Understanding thermal comfort and heating energy use in Dutch dwellings: Analysis of smart meter data, indoor climate and comfort in 78 Dutch dwellings

Abstract

According to the European Commission, buildings are responsible for approximately 40% of the EU energy consumption and 36% of the CO2 emissions, resulting in the largest energy consumer in Europe. Especially, the reduction of energy consumption for heating of residential dwellings has gained more and more interest in the last decades. Although some reduction has been achieved by implementing passive or active strategies and improving the thermal performance of new or renovated dwellings, the decrease is found to be lower than predicted by the energy performance models. One possible reason is that the existing thermal comfort models, the PMV and the adaptive thermal comfort model, are not appropriate for all different types of buildings and climates, resulting in inaccurate predictions of thermal comfort and leading to amounts of energy use different than the actual ones. In addition, the occupants’ behaviour, which is determined by their thermal comfort preferences might play a significant role on the energy use which needs to be explored. Therefore, the main topic of the current thesis is to explore the relationship that different parameters such as dwelling and installation characteristics, indoor and outdoor climate parameters, household and physiological characteristics as well as occupants’ behaviour have with thermal comfort and energy use.

A measurement campaign took place within the context of the OPSCHALER project and data related to thermal comfort perception, indoor climate and energy consumption was collected for 96 dwellings in the Netherlands during periods ranging from two to twelve months over a one-year period (2017-2018). The gathered data included both quantitative and subjective data. The quantitative data included the indoor climate data (air temperature, relative humidity, CO2, presence), the gas use and the electricity use collected through smart meters and sensors. The subjective data related to comfort was gathered by a comfort mobile application where users recorded their thermal sensation and other comfort related parameters (thermal preference, clothing, metabolic activities, actions), while questionnaires and inspections did also take place. This data was analysed using different types of graphical representation (bar charts, pie charts, stacked bar plots, scatterplots) as well as statistical tests such as chi-square tests, regression analysis, Kruskal-Wallis and correlation tests. Due to limited or unavailable data only 78 dwellings were included in the analysis, while the reliability and generalisation of the results entails some uncertainty due to the number of dwellings and data points available.

All in all, it was found that the thermal sensation is correlated with the four comfort related factors (thermal preference, clothing, metabolic activity, action), the indoor air temperature as well as the energy labels and the ventilation types, while only age was resulted in not being associated with thermal sensation. Indoor air temperature was found to be related with the energy label of the dwellings, but not with their ventilation type. In addition, the results indicated that the heating energy use is related with the outdoor air temperature as well as with the indoor-outdoor air temperature difference, but not with the indoor air temperature. Energy labels and ventilation types were found to be related with the heating energy use, while thermal sensation, actions and income did not show any correlation with it.

Based on the findings it is concluded that the adaptive thermal comfort model is more accurate and closer to the reality of a residential environment in comparison with the steady-state PMV model, where no adaptations take place. However, it is highly recommended that instead of improving the existing thermal comfort models, personalised comfort models could be developed for each single person separately by gathering data using sensors. This would help to trace all different parameters that influence the thermal comfort and the energy use as well and lead to more accurate prediction and lower actual energy use.