IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v219y2023ip1s0960148123012892.html

A hybrid meteorological data simulation framework based on time-series generative adversarial network for global daily solar radiation estimation

Author

Listed:
  • Liu, Jingxuan
  • Zang, Haixiang
  • Zhang, Fengchun
  • Cheng, Lilin
  • Ding, Tao
  • Wei, Zhinong
  • Sun, Guoqiang

Abstract

Solar energy has received considerable attention worldwide because of its clean, safe and easily accessible characteristics. Accurate global solar radiation estimation is crucial for local energy potential assessments and deployment of photovoltaic power generation systems. However, the distribution of metrological data varies considerably between regions, and its quality considerably affects the accuracy of the estimation model. Motivated by these concerns, a metrological data estimation framework based on the time-series generative adversarial network (TimeGAN) and deep belief network (DBN) was proposed. First, the metrological data collected from 30 urban meteorological stations in China were analyzed through time-series aggregation. Next, a novel metrological data simulation model based on TimeGAN was proposed to enrich the quality of training samples. Furthermore, a generalized estimation model of daily solar radiation based on DBN was constructed. In the case study in China, TimeGAN outperforms other conventional generative adversarial networks in terms of data generation performance. The simulation results reveal that the TimeGAN-based simulation model can capture the inherent temporal characteristics of the original data, generate high-quality simulation data to reasonably improve the distribution of training data in various cities. In addition, the estimation results indicate that the proposed hybrid framework improve the accuracy of daily solar radiation estimation.

