•Impacts of climate change on the energy demand and thermal comfort of buildings.•13 future climate scenarios with 90 years of hourly data in 38 European cities.•Long- and short-term variations ...considering climate extremes and uncertainties.•Extreme climate events notably affect peak loads and thermal comfort of buildings.•Cooling demand can increase to 28% on average while heating decrease for 16%.
In recent years, climate change and the corresponding expected extreme weather conditions have been widely recognized as potential problems. The building industry is taking various actions to achieve sustainable development, implement energy conservation strategies, and provide climate change mitigation. In addition to mitigation, it is crucial to adapt to climate change, and to investigate the possible risks and limitations of mitigation strategies. Although the importance of climate change adaptation is well-understood, there are still challenges in understanding and modeling the impacts of climate change, and the consequent risks and extremes. This work provides a comprehensive study of the impacts of climate change on the energy performances and thermal comfort of European residential building stocks. To perform an unbiased assessment and account for climate uncertainties and extreme events, a large set of future climate data was used for a 90-year period (2010–2099). Climate data for 38 European cities in five different climate zones, downscaled by the “RCA4” regional climate model, were synthesized and applied to simulate the respective energy performances of the residential building stocks in the cities. The results suggest that there will be larger needs for cooling buildings in the future and less heating demand; however, there are differences in the variation rates between zones and cities. Discomfort hours will increase notably in cities within cooling-dominated zones, but will not be affected considerably in cities within heating-dominated zones. In addition to long-term changes, climate-induced extremes can considerably affect future energy demands, especially the cooling demand; this may become challenging for both buildings and energy systems.
We analyzed the ASHRAE Global Thermal Comfort Database II to answer a fundamental but overlooked question in thermal comfort studies: how many and which subjective metrics should be used for the ...assessment of the occupants' thermal experience. We found that the thermal sensation is the most frequently used metrics in Thermal Comfort Database II, followed by thermal preference, comfort and acceptability. The thermal sensation/thermal preference, thermal comfort/air movement acceptability and thermal comfort/thermal preference are the top three most dependent metrics pairs. A principal component analysis confirmed that the personal experience of thermal conditions in built environment is not a one-dimensional problem, but at least a two-dimensional problem, and suggested thermal sensation and thermal comfort should be asked in right-now surveys as the first two Principal Component are majorly constructed by thermal sensation and thermal comfort. To further confirm the predictive power of thermal sensation and comfort, we used logistic regression and support vector machine to predict thermal acceptability and thermal preference with thermal sensation and comfort. The prediction accuracy is 87% for thermal acceptability and 64% for thermal preference. The prediction error might be due to occupants' individual difference and people errors in answering survey. These findings could help the design of chamber experiments, field studies, and human-building interaction interfaces by shedding light on the choice of subjective thermal metrics to effectively and accurately collect information on occupants’ thermal experience.
•Correlation matrix and Principal Component Analysis were conducted.•The evaluation on thermal environment is found to be a two-dimensional problem.•Thermal sensation and thermal comfort are suggested to be used in right-now surveys.•Logistic regression and support vector machine are used to confirm the result.•These findings could help the survey design of thermal comfort studies.
A personal comfort model is a new approach to thermal comfort modeling that predicts individuals' thermal comfort responses, instead of the average response of a large population. However, securing ...consistent occupant feedback for model development is challenging as the current methods of data collection rely on individuals' survey participation. We explored the use of a new type of feedback, occupants' heating and cooling behavior with a personal comfort system (PCS) for the development of personal comfort models to predict individuals' thermal preference. The model development draws from field data including PCS control behavior, environmental conditions and mechanical system settings collected from 38 occupants in an office building, and employs six machine learning algorithms. The results showed that (1) personal comfort models based on all field data produced the median accuracy of 0.73 among all subjects and improved predictive accuracy compared to conventional models (PMV, adaptive) which produced a median accuracy of 0.51; (2) the PMV and adaptive models produced individual comfort predictions only slightly better than random guessing under the relatively mild indoor environment observed in the field study; and (3) the models based on PCS control behavior produced the best prediction accuracy when individually assessing all categories of field data acquired in the study. We conclude that personal comfort models based on occupants' heating and cooling behavior can effectively predict individuals' thermal preference and can therefore be used in everyday comfort management to improve occupant satisfaction and energy use in buildings.
