Abstract
Increasing population and urbanization call for smarter cities where the cycles of matter and energy are optimized, notably in buildings which are actually a source of pollution consuming a lot of energy. The efficiency of building energy has been improved by modelling earth-air heat exchangers, yet selecting the suitable models is challenging. Here we review data-driven earth-air heat exchanger models used for buildings. We discuss issues brought about by assumptions, unmeasured disruptions, and uncertainties in numerical and experimental works. We found that high accuracy can be reached if sufficient data is available. Models are appropriate for real-time activity due to their structure simplicity, yet they display a poor generalization capacity. Model development is limited by the constrained parameters and the complex boundary conditions of the heat exchangers.
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Abbreviations
- HVAC:
-
Heating, ventilation, and air conditioning
- CART:
-
Classification and regression trees
- ANFIS:
-
Adaptive-network-based fuzzy interface system
- ARIMA:
-
Autoregressive integrated moving average
- ARMAX:
-
Autoregressive moving average exogenous
- ARMA:
-
Autoregressive moving average
- SPSS:
-
Statistical package for the social sciences
- \(c_{p}\) :
-
Specific heat of the material
- \(C_{p,air}\) :
-
Specific heat of the air (kJ/kg K)
- \(C_{p,wa}\) :
-
Specific heat of water (kJ/kg K)
- \(I_{C}\) :
-
Compressor current (A)
- \(I_{ef}\) :
-
Evaporative fan current (A)
- \(I_{P}\) :
-
Pump current (A)
- \(k\) :
-
Materials’ thermal conductivity
- \(\dot{m}_{wa}\) :
-
Rate of mass flow of water (kg/s)
- \(\dot{Q}_{cl}\) :
-
Cooling load of the space (kW)
- \(\dot{Q}_{hl}\) :
-
Heating load of the space (kW)
- r :
-
Radius
- \(T\) :
-
Transient temperature (oC)
- \(T_{air,i}\) :
-
Average inlet air temperature (oC)
- \(T_{air,o}\) :
-
Average outlet air temperature (oC)
- \(T_{wa,i}\) :
-
Average inlet water temperature (oC)
- \(T_{wa,o}\) :
-
Average outlet water temperature (oC)
- \(t\) :
-
Time (s)
- \(U_{C}\) :
-
Compressor voltage (V)
- \(U_{P}\) :
-
Pump voltage (V)
- \(\dot{V}_{air}\) :
-
Volumetric airflow rate (m3 /s)
- \(\dot{W}_{c}\) :
-
Power input into the compressor (kW)
- \(\dot{W}_{p}\) :
-
Water-antifreeze flowing heat pump (kW)
- \(\dot{W}_{ef}\) :
-
Evaporator fan power (kW)
- \(\alpha ,\beta ,\gamma ,\theta ,\delta\) :
-
Polynomials
- \(g(x,y,t)\) :
-
Random function of flux density
- y(t), u(t), w(t) :
-
Output, input, and noise for the polynomials
- \(\dot{Q}_{con}\) :
-
Rejected heat from the ground in cooling mode (kW)
- \(\dot{Q}_{eva}\) :
-
Heat extracted by the ground in heating mode (kW)
- \(q^{ - 1}\) :
-
Backshift operator
- \(\cos \phi\) :
-
Power factor
- x :
-
Axes of symmetry
- \(\rho_{air}\) :
-
Air density (kg/m3)
- \(\rho\) :
-
Specific density of the material
- \(T_{0}\) :
-
Random initial temperature
- kW :
-
Kilowatt
- kWh :
-
Kilowatt-hour
- m :
-
Meter
References
Afram A, Janabi-Sharifi F (2014) Review of modeling methods for HVAC systems. Appl Therm Eng 67:507–519. https://doi.org/10.1016/j.applthermaleng.2014.03.055
Agrawal KK, Bhardwaj M, Misra R, Agrawal GD, Bansal V (2018a) Optimization of operating parameters of earth air tunnel heat exchanger for space cooling: taguchi method approach. Geotherm Energy 6:1–17. https://doi.org/10.1186/s40517-018-0097-0
Agrawal KK, Misra R, Yadav T, Agrawal GD, Jamuwa DK (2018b) Experimental study to investigate the effect of water impregnation on thermal performance of earth air tunnel heat exchanger for summer cooling in hot and arid climate. Renew Energy 120:255–265. https://doi.org/10.1016/j.renene.2017.12.070
Ahasan T, Ahmed S, Rasul M, Khan M, Azad A (2014) Performance evaluation of hybrid green roof system in a subtropical climate using fluent. J Power Energy Eng 2:113
Ahmed SF, Khan MMK, Amanullah M, Rasul M, Hassan N (2013) Thermal performance analysis of earth pipe cooling system for subtropical climate. 12th International Conference on Sustainable Energy Technologies, Hong Kong. pp 1795–1803.
