Assessing the energy saving potential of using adaptive setpoint temperatures:The case study of a regional adaptive comfort model for Brazil in both the present and the future

It has been found in recent years that using setpoint temperatures based on adaptive thermal comfort models is a successful method of energy conservation.Recent studies using adaptive setpoint temperatures incorporate international models from ASHRAE Standard 55 and EN16798-1.This study,however,has instead considered a regional Brazilian adaptive comfort model.This study investigates the energy demand arising from the use of a local Brazilian comfort model in order to assess the energy implications from the use of the worldwide ASHRAE Standard 55 adaptive model and various fixed setpoint temperatures.All of Brazil’s climate zones,full air-conditioning,mixed-mode building operating modes,present-day climate change scenarios,and future scenarios—specifically Representative Concentration Pathways(RCP)2.6,4.5,and 8.5 for the years 2050 and 2100—have all been taken into account in building energy simulations.The use of adaptive setpoint temperatures based on the Brazilian local model considering mixed-mode has been found to significantly reduce energy consumption when compared to static setpoint temperatures(average energy-saving values ranging from 52%to 58%)and the ASHRAE 55 adaptive model(average values ranging from 15%to 21%).Considering climate change and the mixed-mode Brazilian model,the overall energy demand for the three groups of climatic zones(annual average outdoor temperatures≤21℃,>21 and≤25℃and>25℃)ranged between 2%decrease and 5%increase,4%and 27%increase,and 13%and 45%increase,respectively.It is concluded as a consequence that setting setpoint temperatures based on the Brazilian local adaptive comfort model is a very efficient energy-saving method.

Adaptive Thermal Comfort for Occupants of Low-Cost Dwellings in a Hot Dry Climate

Adaptive models are based on the observation that there are some actions that people can and actually do take to achieve thermal comfort. Studies regarding thermal comfort conditions in economical dwellings were carried out simultaneously in seven Mexican cities, corresponding to warm dry and warm humid climates. In this article, case studies of low-cost dwellings in the city of Hermosillo （in northwest Mexico）, are presented and analyzed. Field surveys were carried out to obtain information about the physical characteristics of the dwellings and their occupants, as well as the indoor thermal environment. Neutral temperature was obtained from the applied survey. The high neutral temperature reveals the effect of inhabitants＇ adaptation mechanism to extreme climates. Occupant comfort votes as a function of indoor air temperatures were analyzed, and different characteristics such as age, size and gender were evaluated separately. The results show the variability of the neutral temperature and the tolerance to temperature changes, depending on the population＇s specific characteristics. In many cases where the population does not have access to artificial acclimatization devices, the neutral temperature values for specific climates and people can inform architects when choosing the most suitable thermal strategies for building design.

Field study of seasonal thermal comfort and adaptive behavior for occupants in residential buildings of Xi’an,China

The study aims to investigate the thermal comfort requirements in residential buildings and to establish an adaptive thermal comfort model in the cold zone of China.A year-long field study was conducted in residential buildings in Xi’an,China.A total of 2069 valid questionnaires,along with indoor environmental parameters were obtained.The results indicated occupants’thermal comfort requirements varied with seasons.The neutral temperatures were 17.9,26.1(highest),25.2,and 17.4℃(lowest),and preferred temperatures were 23.2,25.6(highest),24.8,and 22.4℃(lowest),respectively for spring,summer,autumn,and winter.The neutral temperature and preferred temperature in autumn are close to the neutral temperature in summer,while the neutral temperature and preferred temperature in spring are close to that in winter.Besides,the 80%and 90%acceptable temperature ranges,adaptive thermal comfort models,and thermal comfort zones for each season were established.Human’s adaptability is related to his/her thermal experience of the current season and the previous season.Therefore,compared with the traditional year-round adaptive thermal comfort model,seasonal models can better reflect seasonal variations of human adaptation.This study provides fundamental knowledge of the thermal comfort demand for people in this region.

Optimisation of Climate Robust Buildings under Summer Conditions with ＂Primero-Comfort＂ Simulation Software Including Detailed User Behaviour and Comfort Expectance

In this paper the possibilities for avoiding active air conditioning by all means of the room itself （window size, glazing, shading system, natural ventilation, and furniture）, artificial light and control strategy of these systems are investigated. A very important component of the system is the user with his ability to adapt to changing conditions in his surrounding and with his possibilities to manipulate the window, the shading system, the light switch etc. All these aspects interact together. It is necessary to optimize them simultaneously. But real planning often separates them into single sections. Simulation tools also handle normally only one or a few aspects, we know for example the thermal simulation or the daylight simulation. Primero-Comfort （2009） is a simulation tool based on energy＋, what is able to consider thermal simulation as well as daylight simulation as well as user behaviour in regard to the probability of window openings. The resulting thermal comfort is rated by an adaptive comfort model, the Dutch ISSO 74 （2004）. This allows designing office rooms more realistic. And it shows that an optimized solution has to include the interactions of aU mentioned aspects. Investigations with Primero-Comfort for a moderate European climate （Hamburg） show that a very good comfort can be reached only by passive means of building design also for hot summer weather just like the summer in the year 2003. The keys for such hot-summer-robust-buildings are night ventilation with height difference, heat protection glazing and good shading system, reduced internal heat gains for artificial light by accepting a threshold of 300 lx of daylight as comfortable and a reduced window size oriented on daylighting and the view out of the window.

