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Our joint environmental and energy commitments mean we must reduce the building’s energy use. Improved central heating control can play a role in how this is accomplished. There are three common control strategies: feedforward (traditional), feedback, and model predictive control (MPC). The latter two often work in parallel, where feedback uses indoor temperature sensors to adjust the supply water temperature. In contrast, the supply temperature setpoint is continuously calculated in MPC, fed with weather forecasts. The weather forecasts are often highlighted as essential ingredients in MPC, but at the same time, it is emphasized that temperature sensors are used to ensure a pleasant indoor temperature. To an outside observer, it is difficult to determine what is what in such combined control arrangements. Is energy saved because of the room sensors or because of the model? And what role do the weather forecasts play? This study quantifies the impact of the control strategy on energy use and indoor temperature. It concludes that PI-based feedback heating control saves approximately as much energy as MPC, and weather forecasts do not save significantly more energy than real-time weather data but are easier to obtain. The overall results for both control strategies align with the lower end of the result ranges of previous studies. The novelty is that the impact of weather forecasts has been studied separately and that different control strategies are compared against each other based on a model of a typical Swedish multi-family building.

Europe needs to save energy, and lowered indoor temperature is frequently promoted as part of the solution. To facilitate this, heating control systems with feedback from indoor temperature sensors are often required to avoid thermal discomfort and achieve long-term temperature reductions. This article describes a measurement- and interview-based study on feedback control where 107 Swedish multifamily buildings were analysed. The obtained results show that buildings with lowered indoor temperatures had reduced annual heating demand by 4 kWh/m2 and a reduced indoor temperature of 0.4 °C. There were, however, significant individual differences and even buildings with increased indoor temperatures, which harmed the energy savings. Temperature fluctuation was most often significantly reduced, but the impact on heating power demand during cold weather was, on average, only 2%. An interview with different actors indicated higher energy savings, possibly due to their stock’s original room temperature levels. Several interviewees also mentioned other advantages of temperature mapping. Most of the results obtained in this study were in line with several previous investigations. The study’s novelty lies in the large number of investigated buildings with mature commercial heat control technology, including PI-control for adjusting supply temperature, indoor temperature sensors in almost every apartment and a parallel analysis of additional affected parameters.