Indoor Temperature Research Articles

Energy Efficiency Assessment of Existing Rural Houses in Nantong Based on Human Thermal Comfort

Current research on rural houses in China mainly focuses on improving energy efficiency, with relatively few studies addressing energy-saving measures and enhancing thermal comfort for residents. Therefore, this paper focuses on existing rural houses in Nantong City, Jiangsu Province, as the research object. Through on-site measurements and questionnaire surveys, it was found that the average indoor temperature of rural houses is 28.5 °C in summer and below 10 °C in winter, failing to meet the comfort needs of the villagers. To further study human thermal comfort, a linear regression method was used to establish an indoor Mean Thermal Sensation (MTS) model for Nantong’s rural houses. The neutral temperature in summer was found to be 26.46 °C, and an adaptive thermal comfort model for rural residents in the Nantong area was established. Through single-factor simulation and orthogonal experiments, the optimal comprehensive energy-saving renovation scheme was proposed. Finally, a typical rural house in Zhangzhuang Village was used as a case for building renovation practice. After the renovation, the number of thermal comfort hours increased by 145 h per year, the thermal comfort compliance rate reached 47.07%, and the overall energy-saving rate was 57.41%.

A refined classification method of heat customers to improve inter-household thermal balance intelligent control and regulation

Hydraulic imbalance in district heating systems (DHSs) especially in the secondary pipe network hinders the improvement of system energy efficiency. To improve inter-household thermal balance on the secondary side, this paper proposes a refined classification and intelligent regulation method for heat customers. Firstly, an intelligent balancing system is proposed with the smart valves installed at heat customer's thermal entrance. Then, the refined method classifies the heat customers based on room comprehensive thermal characteristic coefficient. The regulation periods and target return temperature predictive models are determined for each customer type to achieve balance regulation. Finally, the proposed intelligent balance system was applied to a practical DHS secondary pipe network to verify its balance effect. After the regulation, indoor temperature non-uniformity coefficient for each type of heat customer decreased by 15 %; while the return water temperature dispersion reduced by 8 %–18 %. This led to a significant improvement in hydraulic and thermal balance among heat customers, resulting in a 6.2 % heat-saving rate. The application provides a reference for inter-household thermal balance regulation in scenarios where indoor temperature loggers are limited within similar secondary pipe network of DHSs.

The impact of local microclimates and Urban Greening Factor on schools’ thermal conditions during summer: A study in Coventry, UK

Thermal comfort in schools affects children's wellbeing and educational outcomes. Global warming and frequent heatwaves have worsened the overheating issue in schools, especially in Western European countries, like the UK. While previous studies have mainly focused on residential and commercial buildings, school-related research often emphasised indoor thermal conditions, neglecting the broader influence of microclimates on the overall thermal conditions. Therefore, this research explores the thermal conditions in schools, during the summer of 2023, with a specific focus on the impact of greenery and materials. Urban Greening Factor (UGF) and its relationship with indoor and outdoor air temperatures were explored for the first time.Field studies were conducted in four primary schools in Coventry, UK, measuring indoor air temperatures and micrometeorological parameters. Tree shade demonstrated a substantial cooling effect, reducing air temperature and mean radiant temperature by up to 6.4 °C and 22.9 °C, respectively. Considerable difference between measured air temperatures in sunlight and official meteorological records highlights the need for microclimatic studies in schools. Thermal imagery identified high surface temperatures on artificial grass (67 °C) and asphalt (55 °C). Urban Greening Factor showed a strong correlation with classroom temperatures but failed to account for spatial greenery distribution and subsequently outdoor thermal conditions. The study concludes that optimising tree shade and replacing dark and artificial materials, are necessary for effective heat mitigation, offering valuable insights for policymakers and urban planners to create thermally comfortable and sustainable school environments.