Display omitted
•We propose a new modeling approach for thermal comfort – personal comfort models.•We employ machine learning to predict individuals' thermal preference.•Individuals' models improve prediction accuracy compared to PMV and adaptive models.•Occupant heating and cooling behavior is a strong comfort predictor.
In this paper, the effects of thermal environment on occupant IEQ perception and productivity were studied. Seven groups of experiments were carried out in a controlled office environment and the ...physical parameters, including air temperature, globe temperature, relative humidity, carbon dioxide concentration, illuminance and background noise level, were measured. In the experiments, indoor air temperature was the independent variable, which varied from 16 °C to 28 °C with a step of 2 °C, and other constant IEQ parameters were the control variables. The dependent variable would be human perception of various control variables and productivity. Subjects (9 females and 12 males) were recruited to participate every experiment for 2 h. During each experiment, they voted their perceptions of thermal comfort, indoor air quality, lighting and acoustic environment, and performed simulated office tasks to evaluate the productivity. The results showed that the variation of thermal environment not only affected thermal comfort but also had a “comparative” impact on the perception of other IEQ factors. When thermal environment was unsatisfactory, it weakened the “comfort expectation” of other IEQ factors, which accordingly resulted in the less dissatisfaction with other IEQ factors. Conversely, when thermal environment was quite satisfying, it raised “comfort expectation” of other IEQ factors, which lowered the evaluation of the real performance of other IEQ factors retroactively. The quantitative relationship between productivity and thermal environment was established. The optimal productivity was obtained when people felt “neutral” or “slightly cool”, and the increase of thermal satisfaction had a positive effect on productivity.
•Experiments were conducted in a controlled office under conditions from 16 °C to 28 °C.•Objective measurement, subjective survey and productivity test were carried out.•The “comparative” impact between thermal environment and other IEQ factors was found.•The optimal productivity was obtained when people felt “neutral” or “slightly cool”.•The increase of thermal satisfaction had a positive effect on productivity.
The rapid urban expansion in East-Asian cities has increased the need for comfortable public spaces. This study presents field measurements and parametric simulations to evaluate the microclimatic ...characteristics in a university campus in the tropical climate of Kuala Lumpur, Malaysia. The study attempts to identify the thermally uncomfortable areas and their physical and design characteristics while debating on the circumstances of enhancing the outdoor comfort conditions for the campus users. Simulations in Envi-met and IES-VE are used to investigate the current outdoor thermal conditions, using classic thermal metric indices. Findings show high levels of thermal discomfort in most of the studied spaces. As a result, suggestions to improve the design quality of outdoor areas optimizing their thermal comfort conditions are proposed. The study concludes that effective re-design of outdoor spaces in the tropics, through adequate attention to the significant impacts of shading and vegetation, can result in achieving outdoor spaces with high frequency of use and improved comfort level.
Display omitted
•Adequate attention to shading and vegetation results in enhancing outdoor thermal comfort.•The outdoor spaces with the highest PMV/PET values embrace similar characteristics.•The fully shaded areas can be used by users for 80% of the studied period.•The unshaded spots embrace high thermal discomfort levels for over 80% of the time.
The re-integration of trees into the urban landscape is a veritable strategy for urban climate mitigation and adaptation. However, dysfunctional trees in terms of urban heat mitigation are dominant ...in many sub-tropical cities' landscapes due to the lack of scientific basis of tree selection. Therefore, this study proposes and evaluates a methodological framework as an approach for “right tree, right place” for urban heat mitigation through parametric ENVI-met simulations that involve the combination of 54 generic tree forms and 10 characteristic urban morphology – Sky-View Factor (SVF). Results show variable temperature regulation by tree forms (species) with varying magnitude in different urban morphology. Daytime and nighttime temperature regulation effects were between 0.3 °C – 1.0 °C and 0.0 °C – 2.0 °C, respectively depending on tree forms and SVF value. Furthermore, the Heat Reduction Potential (HRP) of trees forms were determined in terms of their human thermal comfort improvement. In general, we found a range of +5% and − 20% depending on SVF, negative and positive values imply heat reduction and increment, respectively. With the competing shading effect of buildings, the HRP of trees reduces from high to low SVF area with variable magnitude among tree forms (species). Hence, the proposed morphology-based tree selection approach was evaluated by comparison with two uninformed selection approaches in a realistic urban neighborhood in Hong Kong. Results clearly indicate the proposed approach's capability in improving human thermal comfort by up two times more than either of the other approaches. Finally, evidence-based recommendations were given for the reference of policy-makers when they make urban green development plan.
