Thermal comfort and energy related occupancy behavior in Dutch residential dwellings

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. The first part of this thesis was based on dynamic building simulations in combination with a Monte Carlo statistical analysis, which tried to shed light to the most influential parameters, including occupancy related ones, that affect the energy consumption and comfort (a factor that is believed to be integral to the energy related behavior of people in buildings). The reference building that was used for the simulations was the TU Delft Concept House that was built for the purposes of the European project SusLab NWE. The concept house was simulated as an A energy label (very efficient) and F label (very inefficient) dwelling and with three different heating systems. The analysis revealed that if behavioral 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 these parameters increases, the impact of the wall conductivity on heating consumption increases considerably. The most important finding was that when behavioral parameters like thermostat use and ventilation flow rate are added to the analysis, they dwarf the importance of the building parameters in relation to the energy consumption. For the thermal comfort (the PMV index was used as the established model for measuring indoor thermal comfort) the most influential parameters were found to be metabolic activity and clothing, while the thermostat had a secondary impact. The simulations were followed by an extensive measurement campaign where an in-situ, non-intrusive, wireless sensor system was installed in 32, social housing, residential dwellings in the area of Den Haag. This sensor system was transmitting quantitative data such as temperature, humidity, CO2 levels, and motion every five minutes for a period of six months (the heating period between November to April) and from every room of the 32 dwellings that participated in the campaign. Furthermore, subjective data were gathered during an initial inspection during the installation of the sensor system, concerning the building envelope, the heating and ventilation systems of the dwellings. More importantly though, subjective data were gathered related to the indoor comfort of the occupants with the use of an apparatus that was developed specifically for the SusLab project. This gimmick, named the comfort dial, allowed us to capture data such as the occupants’ comfort level in the PMV 7 point scale. In addition further comfort related data like the occupants’ clothing ensemble, actions related to thermal comfort, and their metabolic activity were captured with the use of a diary. The subjective data measurement session lasted for a week for each dwelling. These data were time coupled real time with the quantitative data that were gathered by the sensor system. The data analysis focused on the two available indoor thermal comfort models, Fanger’s PMV index and the adaptive model. Concerning the PMV model the analysis showed that while the neutral temperatures are well predicted by the PMV method, the cold and warm sensations are not. It appears that tenants reported (on a statistically significant way) comfortable sensations while the PMV method does not predict such comfort. This indicates a certain level of psychological adaptation to occupant’s 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. Specifically in houses with a good energy rating, the neutral temperature was higher than in houses with a poor rating. Concerning the adaptive model, which was developed as the answer to the discrepancies of Fanger’s model related to naturally ventilated buildings (the majority of the residential sector), data analysis showed that while indoor temperatures are within the adaptive model’s comfort bandwidth, occupants often reported comfort sensations other than neutral. In addition, when indoor temperatures were below the comfort bandwidth, tenants often reported that they felt ‘neutral’. The adaptive model could overestimate as well as underestimate the occupant’s adaptive capacity towards thermal comfort. Despite the significant outdoors temperature variation, the indoor temperature of the dwellings, as well as the clothing of the tenants, were largely constant. Certain actions towards thermal comfort such as ‘turning the thermostat up’ were taking place while tenants were reporting thermal sensation ‘neutral’ or ‘a bit warm’. This indicates that either there is an indiscrimination among the various thermal sensation levels or alliesthesia, a new concept introduced by the creators of the adaptive model, plays an increased role. Most importantly there was an uncertainty on whether the neutral sensation means at the same time comfortable sensation while many actions are happening out of habit and not in order to improve one’s thermal comfort. A chi² analysis showed that only six actions were correlated to thermal sensation in thermally poorly efficient dwellings, and six in thermally efficient dwellings. Finally, the abundance of data collected during the measurement campaign led the last piece of research of this thesis to data mining and pattern recognition analysis. Since the introduction of computers, the way research is performed has changed significantly. Huge amounts of data can be gathered and handled by evermore faster computers; the analysis of these data a couple of decades ago would take years. Sequential pattern mining reveals frequently occurring patterns from time-ordered input streams of data. A great deal of nature behaves in a periodic manner and these strong periodic elements of our environment have led people to adopt periodic behavior in many aspects of their lives such as the time they wake up in the morning, the daily working hours, the weekend days off, the weekly sports practice. These periodic interactions could extend in various aspects of our lives including the relationship of people with their home thermal environment. Repetitive behavioural actions in sensor rich environments, such as the dwellings of the measurement campaign, can be observed and categorized into patterns. These discoveries could form the basis of a model of tenant behaviour that could lead to a self-learning automation strategy or better occupancy data to be used for better predictions of building simulating software such as Energy+ or ESP-r and others. The analysis revealed various patterns of behaviour; indicatively 59% of the dwellings during the morning hours (7-9 a.m.) were increasing their indoor temperature from 20 oC< T< 22 oC to T> 22oC or that the tenants of 56% of the dwellings were finding the temperature 20 oC< T< 22 oC to be a bit cool and even for temperatures above 22 oC they were having a warm shower leading to the suspicion that a warm shower is a routine action not related to thermal comfort. Such pattern recognition algorithms can be more effective in the era of mobile internet, which allows the capturing of huge amounts of data. Increased computational power can analyse these data and define useful patterns of behaviour that could be tailor made for each dwelling, for each room of a dwelling, even for each individual of a dwelling. The occupants could then have an overview of their most common behavioural patterns, see which ones are energy consuming, which ones are related to comfort and which are redundant, and therefore, could be discarded leading to energy savings. In any case the balance between indoor comfort and energy consumption will be the final factor that would lead the occupant to decide on a customised model of his indoor environment. The general conclusion of this thesis is that the effect of energy related occupancy behaviour on the energy consumption of dwellings should not be statistically defined for large groups of population. There are so many different types of people inhabiting so many different types of dwellings that embarking in such a task would be a considerable waste of time and resources. The future in understanding the energy related occupancy behaviour, and therefore using it towards a more sustainable built environment, lies in the advances of sensor technology, big data gathering, and machine learning. Technology will enable us to move from big population models to tailor made solutions designed for each individual occupant