Optimizing the indoor thermal environment and daylight performance of buildings with PCM glazing

With the development of energy efficient technologies for glass envelopes, using thermal storage techniques to improve their thermal inertia has become a trend in research. In the present study, the heat transfer and day-lighting models of a building with PCM glazing curtain wall (PGC) were developed. Multiple parameters such as glazing internal surface temperature, PCM liquid phase rate, indoor temperature, natural illumination, and daylighting coefficient were quantitatively analyzed to evaluate their indoor thermal environment and daylighting performance. The results show that the application of PGC can significantly improve indoor temperature uniformity and indoor thermal comfort compared to silica aerogel glass curtain wall (SGC) and hollow glass curtain wall (HGC). By filling 10 mm thick PCM, the temperature fluctuation of the inner surface of the glass and the midpoint temperature of the room can be reduced by 35 %, the peak temperature of the inner surface of the glass can be delayed by 0.89 h, and the average indoor natural light value and indoor daylight factor are satisfactory at 453.26 lx and 9.63 %, respectively. However, as the thickness of the PCM increases to 15 and 20 mm, the liquid fraction of the PCM and the energy storage performance of the PGC are greatly reduced, and natural daylighting does not meet the GB/t-50033-2013 limit.

Cooling collars incorporated with PCM packs and gels to improve indoor thermal comfort in healthy young females

The human neck is one of the most thermally sensitive body regions to nociceptive stimuli. Neck cooling has been extensively researched to improve athletic performance in the heat. However, there has been very little research into the use of conductive neck cooling for indoor thermal comfort. We thereby designed three ultralight cooling collars with cooling gels or phase change materials (GEL6, PCM15, and PCM18). Their cooling performance on thermal comfort enhancement of 14 healthy young female participants under two warm indoor temperatures (28 °C and 30 °C) was investigated. Thermophysiological and perceptual responses, including skin temperature, heart rate, sweating, thermal sensation vote (TSV), thermal comfort vote (TCV), and thermal preference vote (TPV), were extensively studied. Results showed that, in contrast to GEL6, PCM15 and PCM18 significantly decreased the neck skin temperature, with PCM15 exhibiting the greatest reduction of 4.37 °C at 28 °C. PCM15 and PCM18 significantly decreased overall and local TSVs while also increasing overall and local TCVs (all p < 0.05), thus they could maintain >80 % occupant satisfaction at 28 °C. At 30 °C indoor temperature, however, none of these three cooling collars were found to meet the 80 % occupant satisfaction criterion. When compared to GEL6, PCM15 and PCM18 were found to only significantly reduce local TSVs at the head and neck (all p < 0.05). In sum, PCM15 and PCM18 have the potential to increase acceptable HVAC summer cooling setpoint temperature by 2.5 °C, from 25.5 °C to 28.0 °C, leading to a 25 % reduction in building cooling energy consumption.

Analyzing the thermal performance of walling systems in low-income housing through computational fluid dynamics approach

The thermal properties of wall materials have a direct impact on the thermal performance of the indoor environment, by also influencing the occupant’s well-being. Coupled with airflow dynamics, the thermal performance of wall materials influences the indoor air temperature distribution thereby impacting thermal comfort, particularly in naturally ventilated (NV) spaces. However, defining the relationships between the wall characteristics, airflow dynamics, and thermal comfort for selecting the appropriate wall material for low-income NV buildings is still not evident in the current literature. Therefore, this study proposes a simulation-based framework that considers the relationships between wall materials, airflow dynamics, and thermal comfort using a computational fluid dynamics (CFD) approach by spatially plotting the thermal comfort within the NV spaces.The study analyses locally accessible wall materials, subjecting them to different operating conditions within NV dwelling units of low-income housing in India. Out of the analyzed wall materials, aerated autoclaved blocks exhibited a significant indoor temperature reduction of 20.7 % compared to the compressed stabilized earth blocks, with constrained airflow exacerbating temperature fluctuations. The global implications of these findings are substantial for urban low-income dwellers experiencing excessive heat. Mass housing efforts worldwide can benefit from using thermally efficient materials in construction thereby improving living conditions, and mitigating the impact of climate change on thermal comfort and health.

