Harbin Institute of Technology Uses UbiBot UB-DT-P1 Sensors to Monitor Residential Overheating in Severe Cold and Cold Regions of China
Research Overview
| Paper Title | The Fundamental Approach of the Digital Twin Application in Railway Turnouts with Innovative Monitoring of Weather Conditions |
| Publisher | MDPI |
| Journal | Sensors |
| Publish Time | August 2021 |
| Authors / Institutions | Arkadiusz Kampczyk and Katarzyna Dybeł; AGH University of Science and Technology, Poland |
| UbiBot Product | UbiBot WS1 Wi-Fi wireless temperature, humidity, and illumination logger with external UB-DT-P1 (DS18B20) temperature sensor |
| Data Collected | Ambient temperature, temperature inside the rail head, humidity, and ambient light |
| Sampling Frequency | Every hour |
| Research Period | January 21, 2020 to May 29, 2020 |
| Application Scenario | Railway turnout monitoring, digital twin data acquisition, Continuous Welded Rail temperature analysis, railway infrastructure diagnostics |
| Original Link | https://doi.org/10.3390/s21175757 |
Research Background: What Problem Did This Study Address?
Residential buildings in the severe cold and cold regions of China have traditionally been designed with a strong focus on retaining heat during winter. This design priority is understandable because these regions experience long heating seasons and low winter temperatures. However, as climate change increases summer temperatures and heatwave risks, the same design logic may create a new problem: indoor overheating during summer.
The research addressed a policy and performance gap. Current Chinese building standards for severe cold and cold regions mainly regulate heating demand and winter energy efficiency. Summer heat protection, cooling loads, shading, and ventilation requirements are either limited or absent in many sub-regions. As a result, residential buildings may comply with winter-oriented energy standards while still performing poorly in summer thermal comfort.
The study focused on four representative cities: Yichun, Harbin, Shenyang, and Dalian. These cities represent different sub-regions within China’s severe cold and cold climate zones. The researchers measured indoor temperatures in newly built residential buildings from May to September 2021 and used building performance simulations to extend the analysis over a 15-year typical weather period from 2004 to 2018.
The goal was not only to determine whether overheating occurs, but also to examine whether existing building policies have overlooked summer thermal risks. UbiBot UB-DT-P1（DS18B20) sensors played an important role by collecting real indoor temperature data that were later used to validate the simulation models and support the overheating analysis.
The Specific Role of UbiBot in the Study
In this study, UbiBot UB-DT-P1（DS18B20) sensors were used as field measurement devices to collect indoor temperature data in real residential buildings. The researchers did not use UbiBot to “prove” an overheating conclusion by itself. Instead, UbiBot sensors provided real-world indoor temperature records, which were then used as empirical evidence and as a validation basis for building performance simulations.
The sensors were deployed in selected newly built flats in four representative Chinese cities: Yichun, Harbin, Shenyang, and Dalian. In each monitored dwelling, sensors were placed in three rooms: the living room, the south-facing bedroom, and the north-facing bedroom. The paper’s sensor layout diagram shows the sensors positioned on interior walls in locations not exposed to direct sunlight. This placement helped reduce measurement distortion from direct solar radiation and allowed the researchers to record room-level indoor operative temperature.
The UbiBot UB-DT-P1（DS18B20) sensors collected temperature data every 5 minutes. For analysis, the researchers extracted the initial data point of each hour, resulting in 3672 hourly temperature datasets for each room during the monitoring period from May 1 to September 30, 2021.
The collected data were used in three main ways.
First, the temperature data provided direct empirical evidence of summer indoor thermal conditions in real homes. The researchers used these measurements to assess whether the monitored rooms exceeded overheating thresholds.
Second, the UbiBot data were used to validate simulation models created in IESVE software. The researchers compared measured hourly indoor temperature data with simulated indoor temperature data. Validation was evaluated using Pearson’s correlation coefficient and root mean square error. This step was important because the simulation models were later used to extend the analysis beyond the monitored summer period.
Third, after validation, the models were used with typical meteorological year weather files from 2004 to 2018 to estimate overheating risk under longer-term typical climate conditions. In this workflow, UbiBot did not replace simulation; it provided measured field data that made the simulation results more reliable.