Thermal comfort and energy related occupancy behavior in Dutch residential dwellings

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. The first part of this thesis was based on dynamic building simulations in combination with a Monte Carlo statistical analysis, which tried to shed light to the most influential parameters, including occupancy related ones, that affect the energy consumption and comfort (a factor that is believed to be integral to the energy related behavior of people in buildings). The reference building that was used for the simulations was the TU Delft Concept House that was built for the purposes of the European project SusLab NWE. The concept house was simulated as an A energy label (very efficient) and F label (very inefficient) dwelling and with three different heating systems.  The analysis revealed that if behavioral 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 these parameters increases, the impact of the wall conductivity on heating consumption increases considerably. The most important finding was that when behavioral parameters like thermostat use and ventilation flow rate are added to the analysis, they dwarf the importance of the building parameters in relation to the energy consumption. For the thermal comfort (the PMV index was used as the established model for measuring indoor thermal comfort) the most influential parameters were found to be metabolic activity and clothing, while the thermostat had a secondary impact. The simulations were followed by an extensive measurement campaign where an in-situ, non-intrusive, wireless sensor system was installed in 32, social housing, residential dwellings in the area of Den Haag. This sensor system was transmitting quantitative data such as temperature, humidity, CO2 levels, and motion every five minutes for a period of six months (the heating period between November to April) and from every room of the 32 dwellings that participated in the campaign. Furthermore, subjective data were gathered during an initial inspection during the installation of the sensor system, concerning the building envelope, the heating and ventilation systems of the dwellings. More importantly though, subjective data were gathered related to the indoor comfort of the occupants with the use of an apparatus that was developed specifically for the SusLab project. This gimmick, named the comfort dial, allowed us to capture data such as the occupants’ comfort level in the PMV 7 point scale. In addition further comfort related data like the occupants’ clothing ensemble, actions related to thermal comfort, and their metabolic activity were captured with the use of a diary. The subjective data measurement session lasted for a week for each dwelling. These data were time coupled real time with the quantitative data that were gathered by the sensor system.  The data analysis focused on the two available indoor thermal comfort models, Fanger’s PMV index and the adaptive model. Concerning the PMV model the analysis showed that while the neutral temperatures are well predicted by the PMV method, the cold and warm sensations are not. It appears that tenants reported (on a statistically significant way) comfortable sensations while the PMV method does not predict such comfort. This indicates a certain level of psychological adaptation to occupant’s 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. Specifically in houses with a good energy rating, the neutral temperature was higher than in houses with a poor rating. Concerning the adaptive model, which was developed as the answer to the discrepancies of Fanger’s model related to naturally ventilated buildings (the majority of the residential sector), data analysis showed that while indoor temperatures are within the adaptive model’s comfort bandwidth, occupants often reported comfort sensations other than neutral. In addition, when indoor temperatures were below the comfort bandwidth, tenants often reported that they felt ‘neutral’. The adaptive model could overestimate as well as underestimate the occupant’s adaptive capacity towards thermal comfort. Despite the significant outdoors temperature variation, the indoor temperature of the dwellings, as well as the clothing of the tenants, were largely constant. Certain actions towards thermal comfort such as ‘turning the thermostat up’ were taking place while tenants were reporting thermal sensation ‘neutral’ or ‘a bit warm’. This indicates that either there is an indiscrimination among the various thermal sensation levels or alliesthesia, a new concept introduced by the creators of the