Multi-objective optimization of air conditioning systems in residential buildings with an economic attitude

Ahmadi Nadooshan, Afshin; Hashemi Dehkordi, Seyed Amir Hossein; Bayareh, Morteza; Baghoolizadeh, Mohammadreza Baghoolizadeh; Bahrami Eisaabadi, Dariush

doi:10.22059/ees.2024.2026357.1463

Multi-objective optimization of air conditioning systems in residential buildings with an economic attitude

Document Type : Research Paper

Authors

Afshin Ahmadi Nadooshan ¹

Seyed Amir Hossein Hashemi Dehkordi ¹

Morteza Bayareh ¹

Mohammadreza Baghoolizadeh Baghoolizadeh ¹

Dariush Bahrami Eisaabadi ²

¹ Department of Mechanical Engineering, Shahrekord University, Shahrekord, Iran

² Chemical Engineering and Industrial Chemistry, Faculty of Engineering, Vrije Universiteit Brussel, Belgium

10.22059/ees.2024.2026357.1463

Abstract

The use of fossil fuels for energy supply causes air pollution and environmental degradation factors, which itself causes climate change in the world. Therefore, researches are looking for ways to optimize energy consumption in the world. For this purpose, in this study, by optimizing the amount of energy consumption of air conditioning systems (ACS) in a residential building, it helps significantly in reducing the energy consumption of the building. For this purpose, a six residential building in 4 cities of Iran with different climates have been investigated. For this purpose, 5 design variables of summer clothing insulation level (SCIL), winter clothing insulation level (WCIL), ACS (40 different ACS), cooling thermostat temperature (CTT) and heating thermostat temperature (HTT) have been considered. The objective functions in this study are the cost of installing the air conditioning system (CIACS), the annual cost of bills (ACB) and the predicted percentage of dissatisfied (PPD). The effect of design factors on objective functions has been investigated by Morris’s sensitivity analysis. Three Energy Plus, JEPLUS and JEPLUS+EA software is used for optimization. For this purpose, non-dominant sorting genetic algorithm (NSGA-II) - is used for optimization. The optimization results after the calculations showed that Tehran's annual energy consumption has decreased by 81.3%, for Rasht by 63.64%, for Bandar Abbas by 82.66% and for Shahrekord by 77.2%. Also, the PPD for the cities of Tehran, Rasht, Bandar Abbas and Shahrekord improved by 44.42, 50.1, 31.1 and 56.3% respectively.

Keywords

PPD

ACS

ACB

CIACS

NSGA-II

[1] Khoukhi, M., The combined effect of heat and moisture transfer dependent thermal conductivity of polystyrene insulation material: Impact on building energy performance. Energy and buildings, 2018. 169: p. 228-235.

[2] Fischedick, M., et al., Industry In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Technical Report. 2014.

[3] Marin, P., et al., Energy savings due to the use of PCM for relocatable lightweight buildings passive heating and cooling in different weather conditions. Energy and Buildings, 2016. 129: p. 274-283.

[4] Heubaum, H. and F. Biermann, Integrating global energy and climate governance: The changing role of the International Energy Agency. Energy Policy, 2015. 87: p. 229-239.

[5] 2023; Available from: https://www.bsria.com/uk/news/article/world-air-conditioning-market-grows-thanks-to-hot-spots/.

[6] Léger, J., et al., Comparing electric heating systems at equal thermal comfort: An experimental investigation. Building and Environment, 2018. 128: p. 161-169.

[7] Aktacir, M.A., O. Büyükalaca, and T. Yılmaz, A case study for influence of building thermal insulation on cooling load and air-conditioning system in the hot and humid regions. Applied Energy, 2010. 87(2): p. 599-607.

[8] Cen, C., et al., Experimental comparison of thermal comfort during cooling with a fan coil system and radiant floor system at varying space heights. Building and Environment, 2018. 141: p. 71-79.

[9] Pandelidis, D., et al., Performance study of a novel dew point evaporative cooler in the climate of central Europe using building simulation tools. Building and Environment, 2020. 181: p. 107101.

[10] Tewari, P., et al., Field study on indoor thermal comfort of office buildings using evaporative cooling in the composite climate of India. Energy and Buildings, 2019. 199: p. 145-163.

[11] Naderi, E., et al., Simulation-based performance analysis of residential direct evaporative coolers in four climate regions of Iran. Journal of Building Engineering, 2020. 32: p. 101514.

[12] Yu, X., et al., Comparative study of the cooling energy performance of variable refrigerant flow systems and variable air volume systems in office buildings. Applied energy, 2016. 183: p. 725-736.

[13] Jiang, Y., T. Ge, and R. Wang, Performance simulation of a joint solid desiccant heat pump and variable refrigerant flow air conditioning system in EnergyPlus. Energy and buildings, 2013. 65: p. 220-230.

