Heat Comfort Research Articles

Ensuring accurate microclimate research: How to select representative meteorological data of local climate in microclimate studies

Microclimate research has seen significant growth in recent years, particularly in areas such as outdoor thermal comfort, urban ecology, and urban heat mitigation. However, the short-term nature of many studies in this field presents challenges in ensuring that the collected data accurately represents local climate conditions. This paper introduces a novel method to enhance the quality and applicability of microclimate research by quantifying the representativeness of short-term meteorological data. Our approach employs the Kolmogorov-Smirnov (KS) statistic to compare daily meteorological data from nearby stations against long-term climate trends. Key findings demonstrate that this method effectively identifies representative data periods. This method allows researchers to evaluate the representativeness of each day's data according to their specific study objectives, whether focusing on typical or extreme weather conditions. By implementing this framework, researchers can: (a) Post-filter existing data to identify the most representative samples. (b) Quantify the climate representativeness of their findings, enhancing result interpretation and applicability. (c) More confidently generalize conclusions from short-term studies. The paper also provides simplified alternatives to the full method, making it accessible to a wider range of researchers. By adopting this approach, microclimate studies can achieve greater confidence in their data's representativeness, leading to more robust and generalizable conclusions. Our method addresses a key methodological challenge in microclimate research and provides a flexible data assessment framework. This framework enables researchers to systematically evaluate climate data representativeness, enhancing the reliability and applicability of their findings across various urban climate studies, from thermal comfort assessments to climate adaptation strategies.

Enhancing demand response and heating performance of air source heat pump through optimal water temperature scheduling: Method and application

Air source heat pump (ASHP) is a key technology for the electrification of building heating, and its participation in demand response (DR) has important implications for reducing the grid peak load. However, current DR strategies mainly focus on changing the setpoint of indoor temperature, using the building’s thermal mass as a passive thermal storage, while the impacts of water temperature on the performance of ASHP units are often overlooked. Therefore, this study proposes an optimal water temperature scheduling (OWTS) method to respond time–of–use tariffs. It is developed based on a room thermal dynamic prediction model and an ASHP power consumption prediction model, optimizing supply water temperature schedule with objectives of maintaining indoor thermal comfort and reducing heating operating costs. The effectiveness of the OWTS method was tested on a field ASHP system in Beijing, and compared with two benchmark methods. Results demonstrate that the application of the OWTS method can enhance the DR capability of the ASHP heating system, with the average value of Flexibility Factor increasing from 0.134 to 0.728, and can reduce the heating operating costs by 20.8%–43.0%. This study thereby offers a promising method for ASHP heating systems to effectively participate in DR.

Development of a Capsule-Type Inspection Robot Customized for Ondol Pipelines

Ondol is a heating system unique to Korean homes that increases indoor temperatures by supplying hot water through pipes embedded in floor slabs. Known for its comfort and sustained heating advantages, ondol has garnered international interest in countries requiring efficient heating solutions. Given the inherent challenges faced during installation and operation, timely inspection of ondol is crucial due to difficulties in detecting and locating defects in buried concrete pipes, often leading to costly rework and removal. However, specialized inspection systems tailored to ondol pipes remain underexplored. Therefore, this paper proposes a robotic inspection system capable of assessing the conditions of ondol pipelines. We analyze the characteristics of ondol piping to establish system requirements and develop a prototype of a compact capsule-shaped inspection robot tailored for ondol pipe inspection. Subsequent laboratory testing validates system performance and usability, confirming field applications through curvature maneuverability and image reception quality tests. This study aims to motivate advancements in ondol-specific system implementation and performance validation, potentially contributing to the smartification of ondol maintenance practices.

