Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Annals of Data Science
Erschienen in: Annals of Data Science 3/2021

21.11.2019

A Comprehensive Review on Performance Prediction of Solar Air Heaters Using Artificial Neural Network

verfasst von: Harish Kumar Ghritlahre, Purvi Chandrakar, Ashfaque Ahmad

Erschienen in: Annals of Data Science | Ausgabe 3/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Solar air heater (SAH) is a most commonly used solar energy utilization system, which collects solar radiation on absorber plate and transmits absorbed thermal energy to the flowing air. Many techniques were used by various researchers for increasing the performance of SAHs by experimental examination, but analytical and experimental studies takes more time and are very costly. To avoid these types of problems soft computing techniques are used, in which artificial neural network (ANN) technique plays an important role to predict and optimize the performances of SAHs. This technique is very popular due to its fast computing speed and ability to solve complicated problems accurately which is not solved by other conventional approaches. For solving any problem programming code is not required which is the main advantage of this technique. The main purpose of present work is to review the work related to applications of neural model for performance prediction of SAHs and find out the research gap for future investigations. Various research works shown in this paper concluded that ANN is very efficient technique for performance prediction of SAHs.
Nächster Artikel On the Inverse Power Gompertz Distribution

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Sukhatme SP, Nayak JK (1996) Solar energy-principles of thermal collection and storage. Tata McGraw Hill, New Delhi
2.
Garg HP, Prakash J (2006) Solar energy fundamentals and applications. Tata McGraw Hill publishing Co., Ltd
3.
Duffie JA, Beckman WA (2006) Solar engineering of thermal processes, 3rd edn. Wiley Interscience, New York
4.
Goswami DY (2015) Principles of solar engineering, 3rd edn. CRC Press, Taylor & Francis GroupCrossRef
5.
Behura AK (2016) Investigation for heat transfer and friction factor characteristics in three sides artificially roughened solar air heater. Ph.D. Thesis, NIT Jamshedpur, India
6.
Sahu MK, Prasad RK (2016) A review of the thermal and hydrodynamic performance of solar air heater with roughened absorber plates. J Enhanc Heat Transf 23:47–89CrossRef
7.
Sahu MK, Prasad RK (2017) Thermohydraulic performance analysis of an arc shape wire roughened solar air heater. Renew Energy 108:598–614CrossRef
8.
Sahu MK (2017) Thermohydraulic performance of solar air heater with arc-shaped wire roughened absorber plates. Ph.D. Thesis, NIT Jamshedpur, India
9.
Sahu MK, Sharma M, Matheswaran MM, Maitra K (2019) On the use of various configurations of fins to enhance the performance in rectangular duct of solar air heaters—a review. J Sol Energy Eng 141:1–16CrossRef
10.
Gardner MW, Dorling SR (1998) Artificial neural networks (the multilayer perceptron): a review of applications in the atmospheric sciences. Atmos Environ 32:2627–2636CrossRef
11.
Hussain MA (1999) Review of the applications of neural networks in chemical process control- simulation and online implementation. Artif Intell Eng 13:55–68CrossRef
12.
Kalogirou SA (2000) Applications of artificial neural networks for energy systems. Appl Energy 67:17–35CrossRef
13.
Kalogirou SA (2001) Artificial neural networks in renewable energy systems applications: a review. Renew Sustain Energy Rev 5:373–401CrossRef
14.
Kalogirou SA (2003) Artificial intelligence for the modeling and control of combustion processes: a review. Prog Energy Combust Sci 29(6):515–566CrossRef
15.
Mellit A, Kalogirou SA (2008) Artificial intelligence techniques for photovoltaic applications: a review. Prog Energy Combust Sci 34:574–632CrossRef
16.