Optimization of a Solar Chimney with a Horizontal Absorber for Building Ventilation:A Case Study

A study is conducted to optimize the geometry of a solar chimney equipped with a horizontal absorber in order to improve its performances in relation to the provision of ventilation.The problem is tackled through numerical solution of the governing equations for mass,momentum and energy in their complete three-dimensional and unsteady formulation.The numerical framework also includes a turbulence model(k-ε)and a radiant heat transfer(DO)model.Moreover,a Multi-Objective Genetic Algorithm(MOGA)is employed to derive the optimal configuration of the considered solar chimney.It is shown that an air velocity of 0.2 m/s can be obtained.This value is the minimum allowed air velocity according to the ASHRAE’s(American Society of Heating,Refrigerating and Air-Conditioning Engineers)adaptive comfort approach.

Adaptive thermal comfort in naturally ventilated hostels of warm and humid climatic region,Tiruchirappalli,India

Thermal comfort is an important factor in hostel buildings when the aim is to maximize the productivity of the students.Due to the extreme weather conditions,achieving thermal comfort in a hostel building in a hot and humid climate is even more difficult.Studies conducted in naturally ventilated hostel buildings in warm-humid climates involved the influence of outdoor air temperature only up to 34.4℃ and have been conducted in a specific season.In contrast,the Tiruchirappalli climate is characterized by a higher range of environmental variables.Therefore,to understand the thermal comfort conditions and usage of the environmental controls in naturally ventilated hostel buildings at the higher range of the environmental variables,a thermal comfort field study spread over one year was carried out at the National Institute of Technology,Tiruchirappalli,India,in twenty-seven hostel buildings.This study relies on field observation and thermal comfort responses from 2028 questionnaires collected from the students between September 2019 to August 2020.The analysis revealed a neutral temperature of 29.5℃ and a comfort range from 26.1℃ to 32.8℃,indicating a wide range of ther-mal adaptation than suggested by the National Building Code of India and ASHRAE standard 55.The preferred temperature was 27.8℃,indicating that students preferred a cooler environment.Acceptability with sweating conditions extended the upper limit of thermal acceptability from 31.8℃ to 32.4℃.The use of a mosquito net can increase the probability of opening a window.Results indicated that overall behavioral adjustment could extend the comfort limits.The study results would be helpful to develop guidelines and designs for naturally ventilated hostel buildings in warm and humid climates that will contribute to reducing energy demand.

Sensitivity Numerical Analysis of Human Body Exergy Balance under an Unsteady-State Thermal Environment —Behavioral Adaptation Induced by Undesirable Cold Storage by Building Envelope in Winter

We analyzed the relationships between the human body exergy balance and behavioral adaptations induced by undesirable cold storage by a building envelope under an unsteady-state thermal environment in winter. The complex interaction of the warm exergy production by shivering, lifting of the shell ratio, and reduction of the blood flow rate was considered to constitute the physiological adaptation necessary for maintaining the constant core temperature, which was an important aspect in living organisms. In the case of intermittent use room, it was suggested that better thermal comfort and desirable behavioral adaptations, which decreased the consumption of fossil fuels, could be achieved if interior wooden cladding was used in constructions with building envelopes that had a comparatively large heat capacity, or in cases of wooden constructions in which the building envelope heat capacity was comparatively small.

Zero-Energy-Buildings in Different Climates： Design Strategies, Simulation and Prognosis METHOD for Energy Demand

The European Directive 2010/31 claims that by 2020 only （nearly-） ZEB （zero-energy-buildings） may be built. To reach this goal, it is pertinent for buildings to be energetically optimized first. The remaining energy demand must then be covered by on-site renewable energies （PV, geothermal, etc.）. With the area of use （energy demand） and the size of the building envelope/estate （renewable energy supply） in competition with each other, the maximum number of building stories will be most likely limited. For 15 different climatic locations worldwide, the energy demand of optimised office rooms has been simulated and compared with the possible renewable energy production on site. For every location, a good correlation has been found between the simulated energy demand and data like heating and cooling degree hours. Correspondent linear equations are given here. As another result, the maximum numbers of possible stories for ZEBS have been derived, being between 3 and 10 depending on the location.

