A Review on the Impact of Outdoor Environment on Indoor Thermal Environment
Abstract
:1. Introduction
2. Methods
3. Coupling Method to Evaluate the Impact of Outdoor Environment on Indoor Environment
4. Influence of Surrounding Physical Environment on Indoor Thermal Environment and Energy Consumption
4.1. Neighboring Buildings
4.2. Greening
4.3. Road Surface
4.4. Water Body
4.5. Sky
5. Weather Condition
5.1. Outdoor Temperature
5.2. Outdoor Humidity
5.3. External Wind
5.4. Global Warming
5.5. Extreme Weather Conditions
5.6. Solar Radiation
6. Outdoor Air Pollutants
6.1. Correlation of Indoor and Outdoor Pollutants
6.2. Impact of Outdoor Pollutants on Building Energy Consumption
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AT | Artificial turf |
BCTVB | Building controls virtual test bed |
BEM | Building energy model |
BEP | Building effect parameterization |
CDD | Cooling degree days |
CFD | Computational fluid dynamic |
CityBES | City building energy saver |
DGP | Daylight glare probability |
EHY | Extreme hot year |
ERV | Energy recovery ventilator |
FSR | Facade to site ratio |
GIS | Geographical information system |
GWR | Geographically weighted regression |
H/W | Height to street width |
HAVC | Heating ventilation and air conditioning system |
HDD | Heating degree days |
IPCC | Intergovernmental panel on climate change |
LAD | Leaf area density |
LAI | Leaf area indices |
MLR | Multiple linear regression |
PH | Prefabricated house |
PMV | Predicted mean vote |
POS | Percentage of Synchronization |
RBF | Radial basis function |
RCM | Regional climate model |
SCR | Site coverage ratio |
SRY | Summer reference year |
SVF | Sky view factor |
TCR | Tree coverage ratio |
TDY | Typical decay years |
THY-E | Typical Hot Year-Event |
THY-I | Typical Hot Year-Intensity |
UGBE | Urban greening and built environment modeling |
UHI | Urban heat island |
VCR | Vegetation coverage ratio |
VFP | Very fine particulate |
WRF | Weather research and forecasting |
XMY | Extreme meteorological year |
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Outdoor Environment | Contribution | |
---|---|---|
Surrounding physical environment | Surrounding buildings | 10.4% |
Greening | 17.4% | |
Road surface | 13.9% | |
Water body | 7.9% | |
Sky | 6.0% | |
Climatic condition | Outdoor air temperature | 7.0% |
Outdoor air humidity | 4.3% | |
External wind | 4.3% | |
Global warming | 13.0% | |
Extreme climate | 4.4% | |
Solar radiation | 5.2% | |
Outdoor pollutants on indoor environment and energy consumption | 6.0% |
Year | Method | Pros. | Cons. | Ref. |
---|---|---|---|---|
2012 | Couple ENVI-met with EnergyPlus | ENVI-met can perform dynamic simulations based on weather data, which provide more realistic descriptions of urban microclimates compared to simulations based on static weather data. It can also help assess the importance of each microclimate factor in a given city. | The time for simulation increases rapidly with the increase in model resolution; there are differences in the physical models and numerical solution schemes of ENVI-met and EnergyPlus; there are uncertainties in the coupling of equations on long-wave radiation and air temperature. | [21] |
2017 | CityBES | CityBES can automatically generate an urban building energy model, which overcomes the difficulty of model generation. | GIS datasets have quality problems, such as missing information; shading and transpiration effects of vegetation cannot be considered. | [23] |
2021 | CitySim | CitySim offers a trade-off between input data requirements, output accuracy, and computational effort. | Not combined with computational fluid methods; CitySim models individual thermal zones for each building. | [24] |
2021 | Couple OpenFOAM with EnergyPlus | The waste heat generation of the building is taken into account; the outdoor environmental model is simplified; it is easy to estimate various physical parameters; the number of parameters of the model is reduced; and the model uncertainty is reduced. | The outdoor environment model is simplified, which might not represent the actual boundary condition; airflow and outdoor temperature are modeled in 2D, while turbulence and shortwave and longwave radiation are modeled in 3D, which could have an impact on the accuracy of simulation results; estimation on the net longwave radiation is inaccurate; the effect of rainwater on the outdoor environment is not taken into account. | [26] |
2022 | Urban Greening and Built Environment Modeling (UGBE) | Taking into account anthropogenic waste heat generation from traffic and buildings, 20 times faster than ENVI-met in simulation. | Not taking into account plant watering time. | [27] |
Year | H to W Ratio | Area | Cooling Demand | Heating Demand | Ref. |
---|---|---|---|---|---|