Therefore, the role of UbiBot in the research can be summarized as follows: the sensors collected real indoor temperature data from occupied residential buildings, and these data supported overheating assessment, model validation, comparison between measured and simulated performance, and policy discussion on summer heat protection.
Research Methods and Data Collection Approach
The study combined field monitoring and building performance simulation.
For the field monitoring, the researchers selected one newly built reinforced concrete residential dwelling in each of the four representative cities. Each dwelling had one south-facing living room and two bedrooms, one facing south and the other facing north. The rooms were predominantly naturally ventilated during the summer monitoring period. The Dalian dwelling was slightly affected by air-conditioning on weekends during September, and this was noted in the paper.
UbiBot UB-DT-P1（DS18B20) sensors were used to measure indoor operative temperature. The sensors had a measurement range from 10 °C to 55 °C and an accuracy of ±0.3 °C. They recorded data every 5 minutes and uploaded the measurement data synchronously through the network. The researchers then extracted hourly data, generating 3672 hourly datasets for each monitored room across the May–September period.
For the simulation part, the researchers used IESVE software to model the indoor operative temperature of residential buildings. The simulation model was based on the design of the monitored dwelling in Shenyang, representing a typical newly built reinforced concrete apartment building in these regions. The model included a living room facing south, a south-facing bedroom, and a north-facing bedroom.
Two types of weather files were used. Locally recorded 2021 weather files were used to generate simulations that could be compared with the measured data. Typical meteorological year weather files from 2004 to 2018 were then used to extend the observation period and evaluate overheating under longer-term typical climate conditions.
The study used the CIBSE TM59 criteria to evaluate overheating. For living rooms, an adaptive overheating criterion was applied. For bedrooms, the study used a static threshold of 26 °C during sleeping hours from 10 p.m. to 7 a.m. The main indicator was hours of exceedance, which means the number and percentage of hours when the indoor operative temperature exceeded the defined threshold.
Key Research Findings
The field monitoring results from 2021 showed that overheating occurred in all monitored dwellings to varying degrees. The monitored dwellings did not simultaneously satisfy the CIBSE TM59 criteria for living rooms and bedrooms. Bedrooms were particularly problematic.
In the 2021 measured data, the average percentage of overheating hours in bedrooms was 25.4% in Yichun, 39.8% in Harbin, 22.5% in Shenyang, and 50.5% in Dalian, compared with the 1% threshold used for sleeping hours. In living rooms, overheating hours accounted for 12.6% in Yichun, 14.6% in Harbin, 5.7% in Shenyang, and 0% in Dalian, compared with the 3% threshold used for the adaptive living-room assessment.
The simulation validation showed moderate to strong agreement between measured and simulated data. For south-facing bedrooms, Pearson’s correlation coefficients ranged from approximately 0.86 to 0.93, and RMSE values ranged from about 1.2 °C to 2.0 °C. This indicated that the measured UbiBot data could support model validation and improve confidence in the simulation-based extension.
Using typical meteorological year data from 2004 to 2018, the researchers found that overheating occurred to varying degrees across the studied cities. In the south-facing bedrooms, overheating was recorded for 6 hours in Yichun, 191 hours in Harbin, 483 hours in Shenyang, and 578 hours in Dalian during the May–September simulation period. These corresponded to 0.4%, 12.4%, 31.6%, and 37.8% of sleeping-period hours, respectively.
The findings showed that overheating risk increased from the more northerly severe cold region toward the warmer cold-region city of Dalian. Bedrooms generally performed worse than living rooms, indicating that night-time thermal comfort and sleep conditions require special attention.
The study also found a clear gap between Chinese building policy and building performance. Current standards in these regions focus heavily on winter heat retention, while summer heat protection measures such as ventilation, shading, and solar heat gain control are insufficiently addressed.
What This Means for Residential Overheating Assessment
This study suggests that residential overheating in China’s severe cold and cold regions should no longer be treated as a marginal issue. Although these regions have historically been associated with winter heating demand, the field measurements and simulations show that summer indoor overheating can occur for substantial periods, especially in bedrooms.
For building performance research, the study demonstrates the value of combining sensor-based monitoring with simulation. Field data from UbiBot UB-DT-P1（DS18B20) sensors helped verify whether simulation outputs reflected real indoor thermal conditions. Once validated, simulations could then be used to extend the analysis across longer weather periods.