adaptive model, plays an increased role. Most importantly there was an uncertainty on whether the neutral sensation means at the same time comfortable sensation while many actions are happening out of habit and not in order to improve one’s thermal comfort. A chi² analysis showed that only six actions were correlated to thermal sensation in thermally poorly efficient dwellings, and six in thermally efficient dwellings. Finally, the abundance of data collected during the measurement campaign led the last piece of research of this thesis to data mining and pattern recognition analysis. Since the introduction of computers, the way research is performed has changed significantly. Huge amounts of data can be gathered and handled by evermore faster computers; the analysis of these data a couple of decades ago would take years.  Sequential pattern mining reveals frequently occurring patterns from time-ordered input streams of data. A great deal of nature behaves in a periodic manner and these strong periodic elements of our environment have led people to adopt periodic behavior in many aspects of their lives such as the time they wake up in the morning, the daily working hours, the weekend days off, the weekly sports practice. These periodic interactions could extend in various aspects of our lives including the relationship of people with their home thermal environment. Repetitive behavioural actions in sensor rich environments, such as the dwellings of the measurement campaign, can be observed and categorized into patterns. These discoveries could form the basis of a model of tenant behaviour that could lead to a self-learning automation strategy or better occupancy data to be used for better predictions of building simulating software such as Energy+ or ESP-r and others.  The analysis revealed various patterns of behaviour; indicatively 59% of the dwellings during the morning hours (7-9 a.m.) were increasing their indoor temperature from 20 oC< T< 22 oC to T> 22oC or that the tenants of 56% of the dwellings were finding the temperature 20 oC< T< 22 oC to be a bit cool and even for temperatures above 22 oC they were having a warm shower leading to the suspicion that a warm shower is a routine action not related to thermal comfort. Such pattern recognition algorithms can be more effective in the era of mobile internet, which allows the capturing of huge amounts of data. Increased computational power can analyse these data and define useful patterns of behaviour that could be tailor made for each dwelling, for each room of a dwelling, even for each individual of a dwelling. The occupants could then have an overview of their most common behavioural patterns, see which ones are energy consuming, which ones are related to comfort and which are redundant, and therefore, could be discarded leading to energy savings. In any case the balance between indoor comfort and energy consumption will be the final factor that would lead the occupant to decide on a customised model of his indoor environment.  The general conclusion of this thesis is that the effect of energy related occupancy behaviour on the energy consumption of dwellings should not be statistically defined for large groups of population. There are so many different types of people inhabiting so many different types of dwellings that embarking in such a task would be a considerable waste of time and resources. The future in understanding the energy related occupancy behaviour, and therefore using it towards a more sustainable built environment, lies in the advances of sensor technology, big data gathering, and machine learning. Technology will enable us to move from big population models to tailor made solutions designed for each individual occupant. &nbsp

Conclusions and recommendations

The broader aim of this thesis was to contribute towards a more sustainable built environment, by first looking at how to seek ways to improve the existing simulation software’s ability to predict the energy consumption of residential dwellings by identifying the most important parameters that affect energy consumption and indoor comfort, which is tightly related to energy consumption. The second aim of this study was to compare the results of both PMV and adaptive models with data obtained with the use of a sensor rich smart environment. Such environments in the residential sector are still in their infancy but improvements in information technology, sensor miniaturization, software development, and analysis techniques (such as pattern recognition methods) will result to a smarter built environment in the future

