IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v56y2013icp558-567.html
   My bibliography  Save this article

Energetic and economic evaluations of geothermal district heating systems by using ANN

Author

Listed:
  • Keçebaş, Ali
  • Alkan, Mehmet Ali
  • Yabanova, İsmail
  • Yumurtacı, Mehmet

Abstract

This paper proposes an artificial neural network (ANN) technique as a new approach to evaluate the energy input, losses, output, efficiency, and economic optimization of a geothermal district heating system (GDHS). By using ANN, an energetic analysis is evaluated on the Afyon geothermal district heating system (AGDHS) located in the city of Afyonkarahisar, Turkey. Promising results are obtained about the economic evaluation of that system. This has been used to determine if the existing system is operating at its optimal level, and will provide information about the optimal design and profitable operation of the system. The results of the study show that the ANN model used for the prediction of the energy performance of the AGDHS has good statistical performance values: a correlation coefficient of 0.9983 with minimum RMS and MAPE values. The total cost for the AGDHS is profitable when the PWF is higher than 7.9. However, the PWF of the AGDHS was found to be 1.43 for the given values. As a result, while installing a GDHS, one should take into account the influences of the PWF, ambient temperature and flow rate on the total costs of the system in any location where it is to be established.

Suggested Citation

  • Keçebaş, Ali & Alkan, Mehmet Ali & Yabanova, İsmail & Yumurtacı, Mehmet, 2013. "Energetic and economic evaluations of geothermal district heating systems by using ANN," Energy Policy, Elsevier, vol. 56(C), pages 558-567.
    • Handle: RePEc:eee:enepol:v:56:y:2013:i:c:p:558-567
    DOI: 10.1016/j.enpol.2013.01.039
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421513000657
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2013.01.039?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bowitz, Einar & Dang Trong, Maj, 2001. "The social cost of district heating in a sparsely populated country," Energy Policy, Elsevier, vol. 29(13), pages 1163-1173, November.
    2. Dzenajaviciene, E.F. & Kveselis, V. & McNaught, C. & Tamonis, M., 2007. "Economic analysis of the renovation of small-scale district heating systems--4 Lithuanian case studies," Energy Policy, Elsevier, vol. 35(4), pages 2569-2578, April.
    3. Erdogdu, Erkan, 2009. "A snapshot of geothermal energy potential and utilization in Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2535-2543, December.
    4. Popescu, Daniela & Ungureanu, Florina & Hernández-Guerrero, Abel, 2009. "Simulation models for the analysis of space heat consumption of buildings," Energy, Elsevier, vol. 34(10), pages 1447-1453.
    5. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    6. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    7. De, S. & Kaiadi, M. & Fast, M. & Assadi, M., 2007. "Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden," Energy, Elsevier, vol. 32(11), pages 2099-2109.
    8. Lund, H. & Möller, B. & Mathiesen, B.V. & Dyrelund, A., 2010. "The role of district heating in future renewable energy systems," Energy, Elsevier, vol. 35(3), pages 1381-1390.
    9. Dotzauer, Erik, 2002. "Simple model for prediction of loads in district-heating systems," Applied Energy, Elsevier, vol. 73(3-4), pages 277-284, November.
    10. Rong, Aiying & Lahdelma, Risto, 2007. "Efficient algorithms for combined heat and power production planning under the deregulated electricity market," European Journal of Operational Research, Elsevier, vol. 176(2), pages 1219-1245, January.
    11. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    12. Hasan, Afif, 1999. "Optimizing insulation thickness for buildings using life cycle cost," Applied Energy, Elsevier, vol. 63(2), pages 115-124, June.
    13. Fast, M. & Palmé, T., 2010. "Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant," Energy, Elsevier, vol. 35(2), pages 1114-1120.
    14. Difs, Kristina & Danestig, Maria & Trygg, Louise, 2009. "Increased use of district heating in industrial processes - Impacts on heat load duration," Applied Energy, Elsevier, vol. 86(11), pages 2327-2334, November.
    15. Lahdelma, Risto & Hakonen, Henri, 2003. "An efficient linear programming algorithm for combined heat and power production," European Journal of Operational Research, Elsevier, vol. 148(1), pages 141-151, July.
    16. Wei, Bing & Wang, Song-Ling & Li, Li, 2010. "Fuzzy comprehensive evaluation of district heating systems," Energy Policy, Elsevier, vol. 38(10), pages 5947-5955, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Yuqing & Liu, Yingxin & Dou, Jinyue & Li, Mingzhu & Zeng, Ming, 2020. "Geothermal energy in China: Status, challenges, and policy recommendations," Utilities Policy, Elsevier, vol. 64(C).
    2. Abrasaldo, Paul Michael B. & Zarrouk, Sadiq J. & Kempa-Liehr, Andreas W., 2024. "A systematic review of data analytics applications in above-ground geothermal energy operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    3. Zheng, Xinye & Wei, Chu & Qin, Ping & Guo, Jin & Yu, Yihua & Song, Feng & Chen, Zhanming, 2014. "Characteristics of residential energy consumption in China: Findings from a household survey," Energy Policy, Elsevier, vol. 75(C), pages 126-135.
