Abstract
Heating systems must be subjected to hydraulic balancing in order to ensure proper operation. When the heating system is hydraulically imbalanced, heat is unevenly distributed across dwellings resulting in a large temperature spread, overheating, and consequently, waste of energy. In this study, we investigate the extent to which hydraulic imbalance affects the thermal energy consumption in buildings. Furthermore, key variables and interactions that influence the thermal performance in buildings are identified. Results show that higher variation in indoor temperature lead to higher energy consumption. Compared to new buildings, old buildings that have large boiler capacity and small heated floor area are more likely to be hydraulically imbalanced and to consume more energy.
Highlights
In Europe, the building sector is responsible for 41% (27% for residential and 14% for tertiary) of the total energy consumption [1]
While energy efficiency of new buildings has been improved as a consequence of more stringent building codes, the main challenge is related to old existing buildings that represent more than 90% of the existing building stock in Europe
Focusing on the optimization measures, this study investigates hydraulic balancing, which is one of the main approaches applied for heating systems [6]
Summary
In Europe, the building sector is responsible for 41% (27% for residential and 14% for tertiary) of the total energy consumption [1]. With the goal of identifying the key variables that influence hydraulic imbalance and thermal energy consumption, we conduct a two stage-analysis. We investigate the effects of building characteristics on the thermal performance of buildings by applying advanced regularization method which allows to address the issue of multicollinearity among variables. We collected hourly indoor temperature between 2016 and 2018 from all 656 flats within the 13 groups of buildings, and annual final energy consumption for heating between 2016 and 2018. We correlate standard deviation and mean indoor temperature against annual final energy consumption for heating for each group. We subsequently identify a set of variables with the highest explanatory power This technique has been applied to many different domains, as it simplifies the model to make it interpretable [15]. A final model is developed only with the selected variables for which we repeat OLS regression
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