AI-powered sensor fault detection for cost-effective smart greenhouses

Sensor networks in greenhouses play a pivotal role in controlling the stability of environmental and chemical factors. The Internet of Things (IoT) has been widely adopted for monitoring various sensor networks in greenhouses. In the present study, an IoT platform for remote monitoring of greenhouse environment is designed. The platform consists of a sensor node, a sink node, and an edge server. The sensor node measures indoor and outdoor humidity and temperature, interior CO(g), and interior luminosity and transmits data to the sink node, where it is timestamped. The sink node is connected to an edge server through the Mosquitto MQTT broker and the data is subsequently transferred to a MongoDB Cloud infrastructure, where the data of each variable is stored in proper formats. In the second part of the study, four 1D convolutional neural networks (CNNs) were developed for data prediction of each sensor to provide fault-tolerance in the system. The first three models, for predicting inner humidity and temperature, outdoor temperature, and outdoor humidity, directly predict the actual data of the faulty sensor based on regression analysis. The last model is designed for predicting CO and luminosity and performs data classification for faulty sensors. The models provide a high level of precision in their predictions. The RMSE for interior temperature and humidity and exterior temperature and humidity are 0.86 ℃, 3.47%, 0.682 ℃, and 2.74%, respectively. Additionally, the accuracy for luminosity and CO classification are 89.70% and 83.43%. Comparison of 1D CNN, decision tree, and linear regression model revealed that the machine learning models considerably outperform linear regression model, and they can better capture the nonlinear correlations among the variables. Furthermore, the predictive outcomes of the models were consistent across different weather conditions. The proposed methodology can be used to induce tolerance against faulty reads at sensor level in greenhouse sensor networks, independent of time and the data gathered by the faulty sensors. The study indicates that exploiting cloud resources for promoting the use of complex AI models on IoT platforms can provide a suitable solution for real-time monitoring and fault control of greenhouse environmental factors.

Experimental study of a PV-PCM window under hot summer and warm winter climate

An energy-efficient PV-PCM (Photovoltaic-Phase change material) window is proposed in this study. During the day, a portion of the incident solar radiation is converted into electricity, while excessive heat from internal sources and solar radiation is stored within the PCM infill. At night, the stored heat can be released from the PCM. Unlike existing PV windows with PCM infill, the PCM is sealed in separate modules, allowing for easy adjustment or removal to better adapt to different weather conditions. In this study, the light, thermal and electrical performance of the PV-PCM window was tested in comparison with common clear glazing window, PV window and PCM-infill window under hot summer and warm winter climate.The experiment results demonstrated the ability of PV-PCM window in delaying and reducing the peak indoor temperature in hot summer. Compared with common clear glazing window, the peak temperature reduced by 8.67, 5.35 and 10.70 °C in the three days. Correspondingly, the indoor heat gains were reduced by 364.97, 265.96 and 301.10 kJ. SHGC of the proposed PV-PCM window was reduced to <0.30, whereas the U-value was reduced to <2.50 W/(m2·K). Compared with the PV window, more power generation were observed by PV-PCM window because of the cooling effect brought by the PCM module nearby. Whilst balancing thermal regulation, the daily power generations of the 0.36 m2 semi-transparent CdTe PV-PCM window were 548.33, 316.55 and 58.85 kJ.

