IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v188y2022icp637-650.html
   My bibliography  Save this article

Multi-step solar irradiation prediction based on weather forecast and generative deep learning model

Author

Listed:
  • Gao, Yuan
  • Miyata, Shohei
  • Akashi, Yasunori

Abstract

With the rapid development of computer technology, more and more deep learning models are used in solar radiation (irradiation) prediction. There have been a lot of studies discussing the research of this type of model. However, how to better apply the deep learning model in the optimization method of building energy system, such as multi-step solar radiation (irradiation) prediction model in model predictive control (MPC), is still a challenging issue due to the complexity of the time series and the accumulation of errors in multi-step forecasts.

Suggested Citation

  • Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-step solar irradiation prediction based on weather forecast and generative deep learning model," Renewable Energy, Elsevier, vol. 188(C), pages 637-650.
    • Handle: RePEc:eee:renene:v:188:y:2022:i:c:p:637-650
    DOI: 10.1016/j.renene.2022.02.051
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148122001938
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2022.02.051?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. Ceusters, Glenn & Rodríguez, Román Cantú & García, Alberte Bouso & Franke, Rüdiger & Deconinck, Geert & Helsen, Lieve & Nowé, Ann & Messagie, Maarten & Camargo, Luis Ramirez, 2021. "Model-predictive control and reinforcement learning in multi-energy system case studies," Applied Energy, Elsevier, vol. 303(C).
    2. Pang, Zhihong & Niu, Fuxin & O’Neill, Zheng, 2020. "Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons," Renewable Energy, Elsevier, vol. 156(C), pages 279-289.
    3. Li, Jiaming & Ward, John K. & Tong, Jingnan & Collins, Lyle & Platt, Glenn, 2016. "Machine learning for solar irradiance forecasting of photovoltaic system," Renewable Energy, Elsevier, vol. 90(C), pages 542-553.
    4. Pinamonti, Maria & Baggio, Paolo, 2020. "Energy and economic optimization of solar-assisted heat pump systems with storage technologies for heating and cooling in residential buildings," Renewable Energy, Elsevier, vol. 157(C), pages 90-99.
    5. Ahmad, Muhammad Waseem & Mourshed, Monjur & Rezgui, Yacine, 2018. "Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression," Energy, Elsevier, vol. 164(C), pages 465-474.
    6. Long, Huan & Zhang, Zijun & Su, Yan, 2014. "Analysis of daily solar power prediction with data-driven approaches," Applied Energy, Elsevier, vol. 126(C), pages 29-37.
    7. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
    8. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    9. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, vol. 15(4), pages 405-408, October.
    10. Guo, Zhifeng & Zhou, Kaile & Zhang, Chi & Lu, Xinhui & Chen, Wen & Yang, Shanlin, 2018. "Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 399-412.
    11. Chineke, Theo Chidiezie, 2008. "Equations for estimating global solar radiation in data sparse regions," Renewable Energy, Elsevier, vol. 33(4), pages 827-831.
    12. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
    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. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system," Applied Energy, Elsevier, vol. 326(C).
    2. Hu, Zehuan & Gao, Yuan & Ji, Siyu & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data," Applied Energy, Elsevier, vol. 359(C).
    3. Gao, Yuan & Hu, Zehuan & Shi, Shanrui & Chen, Wei-An & Liu, Mingzhe, 2024. "Adversarial discriminative domain adaptation for solar radiation prediction: A cross-regional study for zero-label transfer learning in Japan," Applied Energy, Elsevier, vol. 359(C).

    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. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention," Applied Energy, Elsevier, vol. 321(C).
    2. Hui Huang & Qiliang Zhu & Xueling Zhu & Jinhua Zhang, 2023. "An Adaptive, Data-Driven Stacking Ensemble Learning Framework for the Short-Term Forecasting of Renewable Energy Generation," Energies, MDPI, vol. 16(4), pages 1-20, February.
    3. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    4. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    5. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    6. Kong, Xiangfei & Du, Xinyu & Xue, Guixiang & Xu, Zhijie, 2023. "Multi-step short-term solar radiation prediction based on empirical mode decomposition and gated recurrent unit optimized via an attention mechanism," Energy, Elsevier, vol. 282(C).
    7. Nourani, Vahid & Sharghi, Elnaz & Behfar, Nazanin & Zhang, Yongqiang, 2022. "Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data," Applied Energy, Elsevier, vol. 315(C).
    8. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
    9. Jessica Wojtkiewicz & Matin Hosseini & Raju Gottumukkala & Terrence Lynn Chambers, 2019. "Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units," Energies, MDPI, vol. 12(21), pages 1-13, October.
    10. Pedregal, Diego J. & Trapero, Juan R., 2021. "Adjusted combination of moving averages: A forecasting system for medium-term solar irradiance," Applied Energy, Elsevier, vol. 298(C).
    11. Zhengwei Huang & Jin Huang & Jintao Min, 2022. "SSA-LSTM: Short-Term Photovoltaic Power Prediction Based on Feature Matching," Energies, MDPI, vol. 15(20), pages 1-16, October.
    12. Peng, Tian & Zhang, Chu & Zhou, Jianzhong & Nazir, Muhammad Shahzad, 2021. "An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting," Energy, Elsevier, vol. 221(C).
    13. Zheng, Jianqin & Zhang, Haoran & Dai, Yuanhao & Wang, Bohong & Zheng, Taicheng & Liao, Qi & Liang, Yongtu & Zhang, Fengwei & Song, Xuan, 2020. "Time series prediction for output of multi-region solar power plants," Applied Energy, Elsevier, vol. 257(C).
    14. Guijo-Rubio, D. & Durán-Rosal, A.M. & Gutiérrez, P.A. & Gómez-Orellana, A.M. & Casanova-Mateo, C. & Sanz-Justo, J. & Salcedo-Sanz, S. & Hervás-Martínez, C., 2020. "Evolutionary artificial neural networks for accurate solar radiation prediction," Energy, Elsevier, vol. 210(C).
    15. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Deep Residual model for short-term multi-step solar radiation prediction," Renewable Energy, Elsevier, vol. 190(C), pages 408-424.
    16. Pang, Zhihong & Niu, Fuxin & O’Neill, Zheng, 2020. "Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons," Renewable Energy, Elsevier, vol. 156(C), pages 279-289.
    17. Liu, Da & Sun, Kun, 2019. "Random forest solar power forecast based on classification optimization," Energy, Elsevier, vol. 187(C).
    18. Christil Pasion & Torrey Wagner & Clay Koschnick & Steven Schuldt & Jada Williams & Kevin Hallinan, 2020. "Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data," Energies, MDPI, vol. 13(10), pages 1-14, May.
    19. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    20. Konduru Sudharshan & C. Naveen & Pradeep Vishnuram & Damodhara Venkata Siva Krishna Rao Kasagani & Benedetto Nastasi, 2022. "Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction," Energies, MDPI, vol. 15(17), pages 1-39, August.

    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:188:y:2022:i:c:p:637-650. 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.