Ahmed SF, Khan MMK, Amanullah M, Rasul M, Hassan N (2014a) Numerical modelling of hybrid vertical earth pipe cooling system. The 19th Australasian Fluid Mechanics Conference, Melbourne, Australia.
Ahmed SF, Khan MMK, Amanullah M, Rasul M, Hassan N (2014b) Performance analysis of vertical earth pipe cooling system for subtropical climate. 13th International Conference on Clean Energy. pp 691–700.
Ahmed SF, Khan MMK, Amanullah MTO, Rasul MG (2014c) Selection of suitable passive cooling strategy for a subtropical climate. Int J Mech Mater Eng 9:1–11. https://doi.org/10.1186/s40712-014-0014-7
Ahmed SF, Khan MMK, Rasul MG, Amanullah MTO, Hassan NMS (2014d) Comparison of earth pipe cooling performance between two different piping systems. Energy Procedia 61:1897–1901. https://doi.org/10.1016/j.egypro.2014.12.237
Ahmed SF, Khan MMK, Amanullah M, Rasul M, Hassan N (2015) Numerical modeling of vertical earth pipe cooling system for hot and humid subtropical climate. Progress Clean Energy, volume 2. https://doi.org/10.1007/978-3-319-17031-2_21
Ahmed SF, Khan MMK, Amanullah M, Rasul M, Hassan N (2015b) Performance assessment of earth pipe cooling system for low energy buildings in a subtropical climate. Energy Convers Manage 106:815–825. https://doi.org/10.1016/j.enconman.2015.10.030
Ahmed SF, Amanullah MTO, Khan MMK, Rasul MG, Hassan NMS (2016a) Parametric study on thermal performance of horizontal earth pipe cooling system in summer. Energy Convers Manage 114:324–337. https://doi.org/10.1016/j.enconman.2016.01.061
Ahmed SF, Khan MMK, Amanullah M, Rasul M, Hassan N (2016) Performance evaluation of hybrid earth pipe cooling with horizontal piping system. Thermofluid Model Energy Effic Appl. https://doi.org/10.1016/B978-0-12-802397-6.00001-4
Ahmed SF, Khan MMK, Amanullah M, Rasul M, Hassan N (2018) Integrated model of horizontal earth pipe cooling system for a hot humid climate. Exergy for A Better Environment and Improved Sustainability. https://doi.org/10.1007/978-3-319-62572-0_59
Ahmed SF, Khan MMK, Amanullah MTO, Rasul MG, Hassan NMS (2021a) A parametric analysis of the cooling performance of vertical earth-air heat exchanger in a subtropical climate. Renew Energy 172:350–367. https://doi.org/10.1016/j.renene.2021.02.086
Ahmed SF, Liu G, Mofijur M, Azad AK, Hazrat MA, Chu Y-M (2021b) Physical and hybrid modelling techniques for earth-air heat exchangers in reducing building energy consumption: Performance, applications, progress, and challenges. Sol Energy 216:274–294. https://doi.org/10.1016/j.solener.2021.01.022
Ahn J, Park M, Lee H-S, Ahn SJ, Ji S-H, Song K, Son B-S (2017) Covariance effect analysis of similarity measurement methods for early construction cost estimation using case-based reasoning. Autom Constr 81:254–266. https://doi.org/10.1016/j.autcon.2017.04.009
Al-Ajmi F, Loveday D, Hanby VI (2006) The cooling potential of earth–air heat exchangers for domestic buildings in a desert climate. Build Environ 41:235–244. https://doi.org/10.1016/j.buildenv.2005.01.027
Aresti L, Christodoulides P, Florides G (2018) A review of the design aspects of ground heat exchangers. Renew Sustain Energy Rev 92:757–773. https://doi.org/10.1016/j.rser.2018.04.053
Ascione F, Bianco N, De Stasio C, Mauro GM, Vanoli GP (2017) Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: a novel approach. Energy 118:999–1017. https://doi.org/10.1016/j.energy.2016.10.126
Aydinalp-Koksal M, Ugursal VI (2008) Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector. Appl Energy 85:271–296. https://doi.org/10.1016/j.apenergy.2006.09.012
Azad A, Rasul M, Khan M, Ahasan T, Ahmed S (2014) Energy scenario: production, consumption and prospect of renewable energy in Australia. Journal of Power and Energy Engineering 2:19–25
Babuška R (2012) Fuzzy modeling for control. Springer Science & Business Media.