DATAWARE AND SOFTWARE OF THE AUTOMATED TECHNOLOGY FOR COMPUTER- INTEGRATED CONTROL OF HEAT PUMP SYSTEMS

The paper has solved a scientific and applied problem as for substantiating requirements to technical and functional characteristics of the automated technologies of computer-integrated control of heat pump systems in the heating and cooling modes owing to its dataware and software developing and testing. Following functional procedures have been taken into consideration during the dataware and software development of the computer-integrated subsystem for heating of buildings: monitoring of ambient air temperature, ground temperature, and operating modes of functional elements of the heat pump system as well as zonal monitoring of indoor air temperature; computer-integrated processing of the monitoring data; automated control of the heat pump as well as modes of zonal thermal medium supply to the building; and analysis of energy efficiency as for implementation of the proposed computer-integrated method to control systems of thermodynamic heating. It has been identified that thermal capacity of a heat pump is rather sensitive to indicators of the total heat losses of a building. If the total heat losses increase from 0.2°С/hour up to 0.5°С/hour then constant value of the thermal capacity increases by 48%. Moreover, thermal capacity of a heat pump is quite sensitive to ambient air temperature dynamics: if temperature variation is 12°С (i.e. 3°С-9 °С) then average increase in thermal capacity is 58 %. Following functional procedures have been involved during the dataware and software development of the computer-integrated subsystem for cooling of buildings: monitoring of ambient air temperature and operating modes of functional elements of the heat pump system; zonal computer-integrated monitoring of indoor air temperature; computer-integrated the monitoring data processing based upon microcontrollers; automated control of the thermal pump operating modes (i.e. heat energy outfeed and release to the main intercooler unit); and analysis of energy efficiency as for implementation of the proposed computer-integrated method to control systems of thermodynamic cooling of buildings. The abovementioned has helped identify that thermal capacity of a heat pump is rather sensitive to indicator of heat exchange with surrounding air: four times increase in heat-exchange coefficient (i.e. from 1°С/hour up to 4°С/hour) results in 20% growth of thermal capacity of the cooling subsystem system at similar ambient air temperature (it varies from 30°С up to 36 °С) if the maintained target indoor temperature is 22°С.

Layered laser-engraved wood-based composite capable of photothermal conversion and energy storage for indoor thermal management in buildings

Wood is used more and more in building. Phase change materials (PCM) with automatic temperature regulation and heat storage function have been widely concerned in the field of building energy conservation. However, phase-change materials have poor light induction and cannot absorb sunlight during the day in time, resulting in limited heat storage effect. For the purpose of achieving efficient energy collection and utilization, a laser-engraved method was employed in this study to directly generate a three-dimensional grid structure on the surface of wood. Then, the eutectic mixture of capric acid (CA) and stearic acid (SA) and nano-SiO2 composite PCM (CPCM) was impregnated into the supporting substrate with photothermal conversion capability. The obtained laser-engraved wood (LEW) loaded with CA-SA/Nano-SiO2 showed a latent heat of 64.706 J/g, a thermal conductivity of 0.732 W/(m⋅K) and a photothermal conversion efficiency of 64.46 %. Additionally, the three-dimensional porous structure of wood and the addition of nano-silica set up a double barrier for the leakage of PCM. Therefore, CPCM have prospective applications as indoor thermal insulation materials. In this research, a simple laser engraving method was adopted to endow the wooden substrate with photothermal conversion ability, which provides an idea to expand the application of photothermal conversion materials in the field of phase-change energy storage. In addition, the building simulation software validated the potential of CPCM in stabilizing indoor temperatures and reducing building energy consumption.

Based on Embedded-RAM Smart Home

This design is based on the FS-MP1A development board, using the STM32MP157A processor. The smart home system includes a door access module, data acquisition and monitoring module, device control module, weather forecast module, and Baidu Cloud interaction module. The door access module mainly uses the sqlite3 database to store residents’ login information and implements password login for unlocking. The monitoring system primarily uses the V4L2 driver for the OV5640 camera, sets the video capture format to YUYN, converts the YUYN format video raw frames to RGB format, and then converts the RGB data to QImage for display on the screen. Environmental monitoring mainly involves detecting indoor light intensity, temperature, and humidity. The device control module mainly controls the on/off of LED lights, activates the buzzer, and regulates the fan speed. The weather forecast module mainly forecasts the weather conditions of the local city and displays the weather information on the screen. The Baidu Cloud interaction module mainly facilitates information exchange and controls devices such as fans, lights, and the buzzer.

