IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v119y2014icp99-117.html
   My bibliography  Save this article

A proposal for a site location planning model of environmentally friendly urban energy supply plants using an environment and energy geographical information system (E-GIS) database (DB) and an artificial neural network (ANN)

Author

Listed:
  • Yeo, In-Ae
  • Yee, Jurng-Jae

Abstract

This study proposes a site location potential model for urban energy supply plants and renewable energy availability using an environment and energy geographical information system (E-GIS) database (DB) and an artificial neural network (ANN). This model addresses the technical methodology of examining the potential for the suitability of urban energy supply plants and renewable energy latency in a region for support material for urban energy supply planning in the draft plan development stage. The applicability of this model is examined by applying it for a planned city in the Republic of Korea, where urban planning is in process. The results from this study are as follows:(1)The E-GIS DB was integrated with geography, climate, and energy-related information to construct an ANN model that can manage, in an integrated manner, the factors that affect the site location of the energy supply plants.(2)The input dataset included the topography, land cover classification, accessibility, water usability, and energy demand, and the target dataset included the system capacity of domestically installed energy supply plants.(3)The site location potential model of the ANN for the urban energy supply plants and renewable energy availability was deduced, and the Levenberg–Marquardt (trainlm) and scaled conjugate gradient (trainscg) algorithms were used. The potentiality class map was constructed for 10 types of energy supply systems and renewable energy resources.(4)The applicability of this energy model was tested in the Gwang-myung/Si-heung public housing district area, a domestic ‘planned city’ of the Republic of Korea. The most appropriate urban energy supply systems for the research area were considered to be the general hydraulic power and solar power based on the topographic conditions and profitable locations for solar resources in Korea. Wind power generation was found to be the least suitable.(5)In terms of the wind energy potential, the technical wind power generation by horizontal – axis wind turbines is unattainable even in the area that has the maximum wind speed, and at least a 10-kW rated power wind turbine should be installed for vertical – axis wind turbines in the research area of interest. In terms of the solar energy potential, the maximum electric power generation potential is 413, 186MJ/month·mesh, which is applied by mono-crystalline bulk PV.