Study on personal comfort heating system and human thermal comfort in extremely low-temperature building environments

Exposure to extremely low-temperature building environments around −20 °C causes various hazards to human health. However, few studies have studied human thermal comfort and physiological response in such environments, nor the personal comfort heating system. Thus, this study investigated the heating characteristics of personal comfort heating system and their effects on thermal responses and thermophysiological parameters in extremely low-temperature building environments. We conducted experimental tests and questionnaires in a cryogenic enthalpy difference laboratory with air temperature ranging from −21.2 to −15.6 °C and a relative humidity of 55 %. Eight experimental conditions were designed with several heating measures (heating mat, heating gloves, heating cushion and airwarmer). The heating parameters of the personal comfort heating systems as well as the subjective evaluations and skin temperature of the subjects were recorded and analyzed. The results showed that the airwarmer confinement box condition had a corrective power of up to 38.3 K. The overall thermal sensation vote and overall thermal comfort vote were maintained at 0.25 and −0.25. Further, we found that the face, hands, calves and feet got cold easier with the largest skin temperature difference of −4.9, −8.1, −6.7 and −7.6 °C, respectively. Besides, the relationships between thermal responses and between thermal responses and skin temperature were examined, as well as a thermal comfort assessment methodology was established for extremely low-temperature environments.

Optimal scheduling of a cogeneration system via Q-learning-based memetic algorithm considering demand-side response

An optimization scheduling method for cogeneration systems based on the Q-learning-based memetic algorithm (QMA) is developed to enhance the system's wind power absorption capacity and economical operation. First, an optimal scheduling model for cogeneration systems is constructed. The model integrates time-of-use electricity pricing and heating comfort into the demand side, fully exploiting the potential of demand-side response (DR) in power generation and heating. An additional heat source (AHS), which consists of an electric boiler and heat storage tank, is used to uncouple heat and power. In addition, a QMA algorithm is designed to find the optimal operating scheme for the cogeneration system. The Q-learning algorithm is introduced to dynamically adjust crossover and mutation parameters during the global evolution stage, improving the algorithm's search capability. The Taguchi method is utilized for algorithm parameter calibration. Finally, the simulation results under various operating scenarios are compared and analyzed, verifying the feasibility and effectiveness of the proposed method. The simulation results show that DR and AHS can improve the system's economic performance and wind power utilization rate. Compared with the IMA, IPSO, MA, and IABC algorithms, the QMA algorithm reduces the average economic cost by approximately 1.18 %, 2.23 %, 5.69 %, and 14.69 %, respectively.

Thermal response and performance evaluation of floor radiant heating system based on fuzzy logic control

The floor radiant heating system is widely used in various buildings due to its advantages such as high efficiency, comfort, and uniform heating. Generally, research on floor radiant heating systems focuses on energy saving, but the issue of slow thermal response during the heating process has not been solved. To solve the problem of slow thermal response and uneven indoor thermal environment in floor radiant heating systems, a fuzzy logic control theory based on optimal fuzzy rules was proposed. The floor radiant heating system based on fuzzy logic control was established by the TRNSYS-MATLAB integrated control platform. The study compared the thermal response and thermal comfort performance of PID control, fuzzy logic control, and optimized fuzzy control methods. The results showed that fuzzy logic control performed better than PID control in controlling the system's thermal performance and indoor thermal environment. Compared to PID control, the optimized fuzzy control reduced the thermal response time by 64.60%, and the total energy consumption of the water pump and heat pump decreased by 222 kWh and 329 kWh, respectively. The proposed fuzzy logic control demonstrates favorable thermal response characteristics and holds substantial potential for energy conservation within the increasingly prevalent floor radiant heating systems.

Modelling and evaluating different multi-carrier energy system configurations for a Dutch house

The urge to reduce the dependence on natural gas for heating at the residential level has led to the deployment of different fossil fuel-free alternatives. In the Netherlands, two technologies are leading the transition: heat pumps, due to their high COP, and photovoltaic–thermal systems, due to their dual electric-thermal output. However, both represent a challenge for users and grid operators, aside from their stochastic behavior. Heat pumps alone can surpass a typical Dutch house’s total energy and power consumption. Photovoltaic–thermal systems, as their only electric homologs, usually have a mismatch between generation and demand, causing energy injections to the grid. From the electric perspective, storage systems are a proven solution to reduce the energy exchange with the distribution network. This paper proposes four multi-carrier energy system configurations for a Dutch household, comprising different combinations of a photovoltaic–thermal system, a battery energy storage, a heat pump, and an underground water tank thermal energy system, providing analytical models for every component (including the thermal losses from the thermal storage to the ground), and the space heating and electrical demands. We determined the components’ compatibility and evaluated the combinations considering their thermal performance, electrical performance, and equivalent CO2 emissions. The results suggest that using a heat pump combined with a photovoltaic system and a battery provides the best trade-off. The photovoltaic–thermal system alone could not supply the thermal demand required for comfortable space heating nor reach temperatures high enough to charge the thermal storage. Combining the thermal storage with the heat pump allows a certain degree of flexibility for the heat pump activation at the cost of COPs between 0.8 and 1.38 when used to charge the thermal storage, thus increasing energy consumption and equivalent emissions considerably.