Baldi S, Zhang F, Le Quang T, Endel P, Holub O (2019) Passive versus active learning in operation and adaptive maintenance of heating, ventilation, and air conditioning. Appl Energy 252:113478. https://doi.org/10.1016/j.apenergy.2019.113478
Bansal V, Mathur J (2009) Performance enhancement of earth air tunnel heat exchanger using evaporative cooling. Int J Low-Carbon Tech 4:150–158. https://doi.org/10.1093/ijlct/ctp017
Bansal V, Misra R, Agrawal GD, Mathur J (2009) Performance analysis of earth–pipe–air heat exchanger for winter heating. Energy Buildings 41:1151–1154. https://doi.org/10.1016/j.enbuild.2009.05.010
Bechtler H, Browne M, Bansal P, Kecman V (2001a) Neural networks—a new approach to model vapour-compression heat pumps. Int J Energy Res 25:591–599. https://doi.org/10.1002/er.705
Bechtler H, Browne M, Bansal P, Kecman V (2001b) New approach to dynamic modelling of vapour-compression liquid chillers: artificial neural networks. Appl Therm Eng 21:941–953. https://doi.org/10.1016/S1359-4311(00)00093-4
Belatrache D, Bentouba S, Bourouis M (2017) Numerical analysis of earth air heat exchangers at operating conditions in arid climates. Int J Hydrogen Energy 42:8898–8904. https://doi.org/10.1016/j.ijhydene.2016.08.221
Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA (2008) Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76:965–977. https://doi.org/10.1016/j.talanta.2008.05.019
Bisoniya TS, Kumar A, Baredar P (2014) Cooling potential evaluation of earth-air heat exchanger system for summer season. Int J Eng Tech Res 2:309–316
Blázquez CS, Verda V, Nieto IM, Martín AF, González-Aguilera D (2020) Analysis and optimization of the design parameters of a district groundwater heat pump system in Turin, Italy. Renew Energy 149:374–383. https://doi.org/10.1016/j.renene.2019.12.074
Chen X, Wang Q, Srebric J (2015) A data-driven state-space model of indoor thermal sensation using occupant feedback for low-energy buildings. Energy Buildings 91:187–198. https://doi.org/10.1016/j.enbuild.2015.01.038
Chiesa G (2018) EAHX–Earth-to-air heat exchanger: Simplified method and KPI for early building design phases. Build Environ 144:142–158. https://doi.org/10.1016/j.buildenv.2018.08.014
Crini G, Lichtfouse E, Chanet G, Morin-Crini N (2020) Applications of hemp in textiles, paper industry, insulation and building materials, horticulture, animal nutrition, food and beverages, nutraceuticals, cosmetics and hygiene, medicine, agrochemistry, energy production and environment: a review. Environ Chem Lett 18:1451–1476. https://doi.org/10.1007/s10311-020-01029-2
Cui Y, Zhu J, Twaha S, Riffat S (2018) A comprehensive review on 2D and 3D models of vertical ground heat exchangers. Renew Sustain Energy Rev 94:84–114. https://doi.org/10.1016/j.rser.2018.05.063
de Gennaro G, Dambruoso PR, Loiotile AD, Di Gilio A, Giungato P, Tutino M, Marzocca A, Mazzone A, Palmisani J, Porcelli F (2014) Indoor air quality in schools. Environ Chem Lett 12:467–482. https://doi.org/10.1007/s10311-014-0470-6
Dhanwani R, Prajapati A, Dimri A, Varmora A, Shah M (2021) Smart Earth Technologies: a pressing need for abating pollution for a better tomorrow. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-14481-6
Diaz G, Sen M, Yang K, McClain RL (1999) Simulation of heat exchanger performance by artificial neural networks. Int J HVAC R Res 5:195–208. https://doi.org/10.1080/10789669.1999.10391233
Diaz S, Sierra J, Herrera J (2013) The use of earth–air heat exchanger and fuzzy logic control can reduce energy consumption and environmental concerns even more. Energy Buildings 65:458–463. https://doi.org/10.1016/j.enbuild.2013.06.028
Dincer I, Rosen MA (2015) Exergy analysis of heating, refrigerating and air conditioning: methods and applications. Academic Press
Doğan SZ, Arditi D, Murat Günaydin H (2008) Using decision trees for determining attribute weights in a case-based model of early cost prediction. J Constr Eng Manag 134:146–152. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:2(146)
Dong B, Cao C, Lee SE (2005) Applying support vector machines to predict building energy consumption in tropical region. Energy Buildings 37:545–553. https://doi.org/10.1016/j.enbuild.2004.09.009
dos Santos CL, Askarzadeh A (2016) An enhanced bat algorithm approach for reducing electrical power consumption of air conditioning systems based on differential operator. Appl Therm Eng 99:834–840. https://doi.org/10.1016/j.applthermaleng.2016.01.155
