IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v239y2022ipcs0360544221024580.html
   My bibliography  Save this article

Estimation of the influences of spatiotemporal variations in air density on wind energy assessment in China based on deep neural network

Author

Listed:
  • Liang, Yushi
  • Wu, Chunbing
  • Ji, Xiaodong
  • Zhang, Mulan
  • Li, Yiran
  • He, Jianjun
  • Qin, Zhiheng

Abstract

This study proposes a deep neural network-based wind energy assessment method, aiming to systematically evaluate the effects of the spatiotemporal variations in air density on wind energy assessment in China. On this basis, the spatiotemporal patterns of air density are modelled on a high-spatial-resolution scale (1000 m × 1000 m). The influence of the spatiotemporal distribution characteristics of air density on the wind energy production is quantified, and the spatiotemporal variability of the corresponding wind energy production is estimated. The results demonstrate that changes in air density during the year present typical periodic characteristics. The mean air density value in January is 1.079 kg/m3, the highest throughout the year. The difference in mean air density between cold and warm seasons in the study area shows a decreasing law of higher in the northeast and lower in the southwest. When the elevation is less than 3500 m, it reaches 5.06%. The observed spatiotemporal variability in annual energy production exhibits a distinct seasonal cycle, with the highest production appears in spring (2.968 GWh/yr). The total annual energy production in the cold season is 16.08 GWh/yr, whereas the annual energy production decreases by higher than 23.46% when it comes to the warm season.

