IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v258y2022ics0360544222017170.html
   My bibliography  Save this article

Data-driven optimization of building-integrated ducted openings for wind energy harvesting: Sensitivity analysis of metamodels

Author

Listed:
  • Kaseb, Z.
  • Montazeri, H.

Abstract

Metamodels are developed and used for aerodynamic optimization of a ducted opening integrated into a high-rise building to maximize the amplification factor within the duct. The duct consists of a nozzle, a throat, and a diffuser. 211 high-resolution 3D RANS CFD simulations are performed to generate training and testing datasets. The space-filling design and Genetic algorithm are used for data sampling and optimization, respectively. The performance of five commonly-used metamodels is systematically investigated: Response Surface Methodology (RSM), Kriging (KG), Neural Network (NN), Support Vector Regression (SVR), and Genetic Aggregation Response Surface (GARS). The investigation is based on (i) detailed in-sample and out-of-sample evaluations of the metamodels, (ii) annual available power in the wind (Pavailable), and (iii) annual energy production (AEP) for a 3-bladed horizontal-axis wind turbine (HAWT) installed in the mid-throat for the optimum designs obtained by the metamodels. The results show that converging-diverging ducted openings can magnify the experienced wind speed by the turbine and enhance the available wind power. In addition, the use of different metamodels can lead to a variation of up to 153% in the estimated Pavailable. For a small dataset, crude yet still acceptable accuracy can be achieved for Genetic Aggregation Response Surface and Kriging at a very low computational time.

Suggested Citation

  • Kaseb, Z. & Montazeri, H., 2022. "Data-driven optimization of building-integrated ducted openings for wind energy harvesting: Sensitivity analysis of metamodels," Energy, Elsevier, vol. 258(C).
    • Handle: RePEc:eee:energy:v:258:y:2022:i:c:s0360544222017170
    DOI: 10.1016/j.energy.2022.124814
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222017170
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124814?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. KC, Anup & Whale, Jonathan & Urmee, Tania, 2019. "Urban wind conditions and small wind turbines in the built environment: A review," Renewable Energy, Elsevier, vol. 131(C), pages 268-283.
    2. Zhang, Yishi & Zhu, Ruilin & Chen, Zhijun & Gao, Jie & Xia, De, 2021. "Evaluating and selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data," European Journal of Operational Research, Elsevier, vol. 290(1), pages 235-247.
    3. Tariq Abdulsalam Khamlaj & Markus Peer Rumpfkeil, 2017. "Theoretical Analysis of Shrouded Horizontal Axis Wind Turbines," Energies, MDPI, vol. 10(1), pages 1-19, January.
    4. Leloudas, Stavros N. & Lygidakis, Georgios N. & Eskantar, Alexandros I. & Nikolos, Ioannis K., 2020. "A robust methodology for the design optimization of diffuser augmented wind turbine shrouds," Renewable Energy, Elsevier, vol. 150(C), pages 722-742.
    5. Nunes, Matheus M. & Brasil Junior, Antonio C.P. & Oliveira, Taygoara F., 2020. "Systematic review of diffuser-augmented horizontal-axis turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    6. Sorribes-Palmer, F. & Sanz-Andres, A. & Ayuso, L. & Sant, R. & Franchini, S., 2017. "Mixed CFD-1D wind turbine diffuser design optimization," Renewable Energy, Elsevier, vol. 105(C), pages 386-399.
    7. Alsailani, M. & Montazeri, H. & Rezaeiha, A., 2021. "Towards optimal aerodynamic design of wind catchers: Impact of geometrical characteristics," Renewable Energy, Elsevier, vol. 168(C), pages 1344-1363.
    8. Østergård, Torben & Jensen, Rasmus Lund & Maagaard, Steffen Enersen, 2018. "A comparison of six metamodeling techniques applied to building performance simulations," Applied Energy, Elsevier, vol. 211(C), pages 89-103.
    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. Rahmatian, Mohammad Ali & Hashemi Tari, Pooyan & Majidi, Sahand & Mojaddam, Mohammad, 2023. "Experimental study of the effect of the duct on dual co-axial horizontal axis wind turbines and the effect of rotors diameter ratio and distance on increasing power coefficient," Energy, Elsevier, vol. 284(C).
    2. Allahyarzadeh-Bidgoli, Ali & Yanagihara, Jurandir Itizo, 2023. "Energy efficiency, sustainability, and operating cost optimization of an FPSO with CCUS: An innovation in CO2 compression and injection systems," Energy, Elsevier, vol. 267(C).
    3. M. Abdelsalam, Ali & Abdelmordy, M. & Ibrahim, K.A. & Sakr, I.M., 2023. "An investigation on flow behavior and performance of a wind turbine integrated within a building tunnel," Energy, Elsevier, vol. 280(C).
    4. Rahmatian, Mohammad Ali & Nazarian Shahrbabaki, Amin & Moeini, Seyed Peyman, 2023. "Single-objective optimization design of convergent-divergent ducts of ducted wind turbine using RSM and GA, to increase power coefficient of a small-scale horizontal axis wind turbine," Energy, Elsevier, vol. 269(C).