Dubey M, Bhagoria J, Atullanjewar A (2013) Earth air heat exchanger in parallel connection. Int J Eng Trend Technol 4:2463–2467
Elghamry R, Hassan H (2020) An experimental work on the impact of new combinations of solar chimney, photovoltaic and geothermal air tube on building cooling and ventilation. Sol Energy 205:142–153. https://doi.org/10.1016/j.solener.2020.05.049
Esen H, Inalli M (2009) Modelling of a vertical ground coupled heat pump system by using artificial neural networks. Expert Syst Appl 36:10229–10238. https://doi.org/10.1016/j.eswa.2009.01.055
Esen H, Inalli M, Sengur A, Esen M (2008a) Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system. Energy Buildings 40:1074–1083. https://doi.org/10.1016/j.enbuild.2007.10.002
Esen H, Inalli M, Sengur A, Esen M (2008b) Forecasting of a ground-coupled heat pump performance using neural networks with statistical data weighting pre-processing. Int J Therm Sci 47:431–441. https://doi.org/10.1016/j.ijthermalsci.2007.03.004
Esen H, Inalli M, Sengur A, Esen M (2008c) Modeling a ground-coupled heat pump system by a support vector machine. Renew Energy 33:1814–1823. https://doi.org/10.1016/j.renene.2007.09.025
Esen H, Inalli M, Sengur A, Esen M (2008d) Modelling a ground-coupled heat pump system using adaptive neuro-fuzzy inference systems. Int J Refrig 31:65–74. https://doi.org/10.1016/j.ijrefrig.2007.06.007
Esen H, Esen M, Ozsolak O (2017) Modelling and experimental performance analysis of solar-assisted ground source heat pump system. J Exp Theor Artif Intell 29:1–17. https://doi.org/10.1080/0952813X.2015.1056242
Fawzy S, Osman AI, Doran J, Rooney DW (2020) Strategies for mitigation of climate change: a review. Environ Chem Lett. https://doi.org/10.1007/s10311-020-01059-w
Fazlikhani F, Goudarzi H, Solgi E (2017) Numerical analysis of the efficiency of earth to air heat exchange systems in cold and hot-arid climates. Energy Convers Manage 148:78–89. https://doi.org/10.1016/j.enconman.2017.05.069
Florides G, Kalogirou S (2007) Ground heat exchangers—A review of systems, models and applications. Renew Energy 32:2461–2478. https://doi.org/10.1016/j.renene.2006.12.014
Frausto HU, Pieters J, Deltour J (2003) Modelling greenhouse temperature by means of auto regressive models. Biosys Eng 84:147–157. https://doi.org/10.1016/S1537-5110(02)00239-8
Garnier A, Eynard J, Caussanel M, Grieu S (2014) Low computational cost technique for predictive management of thermal comfort in non-residential buildings. J Process Control 24:750–762. https://doi.org/10.1016/j.jprocont.2013.10.005
Goh YM, Chua D (2009) Case-based reasoning for construction hazard identification: Case representation and retrieval. J Constr Eng Manag 135:1181–1189. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000093
Gross LJ (2013) Use of computer systems and models. Encyclopedia of Biodiversity, 2nd Ed, SA Levin, ed 2:213–220.
Gunay B, Shen W, Newsham G (2017) Inverse blackbox modeling of the heating and cooling load in office buildings. Energy Buildings 142:200–210. https://doi.org/10.1016/j.enbuild.2017.02.064
Han X, Wei C (2021) Household energy consumption: state of the art, research gaps, and future prospects. Environ Dev Sustain. https://doi.org/10.1007/s10668-020-01179-x
Hu M, Weir JD, Wu T (2012) Decentralized operation strategies for an integrated building energy system using a memetic algorithm. Eur J Oper Res 217:185–197. https://doi.org/10.1016/j.ejor.2011.09.008
Inam A, Oncel S (2021) Photobioreactors as potential tools for environmentally friendly and sustainable buildings. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-021-03281-7
Ishaque S, Siddiqui MIH, Kim M-H (2020) Effect of heat exchanger design on seasonal performance of heat pump systems. Int J Heat Mass Transf 151:119404. https://doi.org/10.1016/j.ijheatmasstransfer.2020.119404
Jakhar S, Soni M, Gakkhar N (2016) Performance analysis of earth water heat exchanger for concentrating photovoltaic cooling. Energy Procedia 90:145–153. https://doi.org/10.1016/j.egypro.2016.11.179
Ji S-H, Park M, Lee H-S, Ahn J, Kim N, Son B (2011) Military facility cost estimation system using case-based reasoning in Korea. J Comput Civ Eng 25:218–231. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000082
Ji S-H, Park M, Lee H-S (2012) Case adaptation method of case-based reasoning for construction cost estimation in Korea. J Constr Eng Manag 138:43–52. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000409
