The average energy consumption for heating of dwellings in the Netherlands has been decreasing over the last decades as a result of increasing thermal performance of new and renovated dwellings. However, this decrease is found to be lower than energy performance models predicted. One of the possible reasons is the behaviour of the residents, which is partially determined by the thermal comfort preferences of these residents. In this paper, the relationship between thermal comfort perception, indoor climate and energy consumption is investigated using high-resolution measurement data in 93 dwellings in the Netherlands. In the OPSCHALER project, data about thermal comfort perception, indoor climate and operational energy consumption were collected in 93 dwellings in the Netherlands during periods ranging from two to twelve months over a period of two years. Comfort perception was registered using the Comfort App, an application where users record their comfort data. Indoor climate data were collected per five minutes using sensors for temperature, relative humidity and CO2 concentration in the living room, kitchen and bedrooms of the dwellings. The Comfort App asked the residents for the room they are in, their comfort perception, their activities during the last half hour and the amount of clothing they wear. Significant relationships were found between thermal sensation and thermal preference, clothing level, metabolic activity level, activities related to thermal comfort taken in the last half hour, and indoor air temperature. These data can be used to compare the comfort level registered by the residents with the comfort level predicted by the PMV model, and link this to the indoor climate and the energy consumption for heating. This information can help to understand the relations between user preferences, indoor climate and energy consumption for heating.@en

After the thermal renovation of a dwelling, there exists a gap between the actual and predicted energy performance. One of the reasons contributing to this gap is the poor assumptions of building thermal characteristics during the prediction stage. Nowadays, smart meters for gas and electricity, and home automation systems are becoming increasingly prominent in dwellings. Hence, there is potential to use the on-board monitored data from these sources to estimate the thermal characteristics of the actual dwellings. If it was possible to measure everything in a dwelling, then the estimation of these characteristics would become easy. However, the amount of data from the dwellings is limited. Hence with the available data, assumptions have to be made to estimate characteristics reflective of the actual dwelling. Therefore, this study investigates the impact these assumptions have on the estimated characteristics. First, a simple equation requiring minimum data is formulated to represent the heat dynamics in a building. Then, the characteristics are determined for one Dutch dwelling for the following conditions: 1. Different measurement periods, 2. Different time granularities, 3. With total (space heating + domestic hot water) and decomposed (only space heating) gas consumption data, 4. With different representations of indoor air temperature, and 5. Using electricity data to account for internal heat gains. In general, the estimated characteristics deviated for all the conditions. And thus, this study establishes the importance of well-chosen on-board monitored data.

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