Introduction

The focus of this study is to contribute towards both the above-mentioned directions of research. The first aim is to test the sensitivity of the parameters that affect energy consumption and comfort in the residential built environment in a theoretical basis. The second aim is to investigate if it would be possible, with the help of a sensor rich environment, to validate both prevailing models for indoor comfort, the PMV and adaptive model, and explore the dynamics between occupancy behavior, indoor comfort and energy consumption in the built environment. Sensor rich environments in the residential sector are not present yet in large scale; therefore, this study investigates a small, but still significant, sample of dwellings. The aim is not to achieve representativeness for the complete residential building sector but to research if the methodology of using sensors to gather quantitative and subjective data (related to thermal comfort, occupancy behavior, and energy consumption) is promising enough and could lead to potential energy savings without compromising the indoor comfort of occupants

In-situ and real time measurements of thermal comfort and its determinants in thirty residential dwellings in the Netherlands

Reducing energy consumption in the residential sector is an imperative EU goal until 2020. An important boundary condition in buildings is that energy savings should not be achieved at the expense of thermal comfort. However, there is 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 subjective 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 minutes interval for 6 months. The thermal sensation was gathered wirelessly as well, using a battery powered comfort dial. Other subjective 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 does not 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

Energy Performance and comfort in residential buildings

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 behavior. This paper presents the results of a Monte Carlo sensitivity analysis on the factors (relating to both the building and occupant behavior) 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 Class F dwelling and with three different heating systems. If behavioral 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 behavioral 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

In-situ real time measurements of thermal comfort and comparison with the adaptive comfort theory in Dutch residential dwellings

Indoor thermal comfort is generally assessed using the PMV or the adaptive model. This research presents the results obtained by in-situ real time measurements of thermal comfort and thermal comfort perception in 17 residential dwellings in the Netherlands. The study demonstrates the new possibilities offered by relatively cheap, sensor-rich environments to collect data on clothing, heating, and activities related to thermal comfort, which can be used to improve and validate existing comfort models. The results are analyzed against the adaptive comfort model and its underlying assumptions. Data analysis showed that while indoor temperatures are within the adaptive model’s comfort bandwidth, occupants often reported comfort sensations other than neutral. Furthermore, when indoor temperatures were below the comfort bandwidth, tenants also often reported that they felt ‘neutral’. The adaptive model could overestimate as well as underestimate the occupant’s adaptive capacity towards thermal comfort. Despite the significant outdoors temperature variation, the indoor temperature of the dwellings and the clothing were observed to remain largely constant. Certain actions towards thermal comfort such as ‘turning the thermostat up’ were taking place while tenants were reporting thermal sensation ‘neutral’ or ‘a bit warm’. This indicates that either there is an indiscrimination among the various thermal sensation levels or alliesthesia plays a role and the neutral sensation is not comfortable, or many actions are happening out of habit and not in order to improve one’s thermal comfort. A chi2 analysis showed that only six actions were correlated to thermal sensation in thermally poorly efficient dwellings, and six in thermally efficient dwellings

Somatosensory Evoked Potentials suppression due to remifentanil during spinal operations; a prospective clinical study

<p>Abstract</p> <p>Background</p> <p>Somatosensory evoked potentials (SSEP) are being used for the investigation and monitoring of the integrity of neural pathways during surgical procedures. Intraoperative neurophysiologic monitoring is affected by the type of anesthetic agents. Remifentanil is supposed to produce minimal or no changes in SSEP amplitude and latency. This study aims to investigate whether high doses of remifentanil influence the SSEP during spinal surgery under total intravenous anesthesia.</p> <p>Methods</p> <p>Ten patients underwent spinal surgery. Anesthesia was induced with propofol (2 mg/Kg), fentanyl (2 mcg/Kg) and a single dose of cis-atracurium (0.15 mg/Kg), followed by infusion of 0.8 mcg/kg/min of remifentanil and propofol (30-50 mcg/kg/min). The depth of anesthesia was monitored by Bispectral Index (BIS) and an adequate level (40-50) of anesthesia was maintained. Somatosensory evoked potentials (SSEPs) were recorded intraoperatively from the tibial nerve (P37) 15 min before initiation of remifentanil infusion. Data were analysed over that period.</p> <p>Results</p> <p>Remifentanil induced prolongation of the tibial SSEP latency which however was not significant (p > 0.05). The suppression of the amplitude was significant (p < 0.001), varying from 20-80% with this decrease being time related.</p> <p>Conclusion</p> <p>Remifentanil in high doses induces significant changes in SSEP components that should be taken under consideration during intraoperative neuromonitoring.</p

