Indoor Temperature Research Articles

Latent Autoregressive Student-t Prior Process Models to Assess Impact of Interventions in Time Series

With the advent of the “Internet of Things” (IoT), insurers are increasingly leveraging remote sensor technology in the development of novel insurance products and risk management programs. For example, Hartford Steam Boiler’s (HSB) IoT freeze loss program uses IoT temperature sensors to monitor indoor temperatures in locations at high risk of water-pipe burst (freeze loss) with the goal of reducing insurances losses via real-time monitoring of the temperature data streams. In the event these monitoring systems detect a potentially risky temperature environment, an alert is sent to the end-insured (business manager, tenant, maintenance staff, etc.), prompting them to take remedial action by raising temperatures. In the event that an alert is sent and freeze loss occurs, the firm is not liable for any damages incurred by the event. For the program to be effective, there must be a reliable method of verifying if customers took appropriate corrective action after receiving an alert. Due to the program’s scale, direct follow up via text or phone calls is not possible for every alert event. In addition, direct feedback from customers is not necessarily reliable. In this paper, we propose the use of a non-linear, auto-regressive time series model, coupled with the time series intervention analysis method known as causal impact, to directly evaluate whether or not a customer took action directly from IoT temperature streams. Our method offers several distinct advantages over other methods as it is (a) readily scalable with continued program growth, (b) entirely automated, and (c) inherently less biased than human labelers or direct customer response. We demonstrate the efficacy of our method using a sample of actual freeze alert events from the freeze loss program.

TINY HOUSE IN THE DESERT: A study in indoor comfort using moveable insulation and thermal storage

A tiny house for seasonal rangers was designed for the extreme climate and diurnal temperature fluctuations of Joshua Tree National Park. The challenging climate conditions resulted in an extensive investigation of the building envelope to act as a passive thermal storage system to naturally cool and heat the house. The north wall was designed as a “cold battery,” which uses a dynamic internal and external insulation system with a high-mass concrete wall to achieve coolth absorption at night as part of a thermal management system and to achieve indoor thermal comfort under extreme desert climates. A combination of opening an exterior insulation system at night to absorb coolth, closing the exterior insulation system when the temperature rises, and opening the interior insulation system to release coolth was attempted. The goal was to achieve a comfortable indoor temperature without the use of HVAC systems. Several software programs were tested for their capabilities to simulate time lag in concrete and thermal storage including Opaque and IES VE. The design of the north wall was carried out for different conditions such as different modulating profiles, locations and thicknesses of insulation, and the data were collected and analyzed again. IES VE was able to successfully simulate the effects of moving interior and exterior insulation twice a day on different sides of a concrete wall. In consideration of the special occupancy type, the expansion of the comfort zone for seasonal rangers is achieved by the specification of a comfortable temperature range from 65°F to 85°F. In a standard year, consisting of 8,760 hours, simulations were conducted on the entire residence, primarily during the months when seasonal rangers were present, to investigate thermal comfort conditions. These assessments encompassed various aspects, including ventilation (2,941 comfort hours), the north wall (4,653 comfort hours), the base model (2,883 comfort hours), and enhancements to the south wall (5,543 comfort hours) and the east and west glazed facade (6,137 comfort hours). The culmination of these findings significantly contributed to an enhanced overall comfort experience within the tiny house design, specifically tailored to accommodate the needs of seasonal rangers at Joshua Tree National Park. Natural ventilation proved to be a good strategy.

The Green Cooling Factor: Eco-Innovative Heating, Ventilation, and Air Conditioning Solutions in Building Design

This research investigates the compatibility of conventional air conditioning with the principles of green building, highlighting the need for systems that enhance indoor comfort while aligning with environmental sustainability. Though proficient in regulating indoor temperatures, conventional cooling systems encounter several issues when incorporated into green buildings. These include energy waste, high running costs, and misalignment with eco-friendly practices, which may also lead to detrimental environmental effects and potentially reduce occupant comfort, particularly in retrofit situations. Given the emphasis on sustainability and energy conservation in green buildings, there is a pressing demand for heating, ventilation, and air conditioning (HVAC) solutions that support these goals. This study emphasises the critical need to reconsider traditional HVAC strategies in the face of green building advances. It advocates for the adoption of innovative HVAC technologies designed for eco-efficiency and enhanced comfort. These technologies should integrate seamlessly with sustainable construction, use greener refrigerants, and uphold environmental integrity, driving progress towards a sustainable and occupant-friendly built environment.

