Data-driven method for optimized supply temperatures in residential buildings

Abstract

The energy required for space heating amounts to approximately 68% of the total energy demand of existing buildings in Europe. The heat requirement of a building, and thus its carbon emission, can be lowered by optimizing the supply and return temperature of the heating system. A lower supply temperature enables a wider variety of transition pathways towards sustainable heating with reduced carbon emissions. However, the minimum supply temperature that guarantees acceptable indoor temperatures in existing dwellings during design weather conditions is still unknown. In this study, we determine the minimum supply temperature by fitting a 2 R–2C model to hourly measurement data. The measurement data is obtained from a representative set of 220 existing gas-fired dwellings in the Netherlands. The heating system of each dwelling was equipped with a pulse flowmeter and temperature sensors on both the supply and return side. Additionally, data was collected from the thermostat in the main living room and the gas boiler. The data was supplemented with weather data from a nearby weather station. The data-driven model shows that the minimum supply temperature can be lower than 55 °C for 60% of the dwellings during design weather conditions (i.e., −10 °C in the Netherlands). Moreover, the minimum supply temperature is poorly correlated with general building properties, such as the building typology, construction period or specific annual space heating demand (kWh/(m2yr)). On the contrary, the ratio between the required and installed heat output of the radiators in the heating system is a promising parameter to predict the minimum design supply temperature of an individual dwelling that guarantees an acceptable indoor temperature during design weather conditions.