- 2628 bl, Julianalaan 134, 2628 BL Delft
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Anastasios Ioannou
TU Delft, Architecture and the Built Environment, Department Member
Residential buildings account for a significant amount of the national energy consumption of all OECD countries and consequently the EU and the Netherlands. Therefore, the national targets for CO2 reduction should include provisions for a... more
Residential buildings account for a significant amount of the national energy consumption of all OECD countries and consequently the EU and the Netherlands. Therefore, the national targets for CO2 reduction should include provisions for a more energy efficient building stock for all EU member states. National and European level policies the past decades have improved the quality of the building stock by setting stricter standards on the external envelope of newly made buildings, the efficiency of the mechanical and heating components, the renovation practices and by establishing an energy labelling system. Energy related occupancy behavior is a significant part, and relatively unchartered, of buildings’ energy consumption. This thesis tried to contribute to the understanding of the role of the occupant related to the energy consumption of residential buildings by means of simulations and experimental data obtained by an extensive measurement campaign.
Research Interests:
Reducing energy consumption in the residential sector is an imperative EU goal until 2020. An important boundary condition in buildings is that energy savings shouldn't be achieved at the expense of thermal comfort. There is, however,... more
Reducing energy consumption in the residential sector is an imperative EU goal until 2020. An important boundary condition in buildings is that energy savings shouldn't be achieved at the expense of thermal comfort. There is, however, little known about comfort perception in residential buildings and its relation to the PMV theory. In this research an in-situ method for real time measurements of the quantitative and qualitative parameters that affect thermal comfort as well as the reported thermal comfort perception was developed and applied in 30 residential dwellings in the Netherlands. Quantitative data (air temperature, relative humidity, presence) have been wirelessly gathered with 5 min interval for 6 months. The thermal sensation was gathered wirelessly as well, using a battery powered comfort dial. Other qualitative data (metabolic activity, clothing, actions related to thermal comfort) were collected twice a day using a diary. The data analysis showed that while the neutral temperatures are well predicted by the PMV method, the cold and warm sensations are not. It seems that people reported (on a statistically significant way) comfortable sensation while the PMV method doesn't predict it, indicating a certain level of psychological adaptation to expectations. Additionally it was found that, although clothing and metabolic activities were similar among tenants of houses with different thermal quality, the neutral temperature was different: in houses with a good energy rating, the neutral temperature was higher than in houses with a poor rating.
Research Interests:
This report presents the results of the second part of the Monicair project, which aim was to explore in how far the better determination of a number of parameters, which up to now were measured only seldom, could support the development... more
This report presents the results of the second part of the Monicair project, which aim was to explore
in how far the better determination of a number of parameters, which up to now were measured only
seldom, could support the development of better prediction models for the heating energy consumption
in dwellings. Energy labeling 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 savings
biased. These poor predictions can be hypothesized 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
behavior 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 finally
improve energy prediction models.In total 32 houses were monitored between November 2014 and April
2015, ensuring a 6 months monitoring period. In all houses presence, CO2 concentrations, temperature
and humidity were measured each 5 minutes in all rooms. Gas and electricity were measured at
the start of the campaign, at the end and in some houses on a continuous basis as well. The thermal
comfort perception of occupants was measured real time during a two-weeks period, using a wireless
comfort dial, in combination with a log-book. Additionally, all households had to fill in a survey at
the start of measurement period and the dwellings were inspected. The heat resistance (Rc-value) of
external walls was measured in-situ in three additional dwellings, in order to determine if the Rc-value
was in the same range as the Rc-value estimated in the energy labeling calculations.
in how far the better determination of a number of parameters, which up to now were measured only
seldom, could support the development of better prediction models for the heating energy consumption
in dwellings. Energy labeling 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 savings
biased. These poor predictions can be hypothesized 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
behavior 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 finally
improve energy prediction models.In total 32 houses were monitored between November 2014 and April
2015, ensuring a 6 months monitoring period. In all houses presence, CO2 concentrations, temperature
and humidity were measured each 5 minutes in all rooms. Gas and electricity were measured at
the start of the campaign, at the end and in some houses on a continuous basis as well. The thermal
comfort perception of occupants was measured real time during a two-weeks period, using a wireless
comfort dial, in combination with a log-book. Additionally, all households had to fill in a survey at
the start of measurement period and the dwellings were inspected. The heat resistance (Rc-value) of
external walls was measured in-situ in three additional dwellings, in order to determine if the Rc-value
was in the same range as the Rc-value estimated in the energy labeling calculations.