Acquired resistance to oxaliplatin is not directly associated with increased resistance to DNA damage in SK-N-ASrOXALI4000, a newly established oxaliplatin-resistant sub-line of the neuroblastoma cell line SK-N-AS

The formation of acquired drug resistance is a major reason for the failure of anti-cancer therapies after initial response. Here, we introduce a novel model of acquired oxaliplatin resistance, a sub-line of the non-MYCN-amplified neuroblastoma cell line SK-N-AS that was adapted to growth in the presence of 4000 ng/mL oxaliplatin (SK-N-ASrOXALI4000). SK-N-ASrOXALI4000 cells displayed enhanced chromosomal aberrations compared to SK-N-AS, as indicated by 24-chromosome fluorescence in situ hybridisation. Moreover, SK-N-ASrOXALI4000 cells were resistant not only to oxaliplatin but also to the two other commonly used anti-cancer platinum agents cisplatin and carboplatin. SK-N-ASrOXALI4000 cells exhibited a stable resistance phenotype that was not affected by culturing the cells for 10 weeks in the absence of oxaliplatin. Interestingly, SK-N-ASrOXALI4000 cells showed no cross resistance to gemcitabine and increased sensitivity to doxorubicin and UVC radiation, alternative treatments that like platinum drugs target DNA integrity. Notably, UVC-induced DNA damage is thought to be predominantly repaired by nucleotide excision repair and nucleotide excision repair has been described as the main oxaliplatin-induced DNA damage repair system. SK-N-ASrOXALI4000 cells were also more sensitive to lysis by influenza A virus, a candidate for oncolytic therapy, than SK-N-AS cells. In conclusion, we introduce a novel oxaliplatin resistance model. The oxaliplatin resistance mechanisms in SK-N-ASrOXALI4000 cells appear to be complex and not to directly depend on enhanced DNA repair capacity. Models of oxaliplatin resistance are of particular relevance since research on platinum drugs has so far predominantly focused on cisplatin and carboplatin

Gait speed, cognition and falls in people living with mild-to-moderate Alzheimer disease: Data from NILVAD

Background: Previous evidence suggests that slower gait speed is longitudinally associated with cognitive impairment, dementia and falls in older adults. Despite this, the longitudinal relationship between gait speed, cognition and falls in those with a diagnosis of dementia remains poorly explored. We sought to assess this longitudinal relationship in a cohort of older adults with mild to-moderate Alzheimer Disease (AD). Methods: Analysis of data from NILVAD, an 18-month randomised-controlled trial of Nilvadipine in mild to moderate AD. We examined: (i) the cross-sectional (baseline) association between slow gait speed and cognitive function, (ii) the relationship between baseline slow gait speed and cognitive function at 18 months (Alzheimer Disease Assessment Scale, Cognitive Subsection: ADAS-Cog), (iii) the relationship between baseline cognitive function and incident slow gait speed at 18 months and finally (iv) the relationship of baseline slow gait speed and incident falls over the study period. Results: Overall, one-tenth (10.03%, N = 37/369) of participants with mild-to-moderate AD met criteria for slow gait speed at baseline and a further 14.09% (N = 52/369) developed incident slow gait speed at 18 months. At baseline, there was a significant association between poorer cognition and slow gait speed (OR 1.05, 95% CI 1.01-1.09, p = 0.025). Whilst there was no association between baseline slow gait speed and change in ADAS-Cog score at 18 months, a greater cognitive severity at baseline predicted incident slow gait speed over 18 months (OR 1.04, 1.01-1.08, p = 0.011). Further, slow gait speed at baseline was associated with a significant risk of incident falls over the study period, which persisted after covariate adjustment (IRR 3.48, 2.05-5.92, p < 0.001). Conclusions: Poorer baseline cognition was associated with both baseline and incident slow gait speed. Slow gait speed was associated with a significantly increased risk of falls over the study period. Our study adds further evidence to the complex relationship between gait and cognition in this vulnerable group and highlights increased falls risk in older adults with AD and slow gait speed. Trial registration: Secondary analysis of the NILVAD trial (Clincaltrials.gov NCT02017340; EudraCT number 2012-002764-27). First registered: 20/12/2013