For building design, the findings suggest that a winter-only energy-saving approach may create unintended summer comfort risks. High insulation levels and airtight construction can improve winter performance, but without adequate summer ventilation, shading, and solar control, they may also trap heat indoors.
For policy, the research indicates that Chinese building standards for severe cold and cold regions should consider both winter heating and summer overheating. Future standards may need to include clearer requirements for summer ventilation, shading design, solar heat gain coefficient, and region-specific overheating assessment.
Application Value of UbiBot Devices
The UbiBot UB-DT-P1（DS18B20) sensors demonstrated practical value in this research by providing continuous, room-level indoor temperature data from real residential buildings.
First, the sensors enabled high-frequency monitoring. By recording data every 5 minutes, the system captured indoor temperature changes throughout the entire summer period. This made it possible to convert raw data into hourly datasets for overheating assessment.
Second, the sensors supported multi-room comparison. By placing sensors in living rooms, south-facing bedrooms, and north-facing bedrooms, the researchers could compare how overheating risk varied by room type and orientation.
Third, the sensors supported simulation validation. The measured UbiBot data were compared against IESVE simulation results, allowing the researchers to evaluate and adjust the model before using it for longer-term analysis.
Fourth, the sensors provided real-world evidence for policy discussion. Instead of relying only on theoretical simulation, the study used measured indoor temperature data from actual dwellings. This strengthened the argument that overheating is not just a projected risk, but an observed indoor performance issue.
Fifth, the networked upload function supported continuous data collection and management. In building performance studies, this type of data continuity is important because overheating assessment depends on long-duration time-series records rather than isolated readings.
Overall, UbiBot’s value in this study was its ability to provide empirical indoor temperature data that connected real residential performance with simulation-based analysis and building policy evaluation.
Extended Application Scenarios
The monitoring approach used in this study can be extended to several related scenarios:
- Residential overheating studies
Used to assess indoor temperature exceedance in apartments, houses, dormitories, and other residential buildings during summer. - Building performance simulation validation
Used to provide measured data for calibrating and validating models in IESVE, EnergyPlus, DesignBuilder, or similar tools. - Indoor thermal comfort assessment
Used to compare room-level thermal conditions in bedrooms, living rooms, classrooms, offices, and care facilities. - Passive cooling strategy evaluation
Used before and after applying shading, window-opening strategies, insulation changes, night ventilation, or other heat-protection measures. - Building policy research
Used to provide empirical evidence for revising building energy standards and thermal comfort regulations. - Climate adaptation studies
Used to monitor how existing housing stock responds to warmer summers and more frequent heatwaves. - Healthy housing evaluation
Used to assess night-time bedroom conditions, especially for vulnerable populations such as older adults, children, and people with health risks. - Post-occupancy evaluation
Used after building completion to compare design expectations with actual indoor thermal performance.
FAQ
1. Which UbiBot product was used in the study?
The study used UbiBot UB-DT-P1（DS18B20) sensors for indoor temperature monitoring.
2. What data did the UbiBot sensors collect?
The sensors collected indoor operative temperature data in living rooms, south-facing bedrooms, and north-facing bedrooms.
3. Where were the sensors installed?
The sensors were installed on interior walls of selected rooms in newly built residential dwellings. They were placed where direct sunlight could not reach them.
4. What was the sampling frequency?
The sensors collected data every 5 minutes. The researchers then extracted hourly data for analysis.
5. How long did field monitoring last?
Field monitoring was conducted from May 1 to September 30, 2021.
6. How were the UbiBot data used?
The measured temperature data were used to assess overheating in real dwellings and to validate IESVE building performance simulation models.
7. Did UbiBot prove that Chinese homes overheat?
No. UbiBot collected real indoor temperature data. The research team used those data, together with simulation models and CIBSE TM59 criteria, to analyse overheating risk.
8. Which cities were studied?
The study covered Yichun, Harbin, Shenyang, and Dalian, representing severe cold and cold regions of China.
9. What was the main finding?
The study found that overheating occurred in monitored dwellings and simulated models, especially in bedrooms. The risk was more severe in Harbin, Shenyang, and Dalian than in Yichun.
10. Why is this research important for building policy?
The findings indicate that current Chinese standards in severe cold and cold regions focus too heavily on winter heat retention and should also include summer overheating protection measures.
