Space heating accounts for 36% of the total energy demand in buildings, with district heating systems using panel radiators satisfying a significant proportion of the global heating demand. Occupant operation of heating systems significantly impacts building energy use, especially when occupants control heating and cooling directly. Addressing the heating behaviour of occupants offers an opportunity for significant reductions in energy use through behavioural change that eliminates wasteful use of energy. A better understanding of the behavioural aspects of space heating energy use can provide opportunities to reduce energy consumption, meet carbon and operational cost reduction targets, and improve the comfort and productivity of building occupants.

Behavioural interventions to reduce energy consumption are considered essential, but their effectiveness in achieving emission reduction targets is often compromised, mainly due to the lack of accurate energy consumption data at the individual occupant level. Despite significant advances in the assessment of occupant behaviour, the ongoing challenge is to develop highly granular, non-invasive and cost-effective methods that provide quantitative data on individual heating energy use. Existing methods for quantifying occupant heating behaviour, such as sensor-based methods, provide only aggregated data, and options such as flow meters or cameras are costly and intrusive. In the absence of accurate tools and metrics to quantify occupant heating behaviour, surveys are commonly used to collect data on space heating practices. While surveys are affordable and scalable, they suffer from inaccuracies due to discrepancies between reported and actual behaviour. An important knowledge gap exists in the understanding of occupant behaviour, particularly in the context of radiator-based district heating systems. Addressing this gap can improve the ability to design and test effective energy-related interventions.

To overcome these limitations, this thesis introduces the Radiator Heating and Temperature Measurement (RHTM) technique, which quantifies the operation of panel radiators in single-occupancy offices using transient indoor air and radiator surface temperatures. The technique introduces two innovative metrics, Time-at-Temperature Difference (TTD) and Cumulative Temperature Difference (CTD). The TTD metric communicates a comprehensive characterisation of radiator operation through a bar graph, showing the duration of use at different temperature differences, which are indicators of the intensity of radiator operation. The CTD metric quantifies total radiator energy use over a period of time, allowing occupants to be categorised according to their individual heating energy use. Comparisons between occupants can be easily made using time series data by using the RHTM technique and analysing the TTD and CTD metrics, facilitating the inference and evaluation of heating behaviours.

The new RHTM technique was applied to a case study covering a 13-working-day period, involving 30 office occupants. The technique effectively characterised radiator usage (reflective of occupant heating behaviour), enabling a rigorous comparison between occupants. This approach facilitated non-intrusive, high-granularity, and low-cost data acquisition for various purposes, including (1) assessing total energy consumption among users, (2) contrasting the duration of radiator usage among occupants, (3) comparing the time spent at different temperatures over the course of the study.

The case study demonstrated the effectiveness of the RHTM technique in overcoming the challenges associated with monitoring radiator operation at the individual occupant level. CTD analysis efficiently facilitated the categorisation of occupants based on the total heating energy consumption during a certain period. The TTD bar graphs provided comprehensive insights into radiator operating times and temperature differences, revealing nuanced user interactions with the heating system. In particular, TTD served as a novel approach for pinpointing inefficient heating energy use, thus potentially supporting the design of targeted behavioural interventions. Consequently, the application of the RHTM technique allows for a more robust comparative analysis between users, providing unique information not captured by traditional methods such as energy meters, BMS, indoor temperature measurements, or surveys.

Surveys are a commonly used method for assessing occupant behaviour in buildings, and the proposed technique can complement this widely used method and significantly enhance its insightfulness. The case study includes an examination of survey data in the light of quantitative measures obtained using the RHTM technique. The results revealed subtleties in occupants' heating behaviour that are often overlooked in conventional survey analysis. For example, scenarios were observed where survey responses on heating habits correlated with observed TTD data, while in other cases discrepancies were evident. These findings highlight the importance of using data-rich metrics such as CTD and TTD to improve survey-based assessments of occupant heating behaviour.

Overall, the RHTM technique and the CTD and TTM metrics provide a strong basis for behavioural studies in intervention design and testing, providing a means of obtaining accurate, high granularity data on occupant heating behaviour that is lacking in traditional assessment methods. By providing this type of data, successful behavioural interventions can lead to behavioural changes that contribute to the optimisation of heating system operation and hence energy reduction in buildings. As a result, the RHTM technique for quantifying radiator operation advances knowledge of building energy performance and energy-related occupant behaviour.