IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v235y2024ics0960148124013314.html
   My bibliography  Save this article

Prediction of long-term photovoltaic power generation in the context of climate change

Author

Listed:
  • Liu, Zhenlu
  • Guo, Junhong
  • Wang, Xiaoxuan
  • Wang, Yuexin
  • Li, Wei
  • Wang, Xiuquan
  • Fan, Yurui
  • Wang, Wenwen

Abstract

Accurate long-term prediction of power generation in photovoltaic (PV) power stations is crucial for preparing generation plans and future planning. Quantitative prediction of future power generation from PV stations not only contributes to the stable operation of the local power system but also assists managers in formulating regional energy policies to promote renewable energy consumption. We utilized the NEX-GDDP-CMIP6 high-resolution climate dataset and employed the Vine Copula method for post-downscaling. This approach enabled high-resolution forecasts of key meteorological factors under different shared socioeconomic pathways (SSPs) scenarios (SSP245 and SSP585) for a PV power station in Yunnan, China. Additionally, we developed the KM-PSO-SVR power generation prediction model, which enables future accurate long-term PV power generation prediction. The results show that the Vine Copula multi-model ensemble downscaling model can effectively simulate the changes in key meteorological factors in the PV power station area. The KM-PSO-SVR model exhibited good simulation performance, with a mean absolute error of 0.843, root mean square error of 1.136, and correlation coefficient of 0.874 during the validation period. The results indicate that during the decade spanning from January 1, 2025, to December 31, 2034, radiation and wind speed will be decrease, while the temperature is expected to increase. In the SSP245 scenario, there is a 1.585 % increase in the average annual power generation during the future carbon peaking period (2025–2034). However, the SSP585 scenario, representing higher future emissions, shows a lower increase of 1.479 %.

