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Flexible heating demand in buildings plays an important role in achieving a carbon neutral society. For the district heating system of Copenhagen, heating demand flexibility can help to eliminate the use of fossil-fueled boilers that are used during peak-load periods. In this project, field tests in 16 apartments were conducted, aiming to gain insights into the use of information and communication technologies (ICT) to manage heating systems operation for flexible demand. The apartments are equipped with sensors and devices interfaced to an ICT system composed of blocks responsible for data storage, monitoring and control. In the experiments, we controlled temperature setpoints of individual rooms during defined periods of the day, and continuously developed control strategies throughout the tests in the heating season 2018/19. The final algorithm was implemented with features to reduce the rebound effect and include residents feedback. All the algorithms are generic and can be applied in other smart homes where heating supply to rooms is controlled using thermostats. The ICT system architecture used in the experiments showed to be a feasible way to implement demand side management (DSM) in the heating system, and the learning process of the experiments resulted in improvements on the control strategies, leading to a better system performance.

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This work suggests a method to evaluate residential building occupants’ neutral temperature in winter based on their interaction with their heating system. This study applies the developed method on eight new, low-energy apartments in Copenhagen, Denmark. A set of indoor temperature, heating setpoint, window opening and floor heating valve opening data was collected from mid-January to the end of April, spanning through a large part of the Danish heating season. Semi-structured interviews were performed with occupants of three of the eight apartments in order to understand their use of their heating system. This preliminary study permits to highlight the potential and the current limitations of the proposed method, both for neutral temperature estimation as such and for applications in optimizing the energy flexibility provided by the building. This article suggests directions for further elaboration of the model. The main two influential factors highlighted here affecting setpoint adjustment are the occupants’ acceptability of temperature variation and their ability to control the heating system.