Yang KT (2008) Artificial neural networks (ANNs): a new paradigm for thermal science and engineering. ASME J Heat Transf 130(9):093001-5–093001-19CrossRef
17.
Mellit A, Kalogirou SA, Hontoria L, Shaarid S (2009) Artificial intelligence techniques for sizing photovoltaic systems: a review. Renew Sustain Energy Rev 13(2):406–419CrossRef
18.
Mohanraj M, Jayaraj S, Muraleedharan C (2012) Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—a review. Renew Sustain Energy Rev 16:1340–1358CrossRef
19.
Cong T, Su G, Qiu S, Tian W (2013) Applications of ANN in fluid flow and heat transfer problems in nuclear engineering: a review work. Prog Nucl Energy 62:54–71CrossRef
20.
Karabacak K, Cetin N (2014) Artificial neural networks for controlling wind–PV power systems: a review. Renew Sustain Energy Rev 29:804–827CrossRef
21.
Yadav AK, Chandel SS (2014) Solar radiation prediction using artificial neural network techniques: a review. Renew Sustain Energy Rev 33:772–781CrossRef
22.
Mohanraj M, Jayaraj S, Muraleedharan C (2015) Applications of artificial neural networks for thermal analysis of heat exchangers—a review. Int J Therm Sci 90:150–172CrossRef
23.
Ata R (2015) Artificial neural networks applications in wind energy systems: a review. Renew Sustain Energy Rev 49:534–562CrossRef
24.
Qazi A, Fayaz H, Wadi A, Raj RG, Rahim NA, Khan WA (2015) The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review. J Clean Prod 104:1–12CrossRef
25.
Zahraee SM, Assadi MK, Saidur R (2016) Application of artificial intelligence methods for hybrid energy system optimization. Renew Sustain Energy Rev 66:617–630CrossRef
26.
Jani DB, Mishra M, Sahoo PK (2017) Application of artificial neural network for predicting performance of solid desiccant cooling systems—a review. Renew Sustain Energy Rev 80:352–366CrossRef
27.
Ghritlahre HK, Prasad RK (2018) Application of ANN technique to predict the performance of solar collector systems—a review. Renew Sustain Energy Rev 84:75–88CrossRef
28.
Pacheco-Vega A, Sen M, Yang KT, McClain RL (2001) Neural network analysis of an-tube refrigerating heat exchanger with limited experimental data. Int J Heat Mass Transf 44:763–770CrossRef
29.
Arcaklioglu E, Erisen A, Yilmaz R (2004) Artificial neural network analysis of heat pumps using refrigerant mixtures. Energy Convers Manag 45:1917–1929CrossRef
30.
Abbassi A, Bahar L (2005) Application of neural network for the modeling and control of evaporative condenser cooling load. Appl Therm Eng 25:3176–3186CrossRef
31.
Yigit KS, Ertunc HM (2006) Prediction of the air temperature and humidity at the outlet of a cooling coil using neural networks. Int Commun Heat Mass Transf 33:898–907CrossRef
32.
Hosoz MH, Ertunc M (2006) Modeling of a cascade refrigeration system using artificial neural networks. Int J Energy Res 30:1200–1215CrossRef
33.
Ertunc HM, Hosoz M (2006) Artificial neural network analysis of a refrigeration system with an evaporative condenser. Appl Therm Eng 26:627–635CrossRef
34.
Yilmaz S, Atik K (2007) Modeling of a mechanical cooling system with variable cooling capacity by using artificial neural network. Appl Therm Eng 27:2308–2313CrossRef
35.
Hosoz M, Ertunc HM, Bulgurcu H (2007) Performance prediction of a cooling tower using artificial neural network. Energy Convers Manag 48:1349–1359CrossRef
36.
Hosoz M, Ertun HM, Ozguc AF (2008) Modelling of a direct evaporative air cooler using artificial neural network. Int J Energy Res 32:83–89CrossRef
37.
Sanaye S, Dehghandokht M, Beigi MH, Bahrami S (2011) Modeling of rotary vane compressor applying artificial neural network. Int J Refrig 34:764–772CrossRef
38.
Jani DB, Mishra M, Sahoo PK (2016) Performance prediction of solid desiccant - vapor compression hybrid air-conditioning system using artificial neural network. Energy 103:618–629CrossRef
39.
Jani DB, Mishra M, Sahoo PK (2016) Performance prediction of rotary solid desiccant dehumidifier in hybrid air-conditioning system using artificial neural network. Appl Therm Eng 98:1091–1103CrossRef