    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. Rahmatian, Mohammad Ali & Hashemi Tari, Pooyan & Mojaddam, Mohammad & Majidi, Sahand, 2022. "Numerical and experimental study of the ducted diffuser effect on improving the aerodynamic performance of a micro horizontal axis wind turbine," Energy, Elsevier, vol. 245(C).
    2. Nunes, Matheus M. & Brasil Junior, Antonio C.P. & Oliveira, Taygoara F., 2020. "Systematic review of diffuser-augmented horizontal-axis turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    3. Ali, Qazi Shahzad & Kim, Man-Hoe, 2022. "Power conversion performance of airborne wind turbine under unsteady loads," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    4. Kuang, Limin & Su, Jie & Chen, Yaoran & Han, Zhaolong & Zhou, Dai & Zhang, Kai & Zhao, Yongsheng & Bao, Yan, 2022. "Wind-capture-accelerate device for performance improvement of vertical-axis wind turbines: External diffuser system," Energy, Elsevier, vol. 239(PB).
    5. Keramat Siavash, Nemat & Najafi, G. & Tavakkoli Hashjin, Teymour & Ghobadian, Barat & Mahmoodi, Esmail, 2020. "Mathematical modeling of a horizontal axis shrouded wind turbine," Renewable Energy, Elsevier, vol. 146(C), pages 856-866.
    6. Leloudas, Stavros N. & Lygidakis, Georgios N. & Eskantar, Alexandros I. & Nikolos, Ioannis K., 2020. "A robust methodology for the design optimization of diffuser augmented wind turbine shrouds," Renewable Energy, Elsevier, vol. 150(C), pages 722-742.
    7. Po-Wen Hwang & Jia-Heng Wu & Yuan-Jen Chang, 2024. "Optimization Based on Computational Fluid Dynamics and Machine Learning for the Performance of Diffuser-Augmented Wind Turbines with Inlet Shrouds," Sustainability, MDPI, vol. 16(9), pages 1-31, April.
    8. Bontempo, R. & Manna, M., 2020. "Diffuser augmented wind turbines: Review and assessment of theoretical models," Applied Energy, Elsevier, vol. 280(C).
    9. Kumar, Vedant & Saha, Sandeep, 2019. "Theoretical performance estimation of shrouded-twin-rotor wind turbines using the actuator disk theory," Renewable Energy, Elsevier, vol. 134(C), pages 961-969.
    10. Rezek, Thiago J. & Camacho, Ramiro G.R. & Manzanares-Filho, Nelson, 2023. "A novel methodology for the design of diffuser-augmented hydrokinetic rotors," Renewable Energy, Elsevier, vol. 210(C), pages 524-539.
    11. Calise, Francesco & Cappiello, Francesco Liberato & Dentice d’Accadia, Massimo & Vicidomini, Maria, 2020. "Dynamic modelling and thermoeconomic analysis of micro wind turbines and building integrated photovoltaic panels," Renewable Energy, Elsevier, vol. 160(C), pages 633-652.
    12. N. Aravindhan & M. P. Natarajan & S. Ponnuvel & P.K. Devan, 2023. "Recent developments and issues of small-scale wind turbines in urban residential buildings- A review," Energy & Environment, , vol. 34(4), pages 1142-1169, June.
    13. Li, Shoutu & Chen, Qin & Li, Ye & Pröbsting, Stefan & Yang, Congxin & Zheng, Xiaobo & Yang, Yannian & Zhu, Weijun & Shen, Wenzhong & Wu, Faming & Li, Deshun & Wang, Tongguang & Ke, Shitang, 2022. "Experimental investigation on noise characteristics of small scale vertical axis wind turbines in urban environments," Renewable Energy, Elsevier, vol. 200(C), pages 970-982.
    14. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost," Energy, Elsevier, vol. 192(C).
    15. Lakshmi Srinivasan & Nishanth Ram & Sudharshan Bharatwaj Rengarajan & Unnikrishnan Divakaran & Akram Mohammad & Ratna Kishore Velamati, 2023. "Effect of Macroscopic Turbulent Gust on the Aerodynamic Performance of Vertical Axis Wind Turbine," Energies, MDPI, vol. 16(5), pages 1-24, February.
    16. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
    17. Antonio García Auyanet & Rangga E. Santoso & Hrishikesh Mohan & Sanvay S. Rathore & Debapriya Chakraborty & Patrick G. Verdin, 2022. "CFD-Based J-Shaped Blade Design Improvement for Vertical Axis Wind Turbines," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    18. Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 2020. "A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis," Applied Energy, Elsevier, vol. 267(C).
    19. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    20. Wolf-Gerrit Früh, 2023. "Assessing the Performance of Small Wind Energy Systems Using Regional Weather Data," Energies, MDPI, vol. 16(8), pages 1-21, April.

    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:energy:v:258:y:2022:i:c:s0360544222017170. 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.journals.elsevier.com/energy .

    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.