Suggested Citation

  • Yeo, In-Ae & Yee, Jurng-Jae, 2014. "A proposal for a site location planning model of environmentally friendly urban energy supply plants using an environment and energy geographical information system (E-GIS) database (DB) and an artifi," Applied Energy, Elsevier, vol. 119(C), pages 99-117.
  • Handle: RePEc:eee:appene:v:119:y:2014:i:c:p:99-117
    DOI: 10.1016/j.apenergy.2013.12.060
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261913010696
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2013.12.060?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. Hamzacebi, Coskun, 2007. "Forecasting of Turkey's net electricity energy consumption on sectoral bases," Energy Policy, Elsevier, vol. 35(3), pages 2009-2016, March.
    2. Wei, Zhongbao & Li, Xiaolu & Xu, Lijun & Cheng, Yanting, 2013. "Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 683-692.
    3. Khatib, Tamer & Mohamed, Azah & Sopian, K., 2012. "A review of solar energy modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2864-2869.
    4. Mellit, A. & Kalogirou, S.A. & Hontoria, L. & Shaari, S., 2009. "Artificial intelligence techniques for sizing photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 406-419, February.
    5. Cadenas, Erasmo & Rivera, Wilfrido, 2009. "Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks," Renewable Energy, Elsevier, vol. 34(1), pages 274-278.
    6. Bourbia, F & Awbi, H.B, 2004. "Building cluster and shading in urban canyon for hot dry climate," Renewable Energy, Elsevier, vol. 29(2), pages 249-262.
    7. Dominguez Bravo, Javier & Garcia Casals, Xavier & Pinedo Pascua, Irene, 2007. "GIS approach to the definition of capacity and generation ceilings of renewable energy technologies," Energy Policy, Elsevier, vol. 35(10), pages 4879-4892, October.
    8. Ondreka, Joris & Rüsgen, Maike Inga & Stober, Ingrid & Czurda, Kurt, 2007. "GIS-supported mapping of shallow geothermal potential of representative areas in south-western Germany—Possibilities and limitations," Renewable Energy, Elsevier, vol. 32(13), pages 2186-2200.
    9. Janke, Jason R., 2010. "Multicriteria GIS modeling of wind and solar farms in Colorado," Renewable Energy, Elsevier, vol. 35(10), pages 2228-2234.
    10. Arnette, Andrew N. & Zobel, Christopher W., 2011. "Spatial analysis of renewable energy potential in the greater southern Appalachian mountains," Renewable Energy, Elsevier, vol. 36(11), pages 2785-2798.
    11. Larentis, Dante G. & Collischonn, Walter & Olivera, Francisco & Tucci, Carlos E.M., 2010. "Gis-based procedures for hydropower potential spotting," Energy, Elsevier, vol. 35(10), pages 4237-4243.
    12. Yeo, In-Ae & Yoon, Seong-Hwan & Yee, Jurng-Jae, 2013. "Development of an urban energy demand forecasting system to support environmentally friendly urban planning," Applied Energy, Elsevier, vol. 110(C), pages 304-317.
    13. Yue, Cheng-Dar & Yang, Min-How, 2009. "Exploring the potential of wind energy for a coastal state," Energy Policy, Elsevier, vol. 37(10), pages 3925-3940, October.
    14. Fadare, D.A., 2010. "The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria," Applied Energy, Elsevier, vol. 87(3), pages 934-942, March.
    15. Ragaglini, G. & Triana, F. & Villani, R. & Bonari, E., 2011. "Can sunflower provide biofuel for inland demand? An integrated assessment of sustainability at regional scale," Energy, Elsevier, vol. 36(4), pages 2111-2118.
    16. Mari, Riccardo & Bottai, Lorenzo & Busillo, Caterina & Calastrini, Francesca & Gozzini, Bernardo & Gualtieri, Giovanni, 2011. "A GIS-based interactive web decision support system for planning wind farms in Tuscany (Italy)," Renewable Energy, Elsevier, vol. 36(2), pages 754-763.
    17. Aydin, Nazli Yonca & Kentel, Elcin & Duzgun, Sebnem, 2010. "GIS-based environmental assessment of wind energy systems for spatial planning: A case study from Western Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 364-373, January.
    18. Kalogirou, Soteris A., 2000. "Long-term performance prediction of forced circulation solar domestic water heating systems using artificial neural networks," Applied Energy, Elsevier, vol. 66(1), pages 63-74, May.
    19. Höhn, J. & Lehtonen, E. & Rasi, S. & Rintala, J., 2014. "A Geographical Information System (GIS) based methodology for determination of potential biomasses and sites for biogas plants in southern Finland," Applied Energy, Elsevier, vol. 113(C), pages 1-10.
    20. Olanrewaju, O.A. & Jimoh, A.A. & Kholopane, P.A., 2012. "Integrated IDA–ANN–DEA for assessment and optimization of energy consumption in industrial sectors," Energy, Elsevier, vol. 46(1), pages 629-635.
    21. Sozen, Adnan & Gulseven, Zafer & Arcaklioglu, Erol, 2007. "Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies," Energy Policy, Elsevier, vol. 35(12), pages 6491-6505, December.
    22. Cadenas, E. & Jaramillo, O.A. & Rivera, W., 2010. "Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method," Renewable Energy, Elsevier, vol. 35(5), pages 925-930.
    23. Ramachandra, T.V. & Shruthi, B.V., 2007. "Spatial mapping of renewable energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(7), pages 1460-1480, September.
    24. Charabi, Yassine & Gastli, Adel, 2011. "PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation," Renewable Energy, Elsevier, vol. 36(9), pages 2554-2561.
    25. Nguyen, Khanh Q., 2007. "Wind energy in Vietnam: Resource assessment, development status and future implications," Energy Policy, Elsevier, vol. 35(2), pages 1405-1413, February.
    26. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    27. Azadeh, A. & Babazadeh, R. & Asadzadeh, S.M., 2013. "Optimum estimation and forecasting of renewable energy consumption by artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 605-612.
    28. Kavaklioglu, Kadir, 2011. "Modeling and prediction of Turkey's electricity consumption using Support Vector Regression," Applied Energy, Elsevier, vol. 88(1), pages 368-375, January.
