IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i17p12885-d1225337.html
   My bibliography  Save this article

Mixed Multi-Pattern Regression for DNI Prediction in Arid Desert Areas

Author

Listed:
  • Tian Han

    (Systems Engineering Institute, School of Automation, Xi’an Jiaotong University, Xi’an 710049, China)

  • Ying Wang

    (Systems Engineering Institute, School of Automation, Xi’an Jiaotong University, Xi’an 710049, China)

  • Xiao Wang

    (Qinghai Photovoltaic Industry Centre Co., Ltd., State Power Investment Corporation, Xining 810000, China)

  • Kang Chen

    (Northwest Engineering Corporation Limited, PowerChina, Xi’an 710065, China)

  • Huaiwu Peng

    (Northwest Engineering Corporation Limited, PowerChina, Xi’an 710065, China)

  • Zhenxin Gao

    (Northwest Engineering Corporation Limited, PowerChina, Xi’an 710065, China)

  • Lanxin Cui

    (Systems Engineering Institute, School of Automation, Xi’an Jiaotong University, Xi’an 710049, China)

  • Wentong Sun

    (Systems Engineering Institute, School of Automation, Xi’an Jiaotong University, Xi’an 710049, China)

  • Qinke Peng

    (Systems Engineering Institute, School of Automation, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

As a crucial issue in renewable energy, accurate prediction of direct normal solar irradiance (DNI) is essential for the stable operation of concentrated solar power (CSP) stations, especially for those in arid desert areas. In this study, in order to fully explore the laws of climate change and assess the solar resources in arid desert areas, we have proposed a mixed multi-pattern regression model (MMP) for short-term DNI prediction using prior knowledge provided by the clear-sky solar irradiance (CSI) model and time series patterns of key meteorological factors mined using PR-DTW on different time scales. The contrastive experimental results demonstrated that MMP can outperform existing DNI prediction models in terms of three recognized statistical metrics. To address the challenge of limited data in arid desert areas, we presented the T-MMP model involving combined transfer learning and MMP. The experimental results demonstrated that T-MMP outperformed MMP in DNI prediction by exploiting the significant correlation between meteorological time series patterns in similar areas for data augmentation. Our study provided a valuable prediction model for accurate DNI prediction in arid desert areas, facilitating the economical and stable operation of CSP plants.

Suggested Citation

  • Tian Han & Ying Wang & Xiao Wang & Kang Chen & Huaiwu Peng & Zhenxin Gao & Lanxin Cui & Wentong Sun & Qinke Peng, 2023. "Mixed Multi-Pattern Regression for DNI Prediction in Arid Desert Areas," Sustainability, MDPI, vol. 15(17), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12885-:d:1225337
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/17/12885/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/17/12885/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
    2. Tingting Zhu & Yiren Guo & Zhenye Li & Cong Wang, 2021. "Solar Radiation Prediction Based on Convolution Neural Network and Long Short-Term Memory," Energies, MDPI, vol. 14(24), pages 1-16, December.
    3. Matthijs Tollenaar & Jon Fridgen & Priyanka Tyagi & Paul W. Stackhouse Jr & Saratha Kumudini, 2017. "The contribution of solar brightening to the US maize yield trend," Nature Climate Change, Nature, vol. 7(4), pages 275-278, April.
    4. Steiner, Andrea & Köhler, Carmen & Metzinger, Isabel & Braun, Axel & Zirkelbach, Mathias & Ernst, Dominique & Tran, Peter & Ritter, Bodo, 2017. "Critical weather situations for renewable energies – Part A: Cyclone detection for wind power," Renewable Energy, Elsevier, vol. 101(C), pages 41-50.
    5. Nematollahi, Omid & Alamdari, Pouria & Jahangiri, Mehdi & Sedaghat, Ahmad & Alemrajabi, Ali Akbar, 2019. "A techno-economical assessment of solar/wind resources and hydrogen production: A case study with GIS maps," Energy, Elsevier, vol. 175(C), pages 914-930.
    6. Ohba, Masamichi & Kadokura, Shinji & Nohara, Daisuke, 2016. "Impacts of synoptic circulation patterns on wind power ramp events in East Japan," Renewable Energy, Elsevier, vol. 96(PA), pages 591-602.
    7. Corrocher, Nicoletta & Cappa, Elisabetta, 2020. "The Role of public interventions in inducing private climate finance: An empirical analysis of the solar energy sector," Energy Policy, Elsevier, vol. 147(C).
    8. Vanessa María Serrano Ardila & Joylan Nunes Maciel & Jorge Javier Gimenez Ledesma & Oswaldo Hideo Ando Junior, 2022. "Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power Forecasting," Energies, MDPI, vol. 15(3), pages 1-21, January.
    9. Köhler, Carmen & Steiner, Andrea & Saint-Drenan, Yves-Marie & Ernst, Dominique & Bergmann-Dick, Anja & Zirkelbach, Mathias & Ben Bouallègue, Zied & Metzinger, Isabel & Ritter, Bodo, 2017. "Critical weather situations for renewable energies – Part B: Low stratus risk for solar power," Renewable Energy, Elsevier, vol. 101(C), pages 794-803.
    Full references (including those not matched with items on IDEAS)

