Research Overview

Research Background: What Problem Did This Study Address?

Residential buildings in the severe cold and cold regions of China have historically been designed with winter heating as the primary concern. Building envelopes in these regions often emphasise insulation, airtightness, and heat retention because winters are long and outdoor temperatures can be extremely low. However, under global warming, these same design priorities may create a new summer problem: indoor overheating.

The study focused on residential buildings without air-conditioning or with limited cooling use. In such buildings, summer overheating can directly affect indoor thermal comfort, sleep quality, health, and productivity. It can also increase future cooling-energy demand if occupants start relying more heavily on air-conditioning.

Previous research had shown that phase change materials, or PCM, can help stabilise indoor temperatures by storing and releasing latent heat during phase transitions. However, most PCM overheating studies focused on temperate or hot climates, while limited attention had been given to China’s severe cold and cold regions. It was unclear whether PCM would be effective in these regions, where summer temperature fluctuations and winter heating requirements differ from other climate zones.

To address this gap, the research team combined field monitoring and simulation. UbiBot UB-DT-P1 sensors were used to collect real indoor temperature data from residential buildings in four representative cities. These measured data were then used to validate EnergyPlus simulation models before assessing whether PCM layers in the building envelope could reduce overheating and cooling-energy consumption.

The Specific Role of UbiBot in the Study

In this study, UbiBot UB-DT-P1 sensors were used as field measurement devices for collecting real indoor temperature data in residential buildings. The paper does not present UbiBot as proving the effectiveness of PCM by itself. Instead, UbiBot provided the empirical indoor temperature dataset used to reveal actual summer overheating conditions and to validate the simulation models.

The researchers selected one residential building in each of four representative cities in China’s severe cold and cold regions: Yichun, Harbin, Shenyang, and Dalian. These cities represent sub-regions 1A, 1B, 1C, and 2A. The monitored residential units were typical apartment layouts containing a living room and two bedrooms facing different directions.

The UbiBot UB-DT-P1 sensors were installed on the walls of the selected residential units. According to the paper’s sensor layout figure, the sensors were placed away from direct sunlight, ventilation airflow, and heat-generating equipment. This placement was important because the goal was to measure representative indoor temperature rather than a localised temperature distorted by solar radiation, air jets, or internal heat sources.

The monitoring period lasted from May 1 to September 30, 2021, covering the summer overheating assessment period used in the study. The paper reports that the monitoring yielded 3672 hours of indoor temperature data. The sensors had a temperature measurement range from 10 °C to 55 °C and an accuracy of ±0.3 °C. The collected data were uploaded to the Internet.

The UbiBot data were used in two main research workflows.

First, the measured indoor temperature data provided direct evidence of overheating in the four monitored residential buildings. The researchers evaluated the monitored data using CIBSE TM59 and CIBSE Guide A criteria to determine how many hours exceeded overheating thresholds in bedrooms and living rooms.

Second, the measured UbiBot data were used to calibrate and validate EnergyPlus simulation models. The research team compared measured indoor temperature values with simulated indoor temperatures, using Pearson’s correlation coefficient, RMSE, NMBE, and CV(RMSE) as validation indicators. After validation, the models were used to compare scenarios without PCM and with PCM layers in the building envelope.

Therefore, UbiBot’s role in the study was to provide the real indoor thermal dataset that connected field conditions with simulation-based PCM performance analysis.

Research Methods and Data Collection Approach

The study combined on-site monitoring and EnergyPlus simulation.

For field monitoring, the researchers selected residential buildings in Yichun, Harbin, Shenyang, and Dalian. These cities represent different severe cold and cold climate sub-regions. The monitored residential units had typical layouts, with a living room and two bedrooms. UbiBot UB-DT-P1 sensors were mounted on the interior walls of the selected units, away from direct sunlight, ventilation airflow, and heat-generating equipment.

The monitoring period covered May 1 to September 30, 2021. The dataset contained 3672 hours of indoor temperature data. These monitored data were used to evaluate overheating and to validate the simulation model.