Jimenez MJ, Madsen H (2008) Models for describing the thermal characteristics of building components. Build Environ 43:152–162. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000082
Kemmler T, Thomas B (2020) Design of heat-pump systems for single-and multi-family houses using a heuristic scheduling for the optimization of PV self-consumption. Energies 13:1118. https://doi.org/10.3390/en13051118
Keniar K, Ghali K, Ghaddar N (2015) Study of solar regenerated membrane desiccant system to control humidity and decrease energy consumption in office spaces. Appl Energy 138:121–132. https://doi.org/10.1016/j.apenergy.2014.10.071
Koo C, Hong T, Hyun C, Koo K (2010) A CBR-based hybrid model for predicting a construction duration and cost based on project characteristics in multi-family housing projects. Can J Civ Eng 37:739–752. https://doi.org/10.1139/L10-007
Kumar R, Kaushik S, Garg S (2006) Heating and cooling potential of an earth-to-air heat exchanger using artificial neural network. Renew Energy 31:1139–1155. https://doi.org/10.1016/j.renene.2005.06.007
Kumar R, Aggarwal R, Sharma J (2013) Energy analysis of a building using artificial neural network: A review. Energy Buildings 65:352–358. https://doi.org/10.1016/j.enbuild.2013.06.007
Kusiak A, Tang F, Xu G (2011) Multi-objective optimization of HVAC system with an evolutionary computation algorithm. Energy 36:2440–2449. https://doi.org/10.1016/j.energy.2011.01.030
Kwon N, Park M, Lee H-S, Ahn J, Kim S (2017) Construction noise prediction model based on case-based reasoning in the preconstruction phase. J Constr Eng Manag 143:04017008. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001291
Kwon N, Lee J, Park M, Yoon I, Ahn Y (2019a) Performance evaluation of distance measurement methods for construction noise prediction using case-based reasoning. Sustainability 11:871. https://doi.org/10.3390/su11030871
Kwon N, Song K, Park M, Jang Y, Yoon I, Ahn Y (2019b) Preliminary service life estimation model for MEP components using case-based reasoning and genetic algorithm. Sustainability 11:3074. https://doi.org/10.3390/su11113074
Kwon N, Song K, Ahn Y, Park M, Jang Y (2020) Maintenance cost prediction for aging residential buildings based on case-based reasoning and genetic algorithm. J Building Eng 28:101006. https://doi.org/10.1016/j.jobe.2019.101006
Li H, Ni L, Yao Y, Sun C (2019) Experimental investigation on the cooling performance of an earth to air heat exchanger (EAHE) equipped with an irrigation system to adjust soil moisture. Energy Buildings 196:280–292. https://doi.org/10.1016/j.enbuild.2019.05.007
Li Y, Li B, Liu C, Su S, Xiao H, Zhu C (2020) Design and experimental investigation of a phase change energy storage air-type solar heat pump heating system. Appl Therm Eng. https://doi.org/10.1016/j.applthermaleng.2020.115506
Liu Q, Huang Y, Ma Y, Peng Y, Wang Y (2021) Parametric study on the thermal performance of phase change material-assisted earth-to-air heat exchanger. Energy Buildings 238:110811. https://doi.org/10.1016/j.enbuild.2021.110811
Lixing D, Jinhu L, Xuemei L, Lanlan L (2010) Support vector regression and ant colony optimization for HVAC cooling load prediction. International Symposium on Computer, Communication, Control and Automation (3CA). IEEE. pp 537–541. doi: https://doi.org/10.1109/3CA.2010.5533861
Lowry G, Lee M-W (2004) Modelling the passive thermal response of a building using sparse BMS data. Appl Energy 78:53–62. https://doi.org/10.1016/S0306-2619(02)00164-2
Mardiana A, Riffat S (2015) Building energy consumption and carbon dioxide emissions: threat to climate change. J Earth Sci Clim Change. https://doi.org/10.4172/2157-7617.S3-001
Mathur A, Surana AK, Verma P, Mathur S, Agrawal G, Mathur J (2015) Investigation of soil thermal saturation and recovery under intermittent and continuous operation of EATHE. Energy Buildings 109:291–303. https://doi.org/10.1016/j.enbuild.2015.10.010
Mba L, Meukam P, Kemajou A (2016) Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region. Energy Buildings 121:32–42. https://doi.org/10.1016/j.enbuild.2016.03.046
Mechaqrane A, Zouak M (2004) A comparison of linear and neural network ARX models applied to a prediction of the indoor temperature of a building. Neural Comput Appl 13:32–37. https://doi.org/10.1007/s00521-004-0401-8
Mirl N, Schmid F, Bierling B, Spindler K (2020) Design and analysis of an ammonia-water absorption heat pump. Appl Therm Eng 165:114531. https://doi.org/10.1016/j.applthermaleng.2019.114531