    4. Shamshirband, Shahaboddin & Petković, Dalibor & Enayatifar, Rasul & Hanan Abdullah, Abdul & Marković, Dušan & Lee, Malrey & Ahmad, Rodina, 2015. "Heat load prediction in district heating systems with adaptive neuro-fuzzy method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 760-767.
    5. Liu, Jian & Cheng, Wen-Long & Nian, Yong-Le, 2018. "The stratigraphic and operating parameters influence on economic analysis for enhanced geothermal double wells utilization system," Energy, Elsevier, vol. 159(C), pages 264-276.
    6. Carotenuto, Alberto & Figaj, Rafal Damian & Vanoli, Laura, 2017. "A novel solar-geothermal district heating, cooling and domestic hot water system: Dynamic simulation and energy-economic analysis," Energy, Elsevier, vol. 141(C), pages 2652-2669.
    7. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    8. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Hai-Chao & Jiao, Wen-Ling & Lahdelma, Risto & Zou, Ping-Hua, 2011. "Techno-economic analysis of a coal-fired CHP based combined heating system with gas-fired boilers for peak load compensation," Energy Policy, Elsevier, vol. 39(12), pages 7950-7962.
    2. Wang, Haichao & Duanmu, Lin & Lahdelma, Risto & Li, Xiangli, 2017. "Developing a multicriteria decision support framework for CHP based combined district heating systems," Applied Energy, Elsevier, vol. 205(C), pages 345-368.
    3. Arslan, Oguz, 2011. "Power generation from medium temperature geothermal resources: ANN-based optimization of Kalina cycle system-34," Energy, Elsevier, vol. 36(5), pages 2528-2534.
    4. Danica Djurić Ilić, 2020. "Classification of Measures for Dealing with District Heating Load Variations—A Systematic Review," Energies, MDPI, vol. 14(1), pages 1-27, December.
    5. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    6. Kljajić, Miroslav & Gvozdenac, Dušan & Vukmirović, Srdjan, 2012. "Use of Neural Networks for modeling and predicting boiler's operating performance," Energy, Elsevier, vol. 45(1), pages 304-311.
    7. Kalogirou, Soteris A. & Florides, Georgios A. & Pouloupatis, Panayiotis D. & Christodoulides, Paul & Joseph-Stylianou, Josephina, 2015. "Artificial neural networks for the generation of a conductivity map of the ground," Renewable Energy, Elsevier, vol. 77(C), pages 400-407.
    8. F. Marta L. Di Lascio & Andrea Menapace & Maurizio Righetti, 2020. "Joint and conditional dependence modelling of peak district heating demand and outdoor temperature: a copula-based approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 373-395, June.
    9. Gunasekar, N. & Mohanraj, M. & Velmurugan, V., 2015. "Artificial neural network modeling of a photovoltaic-thermal evaporator of solar assisted heat pumps," Energy, Elsevier, vol. 93(P1), pages 908-922.
    10. Bloess, Andreas, 2020. "Modeling of combined heat and power generation in the context of increasing renewable energy penetration," Applied Energy, Elsevier, vol. 267(C).
    11. Shamshirband, Shahaboddin & Petković, Dalibor & Enayatifar, Rasul & Hanan Abdullah, Abdul & Marković, Dušan & Lee, Malrey & Ahmad, Rodina, 2015. "Heat load prediction in district heating systems with adaptive neuro-fuzzy method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 760-767.
    12. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
    13. Giuseppe Pinto & Elnaz Abdollahi & Alfonso Capozzoli & Laura Savoldi & Risto Lahdelma, 2019. "Optimization and Multicriteria Evaluation of Carbon-neutral Technologies for District Heating," Energies, MDPI, vol. 12(9), pages 1-19, April.
    14. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
    15. Kalogirou, S.A. & Mathioulakis, E. & Belessiotis, V., 2014. "Artificial neural networks for the performance prediction of large solar systems," Renewable Energy, Elsevier, vol. 63(C), pages 90-97.
    16. Manfren, Massimiliano & Caputo, Paola & Costa, Gaia, 2011. "Paradigm shift in urban energy systems through distributed generation: Methods and models," Applied Energy, Elsevier, vol. 88(4), pages 1032-1048, April.
    17. Guelpa, Elisa & Marincioni, Ludovica & Verda, Vittorio, 2019. "Towards 4th generation district heating: Prediction of building thermal load for optimal management," Energy, Elsevier, vol. 171(C), pages 510-522.
    18. Almonacid, F. & Fernández, Eduardo F. & Rodrigo, P. & Pérez-Higueras, P.J. & Rus-Casas, C., 2013. "Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network," Energy, Elsevier, vol. 53(C), pages 165-172.
    19. Kalogirou, Soteris A. & Florides, Georgios A. & Pouloupatis, Panayiotis D. & Panayides, Ioannis & Joseph-Stylianou, Josephina & Zomeni, Zomenia, 2012. "Artificial neural networks for the generation of geothermal maps of ground temperature at various depths by considering land configuration," Energy, Elsevier, vol. 48(1), pages 233-240.
    20. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:enepol:v:56:y:2013:i:c:p:558-567. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.