Development of a Simulator for Household Refrigerator Using Equation-Based Optimization Control with Bayesian Calibration

Conventional household refrigerators consist of a motor-driven compressor, evaporator, condenser, and expansion valve. To determine the optimal operation strategies of refrigerators, it is essential to investigate the overall system performance, using an appropriate simulator. This study proposed a data-driven simulator based on engineering features and machine learning algorithms for conventional household refrigerators. The most correlated variables for identifying the indoor temperature of refrigerators were identified using variable importance, and these were revealed to be the circulation fan speed, compressor operation status, and refrigerant flow direction. A data-driven simulator was constructed using Bayesian calibration, which considers the important variables, combined with a straightforward heat balance equation. The Markov Chain Monte Carlo approach was used to simultaneously calibrate three coefficients on the critical variables based on the heat balancing equation on each time step, which is consistent with the actual temperature of the container. The results revealed that the proposed approach (equation-based Bayesian calibration outperforms) standard machine learning algorithms, such as linear regression and random forest models, by 38.5%. Additionally, compared to the typical numerical analysis method, it can reduce the delivery time and effort required to develop a reliable simulator for household refrigerators.

Investigation of the Energy-Saving Potential of Buildings with Radiative Roofs and Low-E Windows in China

This study develops a model for buildings with a cooling roof, walls, and low-emissivity (Low-E) windows. This model is verified through experimental analysis. The cooling demands of standard buildings and cooling buildings are compared, and the energy-saving potentials of cooling buildings are analysed. It is found that compared to standard buildings, cooling buildings exhibit superior cooling performances attributable to the application of cooling materials. Considering Hong Kong’s weather data, the indoor temperature of cooling buildings can be sub-ambient. The cooling demands of cooling buildings are decreased from 75 W/m2 to 30 W/m2, indicating a 60% energy-saving potential. The nationwide cooling demand for a standard building across China is approximately 95.7 W/m2, whereas the nationwide summer average cooling demand for cooling buildings is 52.7 W/m2. Moreover, the cooling performance of a cooling roof is adversely affected by hot and humid weather conditions, resulting in lower temperature drops in southern regions compared to northern regions. However, the nationwide temperature drop across China can still be 1.6 °C, demonstrating promising cooling potentials. For the Low-E windows, the temperature can also be sub-ambient, with a nationwide average temperature drop of 1.7 °C. Therefore, the use of Low-E windows across China can also significantly contribute to energy savings for indoor cooling. Overall, the results of this study show that cooling buildings have high energy-saving potential under various climates. The proposed model can provide a reliable tool to facilitate relevant cooling evaluation by stakeholders, thereby benefiting the popularization of this technology.

Pobreza energética y salud en población gitana y en población general de España

ObjectiveTo quantify energy poverty in Roma population and in general population in Spain, in 2016, as well as to observe the association of this phenomenon with self-rated health, adjusted according to the main socio-economic determinants. MethodEnergy poverty has been defined as the financial inability to keep a home warm, the presence of dampness in the dwelling and falling into arrears in utility bills, using data from two European surveys from Spain in 2016: the Survey on Income and Living Conditions (EU-SILC) and the Second Survey on Minorities and Discrimination (EU-MIDIS II). Hierarchical logistic regression models were estimated with self-rated health as the outcome variable, progressively adjusted according to demographic (gender and age), environmental (household temperature, humidity and arrears in utility bills) and socio-economic (level of education, marital status and employment status) variables. ResultsOur results show that 45% of the Roma population had moderate or high levels of energy poverty. The odds ratio (OR) of poor self-rated health was higher in the Roma population (OR: 3.11; 95% confidence interval [95% CI]: 2.59–3.74). The inability to maintain an adequate indoor temperature significantly increased the risk of poor health (OR: 2.10; 95% CI: 1.90–2.32). After adjusting according to demographic, environmental and socio-economic variables, no association was observed between the population of ascription and self-rated health. ConclusionsTaking into account the main social determinants, including energy poverty indicators, being Roma is not associated with reporting poor health. This result points to the importance of tackling socio-economic factors, including energy poverty, to reduce health inequalities.