    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. Sharif, Arshian & Raza, Syed Ali & Ozturk, Ilhan & Afshan, Sahar, 2019. "The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: A global study with the application of heterogeneous panel estimations," Renewable Energy, Elsevier, vol. 133(C), pages 685-691.
    2. Berrueta, Alberto & Heck, Michael & Jantsch, Martin & Ursúa, Alfredo & Sanchis, Pablo, 2018. "Combined dynamic programming and region-elimination technique algorithm for optimal sizing and management of lithium-ion batteries for photovoltaic plants," Applied Energy, Elsevier, vol. 228(C), pages 1-11.
    3. Raynaud, D. & Hingray, B. & François, B. & Creutin, J.D., 2018. "Energy droughts from variable renewable energy sources in European climates," Renewable Energy, Elsevier, vol. 125(C), pages 578-589.
    4. Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
    5. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    6. Li, Muyuan & Yao, Jinfeng & Shen, Yanbo & Yuan, Bin & Simmonds, Ian & Liu, Yunyun, 2023. "Impact of synoptic circulation patterns on renewable energy-related variables over China," Renewable Energy, Elsevier, vol. 215(C).
    7. Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
    8. Thierry Moudiki & Frédéric Planchet & Areski Cousin, 2018. "Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks," Risks, MDPI, vol. 6(1), pages 1-20, March.
    9. Jiang, Hou & Lu, Ning & Huang, Guanghui & Yao, Ling & Qin, Jun & Liu, Hengzi, 2020. "Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data," Applied Energy, Elsevier, vol. 270(C).
    10. Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
    11. Qimeng Pan & Lysa Porth & Hong Li, 2022. "Assessing the Effectiveness of the Actuaries Climate Index for Estimating the Impact of Extreme Weather on Crop Yield and Insurance Applications," Sustainability, MDPI, vol. 14(11), pages 1-24, June.
    12. 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.
    13. Daiki Maki & Yasushi Ota, 2019. "Robust tests for ARCH in the presence of the misspecified conditional mean: A comparison of nonparametric approches," Papers 1907.12752, arXiv.org, revised Sep 2019.
    14. Can Ding & Yiyuan Zhou & Qingchang Ding & Kaiming Li, 2022. "Integrated Carbon-Capture-Based Low-Carbon Economic Dispatch of Power Systems Based on EEMD-LSTM-SVR Wind Power Forecasting," Energies, MDPI, vol. 15(5), pages 1-27, February.
    15. Lifang Zhang & Jianzhou Wang & Zhenkun Liu, 2023. "Power grid operation optimization and forecasting using a combined forecasting system," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 124-153, January.
    16. Cheng, Kai & Lu, Zhenzhou, 2018. "Sparse polynomial chaos expansion based on D-MORPH regression," Applied Mathematics and Computation, Elsevier, vol. 323(C), pages 17-30.
    17. Songhee Han & Hannah Jun, 2023. "Growth, emissions, and climate finance nexus for sustainable development: Revisiting the environmental Kuznets curve," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(1), pages 510-527, February.
    18. Ajith, Meenu & Martínez-Ramón, Manel, 2023. "Deep learning algorithms for very short term solar irradiance forecasting: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    19. Yoshiki Nakajima & Naoya Sueishi, 2022. "Forecasting the Japanese macroeconomy using high-dimensional data," The Japanese Economic Review, Springer, vol. 73(2), pages 299-324, April.
    20. Tang, Yi, 2024. "Nexus of natural resource depletion, corruption and financial inclusion on bio-diversity loss: A systematic study on corrupt economies," Resources Policy, Elsevier, vol. 92(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:gam:jsusta:v:15:y:2023:i:17:p:12885-:d:1225337. 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.