Flexible Regulation and Synergy Analysis of Multiple Loads of Buildings in a Hybrid Renewable Integrated Energy System

The insufficient flexibility of the hybrid renewable integrated energy system (HRIES) causes renewable power curtailment and weak operational performance. The regulation potential of flexible buildings is an effective method for handling this problem. This paper builds a regulation model of flexible heat load according to the dynamic heat characteristics and heat comfort elastic interval of the buildings, as well as a regulation model of the flexible electrical load based on its transferability, resectability, and rigidity. An operation optimization model, which incorporates flexible regulation of multiple loads and a variable load of devices, is then developed. A case study is presented to analyze the regulation and synergy mechanisms of different types of loads. Its results show a saturation effect between heat and electrical loads in increasing renewable energy consumption and a synergistic effect in decreasing the operating cost. This synergy can reduce the operating cost by 0.73%. Furthermore, the operating cost can be reduced by 15.13% and the curtailment rate of renewable energy can be decreased by 12.08% when the flexible electrical and heat loads are integrated into the operation optimization of HRIES.

Assessing the impact of green infrastructure on thermal comfort in relation to humidity: A case study in Korea

Problems arising from heatwaves and urban heat islands (UHIs) exacerbated by climate change have prompted diverse adaptation strategies. Although green infrastructure, which positively affects climate change adaptation, has been extensively evaluated, global-scale assessments of UHIs indicate that green infrastructure may not be sufficient to mitigate UHIs in humid regions with annual precipitation exceeding 1000 mm or summer precipitation exceeding 570 mm. However, these assessments primarily focused on UHIs; different results may be obtained for individual heat comfort. As relative humidity increases, the cooling effect of green areas potentially decreases. This suggests the need for additional studies on the impact of short-term precipitation, which increases relative humidity and induces cooling effects on green infrastructure. Korea experiences a humid summer due to the concentration of precipitation (approximately 711 mm of summer precipitation); thus, the contribution of green infrastructure to improving heat comfort might be limited. Therefore, this study investigated the impact of green infrastructure on thermal comfort in relation to precipitation levels using the Wet-Bulb Globe Temperature (WBGT) index and data from weather stations in Korea. Precipitation intensity was categorized into 2-day periods and 10 mm/day. Subsequently, thermal comfort was compared for summers from 1993 to 2022 according to the green infrastructure ratio around the stations. The results indicated that green infrastructure had a cooling effect on clear days but only had a cooling effect on precipitation days when the level of green infrastructure was above the critical ratio. Additionally, green infrastructure reduced the WBGT index if minimal precipitation occurred during the five-day cooling period following precipitation. While green infrastructure remains crucial for climate change adaptation, other strategies must be implemented to improve the thermal environment, particularly in areas with frequent precipitation.

Protection Against Energy Exclusion as a Social Welfare Task

Abstract Many households in Poland are currently experiencing a situation in which they must choose between drastic energy savings, resulting in a noticeable lack of heat comfort, and spending on energy costs at the expense of other basic needs, such as food or medications. This phenomenon has negative consequences, from a deteriorating state of health, especially among children and older people, to ‘saving themselves’ by burning coal dust or waste, which, in turn, increases emissions and leads to pollution of the air. It also causes energy exclusion in many cases, which can cause breaches of human dignity. The question therefore arises: how does social welfare deal with energy exclusion on legal and social grounds with regard to satisfying the energy needs of citizens and what are the role and tasks of social welfare in conditions of an energy crisis? The answers to these questions are accompanied by an analysis of the concepts of energy poverty and energy exclusion, as well as a discussion and assessment of selected forms of social welfare in counteracting the energy exclusion of citizens.