Simulation optimization and energy consumption evaluation on the wood solar drying kiln with heat storage and dehumidification system

The conventional wood drying process has high energy consumption and detrimental environmental impact. While solar energy has found widespread application in material drying, simulation software has been instrumental in optimizing drying processes and curbing energy consumption. Nevertheless, there remains a dearth of simulation and optimization studies specifically focusing on wood drying systems, particularly those incorporating phase-change heat storage devices.This study delves into the wood drying process in one year within a solar drying kiln equipped with a phase-change heat storage and dehumidification system. The investigation scrutinizes the impact of different operating parameters and heat storage devices on the temperature distribution across the entire system. Results indicate that through a new programming method, the simulation of the temperature change of the phase change thermal storage material is realized, and the visualization degree of the system is improved. The average relative error of simulated temperature and measured temperature is 2.29 %, which has a strong consistency. When dehumidifier air flow rate is 1500 m3/h, solar panel angle is 36°, the dehumidifier is turned off when the drying room temperature reaches 55 °C, and the dehumidifier is turned on when the drying room temperature drops below 55 °C, the highest energy efficiency can be achieved. Moreover, through simulation and optimization, the average indoor temperature increased by 24.05 %, and the system energy consumption reduced by about 56.23 %. This is equivalent to a reduction of 3,312.22 kg of CO2 emissions.In essence, this study provides a pathway for enterprises to realize tangible energy savings and emission reductions in the wood drying process, showcasing the potential of integrating solar energy, phase-change heat storage, and dehumidification for enhanced efficiency.

Thermal performance analysis and thermal environment creates effect of a novel solar hot-air phase change bed

Comprehensive heating provides a comfortable indoor but consumes significant energy. Temporal-spatial partitioned heating can meet differentiated thermal demands while reducing energy consumption. A novel solar hot-air phase change bed heating system has been proposed for achieving temporal-spatial partitioned heating. Theoretical analysis models have been established to study the heat transfer process of the hot-air phase change heating bed, and numerical simulations have been conducted to analyze its thermal performance and creation of a comfortable environment. The results demonstrate that at the end of the heat storage, the bed board temperature increased by 1.5 °C for every 5 °C increase in the wind temperature. The maximum temperature difference of the bed board reached 6.1 °C under three different wind speed schemes. During heat release, the bed board heat flux was affected by the phase change material heat storage difference, and the maximum temperature difference of the bed board reached 4.3 °C ∼ 6.7 °C under different schemes. After the first cycle, both indoor and bed board temperature were higher than the initial values. During the daytime, there was excessive heat indoors, and the maximum environmental temperature exceeded the comfortable range by 8.7 °C. At night, the bed was covered with a high-temperature layer, and the bed surface temperature was uniformly distributed, remaining in the thermal comfort zone for 3.8 h to 5.1 h. The research results provide a basis and guidance for utilizing solar hot-air phase change beds to improve differentiated thermal demands.

Relativistic Digital Twin: Bringing the IoT to the future

Complex IoT ecosystems often require the usage of Digital Twins (DTs) of their physical assets in order to perform predictive analytics and simulate what-if scenarios. DTs are able to replicate IoT devices and adapt over time to their behavioral changes. However, DTs in IoT are typically tailored to a specific use case, without the possibility to seamlessly adapt to different scenarios. Further, the fragmentation of IoT poses additional challenges on how to deploy DTs in heterogeneous scenarios characterized by the usage of multiple data formats and IoT network protocols. In this paper, we propose the Relativistic Digital Twin (RDT) framework, through which we automatically generate general-purpose DTs of IoT entities and tune their behavioral models over time by constantly observing their real counterparts. The framework relies on the object representation via the Web of Things (WoT), to offer a standardized interface to each of the IoT devices as well as to their DTs. To this purpose, we extended the W3C WoT standard in order to encompass the concept of behavioral model and define it in the Thing Description (TD) through a new vocabulary. Finally, we evaluated the RDT framework over two disjoint use cases to assess its correctness and learning performance, i.e., the DT of a simulated smart home scenario with the capability of forecasting the indoor temperature, and the DT of a real-world drone with the capability of forecasting its trajectory in an outdoor scenario. Experiments show that the generated DT can estimate the behavior of its real counterpart after an observation stage, regardless of the considered scenario.

