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Identifying cost-optimal options for a typical residential nearly zero energy building’s design in developing countries

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Abstract

High population growth rate and the limited non-renewable energy sources such as fossil fuels have made different challenges for the energy supply in the building sector, especially for the residential buildings. Nearly zero energy buildings are viable solutions for reducing the dependency of the building sector on non-renewable energy sources and reducing the destructive environmental impacts of building sector during their operational period. Besides, nearly zero energy buildings are climate-specific. That means their development needs to be addressed in various economic, technical, and climatic conditions of different countries. Consequently, the present study tries to identify the cost-optimal options for designing a residential nearly zero energy building in Kabul city, the capital and largest city of Afghanistan. In this regard, the optimal options are identified using energy simulation and non-dominated sorting genetic algorithm. The results showed that due to some economic limitations such as high interest rates and low energy prices, improving the performance of the building envelope has played a crucial role in determining the cost-optimal options for a nearly zero energy building design in Kabul city. For the optimal options, the payback period is two years, and the total energy consumption is reduced by 83%.

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Abbreviations

CSO:

Central Statistics Organization

CvRMSE:

Coefficient of variation of the root mean square error

LCC:

Life cycle cost

NEPA:

National Environmental Protection Agency

NMBE:

Normalized mean bias error

NPV:

Net present value

nRES:

Non-renewable energy source

NSGA-II:

Non-dominated sorting genetic algorithm

NSIA:

National Statistics and Information Authority

nZEB:

Nearly zero energy building

PMV:

Predicted mean vote

PPD:

Predicted percentage of dissatisfied

PV:

Photovoltaic

RES:

Renewable energy source

RECR:

Renewable energy contribution ratio

STC:

Solar thermal collectors

WMO:

World Meteorological Organization

ZEB:

Zero energy building

A R/Ci :

Roof/ceiling area (m2)

A Wi :

Wall area (m2)

A Fi :

Floor area (m2)

A Gi :

Glazing area (m2)

C W :

Cost of wall construction (USD)

C R/C :

Cost of roof/ceiling construction (USD)

C F :

Cost of floor construction (USD)

C G :

Cost of glazing construction (USD)

C PV :

Cost of PV panels (USD)

C STC :

Cost of STC panels (USD)

C E :

Cost of annual energy consumption (USD)

C Coal :

Cost of coal consumption (USD/kWh)

C El :

Cost of electricity consumption (USD/kWh)

C C :

Total construction cost (USD)

E El :

Annual electricity consumption (kWh)

E Coal :

Annual coal consumption (kWh)

i :

Annual interest rate

I cl :

Clothing insulation layer (clo)

M :

Metabolic rate (met)

mi :

Measured value for the monthly energy consumption (KWh)

\(\overline{m}\) :

Mean value for the monthly energy consumption (KWh)

RH:

Relative humidity (%)

S i :

Simulated value for the monthly energy consumption (KWh)

t :

Air temperature (°C)

t mrt :

Mean radiant temperature (°C)

v :

Air velocity (m/s

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Authors and Affiliations

  1. School of Civil Engineering, College of Engineering, University of Tehran, 16 Azar Ave, P.O. Box 11155-4563, Tehran, Iran

    Gholamreza Heravi, Mohammad Mahdi Salehi & Milad Rostami

  2. Department of Civil Engineering, College of Engineering, Kateb University, Darul Aman Road, Kabul, Afghanistan

    Mohammad Mahdi Salehi

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  1. Gholamreza Heravi
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  2. Mohammad Mahdi Salehi
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  3. Milad Rostami
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Correspondence to Gholamreza Heravi.

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Heravi, G., Salehi, M.M. & Rostami, M. Identifying cost-optimal options for a typical residential nearly zero energy building’s design in developing countries. Clean Techn Environ Policy 22, 2107–2128 (2020). https://doi.org/10.1007/s10098-020-01962-4

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