Suggested Citation

  • Liang, Yushi & Wu, Chunbing & Ji, Xiaodong & Zhang, Mulan & Li, Yiran & He, Jianjun & Qin, Zhiheng, 2022. "Estimation of the influences of spatiotemporal variations in air density on wind energy assessment in China based on deep neural network," Energy, Elsevier, vol. 239(PC).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pc:s0360544221024580
    DOI: 10.1016/j.energy.2021.122210
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221024580
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.122210?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. Kruyt, Bert & Lehning, Michael & Kahl, Annelen, 2017. "Potential contributions of wind power to a stable and highly renewable Swiss power supply," Applied Energy, Elsevier, vol. 192(C), pages 1-11.
    2. Jung, Sungmoon & Arda Vanli, O. & Kwon, Soon-Duck, 2013. "Wind energy potential assessment considering the uncertainties due to limited data," Applied Energy, Elsevier, vol. 102(C), pages 1492-1503.
    3. Shahriari, Mehdi & Blumsack, Seth, 2017. "Scaling of wind energy variability over space and time," Applied Energy, Elsevier, vol. 195(C), pages 572-585.
    4. Nugent, Daniel & Sovacool, Benjamin K., 2014. "Assessing the lifecycle greenhouse gas emissions from solar PV and wind energy: A critical meta-survey," Energy Policy, Elsevier, vol. 65(C), pages 229-244.
    5. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2013. "Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions," Renewable Energy, Elsevier, vol. 52(C), pages 273-282.
    6. Carta, José A. & Díaz, Santiago & Castañeda, Alberto, 2020. "A global sensitivity analysis method applied to wind farm power output estimation models," Applied Energy, Elsevier, vol. 280(C).
    7. Carreno-Madinabeitia, Sheila & Ibarra-Berastegi, Gabriel & Sáenz, Jon & Ulazia, Alain, 2021. "Long-term changes in offshore wind power density and wind turbine capacity factor in the Iberian Peninsula (1900–2010)," Energy, Elsevier, vol. 226(C).
    8. Jafarian, M. & Ranjbar, A.M., 2010. "Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine," Renewable Energy, Elsevier, vol. 35(9), pages 2008-2014.
    9. Ucar, Aynur & Balo, Figen, 2009. "Evaluation of wind energy potential and electricity generation at six locations in Turkey," Applied Energy, Elsevier, vol. 86(10), pages 1864-1872, October.
    10. Liu, Fa & Sun, Fubao & Liu, Wenbin & Wang, Tingting & Wang, Hong & Wang, Xunming & Lim, Wee Ho, 2019. "On wind speed pattern and energy potential in China," Applied Energy, Elsevier, vol. 236(C), pages 867-876.
    11. Safari, Bonfils & Gasore, Jimmy, 2010. "A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda," Renewable Energy, Elsevier, vol. 35(12), pages 2874-2880.
    12. Zhang, Shuangyi & Li, Xichen, 2021. "Future projections of offshore wind energy resources in China using CMIP6 simulations and a deep learning-based downscaling method," Energy, Elsevier, vol. 217(C).
    13. Zhang, Sufang & Li, Xingmei, 2012. "Large scale wind power integration in China: Analysis from a policy perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1110-1115.
    14. Li, Yi & Wu, Xiao-Peng & Li, Qiu-Sheng & Tee, Kong Fah, 2018. "Assessment of onshore wind energy potential under different geographical climate conditions in China," Energy, Elsevier, vol. 152(C), pages 498-511.
    15. Sun, Shengpeng & Liu, Fengliang & Xue, Song & Zeng, Ming & Zeng, Fanxiao, 2015. "Review on wind power development in China: Current situation and improvement strategies to realize future development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 589-599.
    16. Ulazia, Alain & Sáenz, Jon & Ibarra-Berastegi, Gabriel & González-Rojí, Santos J. & Carreno-Madinabeitia, Sheila, 2019. "Global estimations of wind energy potential considering seasonal air density changes," Energy, Elsevier, vol. 187(C).
    17. Ling, Yu & Cai, Xu, 2012. "Exploitation and utilization of the wind power and its perspective in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2111-2117.
    18. Jiang, Haiyan & Wang, Jianzhou & Wu, Jie & Geng, Wei, 2017. "Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1199-1217.
    19. Vanvyve, Emilie & Delle Monache, Luca & Monaghan, Andrew J. & Pinto, James O., 2015. "Wind resource estimates with an analog ensemble approach," Renewable Energy, Elsevier, vol. 74(C), pages 761-773.
    20. WenminQin, & Wang, Lunche & Gueymard, Christian A. & Bilal, Muhammad & Lin, Aiwen & Wei, Jing & Zhang, Ming & Yang, Xuefang, 2020. "Constructing a gridded direct normal irradiance dataset in China during 1981–2014," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    21. Jung, Christopher & Schindler, Dirk, 2019. "The role of air density in wind energy assessment – A case study from Germany," Energy, Elsevier, vol. 171(C), pages 385-392.
    22. Isabelle Tobin & Robert Vautard & Irena Balog & François-Marie Bréon & Sonia Jerez & Paolo Ruti & Françoise Thais & Mathieu Vrac & Pascal Yiou, 2015. "Assessing climate change impacts on European wind energy from ENSEMBLES high-resolution climate projections," Climatic Change, Springer, vol. 128(1), pages 99-112, January.
    23. Lin, Zi & Liu, Xiaolei, 2020. "Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network," Energy, Elsevier, vol. 201(C).
    24. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2019. "Characterization of wind resource in China from a new perspective," Energy, Elsevier, vol. 167(C), pages 994-1010.