Suggested Citation

  • Liu, Zhenlu & Guo, Junhong & Wang, Xiaoxuan & Wang, Yuexin & Li, Wei & Wang, Xiuquan & Fan, Yurui & Wang, Wenwen, 2024. "Prediction of long-term photovoltaic power generation in the context of climate change," Renewable Energy, Elsevier, vol. 235(C).
    • Handle: RePEc:eee:renene:v:235:y:2024:i:c:s0960148124013314
    DOI: 10.1016/j.renene.2024.121263
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124013314
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.121263?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. Shayan, Mostafa Esmaeili & Najafi, Gholamhassan & Ghobadian, Barat & Gorjian, Shiva & Mamat, Rizalman & Ghazali, Mohd Fairusham, 2022. "Multi-microgrid optimization and energy management under boost voltage converter with Markov prediction chain and dynamic decision algorithm," Renewable Energy, Elsevier, vol. 201(P2), pages 179-189.
    2. He, Xinbo & Wang, Yong & Zhang, Yuyang & Ma, Xin & Wu, Wenqing & Zhang, Lei, 2022. "A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecasting," Applied Energy, Elsevier, vol. 325(C).
    3. Han, Shuang & Qiao, Yan-hui & Yan, Jie & Liu, Yong-qian & Li, Li & Wang, Zheng, 2019. "Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network," Applied Energy, Elsevier, vol. 239(C), pages 181-191.
    4. Li, Fengyun & Zheng, Haofeng & Li, Xingmei, 2022. "A novel hybrid model for multi-step ahead photovoltaic power prediction based on conditional time series generative adversarial networks," Renewable Energy, Elsevier, vol. 199(C), pages 560-586.
    5. Hongbo Zhu & Bing Zhang & Weidong Song & Jiguang Dai & Xinmei Lan & Xinyue Chang, 2023. "Power-Weighted Prediction of Photovoltaic Power Generation in the Context of Structural Equation Modeling," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    6. Li, Canbing & Shi, Haiqing & Cao, Yijia & Wang, Jianhui & Kuang, Yonghong & Tan, Yi & Wei, Jing, 2015. "Comprehensive review of renewable energy curtailment and avoidance: A specific example in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1067-1079.
    7. Wang, Yun & Hu, Qinghua & Meng, Deyu & Zhu, Pengfei, 2017. "Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive robust multi-kernel regression model," Applied Energy, Elsevier, vol. 208(C), pages 1097-1112.
    8. Dongkyu Lee & Jinhwa Jeong & Sung Hoon Yoon & Young Tae Chae, 2019. "Improvement of Short-Term BIPV Power Predictions Using Feature Engineering and a Recurrent Neural Network," Energies, MDPI, vol. 12(17), pages 1-17, August.
    9. Vicknair, David & Tansey, Michael & O'Brien, Thomas E., 2022. "Measuring fossil fuel reserves: A simulation and review of the U.S. Securities and Exchange Commission approach," Resources Policy, Elsevier, vol. 79(C).
    10. Mahdavi-Meymand, Amin & Sulisz, Wojciech, 2023. "Application of nested artificial neural network for the prediction of significant wave height," Renewable Energy, Elsevier, vol. 209(C), pages 157-168.
    11. He, Gang & Lin, Jiang & Sifuentes, Froylan & Liu, Xu & Abhyankar, Nikit & Phadke, Amol, 2020. "Author Correction: Rapid cost decrease of renewables and storage accelerates the decarbonization of China’s power system," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt11x8b9hc, Department of Agricultural & Resource Economics, UC Berkeley.
    12. Linlin Yu & Gaojun Meng & Giovanni Pau & Yao Wu & Yun Tang, 2023. "Research on Hierarchical Control Strategy of ESS in Distribution Based on GA-SVR Wind Power Forecasting," Energies, MDPI, vol. 16(4), pages 1-17, February.
    13. Sarmas, Elissaios & Spiliotis, Evangelos & Stamatopoulos, Efstathios & Marinakis, Vangelis & Doukas, Haris, 2023. "Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models," Renewable Energy, Elsevier, vol. 216(C).
    14. Gang He & Jiang Lin & Froylan Sifuentes & Xu Liu & Nikit Abhyankar & Amol Phadke, 2020. "Rapid cost decrease of renewables and storage accelerates the decarbonization of China’s power system," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    15. Alen Jakoplić & Dubravko Franković & Juraj Havelka & Hrvoje Bulat, 2023. "Short-Term Photovoltaic Power Plant Output Forecasting Using Sky Images and Deep Learning," Energies, MDPI, vol. 16(14), pages 1-18, July.
    16. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
    17. Pelosi, A. & Chirico, G.B., 2021. "Regional assessment of daily reference evapotranspiration: Can ground observations be replaced by blending ERA5-Land meteorological reanalysis and CM-SAF satellite-based radiation data?," Agricultural Water Management, Elsevier, vol. 258(C).
    18. Zhuo, Chen & Junhong, Guo & Wei, Li & Fei, Zhang & Chan, Xiao & Zhangrong, Pan, 2022. "Changes in wind energy potential over China using a regional climate model ensemble," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    19. Gang He & Jiang Lin & Froylan Sifuentes & Xu Liu & Nikit Abhyankar & Amol Phadke, 2020. "Author Correction: Rapid cost decrease of renewables and storage accelerates the decarbonization of China’s power system," Nature Communications, Nature, vol. 11(1), pages 1-1, December.
