District heating (DH) is often regarded as a sustainable supply of heat - improving energy efficiency and contributing to net zero emission targets. We analyse the expansion potential of DH in several EU Countries from the perspective of the whole energy system, which comprises interconnected heat power and transport sectors under different scenarios of heat demand development. Our results show a strong potential for DH expansion in most of the countries analysed with an overall share of DH increased from 8% to 40%. Among the countries analysed, Germany, Netherlands, UK, France, and Norway showed the most potential as presently, the DH in these Countries is almost non-existent. The role of potential exploitation of waste heat from electrolysers is also evaluated, and the results show a promising expansion with more than 50 TWh of excess heat introduced in the district heating grid with a 4% higher expansion of DH. We ultimately highlight the need for a comprehensive policy and market framework to promote expansion of energy efficient DH system and achieve an affordable decarbonization of the heating sector.

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.

In the digitization of energy systems, sensors and smart meters are increasingly being used to monitor production, operation and demand. Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage, which can serve as a reference for timely initiation of appropriate actions and improving management. However, smart meter data from energy systems often lack labels and contain noise and various patterns without distinctively cyclical. Meanwhile, the vague definition of anomalies in different energy scenarios and highly complex temporal correlations pose a great challenge for anomaly detection. Many traditional unsupervised anomaly detection algorithms such as cluster-based or distance-based models are not robust to noise and not fully exploit the temporal dependency in a time series as well as other dependencies amongst multiple variables (sensors). This paper proposes an unsupervised anomaly detection method based on a Variational Recurrent Autoencoder with attention mechanism. with "dirty" data from smart meters, our method pre-detects missing values and global anomalies to shrink their contribution while training. This paper makes a quantitative comparison with the VAE-based baseline approach and four other unsupervised learning methods, demonstrating its effectiveness and superiority. This paper further validates the proposed method by a real case study of detecting the anomalies of water supply temperature from an industrial heating plant.

Knowledge of borehole heat exchanger efficiency is necessary to optimize the design and performance of ground source heat pump systems. To evaluate the heat transfer performance of the wildly-used vertical U-pipe BHE, a novel one-dimensional numerical model was developed to assess the thermal transfer performance of the BHE from short-term (thermal response test) to long-term (a heating period) for engineering application. The proposed numerical model took into account the internal capacity of the borehole and the thermal resistance between the two legs of U-pipe which are often negligible in traditional one-dimensional numerical models. A thermal response test data from a case at Vorbasse, Denmark and the data from the TRNSYS model were used to validate the feasibility and reliability of the presented model. Then, both a short-term thermal response and a long-term temperature development of the fluid in BHE and surrounding ground were simulated and analyzed based on the model. Additionally, this study did a sensitivity analysis to see what effect the parameters have on the BHE efficiency. Hereby the impact of given assumptions can be able to estimate and the results can serve for the optimum design and control of GSHP systems.

With the growing share of fluctuating renewable energy sources in our energy systems, providing sufficient flexibility on the demand side is becoming more and more important - also in the context of the emergence of Smart Grids. However, it will be difficult to achieve this by concentrating on electricity-only solutions. So, the next step is to focus on electricity-thermal solutions (e.g. heat pumps, electrical heating and cooling) and thermal system components. Here district heating and the building stock are important contributors due to their large share of energy demand. This literature review focuses on energy flexibility in context of heat demand in buildings and district heating systems. First, the theory regarding definitions of energy flexibility found in the literature, its quantification methods and indicators is discussed. Due to a lack of literature on the heating side, most of the theory in this review is based on electrical solutions. Then, the connection between electrical and thermal energy systems is described and the importance of integrated systems approach is explained. A schematic of flexibility sources in the built environment is proposed and technological solutions found in literature on buildings and district heating are presented based on the proposed framework.

This study analysed the dynamic thermal response of a low-energy building using measurement data from an apartment block in Copenhagen, Denmark. Measurements were collected during February and July 2018 on space heating energy use, set-points, room air temperature and temperature from sensors integrated inside concrete elements, i.e. internal walls and ceiling, at different heights and depths. The heating system was controlled by the occupants. During February, there were unusually high set-points for some days and a regular heating pattern for some other days. Overheating was observed during July. A considerable effect of solar gain was observed both during winter and summer months. The room air temperature fluctuations were observed at a certain extent inside the concrete elements; higher in the non-load-bearing internal wall, followed by the load-bearing internal wall and lastly by the ceiling. The phenomenon of delayed thermal response of the concrete elements was observed. All internal concrete masses examined may be regarded as active elements and can contribute to the physically available heat storage potential of the building. The study provides deep insight into the thermal response of concrete elements in low-energy residential buildings, which should be considered when planning a flexible space heating energy use.