[14] Song, G.-S., J.-H. Lim, and T.-K. Ahn, Air conditioner operation behaviour based on students’ skin temperature in a classroom. Applied ergonomics, 2012. 43(1): p. 211-216.

[15] Aynur, T.N., Y. Hwang, and R. Radermacher, Simulation comparison of VAV and VRF air conditioning systems in an existing building for the cooling season. Energy and Buildings, 2009. 41(11): p. 1143-1150.

[16] Moon, H.J. and S.H. Yang, Evaluation of the energy performance and thermal comfort of an air conditioner with temperature and humidity controls in a cooling season. HVAC&R Research, 2014. 20(1): p. 61-71.

[17] Zhang, R., et al., A novel Variable Refrigerant Flow (VRF) heat recovery system model: Development and validation. Energy and Buildings, 2018. 168: p. 399-412.

[18] Nada, S., et al., Experimental investigation of energy and exergy performance of a direct evaporative cooler using a new pad type. Energy and Buildings, 2019. 203: p. 109449.

[19] Chu, C.-M., T.-L. Jong, and Y.-W. Huang, Thermal comfort control on multi-room fan coil unit system using LEE-based fuzzy logic. Energy Conversion and Management, 2005. 46(9-10): p. 1579-1593.

[20] Bishoyi, D. and K. Sudhakar, Experimental performance of a direct evaporative cooler in composite climate of India. Energy and Buildings, 2017. 153: p. 190-200.

[21] Deshmukh, S., et al., Fault detection in commercial building VAV AHU: A case study of an academic building. Energy and Buildings, 2019. 201: p. 163-173.

[22] MARTíNEZ, P., et al., Experimental study on energy performance of a split air-conditioner by using variable thickness evaporative cooling pads coupled to the condenser. Applied Thermal Engineering, 2016. 105: p. 1041-1050.

[23] Tewari, P., et al., Advancing building bioclimatic design charts for the use of evaporative cooling in the composite climate of India. Energy and Buildings, 2019. 184: p. 177-192.

[24] Gheibi, A. and A. Rahmati, An experimental and numerical investigation on thermal performance of a new modified baseboard radiator. Applied Thermal Engineering, 2019. 163: p. 114324.

[25] Yun, G.Y., J.H. Lee, and H.J. Kim, Development and application of the load responsive control of the evaporating temperature in a VRF system for cooling energy savings. Energy and Buildings, 2016. 116: p. 638-645.

[26] Yildiz, A. and M.A. Ersöz, Determination of the economical optimum insulation thickness for VRF (variable refrigerant flow) systems. Energy, 2015. 89: p. 835-844.

[27] Krishnamoorthy, S. and M. Modera, Impacts of duct leakage on central outdoor-air conditioning for commercial-building VAV systems. Energy and Buildings, 2016. 119: p. 340-351.

[28] Yao, Y. and L. Wang, Energy analysis on VAV system with different air-side economizers in China. Energy and Buildings, 2010. 42(8): p. 1220-1230.

[29] Cho, Y.-H. and M. Liu, Minimum airflow reset of single duct VAV terminal boxes. Building and Environment, 2009. 44(9): p. 1876-1885.

[30] Baghoolizadeh, M., et al., Optimization of annual electricity consumption costs and the costs of insulation and phase change materials in the residential building using artificial neural network and genetic algorithm methods. Journal of Energy Storage, 2023. 62: p. 106916.

[31] Baghoolizadeh, M., et al., Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid. Heliyon, 2024.

[32] Baghoolizadeh, M., et al., Multi‐objective optimization of annual electricity consumption and annual electricity production of a residential building using photovoltaic shadings. International Journal of Energy Research: p. 1-45.

[33] Baghoolizadeh, M., et al., The effect of photovoltaic shading with ideal tilt angle on the energy cost optimization of a building model in European cities. Energy for Sustainable Development, 2022. 71: p. 505-516.

[34] Baghoolizadeh, M., et al., Using different machine learning algorithms to predict the rheological behavior of oil SAE40-based nano-lubricant in the presence of MWCNT and MgO nanoparticles. Tribology International, 2023: p. 108759.

[35] Baghoolizadeh, M., et al., A prediction model for CO2 concentration and multi-objective optimization of CO2 concentration and annual electricity consumption cost in residential buildings using ANN and GA. Journal of Cleaner Production, 2022. 379: p. 134753.

[36] Baghoolizadeh, M., et al., Improving CO2 concentration, CO2 pollutant and occupants’ thermal comfort in a residential building using genetic algorithm optimization. Energy and Buildings, 2023: p. 113109.

[37] Baghoolizadeh, M., et al., Multi-objective optimization of Venetian blinds in office buildings to reduce electricity consumption and improve visual and thermal comfort by NSGA-II. Energy and Buildings, 2022. 278: p. 112639.