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Harbin Institute of Technology Uses UbiBot UB-DT-P1 Sensors to Monitor Residential Overheating in Severe Cold and Cold Regions of China
Research Overview
| Paper Title | The Fundamental Approach of the Digital Twin Application in Railway Turnouts with Innovative Monitoring of Weather Conditions |
| Publisher | MDPI |
| Journal | Sensors |
| Publish Time | August 2021 |
| Authors / Institutions | Arkadiusz Kampczyk and Katarzyna Dybeł; AGH University of Science and Technology, Poland |
| UbiBot Product | UbiBot WS1 Wi-Fi wireless temperature, humidity, and illumination logger with external UB-DT-P1 (DS18B20) temperature sensor |
| Data Collected | Ambient temperature, temperature inside the rail head, humidity, and ambient light |
| Sampling Frequency | Every hour |
| Research Period | January 21, 2020 to May 29, 2020 |
| Application Scenario | Railway turnout monitoring, digital twin data acquisition, Continuous Welded Rail temperature analysis, railway infrastructure diagnostics |
| Original Link | https://doi.org/10.3390/s21175757 |
Research Background: What Problem Did This Study Address?
Residential buildings in the severe cold and cold regions of China have traditionally been designed with a strong focus on retaining heat during winter. This design priority is understandable because these regions experience long heating seasons and low winter temperatures. However, as climate change increases summer temperatures and heatwave risks, the same design logic may create a new problem: indoor overheating during summer.
The research addressed a policy and performance gap. Current Chinese building standards for severe cold and cold regions mainly regulate heating demand and winter energy efficiency. Summer heat protection, cooling loads, shading, and ventilation requirements are either limited or absent in many sub-regions. As a result, residential buildings may comply with winter-oriented energy standards while still performing poorly in summer thermal comfort.
The study focused on four representative cities: Yichun, Harbin, Shenyang, and Dalian. These cities represent different sub-regions within China’s severe cold and cold climate zones. The researchers measured indoor temperatures in newly built residential buildings from May to September 2021 and used building performance simulations to extend the analysis over a 15-year typical weather period from 2004 to 2018.
The goal was not only to determine whether overheating occurs, but also to examine whether existing building policies have overlooked summer thermal risks. UbiBot UB-DT-P1（DS18B20) sensors played an important role by collecting real indoor temperature data that were later used to validate the simulation models and support the overheating analysis.
The Specific Role of UbiBot in the Study
In this study, UbiBot UB-DT-P1（DS18B20) sensors were used as field measurement devices to collect indoor temperature data in real residential buildings. The researchers did not use UbiBot to “prove” an overheating conclusion by itself. Instead, UbiBot sensors provided real-world indoor temperature records, which were then used as empirical evidence and as a validation basis for building performance simulations.
The sensors were deployed in selected newly built flats in four representative Chinese cities: Yichun, Harbin, Shenyang, and Dalian. In each monitored dwelling, sensors were placed in three rooms: the living room, the south-facing bedroom, and the north-facing bedroom. The paper’s sensor layout diagram shows the sensors positioned on interior walls in locations not exposed to direct sunlight. This placement helped reduce measurement distortion from direct solar radiation and allowed the researchers to record room-level indoor operative temperature.
The UbiBot UB-DT-P1（DS18B20) sensors collected temperature data every 5 minutes. For analysis, the researchers extracted the initial data point of each hour, resulting in 3672 hourly temperature datasets for each room during the monitoring period from May 1 to September 30, 2021.
The collected data were used in three main ways.
First, the temperature data provided direct empirical evidence of summer indoor thermal conditions in real homes. The researchers used these measurements to assess whether the monitored rooms exceeded overheating thresholds.
Second, the UbiBot data were used to validate simulation models created in IESVE software. The researchers compared measured hourly indoor temperature data with simulated indoor temperature data. Validation was evaluated using Pearson’s correlation coefficient and root mean square error. This step was important because the simulation models were later used to extend the analysis beyond the monitored summer period.
Third, after validation, the models were used with typical meteorological year weather files from 2004 to 2018 to estimate overheating risk under longer-term typical climate conditions. In this workflow, UbiBot did not replace simulation; it provided measured field data that made the simulation results more reliable.