Mongkon S, Thepa S, Namprakai P, Pratinthong N (2014) Cooling performance assessment of horizontal earth tube system and effect on planting in tropical greenhouse. Energy Convers Manage 78:225–236. https://doi.org/10.1016/j.enconman.2013.10.076
More CV, Alsayed Z, Badawi MS, Thabet AA, Pawar PP (2021) Polymeric composite materials for radiation shielding: a review. Environ Chem Lett. https://doi.org/10.1007/s10311-021-01189-9
Moshiri B, Rashidi F (2004) Self-tuning based fuzzy PID controllers: application to control of nonlinear HVAC systems. International Conference on Intelligent Data Engineering and Automated Learning. Springer. pp 437–442. doi: https://doi.org/10.1007/978-3-540-28651-6_64
Mustafaraj G, Chen J, Lowry G (2010) Development of room temperature and relative humidity linear parametric models for an open office using BMS data. Energy and Buildings 42:348–356. https://doi.org/10.1016/j.enbuild.2009.10.001
Mustafaraj G, Lowry G, Chen J (2011) Prediction of room temperature and relative humidity by autoregressive linear and nonlinear neural network models for an open office. Energy Buildings 43:1452–1460. https://doi.org/10.1016/j.enbuild.2011.02.007
Nian Y-L, Wang X-Y, Xie K, Cheng W-L (2020) Estimation of ground thermal properties for coaxial BHE through distributed thermal response test. Renew Energy 152:1209–1219. https://doi.org/10.1016/j.renene.2020.02.006
Niu F, Yu Y, Yu D, Li H (2015) Heat and mass transfer performance analysis and cooling capacity prediction of earth to air heat exchanger. Appl Energy 137:211–221. https://doi.org/10.1016/j.apenergy.2014.10.008
Nozhkin V, Semenov M, Ulshin I (2019) A stochastic approach to the solution to the differential equation of heat transfer in the atmosphere. J Phys: Conf Ser. https://doi.org/10.1088/1742-6596/1368/4/042012
Okochi GS, Yao Y (2016) A review of recent developments and technological advancements of variable-air-volume (VAV) air-conditioning systems. Renew Sustain Energy Rev 59:784–817. https://doi.org/10.1016/j.rser.2015.12.328
Oldewurtel F, Parisio A, Jones CN, Gyalistras D, Gwerder M, Stauch V, Lehmann B, Morari M (2012) Use of model predictive control and weather forecasts for energy efficient building climate control. Energy Buildings 45:15–27. https://doi.org/10.1016/j.enbuild.2011.09.022
Osman AI, Hefny M, Maksoud MA, Elgarahy AM, Rooney DW (2020) Recent advances in carbon capture storage and utilisation technologies: a review. Environ Chem Lett. https://doi.org/10.1007/s10311-020-01133-3
Ozgener O, Hepbasli A (2005) Performance analysis of a solar-assisted ground-source heat pump system for greenhouse heating: an experimental study. Build Environ 40:1040–1050. https://doi.org/10.1016/j.buildenv.2004.08.030
Ozgener L, Ozgener O (2010) Energetic performance test of an underground air tunnel system for greenhouse heating. Energy 35:4079–4085. https://doi.org/10.1016/j.energy.2010.06.020
Page J, Robinson D, Morel N, Scartezzini J-L (2008) A generalised stochastic model for the simulation of occupant presence. Energy Buildings 40:83–98. https://doi.org/10.1016/j.enbuild.2007.01.018
Park S, Ahn Y, Lee S (2018) Analyzing the finishing works service life pattern of public housing in South Korea by probabilistic approach. Sustainability 10:4469. https://doi.org/10.3390/su10124469
Patil S, Tantau H, Salokhe V (2008) Modelling of tropical greenhouse temperature by auto regressive and neural network models. Biosys Eng 99:423–431. https://doi.org/10.1016/j.biosystemseng.2007.11.009
Pereira E, Hermann U, Han S, AbouRizk S (2018) Case-based reasoning approach for assessing safety performance using safety-related measures. J Constr Eng Manag 144:04018088. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001546
Peretti C, Zarrella A, De Carli M, Zecchin R (2013) The design and environmental evaluation of earth-to-air heat exchangers (EAHE). a literature review. Renew Sustain Energy Rev 28:107–116. https://doi.org/10.1016/j.rser.2013.07.057
Prasad R, Bharadwaj K (2002) Stochastic modeling of heat exchanger response to data uncertainties. Appl Math Model 26:715–726. https://doi.org/10.1016/S0307-904X(01)00082-8
Ramos R, Silva A, de Brito J, Gaspar PL (2018) Methodology for the service life prediction of ceramic claddings in pitched roofs. Constr Build Mater 166:386–399. https://doi.org/10.1016/j.conbuildmat.2018.01.111