40.
Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan, New York
41.
Oztop HF, Bayrak F, Hepbasli A (2013) Energetic and exergetic aspects of solar air heating (solar collector) systems. Renew Sustain Energy Rev 21:59–83CrossRef
42.
Cengel YA, Boles MA (2006) Thermodynamics: an engineering approach, 5th edn. McGraw-Hill, New York
43.
Banli H (2013) Experimentally derived efficiency and exergy analysis of a new solar air heater having different surface shapes. Renew Energy 50:58–67CrossRef
44.
Park SR, Pandey AK, Tyagi VV, Tyagi SK (2014) Energy and exergy analysis of typical renewable energy systems. Renew Sustain Energy Rev 30:105–123CrossRef
45.
46.
Ghritlahre HK, Prasad RK (2018) Prediction of heat transfer of two different types of roughened solar air heater using artificial neural network technique. Therm Sci Eng Prog 8:145–153CrossRef
47.
Ghritlahre HK (2019) Performance evaluation of solar air heating systems using artificial neural network. Ph.D. Thesis, NIT Jamshedpur, India
48.
Kisi O, Tombul M, Kermani MZ (2014) Modeling soil temperatures at different depths by using three different neural computing techniques. Theoret Appl Climatol 121(1–2):377–387
49.
Wang L, Kisi O, Kermani MZ, Salazar GA, Zhu Z, Gong W (2016) Solar radiation prediction using different techniques: model evaluation and comparison. Renew Sustain Energy Rev 61:384–397CrossRef
50.
Ghritlahre HK (2019) Performance prediction of porous bed solar air heater using MLP and GRNN model-a comparative study. CSVTU Res J Eng Technol 5(1):70–81CrossRef
51.
Ghritlahre HK, Prasad RK (2018) Investigation of thermal performance of unidirectional flow porous bed solar air heater using MLP, GRNN, and RBF models of ANN technique. Therm Sci Eng Prog 6:226–235CrossRef
52.
Ghritlahre HK, Prasad RK (2018) Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique. J Environ Manag 223:566–575CrossRef
53.
Esen H, Ozgen F, Esen M, Sengur A (2009) Artificial neural network and wavelet neural network approaches for modelling of a solar air heater. Expert Syst Appl 36:11240–11248CrossRef
54.
Cakmak G, Yıldız C (2011) The prediction of seedy grape drying rate using a neural network method. Comput Electron Agric 75:132–138CrossRef
55.
Caner M, Gedik E, Keçebas A (2011) Investigation on thermal performance calculation of two type solar air collectors using artificial neural network. Expert Syst Appl 38:1668–1674CrossRef
56.
Kamthania D, Tiwari GN (2012) Performance analysis of a hybrid photovoltaic thermal double pass air collector using ANN. Appl Solar Energy 48(3):186–192CrossRef
57.
Benli H (2013) Determination of thermal performance calculation of two different types solar air collectors with the use of artificial neural networks. Int J Heat Mass Transf 60:1–7CrossRef
58.
59.
Ghritlahre HK, Prasad RK (2017) Prediction of thermal performance of unidirectional flow porous bed solar air heater with optimal training function using artificial neural network. Energy Procedia 109:369–376CrossRef
60.
Ghritlahre HK, Prasad RK (2017) Energetic and exergetic performance prediction of roughened solar air heater using artificial neural network. Ciência e Técnica Vitivinícola 32(11):2–24
61.
Ghritlahre HK, Prasad RK (2018) Investigation on heat transfer characteristics of roughened solar air heater using ANN technique. Int J Heat Technol 36(1):102–110CrossRef
62.
Ghritlahre HK, Prasad RK (2018) Exergetic performance prediction of roughened solar air heater using artificial neural network. Strojniški vestnik J Mech Eng 64(3):195–206
63.
Ghritlahre HK, Prasad RK (2018) Development of optimal ANN model to estimate the thermal performance of roughened solar air heater using two different learning algorithms. Ann Data Sci 5(3):453–467CrossRef
64.
65.
Ghritlahre HK, Prasad RK (2018) Prediction of exergetic efficiency of arc shaped wire roughened solar air heater using ANN model. Int J Heat Technol 36(3):1107–1115CrossRef
66.