Thermal comfort preferences of occupants and their interactions with building systems are top influential factors of residential space heating demand. Consequently, housing stock models are sensitive to assumptions made on heating temperatures. This study proposes a heat balance approach, inspired by the classical degree-day method, applied to an extensive urban dataset. The goal of this analysis is to determine heterogeneous characteristics, such as temperature setpoints of heating systems and thermal envelope characteristics from an overall population of residential buildings. Measured energy data are utilized for the purpose of the study from the city of Aarhus, Denmark, where the energy usage for heating of circa 14,000 households was monitored over time via smart meters. These data are combined with actual weather data as well as data extracted by a national building database. Using linear regression and heat balance models, temperature setpoints for the whole dataset are determined with a median and average of 19 degrees C and 19.1 degrees C, respectively. Furthermore, building related characteristics such as thermal and ventilation losses per building and overall heat transfer coefficients are extracted at urban scale. The reliability of the method over its complexity is discussed with regards to the big sample that has been applied to. In general, the overall performance of the approach is satisfactory achieving a coefficient of determination with an average of 0.8, and is found to be in line with previous findings, considering also the high uncertainty associated with building-related input parameters. The extracted setpoint distribution should be transferrable across Scandinavia.

•Creating a framework of barriers and motivators for energy renovation in homes.•Identification of four possible areas for improvement in current Danish policy.•Need for improved support through relevant renovation plans and financial support.•Focus on non-energy benefits and on stating maximum allowed energy consumption.

The wide use of smart meters enables collection of a large amount of fine-granular time series, which can be used to improve the understanding of consumption behavior and used for consumption optimization. This paper presents a clustering-based knowledge discovery in databases method to analyze residential heating consumption data and evaluate information included in national building databases. The proposed method uses the K-means algorithm to segment consumption groups based on consumption intensity and representative patterns and ranks the groups according to daily consumption. This paper also examines the correlation between energy intensity and the characteristics of buildings and occupants, load profiles of households, consumption behavior changes over time, and consumption variability. The results show that the majority of the customers can be represented by fairly constant load profiles. Calendar context has an impact not only on the patterns but also on the consumption intensity and user behaviors. The variability studies show that consumption patterns are serially correlated, the customers with high energy consumption have lower variability, and the consumption is more stable over time. These findings will be valuable for district heating utilities and energy planners to optimize their operations, design demand-side management strategies, and develop targeting energy-efficiency programs or policies.

Higher shares of intermittent renewable energy in energy systems have raised the issue of the need for different energy storage solutions. The utilization of existing thermal building mass for storage is a cost-efficient solution. In order to investigate its potential, a detailed building simulation model was coupled with a linear optimization model of the energy system. Different building archetypes were modelled in detail, and their potential preheating and subsequent heat supply cut-off periods were assessed. Energy system optimization focused on the impact of thermal mass for storage on the energy supply of district heating. Results showed that longer preheating time increased the possible duration of cut-off events. System optimization showed that the thermal mass for storage was used as intra-day storage. Flexible load accounted for 5.5%-7.7% of the total district heating demand. Furthermore, thermal mass for storage enabled more solar thermal heating energy to be effectively utilized in the system. One of the sensitivity analyses showed that the large-scale pit thermal energy storage and thermal mass for storage are complimentary. The cut-off duration potential, which did not compromise thermal comfort, was longer in the newer, better insulated buildings, reaching 6 h among different building archetypes. (C) 2018 Elsevier Ltd. All rights reserved.

With the growing share of fluctuating renewable energy sources in our energy systems, providing sufficient flexibility on the demand side is becoming more and more important – also in the context of the emergence of Smart Grids. However, it will be difficult to achieve this by concentrating on electricity-only solutions. So, the next step is to focus on electricity-thermal solutions (e.g. heat pumps, electrical heating and cooling) and thermal system components. Here district heating and the building stock are important contributors due to their large share of energy demand. This literature review focuses on energy flexibility in context of heat demand in buildings and district heating systems. First, the theory regarding definitions of energy flexibility found in the literature, its quantification methods and indicators is discussed. Due to a lack of literature on the heating side, most of the theory in this review is based on electrical solutions. Then, the connection between electrical and thermal energy systems is described and the importance of integrated systems approach is explained. A schematic of flexibility sources in the built environment is proposed and technological solutions found in literature on buildings and district heating are presented based on the proposed framework.

The dataset consists of whole building heating load measurements at 60-min intervals per building, collected continuously over a period of 2 years and 3 months from 47 buildings in Denmark. Moreover, building related information based on the Danish Building Register (BBR) is included in the dataset for some of the buildings. Metadata description id: Building ID (meter IDs have been replaced for anonymization reasons) application_type: Value corresponding to the type of the building (120=single family house, 130=row house, 140=multi-family house, 320=office building, 910=garage, 920=carport, 930=outhouse) constr_year: Year of construction of the building total_floor_area: Total floor area of the building (m2) floors: Number of floors readdate: Date of the reading readhour: Time of the reading rounded up to hourly resolution volume: Volume of the consumed energy in m3 heatenergy: Heating consumption in GJ (including space heating and heating of DHW)

Energy flexibility is proposed as a cost-effective solution facilitating secure operation of the energy system while integrating large share of renewables. With strict building regulations in Denmark, newly built buildings are low-energy buildings. In order to identify the role of low-energy buildings in the energy system, we investigated the physical potential for flexibility and analysed the thermal storage capacity existing inherently in the structural mass. Two building types were studied: single-family house and apartment block. The aim is to quantify the energy that can be added to or curtailed from each building during a time period without compromising thermal comfort. Different scenarios (starting time and duration), building design characteristics and boundary conditions were studied. The findings showed that low-energy buildings are highly robust and can remain autonomous for several hours. Although for individual buildings the available energy for curtailment is limited, if many buildings are aggregated energy flexibility becomes significant. The potential for storage in the thermal mass is considerable. The analysis presented high dependence of flexibility potential on boundary conditions (ambient temperature, solar radiation, internal gains) and underlined the importance of envelope insulation. Heat losses govern the potential for flexibility, while the walls' thermal mass has a secondary influence. (C) 2018 Elsevier B.V. All rights reserved.