    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. Natalia Gonzalez & Paul Serna-Torre & Pedro A. Sánchez-Pérez & Ryan Davidson & Bryan Murray & Martin Staadecker & Julia Szinai & Rachel Wei & Daniel M. Kammen & Deborah A. Sunter & Patricia Hidalgo-Go, 2024. "Offshore wind and wave energy can reduce total installed capacity required in zero-emissions grids," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. Tan, Jiawei & Chen, Xingyu & Bu, Yang & Wang, Feng & Wang, Jialing & Huang, Xianan & Hu, Zhenda & Liu, Lin & Lin, Changzhui & Meng, Chao & Lin, Jian & Xie, Shan & Xu, Jinmei & Jing, Rui & Zhao, Yingru, 2024. "Incorporating FFTA based safety assessment of lithium-ion battery energy storage systems in multi-objective optimization for integrated energy systems," Applied Energy, Elsevier, vol. 367(C).
    3. Yao, En-jian & Zhang, Tian-yu & Wang, David Z.W. & Zhang, Jun-yi, 2024. "Dynamic planning and decarbonization pathways of the highway power supply network," Applied Energy, Elsevier, vol. 376(PB).
    4. Xiang, C. & van Gevelt, T., 2025. "China's global leadership aspirations and domestic support for climate policy," Ecological Economics, Elsevier, vol. 227(C).
    5. Merheb, Caroline, 2024. "Why should imminent international funds for solar photovoltaics go to families and the private sector and not to the government to stop the electricity crisis in Lebanon?," Energy Policy, Elsevier, vol. 192(C).
    6. Zhang, Yun-Long & Kang, Jia-Ning & Liu, Lan-Cui & Wei, Yi-Ming, 2024. "Unveiling the evolution and future prospects: A comprehensive review of low-carbon transition in the coal power industry," Applied Energy, Elsevier, vol. 371(C).
    7. Sánchez-Pérez, P.A. & Staadecker, Martin & Szinai, Julia & Kurtz, Sarah & Hidalgo-Gonzalez, Patricia, 2022. "Effect of modeled time horizon on quantifying the need for long-duration storage," Applied Energy, Elsevier, vol. 317(C).
    8. Zhenyu Zhuo & Ershun Du & Ning Zhang & Chris P. Nielsen & Xi Lu & Jinyu Xiao & Jiawei Wu & Chongqing Kang, 2022. "Cost increase in the electricity supply to achieve carbon neutrality in China," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    9. Shi, Zhengkun & Yang, Yongbiao & Xu, Qingshan & Wu, Chenyu & Hua, Kui, 2023. "A low-carbon economic dispatch for integrated energy systems with CCUS considering multi-time-scale allocation of carbon allowance," Applied Energy, Elsevier, vol. 351(C).
    10. Elkadeem, Mohamed R. & Younes, Ali & Mazzeo, Domenico & Jurasz, Jakub & Elia Campana, Pietro & Sharshir, Swellam W. & Alaam, Mohamed A., 2022. "Geospatial-assisted multi-criterion analysis of solar and wind power geographical-technical-economic potential assessment," Applied Energy, Elsevier, vol. 322(C).
    11. Kuang, Zhonghong & Chen, Qi & Yu, Yang, 2022. "Assessing the CO2-emission risk due to wind-energy uncertainty," Applied Energy, Elsevier, vol. 310(C).
    12. Li, Xiao & Liu, Pan & Feng, Maoyuan & Jordaan, Sarah M. & Cheng, Lei & Ming, Bo & Chen, Jie & Xie, Kang & Liu, Weibo, 2024. "Energy transition paradox: Solar and wind growth can hinder decarbonization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    13. Mou, Dunguo & Wang, Zining, 2022. "A systematic analysis of integrating variable wind power into Fujian power grid," Energy Policy, Elsevier, vol. 170(C).
    14. Tong, Wenxuan & Lu, Zhengang & Chen, Yanbo & Zhao, Guoliang & Hunt, Julian David & Ren, Dawei & Xu, GuiZhi & Han, Minxiao, 2024. "Typical unit capacity configuration strategies and their control methods of modular gravity energy storage plants," Energy, Elsevier, vol. 295(C).
    15. Liu, Yulin & Chen, Lin & Cao, Zhiling & Wen, Fenghua, 2024. "Uncertainty breeds opportunities: Assessing climate policy uncertainty and its impact on corporate innovation," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    16. Zebo Kuldasheva & Raufhon Salahodjaev, 2023. "Renewable Energy and CO2 Emissions: Evidence from Rapidly Urbanizing Countries," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 14(2), pages 1077-1090, June.
    17. Shao, Yunfei & Fan, Shuai & Meng, Yuhang & Jia, Kunqi & He, Guangyu, 2024. "Personalized demand response based on sub-CDL considering energy consumption characteristics of customers," Applied Energy, Elsevier, vol. 374(C).
    18. Jain, Sourabh, 2022. "Exploring structures of power purchase agreements towards supplying 24x7 variable renewable electricity," Energy, Elsevier, vol. 244(PA).
    19. Kim, Haein, 2023. "Grid interconnections and decarbonization pathways for carbon neutrality of Northeast Asia," Renewable Energy, Elsevier, vol. 219(P1).
    20. Tong, Wenxuan & Lu, Zhengang & Chen, Yanbo & Zhao, Guoliang & Hunt, Julian David & Xu, Guizhi, 2025. "Enhancing modular gravity energy storage plants: A hybrid strategy for optimal unit capacity configuration," Applied Energy, Elsevier, vol. 378(PA).

    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:renene:v:235:y:2024:i:c:s0960148124013314. 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/renewable-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.