    25. Gao, Yang & Ma, Shaoxiu & Wang, Tao, 2019. "The impact of climate change on wind power abundance and variability in China," Energy, Elsevier, vol. 189(C).
    26. Alain Ulazia & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia & Santos J. González-Rojí, 2019. "Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula," Sustainability, MDPI, vol. 11(13), pages 1-22, July.
    27. Liu, Li-qun & Wang, Zhi-xin, 2009. "The development and application practice of wind-solar energy hybrid generation systems in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1504-1512, August.
    28. Eurek, Kelly & Sullivan, Patrick & Gleason, Michael & Hettinger, Dylan & Heimiller, Donna & Lopez, Anthony, 2017. "An improved global wind resource estimate for integrated assessment models," Energy Economics, Elsevier, vol. 64(C), pages 552-567.
    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. Li, Chunxiao & Cui, Can & Li, Ming, 2023. "A proactive 2-stage indoor CO2-based demand-controlled ventilation method considering control performance and energy efficiency," Applied Energy, Elsevier, vol. 329(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. He, J.Y. & Chan, P.W. & Li, Q.S. & Lee, C.W., 2022. "Characterizing coastal wind energy resources based on sodar and microwave radiometer observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    2. Gao, Yang & Ma, Shaoxiu & Wang, Tao & Miao, Changhong & Yang, Fan, 2022. "Distributed onshore wind farm siting using intelligent optimization algorithm based on spatial and temporal variability of wind energy," Energy, Elsevier, vol. 258(C).
    3. Martin, Sean & Jung, Sungmoon & Vanli, Arda, 2020. "Impact of near-future turbine technology on the wind power potential of low wind regions," Applied Energy, Elsevier, vol. 272(C).
    4. Sahu, Bikash Kumar, 2018. "Wind energy developments and policies in China: A short review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1393-1405.
    5. Zhuo Chen & Wei Li & Junhong Guo & Zhe Bao & Zhangrong Pan & Baodeng Hou, 2020. "Projection of Wind Energy Potential over Northern China Using a Regional Climate Model," Sustainability, MDPI, vol. 12(10), pages 1-16, May.
    6. He, J.Y. & Li, Q.S. & Chan, P.W. & Zhao, X.D., 2023. "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach," Applied Energy, Elsevier, vol. 329(C).
    7. Zi Lin & Xiaolei Liu & Ziming Feng, 2020. "Systematic Investigation of Integrating Small Wind Turbines into Power Supply for Hydrocarbon Production," Energies, MDPI, vol. 13(12), pages 1-16, June.
    8. Guangyu Fan & Yanru Wang & Bo Yang & Chuanxiong Zhang & Bin Fu & Qianqian Qi, 2022. "Characteristics of Wind Resources and Post-Project Evaluation of Wind Farms in Coastal Areas of Zhejiang," Energies, MDPI, vol. 15(9), pages 1-18, May.
    9. Gao, Yang & Ma, Shaoxiu & Wang, Tao, 2019. "The impact of climate change on wind power abundance and variability in China," Energy, Elsevier, vol. 189(C).
    10. Bilal, Boudy & Adjallah, Kondo Hloindo & Yetilmezsoy, Kaan & Bahramian, Majid & Kıyan, Emel, 2021. "Determination of wind potential characteristics and techno-economic feasibility analysis of wind turbines for Northwest Africa," Energy, Elsevier, vol. 218(C).
    11. Song, Dongran & Yang, Yinggang & Zheng, Songyue & Tang, Weiyi & Yang, Jian & Su, Mei & Yang, Xuebing & Joo, Young Hoon, 2019. "Capacity factor estimation of variable-speed wind turbines considering the coupled influence of the QN-curve and the air density," Energy, Elsevier, vol. 183(C), pages 1049-1060.
    12. Martinez, A. & Murphy, L. & Iglesias, G., 2023. "Evolution of offshore wind resources in Northern Europe under climate change," Energy, Elsevier, vol. 269(C).
    13. Zhang, Chongyu & Lu, Xi & Ren, Guo & Chen, Shi & Hu, Chengyu & Kong, Zhaoyang & Zhang, Ning & Foley, Aoife M., 2021. "Optimal allocation of onshore wind power in China based on cluster analysis," Applied Energy, Elsevier, vol. 285(C).
    14. Alain Ulazia & Ander Nafarrate & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia, 2019. "The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential," Energies, MDPI, vol. 12(13), pages 1-18, July.
    15. Alain Ulazia & Gabriel Ibarra-Berastegi, 2020. "Problem-Based Learning in University Studies on Renewable Energies: Case of a Laboratory Windpump," Sustainability, MDPI, vol. 12(6), pages 1-15, March.
    16. Zhang, Shuangyi & Li, Xichen, 2021. "Future projections of offshore wind energy resources in China using CMIP6 simulations and a deep learning-based downscaling method," Energy, Elsevier, vol. 217(C).
    17. Adaramola, M.S. & Oyewola, O.M., 2011. "Evaluating the performance of wind turbines in selected locations in Oyo state, Nigeria," Renewable Energy, Elsevier, vol. 36(12), pages 3297-3304.
    18. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
    19. Liu, Fa & Sun, Fubao & Liu, Wenbin & Wang, Tingting & Wang, Hong & Wang, Xunming & Lim, Wee Ho, 2019. "On wind speed pattern and energy potential in China," Applied Energy, Elsevier, vol. 236(C), pages 867-876.
    20. Vu Dinh, Quang & Doan, Quang-Van & Ngo-Duc, Thanh & Nguyen Dinh, Van & Dinh Duc, Nguyen, 2022. "Offshore wind resource in the context of global climate change over a tropical area," Applied Energy, Elsevier, vol. 308(C).

    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:239:y:2022:i:pc:s0360544221024580. 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.