Therefore, the role of UbiBot in the research can be summarized as follows: the sensors collected real indoor temperature data from occupied residential buildings, and these data supported overheating assessment, model validation, comparison between measured and simulated performance, and policy discussion on summer heat protection.
Research Methods and Data Collection Approach
The study combined field monitoring and building performance simulation.
For the field monitoring, the researchers selected one newly built reinforced concrete residential dwelling in each of the four representative cities. Each dwelling had one south-facing living room and two bedrooms, one facing south and the other facing north. The rooms were predominantly naturally ventilated during the summer monitoring period. The Dalian dwelling was slightly affected by air-conditioning on weekends during September, and this was noted in the paper.
UbiBot UB-DT-P1（DS18B20) sensors were used to measure indoor operative temperature. The sensors had a measurement range from 10 °C to 55 °C and an accuracy of ±0.3 °C. They recorded data every 5 minutes and uploaded the measurement data synchronously through the network. The researchers then extracted hourly data, generating 3672 hourly datasets for each monitored room across the May–September period.
For the simulation part, the researchers used IESVE software to model the indoor operative temperature of residential buildings. The simulation model was based on the design of the monitored dwelling in Shenyang, representing a typical newly built reinforced concrete apartment building in these regions. The model included a living room facing south, a south-facing bedroom, and a north-facing bedroom.
Two types of weather files were used. Locally recorded 2021 weather files were used to generate simulations that could be compared with the measured data. Typical meteorological year weather files from 2004 to 2018 were then used to extend the observation period and evaluate overheating under longer-term typical climate conditions.
The study used the CIBSE TM59 criteria to evaluate overheating. For living rooms, an adaptive overheating criterion was applied. For bedrooms, the study used a static threshold of 26 °C during sleeping hours from 10 p.m. to 7 a.m. The main indicator was hours of exceedance, which means the number and percentage of hours when the indoor operative temperature exceeded the defined threshold.
Key Research Findings
The field monitoring results from 2021 showed that overheating occurred in all monitored dwellings to varying degrees. The monitored dwellings did not simultaneously satisfy the CIBSE TM59 criteria for living rooms and bedrooms. Bedrooms were particularly problematic.
In the 2021 measured data, the average percentage of overheating hours in bedrooms was 25.4% in Yichun, 39.8% in Harbin, 22.5% in Shenyang, and 50.5% in Dalian, compared with the 1% threshold used for sleeping hours. In living rooms, overheating hours accounted for 12.6% in Yichun, 14.6% in Harbin, 5.7% in Shenyang, and 0% in Dalian, compared with the 3% threshold used for the adaptive living-room assessment.
The simulation validation showed moderate to strong agreement between measured and simulated data. For south-facing bedrooms, Pearson’s correlation coefficients ranged from approximately 0.86 to 0.93, and RMSE values ranged from about 1.2 °C to 2.0 °C. This indicated that the measured UbiBot data could support model validation and improve confidence in the simulation-based extension.
Using typical meteorological year data from 2004 to 2018, the researchers found that overheating occurred to varying degrees across the studied cities. In the south-facing bedrooms, overheating was recorded for 6 hours in Yichun, 191 hours in Harbin, 483 hours in Shenyang, and 578 hours in Dalian during the May–September simulation period. These corresponded to 0.4%, 12.4%, 31.6%, and 37.8% of sleeping-period hours, respectively.
The findings showed that overheating risk increased from the more northerly severe cold region toward the warmer cold-region city of Dalian. Bedrooms generally performed worse than living rooms, indicating that night-time thermal comfort and sleep conditions require special attention.
The study also found a clear gap between Chinese building policy and building performance. Current standards in these regions focus heavily on winter heat retention, while summer heat protection measures such as ventilation, shading, and solar heat gain control are insufficiently addressed.
What This Means for Residential Overheating Assessment
This study suggests that residential overheating in China’s severe cold and cold regions should no longer be treated as a marginal issue. Although these regions have historically been associated with winter heating demand, the field measurements and simulations show that summer indoor overheating can occur for substantial periods, especially in bedrooms.
For building performance research, the study demonstrates the value of combining sensor-based monitoring with simulation. Field data from UbiBot UB-DT-P1（DS18B20) sensors helped verify whether simulation outputs reflected real indoor thermal conditions. Once validated, simulations could then be used to extend the analysis across longer weather periods.