This study proposes a methodology for intelligent scheduling of a heat pump installed in a refurbished grid-connected detached house in Denmark. This scheduling is conducted through the coupling of a dynamic building simulation tool with an optimization tool. The optimization of the operation of the system is based on a price-signal considering a three-day period for different weather cases. The results show that the optimal scheduling of the system is successful in terms of reducing the peak load during times when electricity prices are high, thus achieving cost savings as well as maintaining good thermal comfort conditions. The proposed methodology bridges dynamic building modelling with optimization of real-time operation of HVAC systems offering a detailed model for building physics, especially regarding thermal mass and a stochastic price-based control.

The pervasive use of Internet of Things and smart meter technologies in smart cities increases the complexity of managing the data, due to their sizes, diversity, and privacy issues. This requires an innovate solution to process and manage the data effectively. This paper presents an elastic private scientific cloud, SciCloud, to tackle these grand challenges. SciCloud provides on-demand computing resource provisions, a scalable data management platform and an in-place data analytics environment to support the scientific research using smart city data.

Smart city data come from heterogeneous sources including various types of the Internet of Things such as traffic, weather, pollution, noise, and portable devices. They are characterized with diverse quality issues and with different types of sensitive information. This makes data processing and publishing challenging. In this paper, we propose a framework to streamline smart city data management, including data collection, cleansing, anonymization, and publishing. The paper classifies smart city data in sensitive, quasi-sensitive, and open/public levels and then suggests different strategies to process and publish the data within these categories. The paper evaluates the framework using a real-world smart city data set, and the results verify its effectiveness and efficiency. The framework can be a generic solution to manage smart city data.

In the future energy system, based wholly on renewable energy sources, biomass is likely to become a scarce resource because of high demand especially by the transport sector. The current paper investigates, what is the possibility of utilizing excess electrical energy from renewable generation to decrease biomass use in a district heating system. The paper focuses on the renewable energy-based district heating system in Marstal, Denmark, with heat produced in central solar heating plant, wood pellet boiler, heat pump and bio-oil boiler. The plant has been the object of research and developments since its construction in 1996 and its operation is well documented. In the first part of the paper, the background of the current study is explained and the system in question is presented. Subsequently, the methodology of the study is explained and the model used in the study is described. Due to lack of widely accepted definition of a metrics for comparing system flexibility the paper proposes such an indicator. It was concluded, that cheap electricity can partially replace scarce biomass for heat production for district heating system.

Cities are densely populated and heavily equipped areas with a high level of service provision. Smart cities can use these conditions to achieve the goals of a smart society for their citizens. To facilitate such developments, the necessary IT-infrastructure has to be in place for supporting, amongst many other things, the whole lifecycle of big data management and analytics for research activities. At the Centre for IT-Intelligent Smart Energy for Cities, we have therefore been developing a flexible infrastructure, based on open sourcetechnologies. This paper presents this solution and its application in a city and building research.

One way of reducing the use of fossil fuels in Denmark is to explore possible energy savings in the building stock, especially the large number of single-family houses built from 1960 through 1980. Energy renovation in this housing segment is progressing slowly. The aim of this project was to determine how a one-stop-shop (OSS) or full-service concept could be used to guide the extensive energy renovation of single-family houses. The purpose was partly to identify the benefits and disadvantages of using the concept and partly to evaluate the potential of the OSS concept for increasing the degree of renovation. The scope of the project was to carry out renovations on up to three houses. The project revealed that the concept on its own was not enough to motivate the house owners to engage in extensive renovation. However, interviews with the house owners indicated that the renovations that took place had probably been expanded and improved with the use of the concept and that the renovations in general benefitted from an independent adviser. (C) 2017 American Society of Civil Engineers.

Building renovation is too often carried out with only one objective: necessary maintenance, updating design and functions, or reducing energy consumption. But, if a necessary maintenance is exploited as an opportunity for renovation, energy improvements can be implemented, house functions can be updated, and indoor climate improved with minimal nuisance and expense. This paper illustrates this approach by documenting the renovation of a single-family house in Denmark, and monitoring its energy consumption and indoor climate before and after the renovation. Building elements were replaced where necessary, and the total energy consumption was reduced by 23%, giving the house owners a saving of about DKK 8400 per year. The energy consumption for heating was reduced by 53%, close to the 58% found using dynamic simulations. The temperatures reached a more comfortable level, and the house owners were satisfied with the result. The increased value of the house was estimated to cover about 77% of the investment. (C) 2017 Published by Elsevier B.V.