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• An automatic energy leakage detection for district heating systems based on UAV Infrared (IR) images is presented. • A deep learning-based model and 8 different ML classifiers have been developed and evaluated for detection of leakages. • A region extraction algorithm has been incorporated to extract potential leakages enabling the ML analysis. • The proposed ML methods have been applied to ca. 13.4 million image patches and achieved an actual detection rate of 98.6%. In this paper, we propose automatic energy leakage detection in underground pipes of district heating systems based on Infrared (IR) images, captured by an Unmanned Aerial Vehicle (UAV). Hot water or steam is distributed to homes and industries through underground pipes from a central power plan. Leakages in underground pipes pose a very common problem, which can occur for many reasons, e.g. unprofessional installation and end of service life. Potentially, a leakage remains undiscovered for a very long period of time. Therefore, it is of great interest for power supply companies to monitor district heating networks to identify leakages. In this paper, the original IR images are captured in a 16 bit format by a UAV. On ground, potential leakages are extracted using a region extraction algorithm. Thereafter a Convolutional Neural Network (CNN) as well as eight conventional Machine Learning (ML) classifiers are applied on these regions to classify whether or not it is a leakage. In total, twelve UAV sequences are captured at different cities in Denmark. Based on these, around 13.4 million samples of image patches of district heating systems are extracted. Eleven sequences are used for training and the remaining one for testing. This was performed on all splits in the leave-one-out testing. The deep learning CNN achieved an average weighted accuracy of 0.872 with a false positive and negative rate of 12.7 % and 10.4 %, respectively. This CNN model detected around 98.6 % of the true leakages. In comparison, conventional ML classifiers, i.e. Adaboost (AB), Random Forest (RF), etc. provide lower average weighted accuracy, but on the other hand they require less computational resources. We have compared our method with a state-of-art method and the result shows that the proposed method is very competitive. [ABSTRACT FROM AUTHOR]

Energy performance indices are widely used methods for determining and assessing the efficiency of buildings in operation. However, methodologies for indices that compare heat consumption among buildings and at higher temporal granularity than yearly are needed. This makes it possible for consumers to compare themselves to others to encourage them to improve their energy efficiency. For a district heating system, it also provides the district heating company with an overview of consumers' energy efficiency (and consumers' efficiency must be comparable). This is critical due to the interconnectedness of district heating systems, as consumers can indirectly have negative impacts on each other's operations. Having an overview of the efficiency of consumers can provide useful information for the district heating companies to detect unusual consumption and possible faults. This paper therefore has created a framework for the comparison of heat consumption among buildings, by taking into account various contextualities to make comparisons possible. The framework has been tested on a case study, and its capabilities are showcased. • Leveraging building metadata to allow energy consumption comparison among buildings. • Creation of heating consumption performance indexes for use by end-consumers. • Enabling of higher temporal resolution performance indexes increasing usability. • Fault detection based on performance indexes. • Showcasing of methodology on real consumption data from a suburb in Denmark. [ABSTRACT FROM AUTHOR]

This paper investigates the energy performance and sizing criteria of 5th generation district heating and cooling (5GDHC) networks as a function of the heat distribution temperature in the building systems connected to the district network. An energy simulation model of a 5GDHC network was developed in Modelica for a case study located in Denmark. Calculations were carried out for four different building heating systems. Simulation results show that reducing the heat distribution temperatures from 70 °C to 23 °C leads to around 40% annual electric energy savings (from 10.4 kWh/m2 to 6.2 kWh/m2) for the operation of the heat pumps. Heat distribution temperatures of 23 °C cause higher water mass flow rates through the network, leading to annual electric energy consumption for the circulation pumps that are almost doubled (from 0.16 kWh/m2 to 0.3 kWh/m2) compared to the reference case at 70 °C. Furthermore, the paper discusses how the results obtained from the Danish case study can be generalized and applied to other cases using a simplified mathematical approach. It is found that about 1.5% of electric energy savings can be achieved for each temperature degree reduction in the heat distribution system. • Simulation-based analysis of 5th generation district heating and cooling (5GDHC). • Interdependency between building systems and district network is crucial. • Temperatures in building systems have a direct impact on network sizing and operation. • Proper distribution pipe design should take into account efficiency of heat pumps. • Each Kelvin degree reduction in building system leads to 1.5% electric energy savings. [ABSTRACT FROM AUTHOR]