2011 | 0.5~3 | Copenhagen, Denmark | Decreases as H/W increases | Increases as H/W increases | [34] |
2012 | 0.37~1.5 | Phoenix Sky Harbor International Airport, Arizona, USA | Decreases as H/W increases | Decreases as H/W increases | [40] |
2017 | 0.52~1.42 | Shanghai, Wuhan, Changsha, Chengdu, and Chongqing, China | Decreases as H/W increases | Significant increases in Shanghai and Wuhan and small increases in the other cities | [35] |
2020 | 0.6~1.4 | Boston, Massachusetts, USA | Increases slightly when H/W < 1 and decreases rapidly when H/W > 1 | Slightly decrease when H/W < 1 and increases when H/W > 1 | [41] |
2021 | 0.48~0.67 | Jinan, China | Not evaluated | No obvious changes when H/W < 0.52 and increases when 0.52 < H/W < 0.67 | [39] |
Author | Evaluation of Greening Schemes | Results | Ref. |
---|---|---|---|
Akbari et al. | 16 potted trees (8 trees with a height of 6 m and others with a height of 2.4 m) to provide shading for two houses | Energy savings for the two houses were 47% and 26%, respectively. | [57] |
Donovan et al. | The distance, crown area, and aspect relative to 460 houses | The trees on the west and south sides of the houses reduce electricity usage in summer, while the trees on the north side of the house increase electricity usage in winter. | [58] |
Simpson et al. | Trees around 264 houses | Each tree reduces cooling energy consumption by 7.1% and increases heating energy consumption by 1.9% per year. | [2] |
Li et al. | Adding horizontal greening and vertical greening to courtyard areas | The simulation results showed that cooling and heating energy consumption decreased by 8.83% and 1.85%, respectively, after greening, while the electricity consumption recorded by smart meters showed that cooling and heating energy consumption decreased by 28.3% and 28.5%, respectively. | [56] |
Author | Location | Method | Results | Ref. |
---|---|---|---|---|
Frank | Switzerland | Temperatures forecast to rise by 4.4 °C by 2050; HELLIOS was used for building energy simulation. | Compared with 1961–1990, cooling demand increased by 223–1050%, and heating demand decreased by 36–58%. | [108] |
Lam et al. | Hongkong | The atmospheric circulation model MIROC3.2-H was used to predict the meteorological data from 2009 to 2100. | In the low-emission scenario, compared with 1979–2008, the electricity consumption in 2009–2039, 2039–2068, and 2069–2100 increased by 5.7%, 12.8%, and 18.4%, respectively. | [109] |
Wang et al. | USA | Weather data for 2020, 2050, and 2080 were generated using a global circulation model under three CO2 emission scenarios, and EnergyPlus was used for building energy simulations. | Compared with the 1960s, by 2080s, the temperature has risen by 2.3 to 7K. Under the three emission scenarios, the cooling energy consumption increases and the heating energy consumption decreases. | [106] |
Wan et al. | China | MIROC3.2-H was used to generate the weather data from 2001 to 2100 under low forcing and medium forcing scenarios; VisualDOE4.1 was used to perform building energy simulation; regression models were developed to study the relationship among heating energy, cooling energy, total building energy use, and climate variables. | Cooling energy needs increased by 11.4% to 24.2%, while heating energy needs decreased by 13.8% to 55.7%. | [110] |
Hosseini et al. | Sweden | The RCA4 Regional Climate Model (RCM) was used to generate future climate data, and EnergyPlus was used for building energy simulations. | Indoor heating degree days (HDD) decreased by 4% and cooling degree days (CDD) increased by 16%. | [105] |
María Calama-González et al. | Spain | Climate scenarios were selected by taking into account the Emissions Scenario Report from Intergovernmental Panel on Climate Change (IPCC); EnergyPlus was used for building energy simulations. | The percentage of future undercooling hours is 15% to 50%, and the percentage of future overheating hours is about 4% to 37%. | [111] |
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Lin, Y.; Huang, T.; Yang, W.; Hu, X.; Li, C. A Review on the Impact of Outdoor Environment on Indoor Thermal Environment. Buildings 2023, 13, 2600. https://doi.org/10.3390/buildings13102600
Lin Y, Huang T, Yang W, Hu X, Li C. A Review on the Impact of Outdoor Environment on Indoor Thermal Environment. Buildings. 2023; 13(10):2600. https://doi.org/10.3390/buildings13102600
Chicago/Turabian StyleLin, Yaolin, Tao Huang, Wei Yang, Xiancun Hu, and Chunqing Li. 2023. "A Review on the Impact of Outdoor Environment on Indoor Thermal Environment" Buildings 13, no. 10: 2600. https://doi.org/10.3390/buildings13102600
APA StyleLin, Y., Huang, T., Yang, W., Hu, X., & Li, C. (2023). A Review on the Impact of Outdoor Environment on Indoor Thermal Environment. Buildings, 13(10), 2600. https://doi.org/10.3390/buildings13102600