Display omitted
•A “Right tree, right place” approach for optimum urban heat mitigation was proposed.•The approach was developed and evaluated through a parametric case study under hot-humid prevailing climate.•Proposed approach was found significantly more efficient compared to two others in a realistic urban neighborhood.•Heat reduction potential of trees was location-dependent and species-specific.
Whether a journey is pleasant or not usually depends on the climatic conditions which permit to perform outdoor activities. The perception of climatic conditions, determined by physiological and ...psychological factors, can vary according to different adaptation phenomena related to the person involved and the weather conditions of the place where they live. Studying the bioclimatology of a country characterized by a high flux of tourism, as e.g. Italy, can provide some important information about where and when is it better to visit a place. Some differences have to be specified though, like the local tourism, which is used to that type of climate, and international tourism, which is formed by people coming from countries with different types of climates. Therefore this paper examined the climatic conditions and outdoor thermal comfort through the Mediterranean Outdoor Comfort Index (MOCI) for local tourism and through the predicted mean vote (PMV) for international tourism. The cities examined were three (Venice, Rome and Palermo located in the North, Centre and South of Italy, respectively), where average information were collected every week for an entire year. Finally, a map of the entire Italian territory reporting the seasonal average values of these indexes was also reported.
Electric vehicles (EVs) are vehicles that are propelled by electric motors powered by rechargeable battery. They are generally asserted to have GHG emissions, driveability and life cycle cost ...benefits over conventional vehicles. Despite this, EVs face significant challenges due to their limited on-board energy storage capacity. In addition to providing energy for traction, the energy storage device operates HVAC systems for cabin conditioning. This results in reduced driving range. The factors such as local ambient temperature, local solar radiation, local humidity, duration and thermal soak have been identified to affect the cabin conditions. In this paper, the development of a detailed system-level approach to HVAC energy consumption in EVs as a function of transient environmental parameters is described. The resulting vehicle thermal comfort model is used to address several questions such as 1) How does day to day environmental conditions affect EV range? 2) How does frequency of EV range change geographically? 3) How does trip start time affect EV range? 4) Under what conditions does cabin preconditioning assist in increasing the EV range? 5) What percentage increase in EV range can be expected due to cabin preconditioning at a given location?
•Dynamic electric vehicle thermal comfort model based on control volume approach.•Impact of HVAC loads on electric vehicle range.•Electric vehicle range variation across the geography of US and as a function of time of day.•Benefits of cabin pre-conditioning.
The steady-state thermal environment creates obvious challenges to health and energy, which arouse people's concern on the dynamic thermal environment. As an important way to improve thermal comfort ...and reduce energy consumption, airflow has been widely concerned. Being a highly acceptable airflow, simulating natural wind can be adopted to set up a dynamic thermal environment. Influencing factors of the dynamic thermal environment are much more spread out than the steady-state thermal environment and the human thermal comfort problem gets start from the relatively simple steady-state thermal comfort, those two determiners work and led to the consequence that research on the human comfort model of the dynamic thermal environment is relatively limited. Based on the fuzzy comprehensive evaluation method, this paper establishes the human thermal comfort model of simulating natural wind environment. Combined with the human thermal comfort experiment, the evaluation indexes of Mean Thermal Sensation Vote (MTSV) and Dissatisfaction Rate (DR) are obtained. By comparing the PMV-PPD index and MTSV-DR index with the experimental results of human thermal comfort, it was found that there was a large deviation between the PMV-PPD index and the experimental results, but the MTSV-DR index was in good agreement. According to the range of dissatisfaction rate, the appropriate combination of environmental parameters is recommended. It provides a new way of thinking for human thermal comfort evaluation in dynamic thermal environment.
•The simulating natural wind is introduced into the steady-state air-conditioning environment to form a dynamic simulating natural wind environment.•The human thermal comfort model is established by fuzzy comprehensive evaluation, and the MTSV-DR indexes are put forward to evaluate the comfort of the environment.•The MTSV-DR is compared with PMV-PPD and the experimental result to verify the reliability of the MTSV-DR.•Adding simulating natural wind in steady-state air-condition environment can increase the indoor comfortable temperature and achieve the purpose of energy saving.•When the dissatisfaction rate of the population is required to be less than 10% (DR is less than 10%), the MTSV is between -0.05 and +0.05.6)It is recommended that the indoor temperature be 28℃, the turbulence intensity be 0.68, and the blowing time be 5 minutes.
PDF