Model predictive control for adaptive cruise control with multi-objectives: comfort,fuel-economy,safety and car-following

For automated vehicles,comfortable driving will improve passengers’ satisfaction.Reducing fuel consumption brings economic profits for car owners,decreases the impact on the environment and increases energy sustainability.In addition to comfort and fuel-economy,automated vehicles also have the basic requirements of safety and car-following.For this purpose,an adaptive cruise control (ACC) algorithm with multi-objectives is proposed based on a model predictive control (MPC) framework.In the proposed ACC algorithm,safety is guaranteed by constraining the inter-distance within a safe range; the requirements of comfort and car-following are considered to be the performance criteria and some optimal reference trajectories are introduced to increase fuel-economy.The performances of the proposed ACC algorithm are simulated and analyzed in five representative traffic scenarios and multiple experiments.The results show that not only are safety and car-following objectives satisfied,but also driving comfort and fuel-economy are improved significantly.

Physics-informed machine learning for metamodeling thermal comfort in non-air-conditioned buildings

There is a growing need for accurate and interpretable machine learning models of thermal comfort in buildings.Physics-informed machine learning could address this need by adding physical consistency to such models.This paper presents metamodeling of thermal comfort in non-air-conditioned buildings using physics-informed machine learning.The studied metamodel incorporated knowledge of both quasi-steady-state heat transfer and dynamic simulation results.Adaptive thermal comfort in an office located in cold and hot European climates was studied with the number of overheating hours as index.A one-at-a-time method was used to gain knowledge from dynamic simulation with TRNSYS software.This knowledge was used to filter the training data and to choose probability distributions for metamodel forms alternative to polynomial.The response of the dynamic model was positively skewed;and thus,the symmetric logistic and hyperbolic secant distributions were inappropriate and outperformed by positively skewed distributions.Incorporating physical knowledge into the metamodel was much more effective than doubling the size of the training sample.The highly flexible Kumaraswamy distribution provided the best performance with R2 equal to 0.9994 for the cold climate and 0.9975 for the hot climate.Physics-informed machine learning could combine the strength of both physics and machine learning models,and could therefore support building design with flexible,accurate and interpretable metamodels.

Predicting the clothing insulation through machine learning algorithms:A comparative analysis and a practical approach

Since indoor clothing insulation is a key element in thermal comfort models,the aim of the present study is proposing an approach for predicting it,which could assist the occupants of a building in terms of recommendations regarding their ensemble.For that,a systematic analysis of input variables is exposed,and 13 regression and 12 classification machine learning algorithms were developed and compared.The results are based on data from 3352 questionnaires and 21 input variables from a field study in mixed-mode office buildings in Spain.Outdoor temperature at 6 a.m.,indoor air temperature,indoor relative humidity,comfort temperature and gender were the most relevant features for predicting clothing insulation.When comparing machine learning algorithms,decision tree-based algorithms with Boosting techniques achieved the best performance.The proposed model provides an efficient method for forecasting the clothing insulation level and its application would entail optimising thermal comfort and energy efficiency.

Thermal comfort analysis of earth-sheltered buildings: The case of meymand village, Iran

Vernacular buildings are known for their localized passive settings to provide comfortable indoor environment without air conditioning systems.One alternative is the consistent ground temperature over the year that earth-sheltered envelopes take the benefit;however,ensuring annual indoor comfort might be challenging.Thus,this research monitors the indoor thermal indicators of 22 earth-sheltered buildings in Meymand,Iran with a warmdry climate.Furthermore,the observations are used to validate the simulation results through two outdoor and indoor environmental parameters,air temperature and relative humidity during the hottest period of the year.Findings indicated that the main thermal comfort differences among case studies were mainly due to their architectural layouts where the associated variables including length,width,height,orientation,window-to-wall ratio,and shading depth were optimized through a linkage between Ladybug-tools and Genetic Algorithm(GA)concerning adaptive thermal comfort model definition and could enhance the annual thermal comfort by 31%.

Development of data-driven thermal sensation prediction model using quality-controlled databases

Predicting the thermal sensations of building occupants is challenging,but useful for indoor environment conditioning.In this study,a data-driven thermal sensation prediction model was developed using three quality-controlled thermal comfort databases.Different machine-learning algorithms were compared in terms of prediction accuracy and rationality.The model was further improved by adding categorical inputs,and building submodels and general models for different contexts.A comprehensive data-driven thermal sensation prediction model was established.The results indicate that the multilayer perceptron(MLP)algorithm achieves higher prediction accuracy and more rational results than the other four algorithms in this specific case.Labeling AC and NV scenarios,climate zones,and cooling and heating seasons can improve model performance.Establishing submodels for specific scenarios can result in better thermal sensation vote(TSV)predictions than using general models with or without labels.With 11 submodels corresponding to 11 scenarios,and three general models without labels,the final TSV prediction model achieved higher prediction accuracy,with 64.7%–90.7%fewer prediction errors(reducing SSE by 3.2–4.9)than the predicted mean vote(PMV).Possible applications of the new model are discussed.The findings of this study can help in development of simple,accurate,and rational thermal sensation prediction tools.