Better insulation or material with low thermal conductivity is a crucial factor in new construction and retrofitting existing structures where energy efficiency is the objective. Effective energy savings need the careful selection of construction materials to accomplish this. This research work takes into account a typical office building for this purpose. This paper presents the WASPAS-based Taguchi optimization approach, which is used to determine the optimal air-conditioning load of a typical office building. To model a typical office building, AutoCAD software has been utilized, and building energy simulation using HAP software has been conducted. The optimization of the air-conditioning load involved selecting eight factors, namely the type of HVAC system (A), wall material (B), window glass U-value (C), window glass shading coefficient (D), window-to-wall ratio (E), insulation material for the roof (F), thickness of roof insulation (G), and air infiltration flow rate (H) at mixed levels. An orthogonal L18 arrangement is used for the experimental tests, and in each trial, the cooling and heating load as well as the annual energy consumption of the typical office building are calculated. The WASPAS method is then used to convert the multiple-response problem into a single-response problem after a decision matrix is generated by the S/N ratios of all three response variables. The optimal levels for each of the eight factors are determined after reviewing the response table for Taguchi tests. By calculating the percentage contributions of each of the eight factors, the analysis of variance method is also used to pinpoint the primary factors that had a significant impact on the results. The results showed that the A2B3C1D2E1F2G3H1 combination is the most effective set of all eight factors, and the wall material of the office building has the largest impact, contributing 38.23%. Finally, the prediction model has been developed for all three response variables.
GoetzlerWGuernseyMYoungJ, et al. The future of air conditioning for buildings. Burlington: Navigant Consulting, 2016.
2.
YunGYKwokASteemersK, et al. New and advanced materials and technologies in ultralow-energy buildings. J Adv Civ Eng2018; 2018(1): 3481364–3481364.2.
3.
ÖztürkBUğurLYildizA.Investigation of effect on energy consumption of surface roughness in X-axis and spindle servo motors in slot milling operation. Measurement2019; 139: 92–102.
4.
Pérez-LombardLOrtizJCoronelJF, et al. A review of HVAC systems requirements in building energy regulations. Energy Build2011; 43(2): 255–268.
5.
ChungW.Review of building energy-use performance benchmarking methodologies. Appl Energy2011; 88(5): 1470–1479.
6.
BaldiSKorkasCDLvM, et al. Automating occupant-building interaction via smart zoning of thermostatic loads: a switched self-tuning approach. Appl Energy2018; 231: 1246–1258.
7.
Adil ZainalOYumrutaşR. Validation of periodic solution for computing CLTD (cooling load temperature difference) values for building walls and flat roofs. Energy2015; 82: 758–768.
8.
CharlesAMarefWOuellet-PlamondonCM.Case study of the upgrade of an existing office building for low energy consumption and low carbon emissions. Energy Build2019; 183: 151–160.
9.
RuivoCRFerreiraPMVazDC.Prediction of thermal load temperature difference values for the external envelope of rooms with setback and setup thermostats. Appl Therm Eng2013; 51(1–2): 980–987.
10.
AlalouchCAl-SaadiSAlWaerH, et al. Energy saving potential for residential buildings in hot climates: the case of Oman. Sustain Cities Soc2019; 46: 101442.
11.
SadeghifamANMeynaghMMTabatabaeeS, et al. Assessment of the building components in the energy efficient design of tropical residential buildings: an application of BIM and statistical Taguchi method. Energy2019; 188: 116080.
12.
ColangeloFNavarroTGFarinaI, et al. Comparative LCA of concrete with recycled aggregates: a circular economy mindset in Europe. Int J Life Cycle Assess2020; 25(9): 1790–1804.
13.
JannatNHussienAAbdullahB, et al. A comparative simulation study of the thermal performances of the building envelope wall materials in the tropics. Sustainability2020; 12(12): 4892.
14.
SahSKMurugesanKElangovanR.Optimization of energy consumption for indoor climate control using Taguchi technique and utility concept. Sci Technol Built Environ2021; 27(10): 1473–1491.
15.
YoshimiJAltanH. Thermal simulations on the effects of vegetated walls on indoor building environments. In: Proceedings of building simulation, November142011, pp. 1438–1443.