Opportunities and Barriers of Calibrating Residential Building Performance Simulation Models Using Monitored and Survey-Based Occupant Behavioural Data: A Case Study in Northern Spain

The performance gap caused by occupant behaviour (OB) is one of the main challenges to the accuracy of building performance simulations (BPS) models. Calibration of BPS models has shown great improvements in tertiary and single residential buildings. Nevertheless, the calibration in collective residential buildings is still uncertain. This study aims to identify the opportunities and barriers to the calibration of collective residential building BPS models for the analysis of heating energy consumption. For this, the research calibrates a real case study of a social rental housing building located in northern Spain. The method involves the adjustment of input data based on OB clusters, developed by monitorization and survey data and the statistical comparison of the results of normative models, calibrated models and real data. The results show an average improvement of 67% in hourly indoor temperature and 16% in hourly heating energy consumption in calibrated models, but still with a considerable performance gap. The main barriers to a higher accuracy are the wide diversity and lack of uniformity of OB patterns, uncertainty of parameters, and use of auxiliary heating systems. However, deeper monitorization and survey campaigns with the use of OB clusters can be a promising opportunity.

Distributed model predictive control of fan coil system

To address the issues of large overshoot, long system stabilization time, and slow response associated with traditional PID-based and centralized model predictive control (MPC) of fan coil systems, this study proposes a distributed model predictive control (DMPC) method suitable for multi-zone building temperature regulation. Specifically, based on the thermal dynamic model of different zones, and aiming to constrain indoor temperature within set limits and minimize water system pressure drop and fan coil speed, a distributed MPC architecture for fan coil systems is developed. To solve the distributed optimization problem, the Nash equilibrium is introduced to find the global balance point within the distributed structure, and the Beetle Antennae Search-Particle Swarm Optimization (BAS-PSO) hybrid algorithm is used for online optimization of local MPCs. Experimental results show that compared to traditional PID control, the distributed MPC achieves coordinated multi-zone indoor temperature regulation with better dynamic performance, with the average absolute temperature error in each zone below 5 % and an energy savings rate of 16.23 %. Through semi-physical simulation, it is demonstrated that the proposed distributed MPC method has better applicability when the number of fan coils exceeds 10, compared to centralized MPC.

Assessment of the impact of setting the heating curve on reducing gas consumption in a residential building while ensuring thermal comfort

Due to the large share of the residential building sector in CO2 emissions, it is necessary to look for quick-to-implement and cost-free solutions for heating systems aimed at increasing their efficiency and reducing fuel consumption. The aim of the study is a multi-aspect assessment of the proper setting of the heating curve in the weather controller of a low-temperature floor heating system with a condensing gas boiler, and an analysis of the impact of the heating curve slope on the natural gas consumption, the emission of gaseous and dust pollutants and on maintaining appropriate indoor air parameters: air temperature, operative temperature and PMV and PPD. A wide range of simulation tests were carried out using the TRNSYS software for an existing energy-efficient single-family residential building, with mechanical ventilation with heat recovery, located in north-eastern Poland in Bialystok. Changing the heating curve slope by 0.1 can reduce gas consumption and pollutant emissions by 1.6–10.3 %. Low heating curve slopes may result in failure to reach the temperature set by users (difference between the set and achieved indoor temperature up to 1.9 K), while maintaining standard thermal comfort parameters (−0.5<PMV<+0.5; PPD<10 %). Reducing the indoor air temperature by 2 K can contribute to reducing gas consumption by 11 % while maintaining standard thermal comfort parameters. Changing the heating curve slope is one of the effective methods of reducing fuel consumption in the building. Results are useful for users of buildings equipped with low-temperature heating systems with floor heating and installers of these systems.