RazaviAlavi S, AbouRizk S (2017) Site layout and construction plan optimization using an integrated genetic algorithm simulation framework. J Comput Civ Eng 31:04017011. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000653
Ríos-Moreno G, Trejo-Perea M, Castañeda-Miranda R, Hernández-Guzmán V, Herrera-Ruiz G (2007) Modelling temperature in intelligent buildings by means of autoregressive models. Autom Constr 16:713–722. https://doi.org/10.1016/j.autcon.2006.11.003
Rodriguez E, Rasmussen BP (2016) A nonlinear reduced-order modeling method for dynamic two-phase flow heat exchanger simulations. Sci Technol Built Environ 22:164–177. https://doi.org/10.1080/23744731.2015.1085280
Romero J, Navarro-Esbrí J, Belman-Flores J (2011) A simplified black-box model oriented to chilled water temperature control in a variable speed vapour compression system. Appl Therm Eng 31:329–335. https://doi.org/10.1016/j.applthermaleng.2010.09.013
Roy D, Chakraborty T, Basu D, Bhattacharjee B (2020) Feasibility and performance of ground source heat pump systems for commercial applications in tropical and subtropical climates. Renew Energy 152:467–483. https://doi.org/10.1016/j.renene.2020.01.058
Ruano AE, Crispim EM, Conceiçao EZ, Lúcio MMJ (2006) Prediction of building’s temperature using neural networks models. Energy Buildings 38:682–694. https://doi.org/10.1016/j.enbuild.2005.09.007
Sadeghi H, Kalantar V (2018) Performance analysis of a wind tower in combination with an underground channel. Sustain Cities Soc 37:427–437. https://doi.org/10.1016/j.scs.2017.12.002
Seginer I, Boulard T, Bailey B (1994) Neural network models of the greenhouse climate. J Agric Eng Res 59:203–216. https://doi.org/10.1006/jaer.1994.1078
Serageldin AA, Abdelrahman AK, Ookawara S (2016) Earth-air heat exchanger thermal performance in Egyptian conditions: experimental results, mathematical model, and computational fluid dynamics simulation. Energy Convers Manage 122:25–38. https://doi.org/10.1016/j.enconman.2016.05.053
Shahsavar A, Bagherzadeh SA, Afrand M (2021) Application of artificial intelligence techniques in prediction of energetic performance of a hybrid system consisting of an earth-air heat exchanger and a building-integrated photovoltaic/thermal system. J SolEnergy Eng 143:051002. https://doi.org/10.1115/1.4049867
Sharma H, Dhir A (2020) Capture of carbon dioxide using solid carbonaceous and non-carbonaceous adsorbents: A review. Environ Chem Lett. https://doi.org/10.1007/s10311-020-01118-2
Shojaee SMN, Malek K (2017) Earth-to-air heat exchangers cooling evaluation for different climates of Iran. Sustainable Energy Technol Assess 23:111–120. https://doi.org/10.1016/j.seta.2017.09.007
Singh R, Sawhney R, Lazarus I, Kishore V (2018) Recent advancements in earth air tunnel heat exchanger (EATHE) system for indoor thermal comfort application: a review. Renew Sustain Energy Rev 82:2162–2185. https://doi.org/10.1016/j.rser.2017.08.058
Sivasakthivel T, Murugesan K, Sahoo P (2014) Optimization of ground heat exchanger parameters of ground source heat pump system for space heating applications. Energy 78:573–586. https://doi.org/10.1016/j.energy.2014.10.045
Sobti J, Singh SK (2015) Earth-air heat exchanger as a green retrofit for Chandīgarh—a critical review. Geothermal Energy 3:1–9. https://doi.org/10.1186/s40517-015-0034-4
Soni SK, Pandey M, Bartaria VN (2015) Ground coupled heat exchangers: a review and applications. Renew Sustain Energy Rev 47:83–92. https://doi.org/10.1016/j.rser.2015.03.014
Soni SK, Pandey M, Bartaria VN (2016) Hybrid ground coupled heat exchanger systems for space heating/cooling applications: a review. Renew Sustain Energy Rev 60:724–738. https://doi.org/10.1016/j.rser.2016.01.125
Spindler HC, Norford LK (2009) Naturally ventilated and mixed-mode buildings—Part I: thermal modeling. Build Environ 44:736–749. https://doi.org/10.1016/j.buildenv.2008.05.019
Su H, Liu X-B, Ji L, Mu J-Y (2012) A numerical model of a deeply buried air–earth–tunnel heat exchanger. Energy Buildings 48:233–239. https://doi.org/10.1016/j.enbuild.2012.01.029
Sun K, Yan D, Hong T, Guo S (2014) Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration. Build Environ 79:1–12. https://doi.org/10.1016/j.buildenv.2014.04.030
Talari S, Shafie-Khah M, Osório GJ, Aghaei J, Catalão JP (2018) Stochastic modelling of renewable energy sources from operators’ point-of-view: A survey. Renew Sustain Energy Rev 81:1953–1965. https://doi.org/10.1016/j.rser.2017.06.006