Erenturk S, Erenturk K (2018) Comparisons of novel modeling techniques to analyze thermal performance of unglazed transpired solar collectors. Measurement 116:412–421CrossRef
67.
Priddy KL, Keller PE (2007) Artificial neural networks: an introduction. Prentice Hall of India, New Delhi
68.
May RJ, Maier HR, Dandy GC (2010) Data splitting for artificial neural networks using SOM-based stratified sampling. Neural Netw 23:283–294CrossRef
69.
Askarzadeh A, Rezazadeh A (2013) Artificial neural network training using a new efficient optimization algorithm. Appl Soft Comput 1:1206–1213CrossRef
70.
Yin C, Rosendahl L, Luo Z (2003) Methods to improve prediction performance of ANN models. Simul Model Pract Theory 11:211–222CrossRef
71.
Smits JRM, Melssen WJ, Buydens LMC, Kateman G (1994) Using artificial neural networks for solving chemical problems: part I. Multi-layer feed-forward networks. Chemom Intell Lab Syst 22:165–189CrossRef
72.
Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14:35–62CrossRef
73.
Wang Z, Massimo CD, Tham MT, Morris AJ (1994) A procedure for determining the topology of multilayer feed forward neural networks. Neural Netw 7:291–300CrossRef
74.
Kalogirou SA, Bojic M (2000) Artificial neural networks for the prediction of the energy consumption of a passive solar building. Energy 25:479–491CrossRef
75.
Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, San Francisco, CA, p 560
76.
Shibata K, Ikeda Y (2009) Effect of number of hidden neurons on learning in large-scale layered neural networks. In: Proceedings of the ICROS-SICE international joint conference, pp 5008–5013
77.
Mohebbi M, Taheri A, Soltani A (2008) neural network for predicting saturated liquid density using genetic algorithm for pure and mixed refrigerants. Int J Refrig 31:1317–1327CrossRef
78.
Castillo PA, Carpio J, Merelo JJ, Prieto A, Rivas V, Romero G (2000) G-Prop: global optimization of multilayer perceptrons using GAs. Neuro Comput 35:149–163
79.
Han F, Gu TY, Ju SG (2011) An improved hybrid algorithm based on PSO and BP for feed forward neural networks. Int J Digit Content Technol Appl 5:106–115
80.
Nabavi-Kerizi SH, Abadi M, Kabir E (2010) A PSO based weighting method for linear combination of neural networks. Comput Electr Eng 36:886–894CrossRef
81.
Leung SYS, Tang Y, Wong WK (2012) A hybrid particle swarm optimization and its application in neural networks. Expert Syst Appl 3:395–405CrossRef
82.
Sexton RS, Dorsey RE, Johnson JD (1999) Optimization of neural networks: a comparative analysis of the genetic algorithm and simulated annealing. Eur J Oper Res 114:589–601CrossRef
83.
Sivagaminathan RK, Ramakrishnan S (2007) A hybrid approach for feature subset selection using neural networks and ant colony optimization. Expert Syst Appl 33:49–60CrossRef
84.
Da Y, Ge XR (2005) An improved PSO-based ANN with simulated annealing technique. Neurocomput Lett 63:527–533CrossRef
Metadaten
Titel
A Comprehensive Review on Performance Prediction of Solar Air Heaters Using Artificial Neural Network
verfasst von
Harish Kumar Ghritlahre
Purvi Chandrakar
Ashfaque Ahmad
Publikationsdatum
21.11.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Annals of Data Science / Ausgabe 3/2021
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-019-00236-1

Weitere Artikel der Ausgabe 3/2021

Annals of Data Science 3/2021 Zur Ausgabe

OriginalPaper

Modeling Determinants of Low Birth Weight for Under Five-Children in Ethiopia

OriginalPaper

On Step-Stress Accelerated Life Testing for Power Generalized Weibull Distribution Under Progressive Type-II Censoring

OriginalPaper

On the Inverse Power Gompertz Distribution

OriginalPaper

On Discrimination Between the Lindley and xgamma Distributions

OriginalPaper

Predicting Indian Stock Market Using the Psycho-Linguistic Features of Financial News

OriginalPaper

A Clustering Algorithm Based on Document Embedding to Identify Clinical Note Templates

Premium Partner

    Bildnachweise