Experimental Analysis of the Dual Source Heat Pump with Varying Water Temperature

Purpose: The aim is to examine how water temperature influence the coefficient of performance (COP) of a dual-source heat pump (DSHP) and to show how water temperature influence the relationship between heating capacity and power consumption of the DSHP. Methods: An experimental method to examine an installed 5kW DSHP system with an attached converters and other connected components, which arrangement properly controls for the indoor temperature and relative humidity of the laboratory. Results and Conclusions: The findings reflects that the COP of the DSHP pumps increases at low water temperature. As water temperature increases, the heat loss to water and compressor power consumption decreases, and in turn reduce the COP. The average temperature increases from 36.55oC to 59.15oC and COP reduces, by 40%, from 2.83–1.12. Implications: The implication is that the DSHP which is shown to be energy efficient is environmentally friendly, and as a result, would remain very useful in the fifth generation of heating systems with the use of smart technology in future. Originality/Value: The value of the article lies in its focus on the performance of the DSHP as an energy and efficiency device for in heating and cooling in buildings.

Control strategies and performance analysis for a novel nuclear heating system with heat storage

A nuclear heating system integrated with a heat storage tank (HST-NHS) is a promising technical solution for countries with large-scale heating demand. HST-NHS can greatly curtail carbon dioxide emissions and contribute to sustainable development. Appropriate control strategies are not only crucial to realize the real-time matching between HST-NHS heat supply and heat demand, but also ensure the stable and safe operation of HST-NHS. Until now, control strategies for HST-NHS have received limited attention. A refined dynamic simulation model for HST-NHS is established based on APROS software. The verification of the built model in steady state and transient state is completed. Two basic control strategies for the primary heating network (PHN) of HST-NHS, constant supply water temperature and variable flow rate (CT-VF) and variable supply water temperature and constant flow rate (VT-CF) are presented in detail. Based on various typical working conditions of a central heating region in Liaoyuan City, the control effects of two control strategies are investigated. Additionally, a comprehensive performance comparison between the two strategies is explored. Simulation results indicated that under the two control strategies, the indoor temperature can be effectively controlled at a high comfort level. During the same typical day, the total energy consumption cost under the VT-CF control strategy is 0.5 × 105 yuan lower than that under the CT-VF control strategy.

Simulation analysis of Finnish residential buildings’ resilience to hot summers under a changing climate

This study evaluated how residential buildings in Finland's cold climate withstand hot summers under climate change. The buildings' responses to outdoor temperatures, overheating, energy consumption, and cooling power demand were investigated with the actual data of the hot summer of 2018, and its projected conditions for 2050 and 2080 under the highest Representative Concentration Pathway. The results revealed that lightweight structured detached houses reacted about two times faster to outdoor temperatures. The new apartment and the new elderly house, constructed according to the latest Finnish building code, showed slower responses and lower indoor temperatures during current and future hot summers with no degree hours above the Finnish health limits of 30 °C/32 °C. Conversely, the old non-renovated apartment building and elderly house built in the 1970s–80s experienced higher indoor temperatures. Their indoor temperature surpassed 30 °C/32 °C even in the current climate. Although passive solutions reduced indoor temperatures effectively, hot summers necessitated active cooling systems. The cooling energy and power demand in the old buildings were higher compared to the new ones. However, the cooling electricity consumption was considerably small compared to the heating and total energy consumption in all the studied buildings. It was projected to increase in future climates by 40–160 % depending on the building type and climate scenario.

Coupling effect of radiative cooling and phase change material on building wall thermal performance

Phase change material (PCM) featured with high latent heat thermal energy storage capacity and isothermal phase transition is employed into building walls decorated with radiative cooling (RC) coating, in order to modify the cooling effect on building thermal performance. Numerical analysis indicates that the RC coating causes exterior temperature of P-RC walls lower than ambient temperature, achieving maximum exterior temperature drop of 13.63 °C. Thermal buffer effect of PCM enables to shave the temperature peak and shift the temperature valley, inducing exterior temperature of the P-RC wall to fluctuate slightly in comparison to the RC wall. Interior temperature of the P-RC wall is found to approach to target temperature tightly. PCM located closer to the outside completes phase transition more rapidly, which is beneficial to levelling radiative cooling of RC coating. Exterior temperature of P-RC walls increases with augment of solar radiation intensity, ambient temperature and indoor temperature. Augment of PCM thickness, ambient temperature or indoor temperature is conducive to interior temperature. Latent heat thermal energy storage of PCM enlarges effective thermal capacity of walls, which is favorable to maintain interior temperature within target temperature. In conclusion, studied results highlight that radiation cooling can be improved by PCM, with substantial benefits to develop passive cooling available to building energy conservation.

