IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i24p8498-d704139.html
   My bibliography  Save this article

Solar Radiation Prediction Based on Convolution Neural Network and Long Short-Term Memory

Author

Listed:
  • Tingting Zhu

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
    Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University), Ministry of Education, Nanjing 210096, China)

  • Yiren Guo

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Zhenye Li

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Cong Wang

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

Abstract

Photovoltaic power generation is highly valued and has developed rapidly throughout the world. However, the fluctuation of solar irradiance affects the stability of the photovoltaic power system and endangers the safety of the power grid. Therefore, ultra-short-term solar irradiance predictions are widely used to provide decision support for power dispatching systems. Although a great deal of research has been done, there is still room for improvement regarding the prediction accuracy of solar irradiance including global horizontal irradiance, direct normal irradiance and diffuse irradiance. This study took the direct normal irradiance (DNI) as prediction target and proposed a Siamese convolutional neural network-long short-term memory (SCNN-LSTM) model to predict the inter-hour DNI by combining the time-dependent spatial features of total sky images and historical meteorological observations. First, the features of total sky images were automatically extracted using a Siamese CNN to describe the cloud information. Next, the image features and meteorological observations were fused and then predicted the DNI in 10-min ahead using an LSTM. To verify the validity of the proposed SCNN-LSTM model, several experiments were carried out using two-year historical observation data provided by the National Renewable Energy Laboratory (NREL). The results show that the proposed method achieved nRMSE of 23.47% and forecast skill of 24.51% for the whole year of 2014, and it also did better than some published methods especially under clear sky and rainy days.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8498-:d:704139
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/24/8498/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/24/8498/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rodríguez, Fermín & Fleetwood, Alice & Galarza, Ainhoa & Fontán, Luis, 2018. "Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control," Renewable Energy, Elsevier, vol. 126(C), pages 855-864.
    2. Yang, Ying & Campana, Pietro Elia & Stridh, Bengt & Yan, Jinyue, 2020. "Potential analysis of roof-mounted solar photovoltaics in Sweden," Applied Energy, Elsevier, vol. 279(C).
    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. 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.
    2. Eduardo Rangel-Heras & César Angeles-Camacho & Erasmo Cadenas-Calderón & Rafael Campos-Amezcua, 2022. "Short-Term Forecasting of Energy Production for a Photovoltaic System Using a NARX-CVM Hybrid Model," Energies, MDPI, vol. 15(8), pages 1-23, April.
    3. 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.
    4. Benaggoune, Khaled & Yue, Meiling & Jemei, Samir & Zerhouni, Noureddine, 2022. "A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 313(C).
    5. 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).
    6. Victor Hugo Wentz & Joylan Nunes Maciel & Jorge Javier Gimenez Ledesma & Oswaldo Hideo Ando Junior, 2022. "Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models," Energies, MDPI, vol. 15(7), pages 1-23, March.
    7. Feng, Zhong-kai & Huang, Qing-qing & Niu, Wen-jing & Yang, Tao & Wang, Jia-yang & Wen, Shi-ping, 2022. "Multi-step-ahead solar output time series prediction with gate recurrent unit neural network using data decomposition and cooperation search algorithm," Energy, Elsevier, vol. 261(PA).
    8. Christopher K. Wikle & Abhirup Datta & Bhava Vyasa Hari & Edward L. Boone & Indranil Sahoo & Indulekha Kavila & Stefano Castruccio & Susan J. Simmons & Wesley S. Burr & Won Chang, 2023. "An illustration of model agnostic explainability methods applied to environmental data," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    9. Hossam Fraihat & Amneh A. Almbaideen & Abdullah Al-Odienat & Bassam Al-Naami & Roberto De Fazio & Paolo Visconti, 2022. "Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan," Future Internet, MDPI, vol. 14(3), pages 1-24, March.
    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. 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).
    12. Evan Sauter & Maqsood Mughal & Ziming Zhang, 2023. "Evaluation of Machine Learning Methods on Large-Scale Spatiotemporal Data for Photovoltaic Power Prediction," Energies, MDPI, vol. 16(13), pages 1-26, June.
    13. Ouyang, Tiancheng & Pan, Mingming & Huang, Youbin & Tan, Xianlin & Qin, Peijia, 2023. "Thermodynamic design and power prediction of a solar power tower integrated system using neural networks," Energy, Elsevier, vol. 278(PA).
    14. Zhu, Yinlong & Li, Guoliang & Jiang, Yonglei & Li, Ming & Wang, Yunfeng & Zhang, Ying & Liu, Yali & Yao, Muchi, 2025. "Predicting photovoltaic greenhouse irradiance at low-latitudes of plateau based on ultra-short-term time series," Renewable Energy, Elsevier, vol. 239(C).
    15. Wen-Chang Tsai & Chia-Sheng Tu & Chih-Ming Hong & Whei-Min Lin, 2023. "A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation," Energies, MDPI, vol. 16(14), pages 1-30, July.
    16. 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.