16.
BhosaleAZadeNPSarkarP, et al. Mechanical and physical properties of cellular lightweight concrete block masonry. Constr Build Mater2020; 248: 118621.
17.
CookCR (ed.). FIP manual of lightweight aggregate concrete. 2nd ed.London: Surrey University Press, 1983.
18.
IS 2185-4. Concrete masonry units, Part 4: preformed foam cellular concrete blocks. New Delhi: Bureau of Indian Standards, 2008.
19.
ChicaLAlzateA.Cellular concrete review: new trends for application in construction. Constr Build Mater2019; 200: 637–647.
20.
MarwanM.The effect of wall material on energy cost reduction in building. Case Stud Therm Eng2020; 17: 100573.
21.
YuJLengKYeH, et al. Study on thermal insulation characteristics and optimized design of pipe-embedded ventilation roof with outer-layer shape-stabilized PCM in different climate zones. Renew Energy2020; 147: 1609–1622.
22.
AdityaLMahliaTMIRismanchiB, et al. A review on insulation materials for energy conservation in buildings. Renew Sustain Energ Rev2017; 73: 1352–1365.
23.
AlgburiOBeyhanF.Cooling load reduction in a single–family house, an energy–efficient approach. Gazi Univ J Sci2019; 32(2): 385–400.
24.
ChoudharyS.Analysis of energy conservation of an institutional building using design builder software. Int J Recent Adv Mech Eng2015; 4(1): 133–139.
25.
MengXDuJWangY, et al. Thermal performance optimization of building floors under air-conditioning intermittent operation by numerical simulation. J Build Phys2019; 43(2): 99–120.
26.
RanJTangMJiangL, et al. Effect of building roof insulation measures on indoor cooling and energy saving in rural areas in Chongqing. Procedia Eng2017; 180: 669–675.
27.
PorrittSShaoLCropperP, et al. Adapting dwellings for heat waves. Sustain Cities Soc2011; 1(2): 81–90.
28.
IhmPKrartiM.Design optimization of energy efficient residential buildings in Tunisia. Build Environ2012; 58: 81–90.
29.
ASHRAE Standard 62.1-2016. Ventilation for acceptable indoor air quality. ASHRAE, 2016.
30.
PapakostasKTPapadopoulosAM.Energy requirements for the treatment of fresh air in HVAC systems: a case study for Athens and Thessaloniki, Greece. Int J Vent2004; 3(1): 33–39.
31.
ForoughiRMostaviEAsadiS.Determining the optimum geometrical design parameters of windows in commercial buildings: comparison between humid subtropical and humid continental climate zones in the United States. J Archit Eng2018; 24(4): 04018026.
32.
KimSSBaeMJKimYD.Policies and status of window design for energy efficient buildings. Procedia Eng2016; 146: 155–157.
33.
DuttaASamantaA.Reducing cooling load of buildings in the tropical climate through window glazing: a model to model comparison. J Build Eng2018; 15: 318–327.
34.
AynurTNHwangYRadermacherR.Simulation comparison of VAV and VRF air conditioning systems in an existing building for the cooling season. Energy Build2009; 41(11): 1143–1150.
35.
ChenMWangYLiuY.Optimization of centrifugal fan position in a particular VRF outdoor unit for European market. J Phys Conf Ser2021; 1865(3): 032081.
ZhangGLiXShiW, et al. Influence of occupant behavior on the energy performance of variable refrigerant flow systems for office buildings: a case study. J Build Eng2019; 22: 327–334.
39.
MohanAPraveenVAPrasanthkumarP.Validation of HVAC system design using CFD modelling. Int J Sci Eng Technol2016; 4(49): 10079–10083.
40.
LiuXHongT.Comparison of energy efficiency between variable refrigerant flow systems and ground source heat pump systems. Energy Build2010; 42(5): 584–589.
41.
MuslimSA; University of Calgary, Canada. Energy plus-towards the selection of right simulation tool for building energy and power systems research. J Energy Power Technol2021; 03(03): 1.
42.