[38] Baghoolizadeh, M., et al., A multi-objective optimization of a building’s total heating and cooling loads and total costs in various climatic situations using response surface methodology. Energy Reports, 2021. 7: p. 7520-7538.

[39] Rostamzadeh-Renani, M., et al., The effect of canard’s optimum geometric design on wake control behind the car using Artificial Neural Network and Genetic Algorithm. ISA transactions, 2022. 131: p. 427-443.

[40] Rostamzadeh-Renani, M., et al., The effect of vortex generators on the hydrodynamic performance of a submarine at a high angle of attack using a multi-objective optimization and computational fluid dynamics. Ocean Engineering, 2023. 282: p. 114932.

[41] Rostamzadeh-Renani, R., et al., Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling. Alexandria Engineering Journal, 2023. 84: p. 184-203.

[42] Rostamzadeh-Renani, R., et al., Multi-objective optimization of rheological behavior of nanofluids containing CuO nanoparticles by NSGA II, MOPSO, and MOGWO evolutionary algorithms and Group Method of Data Handling Artificial neural networks. Materials Today Communications, 2023: p. 107709.

[43] Jafari Ghahfarokhi, N., et al., Optimization of a novel micromixer with fan-shaped obstacles. Chemical Papers, 2024: p. 1-10.

[44] Hakimazari, M., et al., Multi-objective optimization of daylight illuminance indicators and energy usage intensity for office space in Tehran by genetic algorithm. Energy Reports, 2024. 11: p. 3283-3306.

[45] Rostamzadeh-Renani, M., et al., A multi-objective and CFD based optimization of roof-flap geometry and position for simultaneous drag and lift reduction. Propulsion and Power Research, 2024.

[46] Bayareh, M. and M. Baghoolizadeh, An overview of the magnetic field effect on heat transfer and entropy generation in cavities: Application of the second law of thermodynamics and artificial intelligence. International Communications in Heat and Mass Transfer, 2024. 151: p. 107238.

[47] Kerdan, I.G. and D.M. Gálvez, Artificial neural network structure optimisation for accurately prediction of exergy, comfort and life cycle cost performance of a low energy building. Applied Energy, 2020. 280: p. 115862.

[48] Markarian, E. and F. Fazelpour, Multi-objective optimization of energy performance of a building considering different configurations and types of PCM. Solar Energy, 2019. 191: p. 481-496.

[49] Kim, W., S.W. Jeon, and Y. Kim, Model-based multi-objective optimal control of a VRF (variable refrigerant flow) combined system with DOAS (dedicated outdoor air system) using genetic algorithm under heating conditions. Energy, 2016. 107: p. 196-204.

[50] Lee, J.M., et al., Application of artificial neural networks for optimized AHU discharge air temperature set-point and minimized cooling energy in VAV system. Applied Thermal Engineering, 2019. 153: p. 726-738.

[51] Xu, X., et al., A model-based optimal ventilation control strategy of multi-zone VAV air-conditioning systems. Applied Thermal Engineering, 2009. 29(1): p. 91-104.

[52] Baghoolizadeh, M., et al., Multi-objective optimization of Venetian blinds in office buildings to reduce electricity consumption and improve visual and thermal comfort by NSGA-II. Energy and Buildings, 2023. 278: p. 112639.

[53] Baghoolizadeh, M., et al., Improving CO2 concentration, CO2 pollutant and occupants’ thermal comfort in a residential building using genetic algorithm optimization. Energy and Buildings, 2023. 291: p. 113109.

[54] Fathalian, A. and H. Kargarsharifabad, Actual validation of energy simulation and investigation of energy management strategies (Case Study: An office building in Semnan, Iran). Case studies in thermal engineering, 2018. 12: p. 510-516.

[55] Pourvahidi, P. and M.B. Ozdeniz, Bioclimatic analysis of Iranian climate for energy conservation in architecture. Scientific Research and Essays, 2013. 8(1): p. 6-16.

[56] Code No.19: Energy Efficiency Bureau for compiling and promoting National regulations for Buildings, Ministry of Housing and Urbanism. 2011: Tehran, Iran.

[57] National Building Code. Part 13 Design and Implementation of Electrical Installations for Buildings. 2009, Iran Building and Housing Research Center: Tehran, Iran.

[58] Iran radiator propertice. 2023; Available from: https://iranradiatorco.com/product-category/radiator/

[59] Butane radiator propertice. 2023; Available from: https://butaneindustrial.com/%d9%85%d8%ad%d8%b5%d9%88%d9%84%d8%a7%d8%aa/butane-products/radiators/.