For simulation, the researchers used EnergyPlus 22.1. The software modelled indoor dry-bulb temperature and allowed the researchers to represent phase change materials through parameters such as thickness, conductivity, density, and phase change properties. PCM was integrated into mortar, a plastering finishing material, and placed on the inner side of the building envelope, including external walls and roofs. The PCM layer thickness was 30 mm.

The study selected PCM with different phase change temperatures for different cities. The selected materials included SP24E for Yichun, SP26E for Harbin, RT28HC for Shenyang, and SP26E for Dalian. The selection was based on calculated optimal phase change temperature and local climate conditions.

Two types of simulation models were compared: residential buildings without PCM layers and residential buildings with PCM layers. The researchers also modelled natural ventilation and infiltration. Buildings were simulated as naturally ventilated and without air-conditioning for overheating analysis. For cooling-energy analysis, the cooling system was assumed to operate automatically when the indoor temperature exceeded 26 °C during occupied hours.

The simulation results were validated using measured UbiBot data. The researchers applied Pearson’s R, RMSE, NMBE, and CV(RMSE). The boundary limits for hourly calibration were set at ±10% for NMBE and ≤30% for CV(RMSE), following ASHRAE Guide 14–2002.

Key Research Findings

The field measurements showed clear overheating risk in the monitored residential buildings. According to CIBSE TM59, all monitored dwellings experienced overheating to varying degrees, except for living rooms in Dalian and Shenyang where air-conditioning affected the results.

In the monitored south-facing bedrooms, overheating hours from May to September reached 395 hours in Yichun, 618 hours in Harbin, 857 hours in Shenyang, and 713 hours in Dalian. These accounted for 25.82%, 40.39%, 56.01%, and 46.60% of the assessed sleeping-period hours, respectively. North-facing bedrooms also showed substantial overheating, with 373 hours in Yichun, 650 hours in Harbin, 806 hours in Shenyang, and 397 hours in Dalian.

The EnergyPlus models were validated against the measured data. Most NMBE values were within ±5%, except one value of 6.66% for Yichun, and CV(RMSE) values were generally below 15%, well below the 30% guideline limit. This indicated that the models were acceptable for subsequent PCM simulations.

The PCM simulations showed that integrating PCM into the building envelope reduced overheating hours in both north-facing and south-facing bedrooms. For north-facing bedrooms, overheating hours were reduced by 48 hours in Yichun, 144 hours in Harbin, 114 hours in Shenyang, and 103 hours in Dalian. For south-facing bedrooms, overheating hours were reduced by 57 hours in Yichun, 135 hours in Harbin, 120 hours in Shenyang, and 90 hours in Dalian.

In percentage terms, PCM reduced overheating in south-facing bedrooms by 19.66% in Yichun, 17.95% in Harbin, 15.58% in Shenyang, and 10.87% in Dalian.

The study also found cooling-energy savings. Without PCM, summer cooling energy consumption was 6.00 kWh/m² in Yichun, 17.51 kWh/m² in Harbin, 18.72 kWh/m² in Shenyang, and 16.81 kWh/m² in Dalian. With PCM, these values decreased to 4.46, 14.82, 15.98, and 14.23 kWh/m². The corresponding cooling-energy saving ratios were 25.67%, 15.36%, 14.61%, and 15.34%.

What This Means for PCM-Based Overheating Mitigation

This study suggests that PCM can be a useful passive strategy for reducing summer overheating in residential buildings in China’s severe cold and cold regions. These regions have traditionally focused on winter insulation, but the monitored data show that summer overheating is already a real problem in typical residential buildings.

For building design, PCM offers a way to moderate indoor temperature swings without relying entirely on mechanical cooling. By absorbing heat during warmer periods and releasing it later, PCM can reduce the frequency of high indoor temperatures and lower cooling-energy demand.

However, the study also shows that PCM performance is climate-sensitive. The PCM layer does not work equally across the whole summer. It is most effective when indoor temperature fluctuations are close to the material’s phase change temperature range. This means PCM selection should be region-specific rather than universal.

For building policy, the findings support the need to update design standards in severe cold and cold regions. Current standards focus heavily on winter insulation, while summer overheating prevention remains underdeveloped. PCM could be considered alongside other passive strategies such as ventilation, shading, thermal mass optimisation, and solar-gain control.