Dynamic effects of occupant motion on indoor vertical thermal stratification in the displacement ventilation system

As an inevitable disturbance source, indoor occupant motion can weaken vertical thermal stratification and reduce the ventilation efficiency of the displacement ventilation (DV) system. However, the weakening process of vertical thermal stratification during occupant motion remains unclear. In order to clarify how occupant motion weakens the indoor vertical thermal stratification, occupant motion within a stratified environment in the DV system by numerical simulation was analysed. Initially, the Q criterion number, which represents the second invariant of the velocity gradient tensor, was used to visualise the vortexes in the wake of the moving occupant. Subsequently, the indoor vertical thermal profile variation during long-term occupant motions was analysed. Finally, the indoor vertical thermal profiles under different occupant moving velocities in the DV system were explored. Our research revealed that the reverse horizontal flow generated by the collision of the wake with the walls is the main reason for the weakening of the vertical thermal stratification. The vertical thermal profile undergoes four stages during the weakening process. In the first stage, the profile shape varies, followed by a monolithic translation. Thirdly, temperature variations only occur near the floor. Finally, indoor temperature stabilises even as occupants keep moving. When the human body moves at normal speeds ranging from 1.0 m/s to 1.6 m/s, the temperature gradient can decrease by approximately 45.0 % to 75.7 %. This study provides a more comprehensive understanding of how occupant motion affects the vertical thermal stratification of the DV systems to the designers, thereby offering robust support for research and practices in relevant fields.

A numerical study on the performance of a hybrid ventilation and power generation system

Ventilation, cooling and power are essential requirements of buildings. Integration of solar chimney, earth-air heat exchanger and photovoltaic panel forms a new hybridsystem, which is able to provide simultaneous ventilation, cooling capacity and electricity in an effective manner for buildings. The contribution of this study is to quantitatively identify the thermal and electrical performances of the hybrid system using a comprehensive validated numerical model. In addition, a parametric study was also performed to examine the effect of the number of EAHE pipe and solar collector on the performance of the hybrid system in order to further enhance the system performance. The results revealed that the peak daytime ventilation of the proposed hybrid system is reduced by 22.05 % compared to the ventilation system of solar chimney integrated with earth-air heat exchanger, and the outlet air temperature is reduced by 0.12 °C in average; the indoor temperatures for the hybrid and ventilation systems are slightly different. Moreover, the minimum electrical efficiency of hybrid system is 1.34 % higher than that for stand-alone photovoltaic system. Increasing the number of earth-air heat exchangers leads to more than 36.43 % increase in peak ventilation volume and 3.7 % improvement in electrical efficiency. In addition, increasing the quantity of solar chimney-photovoltaic modules can effectively increase the induced ventilation volume, and the outlet air temperature, but has slight effect on the temperature and electrical efficiency of photovoltaic panel. Therefore, the hybrid system not only can supply appropriate ventilation but also can generate electric power to buildings. The findings provide a preliminary guidance for optimizing design of the hybrid system.

Experimental Study of a Pump-Driven Microchannel-Separated Heat Pipe System

The current situation of high energy consumption in data centers places high demands on the energy consumption and heat-dissipation efficiency of cooling technology. This article studies the steady-state flow and heat-transfer characteristics of a pump-driven separated heat pipe system from an experimental perspective. After designing and selecting the pump-driven microchannel-separated heat pipe system, an experimental platform is built to test the pump-driven microchannel-separated heat pipe system under variable operating conditions. It is found that the optimal filling rate range of the system is 75% to 95%, and the optimal condensing air volume is 4250 m3/h. The relationship between the circulating mass flow rate and the heat-transfer capacity of the heat pipe system is comprehensively influenced by the resistance of each section and the heat-transfer coefficient at the heat exchanger. When the indoor and outdoor temperature difference increases from 10 °C to 30 °C, the heat transfer increases by 261.5%, and the working medium of R410a has a better heat-transfer performance than R134A at outdoor temperatures ranging from 0 to 15 °C. The results contribute to the application of pump-assisted microchannel heat pipe systems in data center machines, which provide guidance for the application of cabinet-level thermal management.

