Data Pre-processing Methods Enhancing Heat Cost Allocator Measurement Usability

Heat cost allocators (HCAs) are devices mounted on radiators to fairly allocate heating consumption among flats within buildings connected to district heating networks or central heating systems. In recent years, HCA data has also been utilized for building heating system analysis, fault detection, diagnosis, and optimization. However, certain inherent limitations of HCAs, such as data truncation and the sparse recording of data points over time, can hinder their direct application in analysis. This underscores the necessity of pre-processing HCA data prior to conducting meaningful analyses. This study aims to develop a methodology for recovering the decimal values of HCA data. By leveraging the continuity of HCA increments and the inverse relationship between external temperature changes and HCA increments, the problem is formulated as an optimization problem. Two case studies were conducted to validate this method. The first case study involved a radiator heating laboratory at the National Metrology Institute of Italy (INRIM), where 40 radiators of various types, geometries, and materials were tested. The lab replicates heating operations typical of real apartment buildings, utilizing specific control strategies and flexible hydraulic connections. The second case study focused on a residential building in Denmark, analyzing HCA data collected from 15 apartments over one month. In both case studies, we used different measures to collect HCA data with decimals as the reference. Results indicate that the proposed method significantly reduces errors and uncertainties associated with data truncation in both laboratory and real-world settings. On average, the root mean square error (RMSE) of the recovered HCA data compared to the reference value decreased by 76.9% and 60.4% when compared to the truncated data in the lab and real buildings, respectively. This demonstrates the method’s effectiveness in enhancing the usability and reliability of HCA data over short time intervals.