    29. Ermis, K. & Midilli, A. & Dincer, I. & Rosen, M.A., 2007. "Artificial neural network analysis of world green energy use," Energy Policy, Elsevier, vol. 35(3), pages 1731-1743, March.
    30. Yeo, In-Ae & Yoon, Seong-Hwan & Yee, Jurng-Jae, 2013. "Development of an Environment and energy Geographical Information System (E-GIS) construction model to support environmentally friendly urban planning," Applied Energy, Elsevier, vol. 104(C), pages 723-739.
    31. Jung, Sungmoon & Kwon, Soon-Duck, 2013. "Weighted error functions in artificial neural networks for improved wind energy potential estimation," Applied Energy, Elsevier, vol. 111(C), pages 778-790.
    32. 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.
    33. Fadare, D.A., 2009. "Modelling of solar energy potential in Nigeria using an artificial neural network model," Applied Energy, Elsevier, vol. 86(9), pages 1410-1422, September.
    34. Safa, M. & Samarasinghe, S., 2011. "Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”," Energy, Elsevier, vol. 36(8), pages 5140-5147.
    35. Sliz-Szkliniarz, Beata & Vogt, Joachim, 2011. "GIS-based approach for the evaluation of wind energy potential: A case study for the Kujawsko-Pomorskie Voivodeship," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(3), pages 1696-1707, April.
    36. Hossain, Jami & Sinha, Vinay & Kishore, V.V.N., 2011. "A GIS based assessment of potential for windfarms in India," Renewable Energy, Elsevier, vol. 36(12), pages 3257-3267.
    37. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
    38. Carolin Mabel, M. & Fernandez, E., 2008. "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, Elsevier, vol. 33(5), pages 986-992.
    39. Bourbia, F & Awbi, H.B, 2004. "Building cluster and shading in urban canyon for hot dry climate," Renewable Energy, Elsevier, vol. 29(2), pages 291-301.
    40. Sözen, Adnan & Arcaklioglu, Erol & Özkaymak, Mehmet, 2005. "Turkey's net energy consumption," Applied Energy, Elsevier, vol. 81(2), pages 209-221, June.
    41. Belmonte, S. & Núñez, V. & Viramonte, J.G. & Franco, J., 2009. "Potential renewable energy resources of the Lerma Valley, Salta, Argentina for its strategic territorial planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1475-1484, August.
    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. Schlör, Holger & Venghaus, Sandra & Hake, Jürgen-Friedrich, 2018. "The FEW-Nexus city index – Measuring urban resilience," Applied Energy, Elsevier, vol. 210(C), pages 382-392.
    2. Karteris, Marinos & Theodoridou, Ifigeneia & Mallinis, Giorgos & Tsiros, Emmanouel & Karteris, Apostolos, 2016. "Towards a green sustainable strategy for Mediterranean cities: Assessing the benefits of large-scale green roofs implementation in Thessaloniki, Northern Greece, using environmental modelling, GIS and," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 510-525.
    3. Antonio Barragán-Escandón & Julio Terrados-Cepeda & Esteban Zalamea-León, 2017. "The Role of Renewable Energy in the Promotion of Circular Urban Metabolism," Sustainability, MDPI, vol. 9(12), pages 1-29, December.
    4. Hammad, Ahmed W A & Akbarnezhad, Ali & Rey, David, 2017. "Sustainable urban facility location: Minimising noise pollution and network congestion," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 107(C), pages 38-59.
    5. Azizkhani, Mostafa & Vakili, Abdullah & Noorollahi, Younes & Naseri, Farzin, 2017. "Potential survey of photovoltaic power plants using Analytical Hierarchy Process (AHP) method in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1198-1206.
    6. Re Cecconi, F. & Moretti, N. & Tagliabue, L.C., 2019. "Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 266-277.
    7. Ma, Jun & Cheng, Jack C.P., 2016. "Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests," Applied Energy, Elsevier, vol. 183(C), pages 193-201.
    8. Franco, Camilo & Bojesen, Mikkel & Hougaard, Jens Leth & Nielsen, Kurt, 2015. "A fuzzy approach to a multiple criteria and Geographical Information System for decision support on suitable locations for biogas plants," Applied Energy, Elsevier, vol. 140(C), pages 304-315.
    9. Costa, Fabrício Rodrigues & Ribeiro, Carlos Antonio Alvares Soares & Marcatti, Gustavo Eduardo & Lorenzon, Alexandre Simões & Teixeira, Thaisa Ribeiro & Domingues, Getulio Fonseca & Castro, Nero Lemos, 2020. "GIS applied to location of bioenergy plants in tropical agricultural areas," Renewable Energy, Elsevier, vol. 153(C), pages 911-918.
    10. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei & Du, Ruijin & Lu, Longxi & He, Yu, 2017. "The effect of energy construction adjustment on the dynamical evolution of energy-saving and emission-reduction system in China," Applied Energy, Elsevier, vol. 196(C), pages 180-189.
    11. Mikkola, Jani & Lund, Peter D., 2014. "Models for generating place and time dependent urban energy demand profiles," Applied Energy, Elsevier, vol. 130(C), pages 256-264.
    12. Tang, J.P. & Lam, H.L. & Abdul Aziz, M.K. & Morad, N.A., 2017. "Palm biomass strategic resource managment – A competitive game analysis," Energy, Elsevier, vol. 118(C), pages 456-463.
    13. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    14. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    15. Barragán-Escandón, Edgar A. & Zalamea-León, Esteban F. & Terrados-Cepeda, Julio & Vanegas-Peralta, P.F., 2020. "Energy self-supply estimation in intermediate cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 129(C).
    16. Chen, Shaoqing & Chen, Bin, 2015. "Urban energy consumption: Different insights from energy flow analysis, input–output analysis and ecological network analysis," Applied Energy, Elsevier, vol. 138(C), pages 99-107.
    17. 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.
    18. Kim, Byungil & Han, SangUk & Heo, Jae & Jung, Jaehoon, 2020. "Proof-of-concept of a two-stage approach for selecting suitable slopes on a highway network for solar photovoltaic systems: A case study in South Korea," Renewable Energy, Elsevier, vol. 151(C), pages 366-377.