[60] Evaporative cooler Mashhad Davam propertice 2023; Available from: https://afshinkala.ir/product/%da%a9%d9%88%d9%84%d8%b1-%d8%a2%d8%a8%db%8c-6500-%d9%85%d8%b4%d9%87%d8%af-%d8%af%d9%88%d8%a7%d9%85-md-6500/?utm_medium=PPC&utm_source=Torob.

[61] Evaporative cooler Absal propertice 2023; Available from: https://damapouya.com/product/%DA%A9%D9%88%D9%84%D8%B1-%D8%A2%D8%A8%DB%8C-%D8%A2%D8%A8%D8%B3%D8%A7%D9%84-%D9%85%D8%AF%D9%84-ac70.

[62] Cast iron boiler Shofajkar propertice 2023; Available from: https://tasisplus.ir/product-category/%d8%aa%d8%ac%d9%87%db%8c%d8%b2%d8%a7%d8%aa-%d9%85%d9%88%d8%aa%d9%88%d8%b1%d8%ae%d8%a7%d9%86%d9%87-%d9%88-%da%af%d8%b1%d9%85%d8%a7%db%8c%d8%b4%db%8c/%d8%a7%d9%86%d9%88%d8%a7%d8%b9-%d8%af%db%8c%da%af/%d8%af%db%8c%da%af-%d8%b4%d9%88%d9%81%d8%a7%da%98-%da%86%d8%af%d9%86%db%8c/mi3/.

[63] Cast iron boiler MI3 propertice 2023; Available from: https://damatajhiz.com/products/5937/%D8%AF%DB%8C%DA%AF-%DA%86%D8%AF%D9%86%DB%8C-%D8%B3%D9%88%D9%BE%D8%B1-200-%D8%B4%D9%88%D9%81%D8%A7%DA%98%DA%A9%D8%A7%D8%B1-4-%D9%BE%D8%B1%D9%87.

[64] Media air cooled chiller propertice. 2023; Available from: https://havasanj.ir/shop/hvac/%DA%86%DB%8C%D9%84%D8%B1-10-%D8%AA%D9%86-%D9%85%DB%8C%D8%AF%DB%8C%D8%A7-%D9%85%D8%AF%D9%84-mgb-d30w-rn1/

[65] Fan coil Green propertice. 2023; Available from: https://damatajhiz.com/products/3933/%D9%81%D9%86-%DA%A9%D9%88%DB%8C%D9%84-%D8%B2%D9%85%DB%8C%D9%86%DB%8C-%D8%AF%DA%A9%D9%88%D8%B1%D8%A7%D8%AA%DB%8C%D9%88-400cfm-%D8%AF%D9%85%D8%A7%D8%AA%D8%AC%D9%87%DB%8C%D8%B2-%D9%85%D8%AF%D9%84-dtgc400.

[66] Fan coil Goldiran ceiling propertice. 2023; Available from: https://damatajhiz.com/products/705/%D9%81%D9%86-%DA%A9%D9%88%DB%8C%D9%84-%D8%B3%D9%82%D9%81%DB%8C-%D8%AA%D9%88%DA%A9%D8%A7%D8%B1-%DA%AF%D9%84%D8%AF%DB%8C%D8%B1%D8%A7%D9%86-400cfm-%D9%85%D8%AF%D9%84-glkt3-400.

[67] Fan coil Goldiran casety propertice. 2023; Available from: https://damatajhiz.com/products/681/%D9%81%D9%86-%DA%A9%D9%88%DB%8C%D9%84-%DA%A9%D8%A7%D8%B3%D8%AA%DB%8C-%DB%8C%DA%A9-%D8%B7%D8%B1%D9%81%D9%87-%DA%AF%D9%84%D8%AF%DB%8C%D8%B1%D8%A7%D9%86-400cfm-%D9%85%D8%AF%D9%84-glkc-400.

[68] Fan coil G-Plus propertice. 2023; Available from: https://damatajhiz.com/products/5569/%D9%81%D9%86-%DA%A9%D9%88%DB%8C%D9%84-%D8%B3%D9%82%D9%81%DB%8C-%D8%AA%D9%88%DA%A9%D8%A7%D8%B1-%D8%AC%DB%8C-%D9%BE%D9%84%D8%A7%D8%B3-%D9%85%D8%AF%D9%84-gfu-lc400g30.

[69] Fan coil Media built-in ceiling propertice. 2023; Available from: https://damatajhiz.com/products/822/%D9%81%D9%86-%DA%A9%D9%88%DB%8C%D9%84-%D8%B3%D9%82%D9%81%DB%8C-%D8%AA%D9%88%DA%A9%D8%A7%D8%B1-%D9%85%DB%8C%D8%AF%DB%8C%D8%A7-%D8%A8%D8%A7-%DA%A9%D9%88%DB%8C%D9%84-%D8%B3%D9%87-%D8%B1%D8%AF%DB%8C%D9%81%D9%87-%D9%85%D8%AF%D9%84-mkt3-400.