Concentration of CO2 in the Local Working Area during the Joint Operation of a Gas Infrared Heater and an Air-Exchange System

The formation of local areas in large buildings with regulated thermal conditions is an urgent task. The use of gas infrared heaters for this purpose raises questions on the utility of an air-exchange system and the monitoring of the combustion product concentration. In this study, the modeling of heat transfer processes on premises with a gas infrared heater and an air-exchange system was conducted. The carbon dioxide concentration in the local working area when using a light-type gas infrared heater was determined. The regularities of current formation for circulating air and combustion products on the premises at various air-exchange rates were analyzed. The profiles of CO2 temperatures and concentrations in the local working areas on the left and right of the equipment model are shown. The article makes a conclusion about the influence of air velocity from the air-exchange system based on average values of carbon dioxide concentration on the premises and in the local working area. The possibility of increasing the temperature in the local working area without exceeding the permissible CO2 concentrations (less than 1000 ppm) has been identified. The formulated approach allows us to predict the available modes of the air-exchange system to create the highest possible comfort heating parameters while maintaining an acceptable degree of air pollution from combustion products.

A bi-objective low-carbon economic scheduling method for cogeneration system considering carbon capture and demand response

Carbon capture and storage (CCS), energy storage (ES), and demand response (DR) mechanisms are introduced into a cogeneration system to enhance their ability to absorb wind energy, reduce carbon emissions, and improve operational efficiency. First, a bi-objective low-carbon economic scheduling model of a cogeneration system considering CCS, ES, and DR was developed. In this model, the ES and CCS remove the coupling between power generation and heating. The DR mechanism, which is based on the time-of-use electricity price and heating comfort, further enhanced the flexibility of the system. In addition, an improved bare-bones multi-objective particle swarm optimisation (IBBMOPSO) was designed to directly obtain the Pareto front of the low-carbon economy scheduling model. The particle position update mode was improved to balance global and local search capabilities in various search stages. The Taguchi method was used to calibrate the algorithm parameters. The inverse generational distance (IGD), hypervolume (HV), and maximum spread (MS) were used to evaluate the distribution and convergence performance of the algorithm. The improved technique for order preference by similarity to an ideal solution (TOPSIS) method was utilised to obtain the optimal compromise solution. Finally, the proposed method was tested on a cogeneration system in Northeast China. According to the comparison results, the average economic cost of the cogeneration system considering CCS, ES, and DR was reduced by approximately 1.13%, and carbon emissions were reduced by 6.79%. The IBBMOPSO is more competitive than the NSGA-II, MOWDO, MOMA, MOPSO, and BBMOPSO in low-carbon economic scheduling for the cogeneration system.

Evaluation of Paraffin based Phase change Material on Thermal Performance in Building

The building sector, which is the primary user of material and energy resources, necessitates the implementation of energy-saving measures and the use of thermally efficient materials. Severe heat waves necessitate a significant level of electricity usage in order to maintain suitable thermal comfort. The current situation poses an economic burden and hinders the progress of sustainable development unless viable alternative solutions are devised. Hence, thermal energy storage systems are considered a viable choice for the effective and environmentally friendly integration of energy in building design. The objective of this study was to examine the customized thermal mass and heat comfort considerations related to recently developed phase change materials (PCMs) known as local paraffin derived from petroleum. These PCMs were evaluated for their prospective application as thermal energy storage (TES) systems in the specific context of Iraq.

Thermographic cameras for thermal comfort applications: simulated and experimental spectral response errors of various long-wave infrared detectors

Modern remote temperature sensing in the form of infrared imaging has become a widely used and important technique, with the ability to measure and characterize important but unseen Radiant heat. As more Long-wave Infrared (LWIR) detectors come to market aiming to meet a wide array of needs and goals, there is a need to differentiate and appraise LWIR detectors based upon the specific needs of thermal comfort research. While most detectors measure in the range of 8-14 μm, only 37.6% of the emitted energy of a blackbody at 300 K is contained in this spectral range. Thus, inherent to the operation of nearly all infrared detectors and cameras is an assumption about the emission curves of the objects sensed. Many materials in the built environment deviate significantly from the blackbody assumption, and the error due to this deviation is one which generic gray-body emissivity corrections are unable to fix — it is akin to taking black-and-white images with only the red channel of a camera, and using exposure compensation to correct the image to attempt a true monochrome rendition: there is simply missing information and the adjusted image will still be very clearly wrong. In this paper, we aim to evaluate by simulation and experiment the potential errors in infrared thermography used to drive thermal comfort heat transfer calculations due to intrinsic spectral assumptions of LWIR detectors.