Wang X, Bjerg BS, Zhang G (2018) Design-oriented modelling on cooling performance of the earth-air heat exchanger for livestock housing. Comput Electron Agric 152:51–58. https://doi.org/10.1016/j.compag.2018.07.006
Wang G, Zhao L, Liang R, Liu Z (2020) Design of remote monitoring system for sewage source heat pump based on PLC and GPRS. E&ES 480:012008. https://doi.org/10.1088/1755-1315/480/1/012008
Wasim M, Shoaib S, Mubarak N, Asiri AM (2018) Factors influencing corrosion of metal pipes in soils. Environ Chem Lett 16:861–879. https://doi.org/10.1007/s10311-018-0731-x
Wei X, Kusiak A, Li M, Tang F, Zeng Y (2015) Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance. Energy 83:294–306. https://doi.org/10.1016/j.energy.2015.02.024
Wengang H, Yanhua L, Mingxin L (2019) Application of earth-air heat exchanger cooling technology in an office building in Jinan city. Energy Procedia 158:6105–6111. https://doi.org/10.1016/j.egypro.2019.01.503
Xia J, Huang Y, Li Q, Xiong Y, Min S (2021) Convolutional neural network with near-infrared spectroscopy for plastic discrimination. Environ Chem Lett. https://doi.org/10.1007/s10311-021-01240-9
Xuemei L, Lixing D, Lanlan L (2010) A novel building cooling load prediction based on SVR and SAPSO. International Symposium on Computer, Communication, Control and Automation (3CA). IEEE. pp 528–532. doi: https://doi.org/10.1109/3CA.2010.5533863
Yang H, Cui P, Fang Z (2010) Vertical-borehole ground-coupled heat pumps: a review of models and systems. Appl Energy 87:16–27. https://doi.org/10.1016/j.apenergy.2009.04.038
Yang D, Guo Y, Zhang J (2016) Evaluation of the thermal performance of an earth-to-air heat exchanger (EAHE) in a harmonic thermal environment. Energy Convers Manage 109:184–194. https://doi.org/10.1016/j.enconman.2015.11.050
Yiu C (2008) Statistical modelling and forecasting schemes for air-conditioning system [PhD thesis]. Hung Hom, Kowloon, Hong Kong: The Hong Kong Polytechnic University
You T, Yang H (2020) Feasibility of ground source heat pump using spiral coil energy piles with seepage for hotels in cold regions. Energy Convers Manage 205:112466. https://doi.org/10.1016/j.enconman.2020.112466
Yu X, Li H, Yao S, Nielsen V, Heller A (2020) Development of an efficient numerical model and analysis of heat transfer performance for borehole heat exchanger. Renewable Energy 152:189–197. https://doi.org/10.1016/j.renene.2020.01.044
Zendehboudi A, Baseer M, Saidur R (2018) Application of support vector machine models for forecasting solar and wind energy resources: a review. J Clean Prod 199:272–285. https://doi.org/10.1016/j.jclepro.2018.07.164
Zeng Y, Zhang Z, Kusiak A (2015) Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms. Energy 86:393–402. https://doi.org/10.1016/j.energy.2015.04.045
Zhang J, Haghighat F (2010) Development of Artificial Neural Network based heat convection algorithm for thermal simulation of large rectangular cross-sectional area Earth-to-Air Heat Exchangers. Energy Buildings 42:435–440. https://doi.org/10.1016/j.enbuild.2009.10.011
Zhao H-x, Magoulès F (2012) A review on the prediction of building energy consumption. Renew Sustain Energy Rev 16:3586–3592. https://doi.org/10.1016/j.rser.2012.02.049
Zhao Y, Zhou S, Li L (2008) Dynamic characteristics modeling of a heat exchanger using neural network. First International Conference on Intelligent Networks and Intelligent Systems. IEEE. pp 13–18. doi: https://doi.org/10.1109/ICINIS.2008.16
Zhou T, Xiao Y, Liu Y, Lin J, Huang H (2018) Research on cooling performance of phase change material-filled earth-air heat exchanger. Energy Convers Manage 177:210–223. https://doi.org/10.1016/j.enconman.2018.09.047
Zhuang Z, Ben X, Yan R, Pang J, Li Y (2017) Accurately predicting heat transfer performance of ground heat exchanger for ground-coupled heat pump systems using data mining methods. Neural Comput Appl 28:3993–4010. https://doi.org/10.1007/s00521-016-2307-7
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The authors highly express their gratitude to Asian University for Women, Chattogram, Bangladesh, for their supports in carrying out this study.
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Ahmed, S.F., Saha, S.C., Debnath, J.C. et al. Data-driven modelling techniques for earth-air heat exchangers to reduce energy consumption in buildings: a review. Environ Chem Lett 19, 4191–4210 (2021). https://doi.org/10.1007/s10311-021-01288-7
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DOI: https://doi.org/10.1007/s10311-021-01288-7