This report presents the results of the second part of the Monicair project1, which aim was to explore in how far the better determination of a number of parameters, which up to now were measured on-ly seldom, could support the development of better prediction models for the heating energy con-sumption in dwellings. Energy labelling calculations, as well as energy consumption forecasts, on which energy policies rely, are based on models. In the past years several studies have demonstrated that these models show large deviations from reality, making the prediction of possible energy sav-ings biased. These poor predictions can be hypothesised to be the result of poor estimation of the U–values of walls, poor estimation of the infiltration and ventilation flow rates and poor estimation of the heated surface area and of the temperature preferences of occupants. Additionally, there is very little knowledge on how occupant’s perception of comfort influences their ventilation and heating behaviour and finally the total energy use for heating. This report presents the results of a field study in which monitoring data was collected in order to further analyse parameters that could influence strongly the heating energy consumption and to fi-nally improve energy prediction models. A mix of modern and older dwellings was studied, as this can give a better idea of possible energy savings when renovating dwellings. @en

Buildings in Europe are the largest end use sector and especially residential buildings account for two thirds of this energy use. Despite the fact that building characteristics play a major role in a dwelling’s energy consumption, occupant characteristics and behaviour significantly affect this energy use as well. The Ecommon campaign monitored 32 residential dwellings for 6 months in the Netherlands, capturing quantitative (temperature, CO2, humidity, movement, boiler and ventilation electricity consumption, real time and total electricity and gas consumption on the meter) and quantitative data (comfort perception, actions taken like closing and opening windows, thermostat use, use and type of clothes, and metabolic activity). Additionally in the beginning of the campaign a survey was given to the tenants with questions on income, gender, education level, thermostat and ventilation preferences, bathing patterns and other related data. This paper describes the experimental set up of the campaign, the temperature and occupancy profiles for each type of room for the 32 dwellings and the findings on the clothing patterns and metabolic activity. Temperature profiles show that these dwellings have higher temperatures through the whole day than the common assumption of the daily average of 18 o C suggested for the calculations of the national simulation software. A method is demonstrated on how a combination of motion detection and CO2 can lead to reliable occupancy profiles.@en