Simulation-based evaluation of low carbon design strategies for extreme climates

Figures & data

Table 1. Extreme climate envelope (Domingos and Rato 2019).

1. Climate	Good material performance	Poorly material performance
Low temperature	Polytetrafluoroethylene (PTFE), Polymers, glass, biopolymers, wood and wood derivates, glass and foams.	Ceramics, concrete, stone and metals
High temperature	Polyethene terephthalate (PET), Polymers, biopolymers, wood, polymers foams and glass	Concrete, stone and metals.

Figure 1. Cold (Dfb), Tropical (Aw, As) and Arid (BWh) climates (1980–2016). Present and future Köppen-Geiger climate classification maps at 1-km resolution (Beck et al. 2020).

Table 2. Cities Climate condition.

City and Category	Climate Condition	Solution
Chennai, India Aw, wet and dry	Minor seasonal differences. During the summer season, it usually rises more than 35°C and nights around 20°C (CIBSE 2017b).	High ceiling and facades. Light thermal mass as a building envelope because it gets cold quickly, but not necessarily as exposed material. On the other hand, shading and reflective building envelope are necessary to reach thermal comfort due to high radiation exposure.
Abu Dhabi BWh, Hot dessert	Minor seasonal differences. During the summer season, it usually arises at more than 40°C and nights around 25°C (CIBSE 2014).	Maximum shading and air movement are ideal for the region as a strategy. Indirect lighting and ventilation catching through wind towers is also effective. Evaporative cooling.
Helsinki, Finland Dfb, Mild summer, long cold winter (humid continental)	Seasonal differences. Winter with variations between −25 °C and 0°C, and Summers between 15 °C and 30°C (CIBSE 2017a).	Insulated envelope, heat recovery systems, skylights facing south and maximising glazing at south façade as well as high thermal mass.

Figure 2. Layout and 3D model used for simulation.

Table 3. Basic model envelope’s U-Value.

Envelope U-Value
Envelope component	U-Value (W$m^2$-K)
Wall	1.485
Roof	2.93

Table 4. Cities site parameters (EnergyPlus and World Meteorological Organization 2022).

Cities	Latitude	Longitude	Altitude (m)	ASHRAE Climate zone	Temp. (° C)	RH (%)
Abu Dhabi	24.43	54.65	27	1B Arid	35.3	19.4	63.83	49.4
Chennai (Nellore)	14.45	79.98	20	1B Tropical	31.3	24.3	80.23	61.7
Helsinki	60.32	24.97	56	6A Cold	18.3	−4.9	87.21	67.8

Table 5. Strategies and coding.

Carbon emission	Design strategy	Sub-Category	Code
Low	Dehumidification/humidification		DHum
Neutral	Shading	Shading 1	SD1
		Shading 2	SD2
	Natural ventilation		NVen
	Insulation	Expandable polystyrene Insulated Pitched roof Insulated Flat roof	EPS InPR InFR
	Energy Storage	Phase Change Material	PCM
	Green Roof	Flat roof	GFR
		Pitched roof	GPR

Figure 3. 3D model with shading options in DB.

Figure 4. Comfort zone annually (8760 h) by ASHRAE 55 Methodology – Cities. Climate consultant.

Figure 5. Strategies per city.

Figure 6. Energy consumption distribution.

Figure 7. Effectiveness of operable energy decrease per strategies in different climates based on Table 5 ($\mathrm{kWh}/m^2$).

Figure 8. Comparison between the base model ($\mathrm{kWh}/m^2$) and most effective design strategies ($\mathrm{kWh}/m^2$).

Table 6. Energy per total building area improvement from base model (kWh/$m^2$).

Energy per total building area improvement from base model (kWh/m^2)
City/Climate	SD1	SD2	Dhum	Nven	EPS	PCM	InPR	InFR	GPR	GFR
Chennai/Tropical	17	18.15	5.44	1.85	40	5	4.68	−1.85	2.86	−7.17
Abu Dhabi/Arid	15.03	16.08	−5.87	2.05	80.39	5	39.5	34.83	2	−6.32
Helsinki/Cold	−9	−9	0	0.96	68.5	3.16	3.93	25.96	86.66	97.89

Table 7. Design Strategies’ Effectiveness in Chennai.

Chennai´s energy per total building area improvement from base model (kWh/m^2)
DS	SD1	SD2	Dhum	Nven	EPS	PCM	InPR	InFR	GPR	GFR
Impact	17	18.15	5.44	1.85	40	5	4.68	−1.85	2.86	−7.17
Base model = 195 kWh/m2		Best design combination decrease = 119.88 kWh/${\mathit{m}}^2$

Figure 9. Base model vs EPS + PCM for Chennai.

Table 8. Abu Dhabi’s design strategies’ effectiveness.

Abu Dhabi´s energy per total building area improvement from base model (kWh/${\mathit{m}}^2$)
DS	SD1	SD2	Dhum	Nven	EPS	PCM	InPR	InFR	GPR	GFR
Impact	15.03	16.08	−5.87	2.05	80.39	5	39.5	34.83	2	−6.32
Base model = 203.65 kWh/m2	Best design combination decrease = 66.63 kWh/${\mathit{m}}^2$

Table 9. Comparison between base fabrics wall and EPS + PCM wall (kWh).

		Comparison between base fabrics wall and EPS + PCM wall (kWh)
	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
Base	−219.8	−122.9	−25.73	227.51	581.9	739.53	892.66	918.48	709.39	396.34	81.66	−176.5
EPS + PCM	−25.56	−7.36	4.63	44.33	95.33	119.73	141.73	145.73	115.43	75.2	32.56	−8.84

Figure 10. Daylight comparison in three cities, from top to bottom: Chennai, Abu Dhabi and Helsinki.

Table 10. Helsinki’s design strategies.

	Helsinki´s energy per total building area improvement from base model (kWh/m^2)
DS	SD1	SD2	Dhum	Nven	EPS	PCM	InPR	InFR	GPR	GFR
Impact	−9	−9	0	0.96	70.92	3.16	3.93	25.96	86	97.89
Base model = 272.82 kWh/m2		Best design combination decrease = 102.31 kWh/m2

Table 11. Helsinki´s Comparison between base roof and GFR (kWh).

Helsinki´s Comparison between base roof and GFR (kWh)
	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
Base	−1919	−1741	−1453	−800	−395	−223.2	−179.6	−219.3	−432.7	−894.1	−1570	−1894
SD2	−255	−315	−487	−728	−969	−535	−729	−398	−303	−185	−223	−251

Domingos, L., and V. Rato. 2019. “Multi-Criteria Material Selection for Buildings in Challenging Environments.” IOP Conference Series. Earth and Environmental Science 297 (1): 012038. doi:10.1088/1755-1315/297/1/012038.

 Google Scholar

Beck, Hylke E., Niklaus E. Zimmermann, Tim R. McVicar, Noemi Vergopolan, Alexis Berg, and Eric F. Wood. 2020. “Present and Future Koppen-Geiger Climate Classification Maps at 1-Km Resolution (Vol 5, 180214, 2018).” Scientific Data 7 (1). doi:10.1038/s41597-020-00616-w.

 Web of Science ®Google Scholar

CIBSE. 2017b. Buildings for Extreme Environments: Tropical. London: CIBSE.

 Google Scholar

CIBSE. 2014. Buildings for Extreme Environments: Arid. London: CIBSE.

 Google Scholar

CIBSE. 2017a. Buildings for Extreme Environments: Cold Climates. London: CIBSE.

 Google Scholar

EnergyPlus, and World Meteorological Organization. 2022. “Weather Data.”

 Google Scholar

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.