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• 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]

[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]

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]

• 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]