AlahmerAAlsaqoorS.Simulation and optimization of multi-split variable refrigerant flow systems. Ain Shams Eng J2018; 9(4): 1705–1715.
43.
OzturkB.Investigation of effects of inverter frequency changes on the specific energy consumption of pipe threading using response surface methodology. Measurement2020; 152: 107296.
44.
SavaşAFÖktemHÖztürkB, et al. Energy consumption, mechanical and metallographic properties of cryogenically treated tool steels. Open Chem2023; 21(1): 20220322.
45.
QuanHChaiYLiR, et al. Influence of circulating-flow’s geometric characters on energy transition of a vortex pump. Eng Comput2019; 36(9): 3122–3137.
46.
American Society of Heating. Refrigerating and air-conditioning engineers [ASHRAE]. Climatic design information. In: ASHRAE handbook: fundamentals SI edition. Punjab: ASHRAE Temperature for Amritsar, 2013.
47.
Energy conservation building code for residential buildings. New Delhi: Bureau Of Energy Efficiency, Ministry of Power, 2017.
NadeemTBAhmedANaqviAA, et al. Designing of heating, ventilation, and air conditioning (HVAC) system for workshop building in hot and humid climatic zone using CLTD method and HAP analysis: a comparison. Arab J Sci Eng2022; 47(7): 9019–9041.
50.
DuarteCDen WymelenbergKVRiegerC.Revealing occupancy patterns in office buildings through the use of annual occupancy sensor data. Idaho Falls, ID: Idaho National Lab (INL), 2013.
51.
GoelSRosenbergMIEleyC.ANSI/ASHRAE/IES standard 90.1-2016 performance rating method reference manual. Richland, WA: Pacific Northwest National Lab (PNNL), 2017.
52.
RoweDM.Activity rates and thermal comfort of office occupants in Sydney. J Therm Biol2001; 26(4–5): 415–418.
53.
NorouziasasAAttiaSHamdyM.Impact of space utilization and work time flexibility on energy performance of office buildings. J Build Eng2024; 98: 111032.
ISHRAE Handbook. HVAC engineers’ handbook, inch pound version. 2nd ed.2017.
56.
ArifRMondalSMandalNP.Optimization of air-conditioning load due to different thermal parameters for a residential building using different techniques. Proc IMechE, Part C: J Mechanical Engineering Science2024; 238(8): 3634–3654.
57.
ArifRMondalSMandalNP.Optimization of air-conditioning load for a residential building using Taguchi method. Proc IMechE, Part E: J Process Mechanical Engineering2023; 09544089231215693. https://doi.org/10.1177/09544089231215693
58.
LeeRKimDYoonJ, et al. Development and calibration of apartment building energy model based on architectural and energy consumption characteristics. Renew Sustain Energ Rev2024; 206: 114874.
59.
BaldiSYuanSEndelP, et al. Dual estimation: constructing building energy models from data sampled at low rate. Appl Energy2016; 169: 81–92.
60.
AS Handbook. Fundamentals. SI ed. Atlanta, GA: American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., 2021.
61.
LiNShiCZhangZ, et al. A mixture proportioning method for the development of performance-based alkali-activated slag-based concrete. Cem Concr Compos2018; 93: 163–174.
62.
ZavadskasEKTurskisZAntuchevicieneJ, et al. Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika2012; 122(6): 3–6.
63.
PamučarDSremacSStević, et al. New multi-criteria LNN WASPAS model for evaluating the work of advisors in the transport of hazardous goods. Neural Comput Appl2019; 31(9): 5045–5068.
64.
SimićVLazarevićDDobrodolacM.Picture fuzzy WASPAS method for selecting last-mile delivery mode: a case study of Belgrade. Eur Transp Res Rev2021; 13(1): 43.
65.
RossPJ (ed.). Taguchi techniques for quality engineering. 2nd ed.New York, NY: McGraw-Hill, 1996.
66.
GouSNikVMScartezziniJL, et al. Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand. Energy Build2018; 169: 484–506.
67.
TanYPengJCurcijaDC, et al. Parametric study of the impact of window attachments on air conditioning energy consumption. Sol Energy2020; 202: 136–143.