Research Interests:
Energy performance simulation is a generally used method for assessing the energy consumption of buildings. Simulation tools, though, have shortcomings due to false assumptions made during the design phase of buildings, limited... more
Energy performance simulation is a generally used method for assessing the energy consumption of buildings. Simulation tools, though, have shortcomings due to false assumptions made during the design phase of buildings, limited information on the building's envelope and installations and misunderstandings over the role of the occupant's behaviour. This paper presents the results of a Monte Carlo sensitivity analysis on the factors (relating to both the building and occupant behaviour) that affect the annual heating energy consumption and the PMV comfort index. The PMV results are presented only for the winter (heating) period which is important for energy consumption in Northern Europe. The reference building (TU Delft Concept House) was simulated as both a Class-A and a Class F dwelling and with three different heating systems. If behavioural parameters are not taken into account, the most critical parameters affecting heating consumption are the window U value, window g value and wall conductivity. When the uncertainty of the building-related parameters increases, the impact of the wall conductivity on heating consumption increases considerably. The most important finding was that when behavioural parameters like thermostat use and ventilation flow rate are added to the analysis, they dwarf the importance of the building parameters. For the PMV comfort index the most influential parameters were found to be metabolic activity and clothing, while the thermostat had a secondary impact.
Research Interests: Monte Carlo Simulation, Sustainable Building Design, Energy Efficiency Buildings, Building Energy Simulation, Built Environment, and 4 moreIndoor environment - thermal comfort, indoor air quality health and wellbeing, Thermal comfort, Energy conservation in buildings, and Domestic energy user behaviour
• Which measurable parameters (including occupant behavior) influence the actual energy use in dwellings? • How can prediction models for energy consumption be improved? Sub-questions: • What is the bandwidth and average behavior for use... more
• Which measurable parameters (including occupant behavior) influence the actual energy use in dwellings? • How can prediction models for energy consumption be improved? Sub-questions: • What is the bandwidth and average behavior for use of electrical appliances, thermostat settings, occupancy of rooms, ventilation, radiator settings, hot tap water use, sun shades and how do these data relate to actual energy use? • Is it possible to define behavioral groups in relation to actual energy use? • Is there a relationship between type of installation, dwelling characteristics , behavior and energy use? • Is it possible to determine a bandwidth of user profiles to be fed in calculation software in order to get a probability of energy use (distribution) instead of one value? • How can prediction (simulation) models be improved in order to match better actual statistical data? • What is the relationship between predicted and actual comfort in Dutch residential dwellings, how can comfort models be improved and how do they relate with the energy consumption of the residential sector.
Research Interests:
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,... more
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.
Research Interests:
Dynamic simulation is widely used for assessing thermal comfort in dwellings. Simulation tools, though, have shortcomings due to false assumptions made during the design phase of buildings, limited information on the building's envelope... more
Dynamic simulation is widely used for assessing thermal comfort in dwellings. Simulation tools, though, have shortcomings due to false assumptions made during the design phase of buildings, limited information on the building's envelope and installations and misunderstandings over the role of the occupant's behaviour. This paper presents the results of a Monte Carlo sensitivity analysis on the factors that affect the PMV comfort index. The reference building was simulated as both Class-A and F according to the Dutch determination method for the energy performance of residential functions and buildings (ISSO 82.3, 2009), with three different heating systems. The study focuses on the heating period which is of main interest concerning residential energy use in the Netherlands. For the PMV the most influential parameters were found to be metabolic activity and clothing, while the thermostat had secondary impact.