IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v88y2011i11p4078-4086.html
   My bibliography  Save this article

Development of a feasibility prediction tool for solar power plant installation analyses

Author

Listed:
  • Kabalci, Ersan

Abstract

The solar energy becomes a challenging area among other renewable sources since the solar energy sources have the advantages of not causing pollution, having low maintenance cost, and not producing noise due to the absence of the moving parts. Although these advantages, the installation cost of a solar power plant is considerably high. However, feasibility analyses have a great role before installation in order to determine the most appropriate power plant site. Despite there are many methods used in feasibility analysis, this paper is focused on a new intelligent method based on an agglomerative hierarchical clustering approach. The solar irradiation and insolation parameters of Central Anatolian Region of Turkey are evaluated utilizing the intelligent feasibility analysis tool developed in this study. The clustering operation in the tool is performed by using the nearest neighbor algorithm. At the stage of determining the optimum hierarchical clustering results, Euclidean, Manhattan and Minkowski distance metrics are adapted to the tool. The achieved clustering results based on Minkowski distance metric provide the most feasible inferences to knowledge domain expert according to other distance metrics.

Suggested Citation

  • Kabalci, Ersan, 2011. "Development of a feasibility prediction tool for solar power plant installation analyses," Applied Energy, Elsevier, vol. 88(11), pages 4078-4086.
  • Handle: RePEc:eee:appene:v:88:y:2011:i:11:p:4078-4086
    DOI: 10.1016/j.apenergy.2011.04.047
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261911002820
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2011.04.047?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. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "Models for monitoring wind farm power," Renewable Energy, Elsevier, vol. 34(3), pages 583-590.
    2. Chwieduk, Dorota A., 2009. "Recommendation on modelling of solar energy incident on a building envelope," Renewable Energy, Elsevier, vol. 34(3), pages 736-741.
    3. Mingoti, Sueli A. & Lima, Joab O., 2006. "Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1742-1759, November.
    4. Zahedi, A., 2006. "Solar photovoltaic (PV) energy; latest developments in the building integrated and hybrid PV systems," Renewable Energy, Elsevier, vol. 31(5), pages 711-718.
    5. Margeta, Jure & Glasnovic, Zvonimir, 2010. "Feasibility of the green energy production by hybrid solar + hydro power system in Europe and similar climate areas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(6), pages 1580-1590, 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. Baser, Furkan & Demirhan, Haydar, 2017. "A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation," Energy, Elsevier, vol. 123(C), pages 229-240.
    2. Hsu, David, 2015. "Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data," Applied Energy, Elsevier, vol. 160(C), pages 153-163.
    3. Hong, Taehoon & Koo, Choongwan & Kwak, Taehyun, 2013. "Framework for the implementation of a new renewable energy system in an educational facility," Applied Energy, Elsevier, vol. 103(C), pages 539-551.
    4. Lukač, Niko & Žlaus, Danijel & Seme, Sebastijan & Žalik, Borut & Štumberger, Gorazd, 2013. "Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data," Applied Energy, Elsevier, vol. 102(C), pages 803-812.

    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. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    2. Mahmoudimehr, Javad & Shabani, Masoume, 2018. "Optimal design of hybrid photovoltaic-hydroelectric standalone energy system for north and south of Iran," Renewable Energy, Elsevier, vol. 115(C), pages 238-251.
    3. Colak, Ilhami & Sagiroglu, Seref & Yesilbudak, Mehmet, 2012. "Data mining and wind power prediction: A literature review," Renewable Energy, Elsevier, vol. 46(C), pages 241-247.
    4. Andreas Wunsch & Tanja Liesch & Stefan Broda, 2022. "Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-Ensembles," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 39-54, January.
    5. Moldovan, Camelia Liliana & Păltănea, Radu & Visa, Ion, 2020. "Improvement of clear sky models for direct solar irradiance considering turbidity factor variable during the day," Renewable Energy, Elsevier, vol. 161(C), pages 559-569.
    6. Glasnovic, Zvonimir & Margeta, Karmen & Premec, Krunoslav, 2016. "Could Key Engine, as a new open-source for RES technology development, start the third industrial revolution?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1194-1209.
    7. Kruczek, Tadeusz, 2015. "Use of infrared camera in energy diagnostics of the objects placed in open air space in particular at non-isothermal sky," Energy, Elsevier, vol. 91(C), pages 35-47.
    8. Sampaio, Priscila Gonçalves Vasconcelos & González, Mario Orestes Aguirre, 2017. "Photovoltaic solar energy: Conceptual framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 590-601.
    9. Lydia, M. & Kumar, S. Suresh & Selvakumar, A. Immanuel & Prem Kumar, G. Edwin, 2014. "A comprehensive review on wind turbine power curve modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 452-460.
    10. Szabolcs Szima & Calin-Cristian Cormos, 2021. "CO 2 Utilization Technologies: A Techno-Economic Analysis for Synthetic Natural Gas Production," Energies, MDPI, vol. 14(5), pages 1-18, February.
    11. Ahmad, Naseer & Sheikh, Anwar K. & Gandhidasan, P. & Elshafie, Moustafa, 2015. "Modeling, simulation and performance evaluation of a community scale PVRO water desalination system operated by fixed and tracking PV panels: A case study for Dhahran city, Saudi Arabia," Renewable Energy, Elsevier, vol. 75(C), pages 433-447.
    12. Pérez-Campuzano, Darío & Rubio Andrada, Luis & Morcillo Ortega, Patricio & López-Lázaro, Antonio, 2022. "Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance," Journal of Air Transport Management, Elsevier, vol. 101(C).
    13. Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.
    14. Herrmann, J.K. & Savin, I., 2017. "Optimal policy identification: Insights from the German electricity market," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 71-90.
    15. Noureddine Bouarroudj & Yehya Houam & Abdelhamid Djari & Vicente Feliu-Batlle & Abdelkader Lakhdari & Boualam Benlahbib, 2023. "A Linear Quadratic Integral Controller for PV-Module Voltage Regulation for the Purpose of Enhancing the Classical Incremental Conductance Algorithm," Energies, MDPI, vol. 16(11), pages 1-17, June.
    16. Piotr Olczak, 2022. "Energy Productivity of Microinverter Photovoltaic Microinstallation: Comparison of Simulation and Measured Results—Poland Case Study," Energies, MDPI, vol. 15(20), pages 1-14, October.
    17. Pillai, Dhanup S. & Rajasekar, N., 2018. "A comprehensive review on protection challenges and fault diagnosis in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 18-40.
    18. Guan, Hongyu & Yin, Xiuxing & Jiang, Wei, 2024. "Towards the integration of distributed renewables: Operation analysis of pumped storage system under off-design condition based on CFD," Applied Energy, Elsevier, vol. 355(C).
    19. Pankaj Kumar Medhi & Sandeep Mondal, 2016. "A neural feature extraction model for classification of firms and prediction of outsourcing success: advantage of using relational sources of information for new suppliers," International Journal of Production Research, Taylor & Francis Journals, vol. 54(20), pages 6071-6081, October.
    20. Ouyang, Tinghui & Kusiak, Andrew & He, Yusen, 2017. "Modeling wind-turbine power curve: A data partitioning and mining approach," Renewable Energy, Elsevier, vol. 102(PA), pages 1-8.

    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:88:y:2011:i:11:p:4078-4086. 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.