STATISTICAL DOWNSCALING USING REGRESSION NONPARAMETRIC OF FOURIER SERIES-POLYNOMIAL LOCAL OF CLIMATE CHANGE

Indonesia is a tropical country that is vulnerable to the impacts of climate change. Climate change causes an effect on the level of comfort (heat stress) that can affect the level of human immunity, one of the indices to calculate the level of human comfort (heat stress) is the Thermal Humidity Index (THI). Climate change scenarios modeled in Earth System Models (ESMs). ESM has a coarse resolution and is subject to considerable bias. This research is using secondary data. The data source used in this study comes from the Coupled Model Intercomparison Project (CMIP5). This research will focus on projected heat stress which is calculated based on THI with the temperature and humidity variables. Therefore, in this research to reduce the bias correction method used Statistical Downscaling (SD) and nonparametric regression. The results of the bias correction using the Statistical Downscaling (SD) method and Nonparametric Regression Fourier-Polynomial Local Series in this study the R-square value for Relative Humidity yields 95% and for Temperature yields 94%. The projection of climate change based on the value of the Temperature Humidity Index (THI) in Indonesia in the category of 50% of the population of Indonesians feeling comfortable conditions occurred in 2006-2059. Then the population of citizens in Indonesia felt uncomfortable conditions occurred in 2060 to 2100 with a THI value of 27.0730°C - 27.7800°C.

Investigating the effect of surface urban heat island on the trend of temperature changes

Urbanization has led to the emergence of surface urban heat islands (SUHI) and air pollution, necessitating comprehensive investigations into the urbanization process. This study explores the impact of urbanization on temperature trends in northern Iran using remote sensing and neural networks. MODIS images from 2001, 2010, and 2019 were employed to determine climatic features and vegetation levels. SUHI indices and the Urban Thermal Field Variance Index (UTFVI) were utilized to assess ecological comfort and urban heat islands. Markov chain methods were used for forecasting future thermal pollution. The relationship between temperature, air pollutants, spectral indices, and thermal indices was investigated through regression analysis. Furthermore, multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM) neural networks were employed for predicting indicator values. The findings reveal an increase in thermal pollution indicated by elevated Land Surface Temperature (LST), SUHI, and UTFVI values, along with a decrease in Normalized Difference Vegetation Index (NDVI) values between 2001 and 2019. The Urbanization Index (UI) values demonstrate a 5% increase in urban areas during the same period. The UTFVI values suggest a decline in ecological comfort due to urbanization and reduced vegetation, with the majority of the region experiencing the poorest thermal comfort in 2019. The SUHI index peaked at 11.72 in 2019, with the highest values observed in spring and the lowest in winter. Correlation analysis highlights a significant relationship between LST and various factors, including SUHI, UTFVI, CH4, SO2, and CO2, underscoring the influence of temperature increase on pollution escalation in the study area. Air pollution assessment indicates the highest concentration of CO gas in the northern and southern parts of the region. Tehran exhibits the highest CO gas levels at 0.051 ppm, while Golestan records the lowest at 0.012 ppm. SO2 levels reach their peak in northern regions (0.0088 ppm) and are lowest in southern regions (0 ppm). The Markov and CA-Markov chain methods predict that by 2050, approximately 50% of the region will experience high temperatures. Neural network results demonstrate that the LSTM method outperforms MLP and RBF methods, with R2 values of 0.99 and 0.98, respectively, indicating higher accuracy in LST prediction during testing and validation stages. Overall, this study highlights the potential of neural networks in predicting thermal pollution and facilitating effective land management strategies in urban areas.

