IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12089-d924136.html
   My bibliography  Save this article

Analysis and Prediction of Wind Speed Effects in East Asia and the Western Pacific Based on Multi-Source Data

Author

Listed:
  • Chaoli Tang

    (School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
    Sate Key Laboratory of Space Weather, Chinese Academy of Sciences, Beijing 100190, China)

  • Xinhua Tao

    (School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Yuanyuan Wei

    (School of Internet, Anhui University, Hefei 230039, China)

  • Ziyue Tong

    (School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Fangzheng Zhu

    (School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Han Lin

    (School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

Abstract

With the increasing problem of global warming caused by the massive use of fossil fuels, biomass energy as a renewable energy source has attracted widespread attention throughout the globe. In this paper, we analyzed the spatial and temporal variation in wind energy in the East Asia and Western Pacific areas using IGRA site data, ERA5, and NCEP/NCAR reanalysis data from 2000 to 2021, and multi-variate empirical orthogonal function (MV-EOF) decomposition with the Pettitt mutation test, and the seasonal autoregression integrated moving average (SARIMA) model was used to predict the trend of wind speed. The spatial and temporal variations in wind energy in East Asia and Western Pacific areas were analyzed, and it was found that the richer wind-energy resources were mainly concentrated in the “Three Norths” (North China, Northwest China, and Northeast China) and Mongolia, followed by the Western Pacific areas. In addition, the T’ai-hang Mountains and the Qinghai-Tibet Plateau in China block the wind resources in the eastern and southern regions of East Asia, resulting in a shortage of wind resources in this region. In addition, the summer wind speed is significantly lower than in the other three seasons. The first-mode contributions of the MV-EOF wind field and geopotential heights, respectively, are 29.47% and 37.75%. The results show that: (1) There are significant seasonal differences in wind-energy resources in the study area, with the lowest wind speed in summer and the highest wind speed in winter. (2) The wind energy in the study area has significant regional characteristics. For example, China’s Qinghai-Tibet Plateau, Inner Mongolia, Xinjiang region, and Mongolia are rich in wind-energy resources. (3) Wind-energy resources in the study area have gradually increased since 2010, mainly due to changes in large-scale oceanic and atmospheric circulation patterns caused by global warming.

Suggested Citation

  • Chaoli Tang & Xinhua Tao & Yuanyuan Wei & Ziyue Tong & Fangzheng Zhu & Han Lin, 2022. "Analysis and Prediction of Wind Speed Effects in East Asia and the Western Pacific Based on Multi-Source Data," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12089-:d:924136
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12089/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/12089/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Xu, Bin & Lin, Boqiang, 2016. "A quantile regression analysis of China's provincial CO2 emissions: Where does the difference lie?," Energy Policy, Elsevier, vol. 98(C), pages 328-342.
    3. Lazić, Lazar & Pejanović, Goran & Živković, Momčilo, 2010. "Wind forecasts for wind power generation using the Eta model," Renewable Energy, Elsevier, vol. 35(6), pages 1236-1243.
    4. Salah Vaisi & Hooshmand Alizadeh & Werya Lotfi & Saleh Mohammadi, 2021. "Developing the Ecological Footprint Assessment for a University Campus, the Component-Based Method," Sustainability, MDPI, vol. 13(17), pages 1-18, September.
    5. XU Jianzhong & Albina Assenova & Vasilii Erokhin, 2018. "Renewable Energy and Sustainable Development in a Resource-Abundant Country: Challenges of Wind Power Generation in Kazakhstan," Sustainability, MDPI, vol. 10(9), pages 1-21, September.
    6. Zhi Qiao & Feng Wu & Xinliang Xu & Jin Yang & Luo Liu, 2019. "Mechanism of Spatiotemporal Air Quality Response to Meteorological Parameters: A National-Scale Analysis in China," Sustainability, MDPI, vol. 11(14), pages 1-16, July.
    7. Saeed Salah & Husain R. Alsamamra & Jawad H. Shoqeir, 2022. "Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms," Energies, MDPI, vol. 15(7), pages 1-16, April.
    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. Manuel Jaramillo & Diego Carrión, 2022. "An Adaptive Strategy for Medium-Term Electricity Consumption Forecasting for Highly Unpredictable Scenarios: Case Study Quito, Ecuador during the Two First Years of COVID-19," Energies, MDPI, vol. 15(22), pages 1-19, November.