For building design, the findings suggest that a winter-only energy-saving approach may create unintended summer comfort risks. High insulation levels and airtight construction can improve winter performance, but without adequate summer ventilation, shading, and solar control, they may also trap heat indoors.
For policy, the research indicates that Chinese building standards for severe cold and cold regions should consider both winter heating and summer overheating. Future standards may need to include clearer requirements for summer ventilation, shading design, solar heat gain coefficient, and region-specific overheating assessment.
Application Value of UbiBot Devices
The UbiBot UB-DT-P1（DS18B20) sensors demonstrated practical value in this research by providing continuous, room-level indoor temperature data from real residential buildings.
First, the sensors enabled high-frequency monitoring. By recording data every 5 minutes, the system captured indoor temperature changes throughout the entire summer period. This made it possible to convert raw data into hourly datasets for overheating assessment.
Second, the sensors supported multi-room comparison. By placing sensors in living rooms, south-facing bedrooms, and north-facing bedrooms, the researchers could compare how overheating risk varied by room type and orientation.
Third, the sensors supported simulation validation. The measured UbiBot data were compared against IESVE simulation results, allowing the researchers to evaluate and adjust the model before using it for longer-term analysis.
Fourth, the sensors provided real-world evidence for policy discussion. Instead of relying only on theoretical simulation, the study used measured indoor temperature data from actual dwellings. This strengthened the argument that overheating is not just a projected risk, but an observed indoor performance issue.
Fifth, the networked upload function supported continuous data collection and management. In building performance studies, this type of data continuity is important because overheating assessment depends on long-duration time-series records rather than isolated readings.
Overall, UbiBot’s value in this study was its ability to provide empirical indoor temperature data that connected real residential performance with simulation-based analysis and building policy evaluation.
Extended Application Scenarios
The monitoring approach used in this study can be extended to several related scenarios:
- Residential overheating studies
Used to assess indoor temperature exceedance in apartments, houses, dormitories, and other residential buildings during summer. - Building performance simulation validation
Used to provide measured data for calibrating and validating models in IESVE, EnergyPlus, DesignBuilder, or similar tools. - Indoor thermal comfort assessment
Used to compare room-level thermal conditions in bedrooms, living rooms, classrooms, offices, and care facilities. - Passive cooling strategy evaluation
Used before and after applying shading, window-opening strategies, insulation changes, night ventilation, or other heat-protection measures. - Building policy research
Used to provide empirical evidence for revising building energy standards and thermal comfort regulations. - Climate adaptation studies
Used to monitor how existing housing stock responds to warmer summers and more frequent heatwaves. - Healthy housing evaluation
Used to assess night-time bedroom conditions, especially for vulnerable populations such as older adults, children, and people with health risks. - Post-occupancy evaluation
Used after building completion to compare design expectations with actual indoor thermal performance.
FAQ
1. Which UbiBot product was used in the study?
The study used UbiBot UB-DT-P1（DS18B20) sensors for indoor temperature monitoring.
2. What data did the UbiBot sensors collect?
The sensors collected indoor operative temperature data in living rooms, south-facing bedrooms, and north-facing bedrooms.
3. Where were the sensors installed?
The sensors were installed on interior walls of selected rooms in newly built residential dwellings. They were placed where direct sunlight could not reach them.
4. What was the sampling frequency?
The sensors collected data every 5 minutes. The researchers then extracted hourly data for analysis.
5. How long did field monitoring last?
Field monitoring was conducted from May 1 to September 30, 2021.
6. How were the UbiBot data used?
The measured temperature data were used to assess overheating in real dwellings and to validate IESVE building performance simulation models.
7. Did UbiBot prove that Chinese homes overheat?
No. UbiBot collected real indoor temperature data. The research team used those data, together with simulation models and CIBSE TM59 criteria, to analyse overheating risk.
8. Which cities were studied?
The study covered Yichun, Harbin, Shenyang, and Dalian, representing severe cold and cold regions of China.
9. What was the main finding?
The study found that overheating occurred in monitored dwellings and simulated models, especially in bedrooms. The risk was more severe in Harbin, Shenyang, and Dalian than in Yichun.
10. Why is this research important for building policy?
The findings indicate that current Chinese standards in severe cold and cold regions focus too heavily on winter heat retention and should also include summer overheating protection measures.
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