Intelligent control of district heating system based on RDPG

Given the continuous expansion of heating areas in recent years, the design of a precise and dependable district heating system (DHS) has become increasingly crucial. Traditional control decisions are made based on real-time environmental temperature feedback, often leading to uneven heating on the user side and affecting residents' comfort. This paper proposes an intelligent control strategy based on the deep reinforcement learning recurrent deterministic policy gradient (RDPG) algorithm for DHSs. To explore the control performance of the RDPG algorithm on DHS, we have meticulously modeled the pivotal components of the DHSs, namely plate heat exchangers, secondary heating pipe networks, and heat users. Moreover, taking into account the periodic factors in heating regulation, the traditional recurrent neural network (RNN) in the recurrent deterministic policy gradient (RDPG) algorithm has been replaced with the long short-term memory (LSTM) network. The proposed algorithm was trained using actual data from a heat exchange station in Tianjin and compared with reinforcement learning algorithms such as TD3, DPPO, DDPG, and A3C in terms of training rewards, effectiveness, and training stability. The results of the models are evaluated and visualized. Experimental results show that the proposed control method based on the RDPG algorithm, compared to other control schemes, can achieve the highest training reward and the most stable control performance, with an indoor temperature fluctuation range of only 0.1 °C.

In Search for Untapped Energy-Saving Potential in Green and Smart Higher Educational Buildings—An Empirical Case Study Involving the Building Occupants

Energy-intense activities and the unpredictable and complex behavior of building occupants lead to an increase in building energy demand. It is, therefore, crucial to study underlying factors for building energy demand related to the users. Higher educational buildings are relevant to study for several reasons: they host the future workforce and citizens, they are predicted to increase in numbers, and they represent a building type less studied. Furthermore, green-rated buildings equipped with smart building systems also represent a research gap that is relevant to address since such a building design involves IoT-functionalities and digital features for the building occupants to interact with. There is also a conceivable risk that if the users know that the building is green-rated and technologically advanced, this may alter their perception of the building operation and thus their behavior. To study the relationship between building occupants and such green and smart educational structure, a survey was conducted in a Swedish higher educational building; as a result, 300 responses were collected and analyzed. The responses revealed that the building occupants act with energy awareness, and they are conscious about energy-saving behaviors. One building feature in particular was studied: the Digital Room Panels (DRPs). The DRP allows the building occupants to modify the indoor temperature and is, therefore, essential for thermal comfort. One key finding from the survey revealed that 70% of the building occupants did not know how the DRPs operate. This study argues that this result can be explained with a lack of communication and user friendliness. Inadequate interactions with building systems could also result in opportunities for energy saving might not be realized. The findings of this case study led to valuable recommendations and suggestions for future research endeavors.

Expanded titanium-bearing blast furnace slag phase change aggregate: Preparation, performance and phase change energy storage mortar application

Expanded titanium-bearing blast furnace slag (ETS), containing rich connected pores, largely accumulated, due to low hydration activity and particle strength. In this study, the pore system of ETS was fully utilized to load paraffin for fabrication of phase change aggregate (PCA), and then the PCA was used to prepare phase change energy storage mortar (PCEM) and mechanical properties, thermal performances and energy-saving efficiency were subsequently evaluated. The results indicated that the 28-day and 56-day compressive strength of mortar containing 100 % PCA reached to 7.8 MPa and 8.6 MPa. The energy saving efficiency of building reached to 8.1 %, the peak indoor temperature of building decreased by 1.2 °C and 2.3 °C in spring and summer, respectively. These facts give confidence of ETS consumption in building energy-saving materials.

An integrated computational method for calculating dynamic thermal bridges of building facades in tropical countries

Identifying thermal bridges on building façades has been a great challenge for architects, especially during the conceptual design stage. This is not only due to the complexity of parameters when calculating thermal bridges, but also lack of feature integration between building energy simulation (BES) tools and the actual building conditions. For example, existing BES tools predominantly calculate thermal bridges only in steady state without considering the temperature dynamic behaviour of building outdoors. Consequently, relevant features such as thermal delay, decrement factor, and operative temperature are often neglected, and this can lead to miscalculation of energy consumption. This study then proposes an integrated method to calculate dynamic thermal bridges under transient conditions by incorporating field observations and computational simulations of thermal bridges. More specifically, the proposed method employs several measurement tools such as HOBO data logger to record the actual conditions of indoor and outdoor room temperature and thermal cameras to identify the surface temperature of selected building junctions. The actual datasets are then integrated with the simulation workflow developed in BES tools. This study ultimately enables architects not only to identify potential thermal bridges on existing building façades but also to support material and geometric exploration in early design phase.