    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. Fan, Fei & Dai, Shangze & Yang, Bo & Ke, Haiqian, 2023. "Urban density, directed technological change, and carbon intensity: An empirical study based on Chinese cities," Technology in Society, Elsevier, vol. 72(C).
    2. Heo, SungKu & Byun, Jaewon & Ifaei, Pouya & Ko, Jaerak & Ha, Byeongmin & Hwangbo, Soonho & Yoo, ChangKyoo, 2024. "Towards mega-scale decarbonized industrial park (Mega-DIP): Generative AI-driven techno-economic and environmental assessment of renewable and sustainable energy utilization in petrochemical industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    3. Yong Bian & Zhi Yu & Xuelan Zeng & Jingchun Feng & Chao He, 2018. "Achieving China’s Long-Term Carbon Emission Abatement Targets: A Perspective from Regional Disparity," Sustainability, MDPI, vol. 10(11), pages 1-19, November.
    4. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    5. Chen, Changhua & Luo, Yuqing & Zou, Hong & Huang, Junbing, 2023. "Understanding the driving factors and finding the pathway to mitigating carbon emissions in China's Yangtze River Delta region," Energy, Elsevier, vol. 278(PB).
    6. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
    7. Ying Su & Chunyan Lu & Xiaoqing Lin & Lianxiu Zhong & Yibin Gao & Yifan Lei, 2020. "Analysis of Spatio-temporal Characteristics and Driving Forces of Air Quality in the Northern Coastal Comprehensive Economic Zone, China," Sustainability, MDPI, vol. 12(2), pages 1-23, January.
    8. Chao-Qun Ma & Jiang-Long Liu & Yi-Shuai Ren & Yong Jiang, 2019. "The Impact of Economic Growth, FDI and Energy Intensity on China’s Manufacturing Industry’s CO 2 Emissions: An Empirical Study Based on the Fixed-Effect Panel Quantile Regression Model," Energies, MDPI, vol. 12(24), pages 1-16, December.
    9. Jianzhong Zhou & Han Liu & Yanhe Xu & Wei Jiang, 2018. "A Hybrid Framework for Short Term Multi-Step Wind Speed Forecasting Based on Variational Model Decomposition and Convolutional Neural Network," Energies, MDPI, vol. 11(9), pages 1-18, August.
    10. Natalia Davidson & Oleg Mariev & Sophia Turkanova, 2021. "Does income inequality matter for CO2 emissions in Russian regions?," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 16(3), pages 533-551, September.
    11. Catalão, J.P.S. & Pousinho, H.M.I. & Mendes, V.M.F., 2011. "Short-term wind power forecasting in Portugal by neural networks and wavelet transform," Renewable Energy, Elsevier, vol. 36(4), pages 1245-1251.
    12. Piotr Michalak, 2023. "Simulation and Experimental Study on the Use of Ventilation Air for Space Heating of a Room in a Low-Energy Building," Energies, MDPI, vol. 16(8), pages 1-17, April.
    13. Bin Xu & Boqiang Lin, 2021. "Large fluctuations of China's commodity prices: Main sources and heterogeneous effects," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2074-2089, April.
    14. Vusal Gasimli & Ramil Huseyn & Rashad Huseynov, 2024. "What Advantages Arise from the Shift Towards Sustainable Energy Sources in Resource-Rich Economies? Empirical Insights from Azerbaijan," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 12-20, January.
    15. Tingting Wang & Linjie Qin & Chao Dai & Zhen Wang & Chenqi Gong, 2023. "Heterogeneous Learning of Functional Clustering Regression and Application to Chinese Air Pollution Data," IJERPH, MDPI, vol. 20(5), pages 1-21, February.
    16. Thipsukon Khumsaeng & Thongchai Kanabkaew, 2021. "Measurement of Indoor Air Pollution in Bhutanese Households during Winter: An Implication of Different Fuel Uses," Sustainability, MDPI, vol. 13(17), pages 1-12, August.
    17. Lin, Boqiang & Ma, Ruiyang, 2022. "Green technology innovations, urban innovation environment and CO2 emission reduction in China: Fresh evidence from a partially linear functional-coefficient panel model," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    18. Tuan-Viet Hoang & Pouya Ifaei & Kijeon Nam & Jouan Rashidi & Soonho Hwangbo & Jong-Min Oh & ChangKyoo Yoo, 2018. "Optimal Management of a Hybrid Renewable Energy System Coupled with a Membrane Bioreactor Using Enviro-Economic and Power Pinch Analyses for Sustainable Climate Change Adaption," Sustainability, MDPI, vol. 11(1), pages 1-22, December.
    19. Xu, Bin & Lin, Boqiang, 2017. "Factors affecting CO2 emissions in China’s agriculture sector: Evidence from geographically weighted regression model," Energy Policy, Elsevier, vol. 104(C), pages 404-414.
    20. Elena Shadrina, 2020. "Non-Hydropower Renewable Energy in Central Asia: Assessment of Deployment Status and Analysis of Underlying Factors," Energies, MDPI, vol. 13(11), pages 1-29, June.

    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:gam:jsusta:v:14:y:2022:i:19:p:12089-:d:924136. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.