Suggested Citation

  • Liu, Jingxuan & Zang, Haixiang & Zhang, Fengchun & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2023. "A hybrid meteorological data simulation framework based on time-series generative adversarial network for global daily solar radiation estimation," Renewable Energy, Elsevier, vol. 219(P1).
    • Handle: RePEc:eee:renene:v:219:y:2023:i:p1:s0960148123012892
    DOI: 10.1016/j.renene.2023.119374
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123012892
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.119374?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Jiang, Hou & Lu, Ning & Qin, Jun & Tang, Wenjun & Yao, Ling, 2019. "A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    2. Liu, Bai & Yang, Dazhi & Mayer, Martin János & Coimbra, Carlos F.M. & Kleissl, Jan & Kay, Merlinde & Wang, Wenting & Bright, Jamie M. & Xia, Xiang’ao & Lv, Xin & Srinivasan, Dipti & Wu, Yan & Beyer, H, 2023. "Predictability and forecast skill of solar irradiance over the contiguous United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    3. Zhang, Yunfei & Zhou, Zhihua & Liu, Junwei & Yuan, Jianjuan, 2022. "Data augmentation for improving heating load prediction of heating substation based on TimeGAN," Energy, Elsevier, vol. 260(C).
    4. Brester, Christina & Kallio-Myers, Viivi & Lindfors, Anders V. & Kolehmainen, Mikko & Niska, Harri, 2023. "Evaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observations," Renewable Energy, Elsevier, vol. 207(C), pages 266-274.
    5. Dong, Wei & Chen, Xianqing & Yang, Qiang, 2022. "Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability," Applied Energy, Elsevier, vol. 308(C).
    6. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
    7. Liu, Jingxuan & Zang, Haixiang & Ding, Tao & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2023. "Harvesting spatiotemporal correlation from sky image sequence to improve ultra-short-term solar irradiance forecasting," Renewable Energy, Elsevier, vol. 209(C), pages 619-631.
    8. Chen, Fuying & Yang, Qing & Zheng, Niting & Wang, Yuxuan & Huang, Junling & Xing, Lu & Li, Jianlan & Feng, Shuanglei & Chen, Guoqian & Kleissl, Jan, 2022. "Assessment of concentrated solar power generation potential in China based on Geographic Information System (GIS)," Applied Energy, Elsevier, vol. 315(C).
    9. Eşlik, Ardan Hüseyin & Akarslan, Emre & Hocaoğlu, Fatih Onur, 2022. "Short-term solar radiation forecasting with a novel image processing-based deep learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 1490-1505.
    10. Haris, Muhammad & Hasan, Muhammad Noman & Qin, Shiyin, 2021. "Early and robust remaining useful life prediction of supercapacitors using BOHB optimized Deep Belief Network," Applied Energy, Elsevier, vol. 286(C).
    11. Demir, Sumeyra & Mincev, Krystof & Kok, Koen & Paterakis, Nikolaos G., 2021. "Data augmentation for time series regression: Applying transformations, autoencoders and adversarial networks to electricity price forecasting," Applied Energy, Elsevier, vol. 304(C).
    12. Rai, Amit & Shrivastava, Ashish & Jana, Kartick C., 2023. "Differential attention net: Multi-directed differential attention based hybrid deep learning model for solar power forecasting," Energy, Elsevier, vol. 263(PC).
    13. Zou, Ling & Wang, Lunche & Xia, Li & Lin, Aiwen & Hu, Bo & Zhu, Hongji, 2017. "Prediction and comparison of solar radiation using improved empirical models and Adaptive Neuro-Fuzzy Inference Systems," Renewable Energy, Elsevier, vol. 106(C), pages 343-353.
    14. Feng, Cong & Zhang, Jie & Zhang, Wenqi & Hodge, Bri-Mathias, 2022. "Convolutional neural networks for intra-hour solar forecasting based on sky image sequences," Applied Energy, Elsevier, vol. 310(C).
    15. Zhang, Chaobo & Li, Junyang & Zhao, Yang & Li, Tingting & Chen, Qi & Zhang, Xuejun & Qiu, Weikang, 2021. "Problem of data imbalance in building energy load prediction: Concept, influence, and solution," Applied Energy, Elsevier, vol. 297(C).
    16. 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.
    17. Korkmaz, Deniz, 2021. "SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 300(C).
    18. Liu, Jingxuan & Zang, Haixiang & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2023. "A Transformer-based multimodal-learning framework using sky images for ultra-short-term solar irradiance forecasting," Applied Energy, Elsevier, vol. 342(C).
    19. Haider, Syed Altan & Sajid, Muhammad & Sajid, Hassan & Uddin, Emad & Ayaz, Yasar, 2022. "Deep learning and statistical methods for short- and long-term solar irradiance forecasting for Islamabad," Renewable Energy, Elsevier, vol. 198(C), pages 51-60.
    20. Jiang, Yingni, 2009. "Estimation of monthly mean daily diffuse radiation in China," Applied Energy, Elsevier, vol. 86(9), pages 1458-1464, September.
    21. Hao, Daning & Qi, Lingfei & Tairab, Alaeldin M. & Ahmed, Ammar & Azam, Ali & Luo, Dabing & Pan, Yajia & Zhang, Zutao & Yan, Jinyue, 2022. "Solar energy harvesting technologies for PV self-powered applications: A comprehensive review," Renewable Energy, Elsevier, vol. 188(C), pages 678-697.
    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. Guo, Hongxia & Li, Lin & Li, Yuan & Chen, Haoyong & Ma, Qian & Zhang, Xianling, 2026. "CVaR-based trading strategies of renewable energy producers under the integration of environmental attribute markets," Renewable Energy, Elsevier, vol. 260(C).
    2. Xiao, Yaqiu & Hu, Xinle & Lin, Yingshan & Lu, Yang & Jing, Rui & Zhao, Yingru, 2025. "Interpretable short-term electricity load forecasting considering small sample heatwaves," Applied Energy, Elsevier, vol. 398(C).
    3. Li, Jierui & Ren, Xiaoying & Zhang, Fei & Li, Jingtao & Liu, Yulei, 2025. "A novel deep learning-based method for theoretical power fitting of photovoltaic generation," Renewable Energy, Elsevier, vol. 250(C).
    4. Jacek Abramczyk & Wiesław Bielak & Ewelina Gotkowska, 2025. "An Automated Method of Parametric Thermal Shaping of Complex Buildings with Buffer Spaces in a Moderate Climate," Energies, MDPI, vol. 18(15), pages 1-30, July.