This paper presents a laboratory study of Offline-trained Reinforcement Learning (RL) control of a Heating Ventilation and Air-Conditioning (HVAC) system. We conducted the experiments on a radiant floor heating system consisting of two temperature zones located in Denmark. The buildings are subjected to real-world weather. A previous paper describes the algorithm we tested, which we summarize in this paper. First, we present a benchmarking test which we conducted during spring 2021 and winter 2021/2022. This data is used in the Offline RL framework to train and deploy the RL policy, which we then tested during winter 2021/2022 and spring 2022. An analysis of the data shows that the RL policy showed predictive control-like behavior, and reduced the oscillations of the system by a minimum of 40%. Additionally, we show that the RL policy is minimum 14% more cost-effective than the traditional control policy used in the benchmarking test. • Real-world data from laboratory test with underfloor heating. • Offline Multi-Agent Reinforcement Learning can eliminate poor behavior during training while converging. • Long Short Term Memory layers are an effective method for obtaining training models for Reinforcement Learning. • Heating costs are reduced by approximately 15% and comfort is maintained. [ABSTRACT FROM AUTHOR]

• Experiment in three homes using mixed-methods and physical measurements. • Indoor air temperature fluctuations like those of EMPC of space heating. • Night setback-users found the heating control unacceptable. • Acceptance was motivated by economic or environmental benefits. Recent simulation-based studies have indicated that Economic Model Predictive Control (EMPC) of space heating systems can exploit the thermal mass in residential buildings for demand response (DR) purposes in district heating systems. However, there is a lack of studies on residents' perception and acceptance of the fluctuating indoor air temperature behaviour inherent in EMPC of space heating. This paper reports on a case study featuring the residents of three one-story houses located in Denmark. The houses were equipped with technology enabling remote actuation of radiator thermostats and collection of various indoor environmental data. Four different temperature boost interventions mimicking the typical behaviour of EMPC of radiators were executed while a mixed-methods triangulation design, employing questionnaires and semi-structured interviews, was used to collect subjective data. Data showed that residents accepted the behaviour but only after the benefits of the EMPC were explained. The acceptance was motivated by either the prospect of saving money, environmental benefits, or a combination of both. The results thereby indicate that explaining the benefits of EMPC is important if residents are to abandon their current preferences and practices in favour of an EMPC concept. The explanation should consider the diversity in current preferences, expectations, and level of technological pre-knowledge. In addition, the paper also reports on a range of findings related to technical aspects of realising EMPC in practice. [ABSTRACT FROM AUTHOR]

Abstract Fossil fuel-based cogeneration of heat and power plants have a long history of supplying district heating in Denmark, however small-scale systems are progressively switching to biomass boilers for economic reasons. Biomass, however, should be reserved for other purposes where a storable fuel is pertinent. This paper investigates the transition of a biomass-based district heating system which is neither socioeconomically optimal nor optimal in terms of integrating fluctuating renewable energy sources or using biomass resources appropriately. First, EnergyPLAN is used to investigate the introduction of heat pumps in terms of the ability to integrate fluctuating renewables and overall system costs. Secondly, optimal business economic design and operation of the district heating plant are analysed using the energyPRO model where plant operation is optimised against an external electricity market. While heat pumps have a positive impact when factoring in the ability to exploit locally available fluctuating renewable energy sources and observing local biomass availability constraints, business economic analyses demonstrate a more uncertain feasibility of the potential switch and also demonstrate that significant flexibility through heat storage and overcapacity on heat pumps does not pay. Highlights • Comparing straw boilers and heat pump district heating systems. • System-wide analyses using EnergyPLAN. • Business economic feasibility studies using energyPRO. • Positive system's effects of heat pump and storage combination. • Insufficient business economic incentives for implementing solution. [ABSTRACT FROM AUTHOR]

Highlights • The largest solar district heating market worldwide has been presented. • Characters of solar district heating plants in Denmark were summarized. • Possibility of solar district heating in smart renewable energy system was shown. • Potential trend of solar district heating plants in Denmark was discussed. Abstract Large solar collector fields are very popular in district heating system in Denmark, even though the solar radiation source is not favorable at high latitudes compared to many other regions. Business models for large solar heating plants in Denmark has attracted much attention worldwide. Denmark is not only the biggest country in both total installed capacities and numbers of large solar district heating plants, but also is the first and only country with commercial market-driven solar district heating plants. By the end of 2017, more than 1.3 million m2 solar district heating plants are in operation in Denmark. Furthermore, more than 70% of the large solar district heating plants worldwide are constructed in Denmark. Based on the case of Denmark, this study reviews the development of large solar district heating plants in Denmark since 2006. Success factors for Danish experiences was summarized and discussed. Novel design concepts of large solar district heating plants are also addressed to clarify the future development trend. Potential integration of large solar district heating plants with other renewable energy technologies are discussed. This paper can provide references to potential countries that want to exploit the market for solar district heating plants. Policy-makers can evaluate the advantages and disadvantages of solar district heating systems in the national energy planning level based on the know-how and experiences from Denmark. [ABSTRACT FROM AUTHOR]