    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. Calvert, K. & Pearce, J.M. & Mabee, W.E., 2013. "Toward renewable energy geo-information infrastructures: Applications of GIScience and remote sensing that build institutional capacity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 416-429.
    2. 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.
    3. Feng Qing & Xiaohuan Liu & Zhaoyong Jiang & Shaoda Li, 2020. "Assessment of energy strategy pressure based on geographical information system," Energy & Environment, , vol. 31(6), pages 1031-1054, September.
    4. Olanrewaju, O.A & Jimoh, A.A, 2014. "Review of energy models to the development of an efficient industrial energy model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 661-671.
    5. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    6. Olanrewaju, O.A. & Jimoh, A.A. & Kholopane, P.A., 2013. "Assessing the energy potential in the South African industry: A combined IDA-ANN-DEA (Index Decomposition Analysis-Artificial Neural Network-Data Envelopment Analysis) model," Energy, Elsevier, vol. 63(C), pages 225-232.
    7. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    8. Quetzalcoatl Hernandez-Escobedo, 2016. "Wind Energy Assessment for Small Urban Communities in the Baja California Peninsula, Mexico," Energies, MDPI, vol. 9(10), pages 1-24, October.
    9. Baseer, M.A. & Rehman, S. & Meyer, J.P. & Alam, Md. Mahbub, 2017. "GIS-based site suitability analysis for wind farm development in Saudi Arabia," Energy, Elsevier, vol. 141(C), pages 1166-1176.
    10. Uzlu, Ergun & Akpınar, Adem & Özturk, Hasan Tahsin & Nacar, Sinan & Kankal, Murat, 2014. "Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey," Energy, Elsevier, vol. 69(C), pages 638-647.
    11. Hasan Eroğlu, 2021. "Multi-criteria decision analysis for wind power plant location selection based on fuzzy AHP and geographic information systems," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 18278-18310, December.
    12. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    13. Grassi, Stefano & Chokani, Ndaona & Abhari, Reza S., 2012. "Large scale technical and economical assessment of wind energy potential with a GIS tool: Case study Iowa," Energy Policy, Elsevier, vol. 45(C), pages 73-85.
    14. Mekonnen, Addisu D. & Gorsevski, Pece V., 2015. "A web-based participatory GIS (PGIS) for offshore wind farm suitability within Lake Erie, Ohio," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 162-177.
    15. Hernández-Escobedo, Q. & Rodríguez-García, E. & Saldaña-Flores, R. & Fernández-García, A. & Manzano-Agugliaro, F., 2015. "Solar energy resource assessment in Mexican states along the Gulf of Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 216-238.
    16. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    17. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    18. Kaytez, Fazil, 2020. "A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption," Energy, Elsevier, vol. 197(C).
    19. McPherson, Madeleine & Ismail, Malik & Hoornweg, Daniel & Metcalfe, Murray, 2018. "Planning for variable renewable energy and electric vehicle integration under varying degrees of decentralization: A case study in Lusaka, Zambia," Energy, Elsevier, vol. 151(C), pages 332-346.
    20. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.

    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:appene:v:119:y:2014:i:c:p:99-117. 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/wps/find/journaldescription.cws_home/405891/description#description .

    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.