Preparation of phase change Heat storage wood with in-situ generation of thermal conductive particles to improve photothermal conversion efficiency

Wood has been developed with phase change heat storage function using balsa as a natural packaging material, and PEG was employed as a material for phase change heat storage (PCHS). Moreover, the thermal conductivity efficiency of the wood was be improved by in-situ synthesizing Fe3O4 in wood. The prepared PCHS wood was characterized by high solidification and melting enthalpies. The temperature of phase change ranged from 21.47 °C to 35.30 °C, consistent with the temperature range that makes humans comfortable in their body. The in-situ generation of Fe3O4 not only improves the PCHS wood's thermal conductivity efficiency, but also forms a 3D network structure with PCHS materials, thereby improving the dimensional stability. Comparing with original wood, PCHS wood shows good temperature regulation function, which can quickly convert light energy into internal energy and has a longer period of thermal radiation at low temperatures. The generated PCHS wood shows a promising application as indoor temperature regulating materials to promote building energy conservation.

Removal efficiency of restroom ventilation revisited for short-term evaluation

Ventilation efficiency or contaminant removal efficiency is often evaluated using the ratio between the concentrations in the exhaust air and the room air. This ratio does not truly represent the expectation of ventilation in restrooms, where dynamic airflow fields and sources are more typical. This study focuses on a short-term (10 min) pollutant removal percentage in a residential restroom featuring a dynamic airflow field, particularly with the onset of window-induced stack ventilation during toilet uses. Thirteen ventilation scenarios of a residential restroom were studied using the numerical method that was validated by a mock-up experiment. The scenarios differed in the operation of the exhaust fan and window. Results show that the 10-min pollutant removal percentage of a typical exhaust ventilation system at 10 h-1 air change rate (ACH) is only 68.5%. Under exhaust ventilation, opening the window can introduce both adverse short circuit and favorable stack ventilation depending on the difference between the indoor and outdoor temperatures. As the temperature difference increases from 0 to 12.5 °C, the removal percentage increases from below 50%, a drop due to short circuit, to above 98% thanks to a tripled ventilation rate. The human thermal plume has notable effect on the removal percentage, but its effect can be neglected with the presence of stack ventilation. The hybrid ventilation strategy has impact on perceived air quality and thermal comfort. When the outdoor air is colder, opening the window under exhaust ventilation may increase the current sitting user’s exposure to the self-produced pollutants but can reduce the exposure of the next immediate standing user. In addition, opening the window in cold days will make the toilet user thermally uncomfortable with reduced local temperatures and increased airflow velocities. The study highlights the importance of using the short-term removal percentage to evaluate the performance of restroom ventilation.

Photothermal Utility Heating with Diffused Indoor Light via Multiple Transparent Fe3O4@Cu2−xS Thin Films

By introducing a novel photothermal radiator that effectively harnesses diffused light through plasmonic Fe3O4@Cu2−xS nanoparticles, it is sought to offer a sustainable solution for maintaining comfortable indoor temperatures without heavy reliance on traditional solar sources. The approach involves the use of ultraviolet (UV) and infrared (IR) lights to photothermally activate transparent Fe3O4@Cu2−xS thin films, showcasing a proactive strategy to optimize energy capture even in low‐light scenarios such as cloudy days or nighttime hours. This innovative technology carries immense potential for energy‐neutral buildings, paving the way to reduce dependence on external energy grids and promoting a more sustainable future for indoor heating and comfort control. The developed photothermal radiator incorporates multiple transparent thin films infused with plasmonic Fe3O4@Cu2−xS nanoparticles, known for their robust UV and IR absorptions driven by localized surface plasmon resonance. Through the application of UV and IR lights, these thin films efficiently convert incident photons into thermal energy. The experiments within a specially constructed diffused light photothermal box, designed to simulate indoor environments, demonstrate the system's capability to raise temperatures above 50 °C effectively. This pioneering photothermal radiator offers a promising pathway for sustainable heat generation in indoor spaces, harnessing ubiquitous diffused light sources to enhance energy efficiency.

