependencies on the weather conditions as well as the occupancy behaviour. Furthermore, the dependency on flow and temperature in delivering the needed heat demand using the district heating network gives a non-linear dependency on these two signals. This chapter presents several data-driven models to handle the non-linear and time-varying phenomena in order to ensure an efficient operation. First, we introduce forecasts that are used to reach an optimal operation as forecasts are needed for both control and production planning, e.g. heat demand and electricity price forecasts. Second, temperature control of a DHN will be introduced with a focus on how the physical characteristics of the network can be incorporated into a control scheme. A special focus will be on how to ensure that the temperatures in the network are high enough to ensure the needed heat supply for the attached buildings in the entire district heating network is met. We shall also briefly look at the role of smart buildings integrated into a DHN that can be used to enhance the efficiency and flexibility of a DHS.

Operating district heating network using additional data with the traditional data extracted from the SCADA system at the production is discussed. The benefits of including new data in daily operation for the utility is demonstrated. For the past decade, more and more data is becoming available for district heating utilities with the smart meters being installed in every home connected to the district heating network. More local climate stations inside the city are also being installed, and made accessible for everyone. In this report, data from smart meters are presented and how they can be used to operate the network more efficiently. Also, weather forecast in cities is discussed and how they can be improved by localizing them to the local climate using climate stations. The case study in this report is a on-line operation of temperature control in Tingbjerg which is a small area that is operated by HOFOR. HOFOR is a utility company in Copenhagen which handles for example the district heating, and waste water. They also produce energy for the Copenhagen area. The case study demonstrate how to localize heat demand forecast and operate closed-loop temperature control for a small area. The result for the operation is compared to the previous operation where it was done using open-loop temperature control, i.e. no feedback of the system. The report emphasizes how current state-of-the-art methods can be improved by using newly available data (e.g. smart meters as feedback of the network) and thereby enhancing the efficiency of the operation.

Smart and flexible operation of components in district heating systems can play a crucial role in integrating larger shares of renewable energy sources in energy systems. Buildings are one of the crucial components that will enable flexibility in the district heating by using intelligent operation. Recent work suggests that such improved operation at the same time can increase thermal comfort and lower economic costs. We have digitalised the heating system in a Danish school by adding IoT devices, such as smart thermostats and temperature sensors to demonstrate the possibilities of making buildings smart. Based on experimental data, this paper introduces a non-linear grey-box model of the thermal dynamics of the building. A non-linear model predictive control method is presented for the thermostatic set-point control of the building's radiators. Based on the building model and the control algorithm, simulation studies are carried out to show the flexibility potential of the building. When used for lowering the return temperature the results suggest that operational costs can be lowered by around 10% using predictive control.

The growing level of digitalization and data abundance in the energy sector enable increasing system flexibility, reducing CO2 emissions and improving energy efficiency. This report is part of the IDASC (Intelligent Data Use in Smart Cities) project financed by The Capital Region of Denmark (Region H) and contains a mapping of energy data types available in Denmark, an analysis of barriers for data collectionand use and, finally, an overview of the value energy data can bring. Data for this report was collected through literature review and in-depth semi-structured interviews conducted with selected stakeholdersdealing with energy data. The present report is accompanied by two other deliverables: the report "Experience of using Data-Driven Methods in District Heating: Digitalized Operation in Tingbjerg, Copenhagen" and a report in Danish, entitled "Digitalisering af fjernvarmen - erfaringer der luner" ("Digitalization of district heating"). This study demonstrates that key actors believe that legislative, technical and data access-related barriers still exist for efficient data collection and sharing. Other frequently mentioned barriers are organizational,insufficient knowledge, insufficient data security, and insufficient communication. Aspects such as uncertainty about the benefits of the various initiatives, silo mentality due to traditional organization andlegislation complicate the possibilities for increased digitalization. These barriers hinder the possibilities for applying data in analyses that could reveal potentials for increased system flexibility, sector coupling,and implementation of renewables, CO2 emission reductions and energy saving measures. The decisionmaking processes concerning data governance and facilitation of digitalization initiatives on the national,municipal and utility/company level need to be improved to address these barriers.We recommend district heating and electricity operators and IT companies to collaborate in order to share experiences on data handling, as well as prepare and implement templates for data agreements andguidelines for legislative processes. We also recommend municipalities to strategically approach data governance, coordinate with property managers to identify open questions and activate key players (e.g.district heating companies) to utilize the potential for CO2 emission reductions, as well as provide more open source data where available. We suggest that national bodies provide common standards for dataformats, develop legislation that would dynamically follow the developments in data science, and support and develop initiatives for open data and co-creation.