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Sweden has an ambitious plan to fully decarbonise district heating by 2030 and to contribute with negative emissions of greenhouse gases in 2050. The vagaries of the energy market associated with climate, political, and social changes entail cross-sectoral integration that can fulfill these national targets. Fifth-generation district heating and cooling (5GDHC) is a relatively new concept of district energy systems that features a simultaneous supply of heating and cooling using power-to-heat technologies. This paper presents best practices for developing 5GDHC systems in Sweden to reach a consensus view on these systems among all stakeholders. A mixed-method combining best practice and roadmapping workshops has been used to disseminate mixed knowledge and experience from middle agents representing industry professionals and practitioners. Four successful implementations of 5GDHC systems are demonstrated and the important learned lessons are shared. The best practices are outlined for system planning, system modeling and simulation, prevailing business models for energy communities, and system monitoring. A roadmap from the middle agents’ point of view is composed and can be utilised to establish industry standards and common regulatory frameworks.

The energy use of buildings is almost one-third of the global final energy use. Phase change Materials (PCMs) are substances that undergo phase transition when the surrounding temperature reaches their phase transition temperature. PCMs are reported to be a good candidate as a thermal storage buffer in building systems. Accordingly, PCMs may be able to regulate the indoor temperature while using less energy and thereby contributing in improving the energy performance of the building. In this project a trail to analyse the effect of PCMs in indoor temperature was carried out, in an experimental set-up, using a climate chamber. The chamber temperature is regulated as a sinusoidal profile with a cycle of 24 hours, with a maximum of 40 °C and a minimum of -10 °C. A cubic-box, is placed at the centre of the chamber, and is used as a representation of “building”. A board was made by encapsulating PCMs, with a melting temperature of 24 °C, to gypsum with a fraction of 20 wt%. The influence of PCM added gypsum board on inside temperature of the box is studied. Temperatures at different locations have been measured by thermocouples. The results indicated that the presence of PCM resulted in less temperature variation inside the box with the temperature holding close to the PCM transition temperature for a long period. Also, the PCM boards shifted the temperature profile. Further results are expected to determine the location of the PCM board that is most suitable to reduce the temperature variation inside the building.

Hybrid heating systems with ground source heat pumps (GSHP) and district heating and cooling offer flexibility in operation to both building owners and energy providers. The flexibility can be used to make the heating system more economical and environmentally friendly. However, due to the lack of suitable models that can accurately predict the long-term performance of the GSHP, there is uncertainty in their performance and concerns about the long-term stability of the ground temperature, which has limited the utilization of such hybrid heating systems. This work presents a hybrid model of a GSHP system that uses analytical and artificial neural network models to accurately represent a GSHP system's long-term behavior. A method to improve the operation of a hybrid GSHP is also presented. The method was applied to hospital buildings in northern Sweden. It was shown that in the improved case, the cost of providing heating to the building can be reduced by 64 t€, and the CO2 emissions can be reduced by 92 tons while maintaining a stable ground temperature., Originally included in thesis in manuscript form.

Radiators play an important role in providing a comfortable and safe indoor environment while maintaining high-energy efficiency. In the perspective of future climate change with expected larger temperature fluctuations and the rapidly changing heat supply and demand, it is required that the current radiator technology is adaptable. The heat supply is changing towards a lower supply temperature to enable an increase in energy efficiency and an increase in the share of renewable energy. Simultaneously, both the heat supply and demand are expected to have more variations in the future. An additional concern that has come into more focus after the experience with the COVID 19 pandemic is the prevention of the spread of infection in indoor environments. Researchers have extensively studied several innovations in radiator technologies and their deployment that addresses these challenges. Some of the solutions available in the literature include floor heating, ceiling heating, ventilation radiator, stratum ventilation. Researchers have used advanced modeling and experimental techniques to understand how to deploy different types of radiator technologies. This review summarizes solutions in the literature that address these challenges and identifies knowledge gaps that need to be addressed. In particular, this study explores the gaps in knowledge of practical issues, such as the position of furniture and the position of people, which have received less attention in the literature. Research that addresses the effect of radiators on ventilation and a healthy indoor environment is also of particular interest in this review.

Developing accurate models is necessary to optimize the operation of heating systems. A large number of field measurements from monitored heat pumps have made it possible to evaluate different heat pump models and improve their accuracy. This study used measured data from a heating system consisting of three heat pumps to compare five regression and two artificial neural network (ANN) models. The models’ performance was compared to determine which model was suitable during the design and operation stage by calibrating them using data provided by the manufacturer and the measured data. A method to refine the ANN model was also presented. The results indicate that simple regression models are more suitable when only manufacturers’ data are available, while ANN models are more suited to utilize a large amount of measured data. The method to refine the ANN model is effective at increasing the accuracy of the model. The refined models have a relative root mean square error (RMSE) of less than 5%

Optimizing the operation of ground source heat pumps requires simulation of both short-term and long-term response of the borehole heat exchanger. However, the current physical and neural network based models are not suited to handle the large range of time scales, especially for large borehole fields. In this study, we present a hybrid model for long-term simulation of BHE with high resolution in time. The model uses an analytical model with low time resolution to guide an artificial neural network model with high time resolution. We trained, tuned, and tested the hybrid model using measured data from a ground source heat pump in real operation. The performance of the hybrid model is compared with an analytical model, a calibrated analytical model, and three different types of neural network models. The hybrid model has a relative RMSE of 6% for the testing period compared to 22%, 14%, and 12% respectively for the analytical model, the calibrated analytical model, and the best of the three investigated neural network models. The hybrid model also has a reasonable computational time and was also found to be robust with regard to the model parameters used by the analytical model.

Planning the operation of large ground source heat pump (GSHP) systems requires accurate models of borehole heat exchangers (BHEs) that are not computationally intensive. In this paper, we propose parameter estimation using measured data as a method to improve the analytical models of BHE. The method was applied to a GSHP system operating for over 3 years. The deviation between modelled and measured load of the BHE reduced from 22% to 14%. Influence of the calibration data set was tested by changing time resolution and season of the calibration data. We concluded that the time resolution must be high enough to differentiate among the effects of different parameters and that different model parameters must be used for injection and extraction (seasons). The method was also applied to a GSHP that has been monitored for 10 years, which showed that accuracy of the model can be improved by annual updates of parameters.