Household indoor air quality in northeast China: On-site inspection and measurement in 399 Tianjin area residences

There has been an increased concern on indoor air quality (IAQ) in residences since the majority of individuals’ time is mainly spent indoors. We inspected and measured indoor environmental parameters in 399 homes in northeast China in order to study IAQ. We systematically measured multilevel environmental parameters (physical, chemical, and biological) in children’s bedrooms during all seasons. The results indicated that the median values for indoor temperature, relative humidity, total volatile organic compounds (TVOC), and formaldehyde concentrations throughout the year were within the Chinese national standards. However, the median carbon dioxide concentrations exceeded 1000 ppm during spring, autumn, and winter. In the same seasons, the air change rate (ACR) was below the minimum required level of 0.5 h−1. Di-2-ethylhexyl phthalate (DEHP), di-n-butyl phthalate (DnBP), and di-isobutyl phthalate (DiBP) were predominantly detected in settled dust, displaying median concentrations of 126.9, 41.5, and 16.3 μg/g, respectively. Notably, phthalate concentrations were significantly higher in urban houses as compared to rural houses. Furthermore, median concentrations of Dermatophagoides farinae (Der f) and endotoxin were 689.4 ng/g and 3689.1 EU/g, respectively, trending higher in winter than summer. There was a negative correlation between ACR and chemical pollutants (TVOC, formaldehyde, and DiBP). In conclusion, northeast Chinese homes had poor indoor air quality with ubiquitous exposure to modern chemical compounds and insufficient ventilation.

Self-detection of thermal ambient parameters for air-conditioned space by learning operation data and self-prediction of indoor temperature and power consumption

For efficient energy use of air conditioning unit (AC), there is a need to find an optimal control method for operation. To deduce an optimal control algorithm, indoor temperature and power consumption need to be predicted according to given operating conditions. Among the key factors, the thermal ambient parameters such as thermal resistances of the operation sites are most difficult to identify. This study proposed a method for an AC to deduce these parameters for itself by learning operation data obtained from the embedded sensors and autonomously predict indoor temperature and power consumption by solving the transient energy conservation equation incorporating refrigeration cycle simulation. A novel aspect of this study is that the AC can autonomously perceive thermal ambient parameters of its surroundings and utilize these parameters to predict indoor temperature variations and power consumption. These data could be fed back to the control software of the AC in real time. To validate the proposed method, experiments were conducted in model house test facility using a real air-conditioner, and the results were compared with the simulation results. It was confirmed that the time to reach the air temperature setpoint was within 3 min and the power consumption could be predicted within 9 %.

CLIMAEXTREMO: A New Risk Indicator for the Health Risk to Building Occupants during Extreme Weather Events in Portugal

Portugal is the country in Europe where the death rate in winter and summer has the highest correlation with outdoor temperatures. The Portuguese National Institute of Public Health Ricardo Jorge has developed a national warning system for heat waves called ICARO, which has been in place since 1999 (and is the oldest in Europe). However, it presents some limitations, namely, the low spatial resolution (five regions in Portugal’s mainland), the low temporal forecasting period (one day), and the fact that it was only accessible to health authorities until very recently. This work describes the development of a new public dashboard that uses a new early warning index for extreme weather events, the CLIMAEXTREMO index, which extends the current warning system by improving the current forecasting models for risk by integrating new sources of public data and increasing the spatial and time resolution of the warnings to the municipality or the parish level. The new index is a combination of a new model to estimate the relative mortality increase (updating the model used in ICARO) together with a model of the indoor temperature of building archetypes for all municipalities and a vulnerability index that considers socio-demographic economic indicators. This work discusses the results of the new risk indicator for the heat waves that occurred in Portugal at the end of June and mid-August 2023, and it shows that the index was able to indicate a high risk for the municipalities that had an increase in the number of deaths during that period.