Thermal Performance Analysis of a Commercial Space Exposed to Solar Radiations in the Composite Climatic Conditions

People spend majority of their lifetime indoors making comfortable dwellings one of the major requirements otherwise it may primarily impact the occupant’s health and productivity. Indoor temperature and relative humidity (RH) are the two major parameters for determining comfort level, whilst airflow and heat transfer are having an influential role in controlling these parameters. Since solar radiations are the primary source of heat inside a building and ceiling fans play a significant role in improving air circulation. The present study strives to develop a model for evaluating the thermal behavior of commercial space under the influence of solar radiations and ceiling fans. It involves “solar ray tracing” and “3D-Fan zone” models to assess their impact on indoor air quality and thermal comfort. The developed model is validated by comparing the simulated results of air velocity and temperature with experimental data accumulated from space. Further, the validated model is used to evaluate the impact of different levels of solar irradiation between 200 W/m2 and 1000 W/m2 on the indoor environment. Results revealed that an increment of 200 W/m2 irradiations is raising the mean indoor temperature by 0.2 °C, percentage dissatisfaction by 8% while a reduction in RH is reported to be 0.5%.

Research on performance of radiant floor heating system based on heat storage

With the development of the economy, improving energy efficiency and saving energy have become important issues that need to be addressed. In terms of building energy efficiency, combining phase change heat storage material with floor radiation heating is an effective way to improve energy utilization efficiency. In this paper, a composite phase change heat storage material used in radiant floor heating was introduced. Based on the selected materials, experiments on floor radiation heating system based on heat storage in different environments during the heating season were conducted. The results showed that thermal conductivity of composite phase change heat storage material increased by a maximum of 97.2%, and the maximum heat release time can be obtained 6 h. By using this radiant floor heating system, a balance between heating comfort and economy has been achieved, so this system will have a good applied foreground.

THERMAL PERFORMANCE OF DIFFERENT BUILDING TYPOLOGIES IN THIMPHU

The building sector in Bhutan is responsible for 42% of total energy and consumes 242,916 TOE of thermal energy. The maximum thermal energy was used for space heating. As per the studies conducted on the energy efficiency of the buildings in Bhutan, the buildings are found to be the worst performance in terms of thermal and airtightness. Without the proper insulation, lack of choice of the building materials as per the climatic conditions and lacks of awareness in the society about the green building practices has led to poor indoor quality of life. Buildings located inthe high region are found to be uncomfortably cold during winter months.This research focuses on studying the thermal performance of different building typologies. The main findings of this research were on enhancing indoor thermal comfort and reducing heating energy demand by the incorporation of passive measures. The results were achieved by simulating the buildings in EnergyPlus software using the data collected from questionnaires and field surveys. The simulation results showed that among the investigated buildings, a concrete hollow block wall located at Kabesa has the less comfort hour of 15% at the existing condition. When passive interventions were incorporated into the building, the thermal comfort hours were achievable up to 32% with the load reduction potential of 65%.As for the traditional house with the adobe brick wall and timber wall at Changdagang, had more comfort hours than the building located at Kabesa. With the different set of passive intervention, it was found that comfort hour of 38% to 41% and load reduction of 25.9% can be achieved for the building with adobe brick wall and timber wall respectively. Whereas for stone masonry with cement mortar and RCC framed structure located at Begana and Langdru had a thermal comfort hour of more that 30%. However, RCC framed structure was reported to have huge demand for heating energy compared to stone masonry with cement mortar. The passive intervention prepared for RCC and stone masonry had the potential to achieve 41%-57% of thermal comfort hours. The results from all the investigated buildings revealed that the inclusion of passive design could improve indoor thermal comfort and can achieve a reduction in heating energy demand.

A control method combining load prediction and operation optimization for phase change thermal energy storage system

Due to the high latent heat and load-shifting capacity, phase-change thermal energy storage technology is an effective way to reduce energy costs under time-of-use electricity pricing. A favorable operation strategy is essential to exploit the advantage of the phase change thermal energy storage system. Previous studies on the operation strategy lack consideration of load prediction, which could reduce the matching degree of heat supply and demand. This study proposed a control method combing load prediction and operation optimization based on an electric boiler-phase change thermal energy storage heating system. A deep learning-based heating load prediction model was built; on this basis, an operation optimization method using dynamic programming was formulated subsequently. The proposed method was applied to a case building located in Beijing, China. By applying the proposed control method, the energy costs of the system were saved by 10% compared to the original operation strategy in the whole heating season, and the matching degree of heat supply and demand could be close to 100%, demonstrating that the method can provide users with cheaper and the best thermally comfortable heating. The proposed method is capable of achieving optimal operation of a phase change thermal energy storage system in practical applications.