    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. Zang, Haixiang & Chen, Dianhao & Liu, Jingxuan & Cheng, Lilin & Sun, Guoqiang & Wei, Zhinong, 2024. "Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction," Energy, Elsevier, vol. 293(C).
    2. Barancsuk, Lilla & Groma, Veronika & Kocziha, Barnabás, 2025. "Hybrid ultra-short term solar irradiation forecasting using resource-efficient multi-step long-short term memory," Renewable Energy, Elsevier, vol. 247(C).
    3. Zhang, Qingyong & Zeng, Zhuo & Zhou, Quan & Ding, Xinzhi & Dong, Zhengcheng & Tian, Meng & Liu, Dinghao & Mei, Kai & Li, Minglong, 2026. "Multimodal bidirectional MAMBA framework for ultra-short-term photovoltaic power forecasting using sky images," Renewable Energy, Elsevier, vol. 256(PG).
    4. Shi, Chaojun & Zhang, Xiaoyun & Zhang, Ke & Xie, Xiongbin & Lu, Qiaochu & Zhang, Ningxuan & Su, Zibo, 2025. "Ultra-short-term photovoltaic power prediction based on ground-based cloud images: A review," Applied Energy, Elsevier, vol. 402(PA).
    5. Chen, Zhiqiang & Li, Jianbin & Cheng, Long & Liu, Xiufeng, 2023. "Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation," Applied Energy, Elsevier, vol. 334(C).
    6. Wang, Yuwei & Song, Minghao & Jia, Mengyao & Shi, Lin & Li, Bingkang, 2023. "TimeGAN based distributionally robust optimization for biomass-photovoltaic-hydrogen scheduling under source-load-market uncertainties," Energy, Elsevier, vol. 284(C).
    7. Xie, Qiyue & Ma, Lin & Liu, Yao & Fu, Qiang & Shen, Zhongli & Wang, Xiaoli, 2023. "An improved SSA-BiLSTM-based short-term irradiance prediction model via sky images feature extraction," Renewable Energy, Elsevier, vol. 219(P2).
    8. Hammond, Joshua E. & Lara Orozco, Ricardo A. & Baldea, Michael & Korgel, Brian A., 2024. "Short-term solar irradiance forecasting under data transmission constraints," Renewable Energy, Elsevier, vol. 233(C).
    9. Liu, Mengcheng & Ling, Qiang, 2025. "Spatial–temporal multimodal fusion model for intra-hour solar power forecasting under variable weather conditions," Renewable Energy, Elsevier, vol. 248(C).
    10. Mousavi, Rashin & Mousavi, Arash & Mousavi, Yashar & Tavasoli, Mahsa & Arab, Aliasghar & Kucukdemiral, Ibrahim Beklan & Alfi, Alireza & Fekih, Afef, 2025. "Revolutionizing solar energy resources: The central role of generative AI in elevating system sustainability and efficiency," Applied Energy, Elsevier, vol. 382(C).
    11. Liu, Jingxuan & Zang, Haixiang & Ding, Tao & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2023. "Harvesting spatiotemporal correlation from sky image sequence to improve ultra-short-term solar irradiance forecasting," Renewable Energy, Elsevier, vol. 209(C), pages 619-631.
    12. Liao, Zhouyi & Coimbra, Carlos F.M., 2024. "Hybrid solar irradiance nowcasting and forecasting with the SCOPE method and convolutional neural networks," Renewable Energy, Elsevier, vol. 232(C).
    13. Nie, Yuhao & Paletta, Quentin & Scott, Andea & Pomares, Luis Martin & Arbod, Guillaume & Sgouridis, Sgouris & Lasenby, Joan & Brandt, Adam, 2024. "Sky image-based solar forecasting using deep learning with heterogeneous multi-location data: Dataset fusion versus transfer learning," Applied Energy, Elsevier, vol. 369(C).
    14. Tao, Kejun & Zhao, Jinghao & Tao, Ye & Qi, Qingqing & Tian, Yajun, 2024. "Operational day-ahead photovoltaic power forecasting based on transformer variant," Applied Energy, Elsevier, vol. 373(C).
    15. Nadimi, Reza & Goto, Mika, 2025. "A novel decision support system for enhancing long-term forecast accuracy in virtual power plants using bidirectional long short-term memory networks," Applied Energy, Elsevier, vol. 382(C).
    16. Yang, Yanru & Liu, Yu & Zhang, Yihang & Shu, Shaolong & Zheng, Junsheng, 2025. "DEST-GNN: A double-explored spatio-temporal graph neural network for multi-site intra-hour PV power forecasting," Applied Energy, Elsevier, vol. 378(PA).
    17. Liu, Yanfeng & Zhou, Yong & Chen, Yaowen & Wang, Dengjia & Wang, Yingying & Zhu, Ying, 2020. "Comparison of support vector machine and copula-based nonlinear quantile regression for estimating the daily diffuse solar radiation: A case study in China," Renewable Energy, Elsevier, vol. 146(C), pages 1101-1112.
    18. Guo, Su & Fan, Huiying & Huang, Jing, 2025. "Ultra-short-term PV power prediction based on an improved hybrid model with sky image features and data two-dimensional purification," Energy, Elsevier, vol. 331(C).
    19. Li, Hao & Ma, Gang & Wang, Bo & Wang, Shu & Li, Wenhao & Meng, Yuxiang, 2025. "Multi-modal feature fusion model based on TimesNet and T2T-ViT for ultra-short-term solar irradiance forecasting," Renewable Energy, Elsevier, vol. 240(C).
    20. Chen, Ji-Long & He, Lei & Chen, Qiao & Lv, Ming-Quan & Zhu, Hong-Lin & Wen, Zhao-Fei & Wu, Sheng-Jun, 2019. "Study of monthly mean daily diffuse and direct beam radiation estimation with MODIS atmospheric product," Renewable Energy, Elsevier, vol. 132(C), pages 221-232.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:219:y:2023:i:p1:s0960148123012892. 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.