For simulation practice, the study demonstrates the importance of field data. UbiBot-measured indoor temperature records were used to validate the EnergyPlus models before PCM performance was analysed. This made the PCM conclusions more grounded in observed residential conditions.

Application Value of UbiBot Devices

UbiBot UB-DT-P1 sensors demonstrated practical value as field-monitoring tools in this study.

First, they provided real indoor temperature data from residential buildings in four climate sub-regions. This allowed the researchers to document actual overheating rather than relying only on theoretical simulation.

Second, the sensors supported long-duration monitoring. The dataset covered the full May-to-September summer period, generating 3672 hours of indoor temperature data. Such continuous records are essential for overheating assessment, because overheating depends on accumulated hours above comfort thresholds.

Third, the sensors supported model validation. EnergyPlus simulations were calibrated and validated against the UbiBot field measurements. This step was essential before the researchers used the model to compare building envelopes without PCM and with PCM.

Fourth, the monitoring setup helped connect room-level measurements with building envelope design. By placing sensors in monitored units and comparing the measured data with simulation, the study linked indoor thermal performance to passive design strategies.

Fifth, the data upload function supported field data management. The paper states that the data obtained by the sensors were uploaded to the Internet, making the measured dataset available for later model validation and analysis.

Overall, UbiBot’s value in this paper lies in providing empirical indoor temperature data that supported overheating diagnosis, EnergyPlus validation, and simulation-based assessment of PCM as a passive cooling strategy.

Extended Application Scenarios

The monitoring approach used in this study can be extended to several related scenarios:

1. PCM building envelope evaluation
   Used to compare indoor temperatures before and after integrating PCM into walls, roofs, ceilings, or interior finishes.
2. Residential overheating monitoring
   Used to assess summer overheating risk in apartments, dormitories, and naturally ventilated homes.
3. EnergyPlus model validation
   Used to provide measured indoor temperature data for calibrating and validating building performance simulations.
4. Passive cooling strategy research
   Used to assess PCM, external shading, night ventilation, cool roofs, insulation optimisation, and thermal mass design.
5. Cold-climate housing policy research
   Used to provide empirical evidence for updating building standards in regions that need both winter insulation and summer heat protection.
6. Cooling-energy reduction studies
   Used to connect temperature monitoring with simulated or measured cooling-energy demand.
7. Post-occupancy evaluation
   Used to compare actual indoor thermal performance with design expectations after a building is occupied.
8. Climate adaptation studies
   Used to evaluate how residential buildings respond to warmer summers and more frequent heatwaves.

FAQ

1. Which UbiBot product was used in the study?

The study used UbiBot UB-DT-P1 sensors to monitor indoor temperature in residential buildings.

2. What data did UbiBot collect?

The sensors collected indoor temperature data in monitored residential units.

3. Where were the sensors installed?

The sensors were mounted on the walls of selected residential units in Yichun, Harbin, Shenyang, and Dalian. They were placed away from direct sunlight, ventilation airflow, and heat-generating equipment.

4. What was the monitoring period?

The field monitoring period was May 1 to September 30, 2021.

5. What was the sampling frequency?

The paper reports 3672 hours of indoor temperature data across the monitoring period. It does not explicitly state the raw sensor logging interval.

6. How were the UbiBot data used?

The data were used to evaluate real residential overheating and to validate EnergyPlus simulation models before analysing PCM performance.

7. Did UbiBot prove that PCM reduces overheating?

No. UbiBot collected real indoor temperature data. The researchers used those data to validate simulation models, and the validated EnergyPlus simulations were then used to analyse PCM effects.

8. What PCM strategy was tested?

The study simulated PCM integrated into mortar on the inner side of external walls and roofs, with a PCM layer thickness of 30 mm.

9. What were the main PCM results?

PCM reduced south-facing bedroom overheating hours by 19.66% in Yichun, 17.95% in Harbin, 15.58% in Shenyang, and 10.87% in Dalian. It also reduced cooling-energy consumption by 25.67%, 15.36%, 14.61%, and 15.34%, respectively.

10. Why is this research important?

It shows that residential overheating already occurs in China’s severe cold and cold regions, and that PCM may be considered as a passive design strategy to reduce overheating and cooling-energy demand.