[Display omitted] This paper introduces a non-linear grey-box (GB) model based on stochastic differential equations that describes the heat dynamics of a school building in Denmark, equipped with a water-based heating system. The building is connected to a local district heating network through a heat exchanger. The heat is delivered to the rooms mainly through radiators and partially through a ventilation system. A monitoring system based on IoT sensors provides data on indoor climate in the rooms and on the heat load of the building. Using this data, we estimate unknown states and parameters of a model of the building's heating system using the maximum likelihood method. Important novelties of this paper include models of the water flow in the circuit and the state of the valves in the radiator thermostats. The non-linear model accurately predicts the indoor air temperature, return water temperature and heat load. The ideas behind the model lay a foundation for GB models of buildings that use different kinds of water-based heating systems such as air-to-water/water-to-water heat pumps. Such GB models enable model predictive control to control e.g. the indoor air climate or provide flexibility services. [ABSTRACT FROM AUTHOR]

• Freely available online overview of district heating efficiency indicators. • Hourly district heating energy use data from approx. 43 000 buildings. • Building stock segmented into building typologies for analysis of: • -1) Heating energy use intensity, 2) load variation and 3) cooling efficiency This paper provides a state-of-the-art and freely available online overview of the district heating (DH) efficiency of Danish building typologies. The overview is based on DH consumption data from 42 969 buildings constructed up until 2017 that in combination with data from the Danish Building and Dwelling Register constitutes a dataset with a previously unseen level of detail, confidence, sample size, and research opportunities. The paper also presents an initial analysis of the dataset in terms of three heating efficiency indicators, namely 1) Annual heating energy use intensity, 2) Daily heat load variation, and 3) Cooling efficiency. The overall finding was that building typologies perform significantly different from one another concerning indicators 1 and 2, but not for indicator 3. It is therefore not possible to declare one building typology more efficient than the other as this depends on the building construction year and the considered efficiency indicator. The data indicate that further energy-efficiency improvements of the existing building stock are essential to the realization of 4th Generation District Heating technologies in Denmark and that these improvements most likely will lead to increased heat load variations that need to be addressed, for instance by enabling a larger degree of demand-side flexibility in the existing district heating systems. The large temporal variation of the buildings' cooling efficiency indicates that there is a potential for rethinking e.g. domestic hot water production in the summer or introduce differentiated billing based on the daily, weekly, or maybe seasonal cooling performance. The presented dataset can be subject to several future investigations which are also discussed in the paper. [ABSTRACT FROM AUTHOR]

Operating district heating systems with low supply and return temperatures improves heat production and distribution efficiency, permitting greater integration of renewable heat sources. Low-temperature district heating is viable without compromising comfort, but faults in end-users' heating systems constrain temperature reductions. Such faults include malfunctioning valves, improper hydronic balancing, and excessive supply temperature setpoints. Occupants lack the resources to detect and diagnose these faults, so there is a need for automated solutions without requiring additional hardware. This paper proposes a method for improving the operation of an apartment's hydronic floor heating system using data from room thermostats, a heat meter and a circulation pump to identify a grey-box model of the system. The resulting model virtually senses each room loop's heat flux, flow, return temperature, and flow coefficient. The authors tested the model on a low-energy apartment in Denmark, using it to diagnose causes of high return temperatures, including poor hydronic balancing and an excessive supply temperature setpoint and pump setting. The authors also used the model to predict the minimum permissible supply temperature maintaining comfort, yielding a reduction in the energy-weighted supply and return temperatures of 8.6 °C and 6.5 °C, respectively. • Grey-box modelling of residential hydronic space heating yielding virtual sensors. • Detected hydraulic balancing errors, including incorrect pre-settings of valves. • Identified the lowest feasible supply temperature meeting the demand for heating. • Simulated operational improvements reducing supply/return temperatures. • Predicting a reduction of 8.6 °C and 6.5 °C to supply and return temperature. [ABSTRACT FROM AUTHOR]

The paper examines implementation of heat saving measures in buildings in 2050, under the assumption that heat and power supply comes solely from renewable resources in Denmark. Balmorel – a linear optimisation model of heat and power sectors in Denmark is used for investigating economically viable levels of heat savings, which can be implemented by reducing heat transmission losses through building elements and by installing ventilation systems with heat recovery, in different future Danish heat and power system scenarios. Today almost 50% of heat demand in Denmark is covered by district heating. A further expansion of district heating network in Denmark is assessed and penetration of heat savings is analysed in this context. If all heat saving measures, included in the model, are implemented, heat demand in Danish buildings in 2050 could be reduced by around 40%. Results show that it is cost effective to reduce from approximately 12% to 17% of future heat demand in buildings depending on assumed lifetime and costs of heat saving measures. Individual heating areas have higher penetration of heat savings than district heating areas. When district heating systems are expanded, an overall penetration of heat savings slightly decreases along with lower capacity investments and system costs. [ABSTRACT FROM AUTHOR]