    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. Yuan, Feng & Chen, Zhongsheng & Liang, Yujia, 2024. "Precise solar radiation forecasting for sustainable energy integration: A hybrid CEEMD-SCM-GA-LGBM model for day-ahead power and hydrogen production," Renewable Energy, Elsevier, vol. 237(PC).
    2. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    3. Cui, Ye & E, Hanyu & Pedrycz, Witold & Fayek, Aminah Robinson, 2022. "A granular multicriteria group decision making for renewable energy planning problems," Renewable Energy, Elsevier, vol. 199(C), pages 1047-1059.
    4. Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).
    5. Formolli, M. & Kleiven, T. & Lobaccaro, G., 2023. "Assessing solar energy accessibility at high latitudes: A systematic review of urban spatial domains, metrics, and parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 177(C).
    6. Mohsen Beigi & Hossein Beigi Harchegani & Mehdi Torki & Mohammad Kaveh & Mariusz Szymanek & Esmail Khalife & Jacek Dziwulski, 2022. "Forecasting of Power Output of a PVPS Based on Meteorological Data Using RNN Approaches," Sustainability, MDPI, vol. 14(5), pages 1-12, March.
    7. Shahid Nawaz Khan & Syed Ali Abbas Kazmi & Abdullah Altamimi & Zafar A. Khan & Mohammed A. Alghassab, 2022. "Smart Distribution Mechanisms—Part I: From the Perspectives of Planning," Sustainability, MDPI, vol. 14(23), pages 1-109, December.
    8. Negri, Simone & Giani, Federico & Blasuttigh, Nicola & Massi Pavan, Alessandro & Mellit, Adel & Tironi, Enrico, 2022. "Combined model predictive control and ANN-based forecasters for jointly acting renewable self-consumers: An environmental and economical evaluation," Renewable Energy, Elsevier, vol. 198(C), pages 440-454.
    9. Ren, Haoshan & Ma, Zhenjun & Chan, Antoni B. & Sun, Yongjun, 2023. "Optimal planning of municipal-scale distributed rooftop photovoltaic systems with maximized solar energy generation under constraints in high-density cities," Energy, Elsevier, vol. 263(PA).
    10. Guglielmina Mutani & Valeria Todeschi, 2021. "Optimization of Costs and Self-Sufficiency for Roof Integrated Photovoltaic Technologies on Residential Buildings," Energies, MDPI, vol. 14(13), pages 1-25, July.
    11. Maolin Cheng & Jiano Li & Yun Liu & Bin Liu, 2020. "Forecasting Clean Energy Consumption in China by 2025: Using Improved Grey Model GM (1, N)," Sustainability, MDPI, vol. 12(2), pages 1-20, January.
    12. Zhang, Tingsheng & Qi, Lingfei & Zhang, Zutao & Yan, Jinyue, 2024. "A portable balloon integrated photovoltaic system deployed at low altitude," Energy, Elsevier, vol. 313(C).
    13. Tomasz Tietze & Piotr Szulc & Daniel Smykowski & Andrzej Sitka & Romuald Redzicki, 2021. "Application of Phase Change Material and Artificial Neural Networks for Smoothing of Heat Flux Fluctuations," Energies, MDPI, vol. 14(12), pages 1-17, June.
    14. Liu, Jiang & Wu, Qifeng & Lin, Zhipeng & Shi, Huijie & Wen, Shaoyang & Wu, Qiaoyu & Zhang, Junxue & Peng, Changhai, 2023. "A novel approach for assessing rooftop-and-facade solar photovoltaic potential in rural areas using three-dimensional (3D) building models constructed with GIS," Energy, Elsevier, vol. 282(C).
    15. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
    16. Szoplik, Jolanta & Muchel, Paulina, 2023. "Using an artificial neural network model for natural gas compositions forecasting," Energy, Elsevier, vol. 263(PD).
    17. 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.
    18. Nikolaos Kolokas & Dimosthenis Ioannidis & Dimitrios Tzovaras, 2021. "Multi-Step Energy Demand and Generation Forecasting with Confidence Used for Specification-Free Aggregate Demand Optimization," Energies, MDPI, vol. 14(11), pages 1-36, May.
    19. Wang, Zheng-Xin & Wang, Zhi-Wei & Li, Qin, 2020. "Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors," Energy, Elsevier, vol. 200(C).
    20. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(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:jeners:v:14:y:2021:i:24:p:8498-:d:704139. 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.