Abstract: Denmark’s long-term energy goal is to develop an energy system solely based on renewable energy sources by 2050. To reach this goal, energy savings in buildings is essential. Therefore, the focus on energy efficient measures in buildings and net zero energy buildings (NZEBs) has increased. Most buildings in Denmark are connected to electricity grids and around half are connected to district heating (DH) systems. Connecting buildings to larger energy systems enables them to send and receive energy from these systems. This paper’s objective is to examine how excess heat production from NZEBs influences different types of DH systems. In the analysis three different types of DH systems are analyzed, and three technology development scenarios for each are created. The examination is from a technical perspective and looks into how the overall heat production within DH areas is affected by the NZEBs excess heat production from solar thermal collectors. The main findings are that the excess heat from NZEBs can benefit DH systems by decreasing the production from production units utilizing combustible fuels. In DH areas where the heat demand in summer months is already covered by renewable energy, adding seasonal heat storage is essential to utilize the NZEB production. [Copyright &y& Elsevier]

Abstract: In 2007 the Class 1 project commenced. Originally, 442 dwellings were to be designed and constructed as “low-energy class 1” houses according to requirements set by the Municipality of Egedal/Denmark. This means that the energy consumption is 50% below the existing energy regulations. Sixty five dwellings and about 30 single family houses have been constructed. The Concerto community also includes a kindergarten (completed) and an elderly centre. Currently, because of the financial crisis, only about 200 new dwellings will be constructed. Therefore, a contingency plan (plan B) has been developed replacing some of the new dwellings with renovation projects for public buildings. Simultaneously the originally planned district heating network supplied from a relatively small bio-gasification plant has been substantially enlarged to cover a large part of the existing Stenloese town as well and a large wood-chip heating plant has been added. The project demonstrates the benefits of ultra-low-energy buildings integrated with biomass- and solar heating energy supply. The Class 1 project involves four other countries: Estonia, France, Italy and Romania. These countries develop training activities based on the results and experiences gained from the Danish housing projects. This paper describes the comparisons between measured and calculated energy consumption in a social housing settlement and in a detached single-family house. Results show relatively large discrepancies between measured and calculated results, and discussions on possible explanations are given. [Copyright &y& Elsevier]

Abstract: Replacing individual natural gas heating with district heating based to increasing shares of renewable energy sources may further reduce CO2-emissions in the Danish Building mass, while increasing flexibility of the energy system to accommodate significantly larger amounts of variable renewable energy production. The present paper describes a geographical study of the potential to expand district heating into areas supplied with natural gas. The study uses a highly detailed spatial database of the built environment, its current and potential future energy demand, its supply technologies and its location relative to energy infrastructure. First, using a spatially explicit economic model, the study calculates the potentials and costs of connection to expanded district heating networks by supply technology. Then a comprehensive energy systems analysis is carried out to model how the new district heat can be supplied from an energy system with higher shares of renewable energy. It can be concluded on the basis of these analyses that the methods used proved highly useful to address issues of geographically dependent energy supply; however the spatio-economic model still is rather crude. The analyses suggest to expand district heating from present 46% to somewhere in between 50% and 70%. The most attractive potential is located around towns and cities. The study also suggests that CO2-emissions, fuel consumption and socio-economic costs can be reduced by expanding district heating, while at the same time investing in energy savings in the building mass as well as increased district heating network efficiency. [Copyright &y& Elsevier]

• Comprehensive analysis of country-specific boundary conditions and success factors. • National heat supply systems and incentive policies are essential. • Role of district heating utilities in Denmark for solar district heating success. • Technological solutions and best practice examples of large-scale systems. • First-hand market research on China shows higher market penetration than assumed. Large-scale solar thermal systems are a cost-efficient technology to provide renewable heat. The rapid market growth in the last decade has been concentrated on a small number of countries, with the outstanding position of Denmark followed by China, Germany and Austria. This paper provides a comprehensive overview of the market and common technological solutions for large-scale solar thermal systems in these countries. Country-specific factors, including solar resources, heat supply systems, competing technologies, promotion schemes and business models, which put these countries in a leading role, are analyzed in detail using an integrated assessment framework. For each country, a best practice solar heating system is introduced. The analysis shows that heat supply by large-scale solar thermal systems is a mature technology with a broad field of applications and that mainly country-specific boundary conditions regarding the heat supply system and incentive policy are responsible for the preeminent role of these four countries. The unique role of district heating utilities in Denmark, which are subject to a national emissions trading system and often act both as investors and operators of large-scale solar thermal systems, can serve as a role model for other countries. [ABSTRACT FROM AUTHOR]