IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i10p2714-d174952.html
   My bibliography  Save this article

Outlier Events of Solar Forecasts for Regional Power Grid in Japan Using JMA Mesoscale Model

Author

Listed:
  • Hideaki Ohtake

    (National Institute of Advanced Industrial Science and Technology, Ibaraki 3058568, Japan
    Meteorological Research Institute, Japan Meteorological Agency, Ibaraki 3050052, Japan)

  • Fumichika Uno

    (National Institute of Advanced Industrial Science and Technology, Ibaraki 3058568, Japan
    Meteorological Research Institute, Japan Meteorological Agency, Ibaraki 3050052, Japan)

  • Takashi Oozeki

    (National Institute of Advanced Industrial Science and Technology, Ibaraki 3058568, Japan)

  • Yoshinori Yamada

    (Meteorological Research Institute, Japan Meteorological Agency, Ibaraki 3050052, Japan)

  • Hideaki Takenaka

    (Japan Aerospace Exploration Agency, Ibaraki 3058505, Japan)

  • Takashi Y. Nakajima

    (Research and Information Center, Tokai University, Tokyo 1510063, Japan)

Abstract

To realize the safety control of electric power systems under high penetration of photovoltaic power systems, accurate global horizontal irradiance (GHI) forecasts using numerical weather prediction models (NWP) are becoming increasingly important. The objective of this study is to understand meteorological characteristics pertaining to large errors (i.e., outlier events) of GHI day-ahead forecasts obtained from the Japan Meteorological Agency, for nine electric power areas during four years from 2014 to 2017. Under outlier events in GHI day-ahead forecasts, several sea-level pressure (SLP) patterns were found in 80 events during the four years; (a) a western edge of anticyclone over the Pacific Ocean (frequency per 80 outlier events; 48.8%), (b) stationary fronts (20.0%), (c) a synoptic-scale cyclone (18.8%), and (d) typhoons (tropical cyclones) (8.8%) around the Japanese islands. In this study, the four case studies of the worst outlier events were performed. A remarkable SLP pattern was the case of the western edge of anticyclone over the Pacific Ocean around Japan. The comparison between regionally integrated GHI day-ahead forecast errors and cloudiness forecasts suggests that the issue of accuracy of cloud forecasts in high- and mid-levels troposphere in NWPs will remain in the future.

Suggested Citation

  • Hideaki Ohtake & Fumichika Uno & Takashi Oozeki & Yoshinori Yamada & Hideaki Takenaka & Takashi Y. Nakajima, 2018. "Outlier Events of Solar Forecasts for Regional Power Grid in Japan Using JMA Mesoscale Model," Energies, MDPI, vol. 11(10), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2714-:d:174952
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/10/2714/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/10/2714/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huva, Robert & Dargaville, Roger & Caine, Simon, 2012. "Prototype large-scale renewable energy system optimisation for Victoria, Australia," Energy, Elsevier, vol. 41(1), pages 326-334.
    2. Fernandez-Jimenez, L. Alfredo & Muñoz-Jimenez, Andrés & Falces, Alberto & Mendoza-Villena, Montserrat & Garcia-Garrido, Eduardo & Lara-Santillan, Pedro M. & Zorzano-Alba, Enrique & Zorzano-Santamaria,, 2012. "Short-term power forecasting system for photovoltaic plants," Renewable Energy, Elsevier, vol. 44(C), pages 311-317.
    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. Verdone, Alessio & Scardapane, Simone & Panella, Massimo, 2024. "Explainable Spatio-Temporal Graph Neural Networks for multi-site photovoltaic energy production," Applied Energy, Elsevier, vol. 353(PB).
    2. De Rosa, Luca & Castro, Rui, 2020. "Forecasting and assessment of the 2030 australian electricity mix paths towards energy transition," Energy, Elsevier, vol. 205(C).
    3. Gulin, Marko & Pavlović, Tomislav & Vašak, Mario, 2016. "Photovoltaic panel and array static models for power production prediction: Integration of manufacturers’ and on-line data," Renewable Energy, Elsevier, vol. 97(C), pages 399-413.
    4. Wu, Yunyang & Reedman, Luke J. & Barrett, Mark A. & Spataru, Catalina, 2018. "Comparison of CST with different hours of storage in the Australian National Electricity Market," Renewable Energy, Elsevier, vol. 122(C), pages 487-496.
    5. Keck, Felix & Jütte, Silke & Lenzen, Manfred & Li, Mengyu, 2022. "Assessment of two optimisation methods for renewable energy capacity expansion planning," Applied Energy, Elsevier, vol. 306(PA).
    6. Hasan, Kazi Nazmul & Saha, Tapan Kumar & Eghbal, Mehdi, 2014. "Investigating the priority of market participants for low emission generation entry into the Australian grid," Energy, Elsevier, vol. 71(C), pages 445-455.
    7. Nolan, Tahlia, 2024. "Is pivoting offshore the right policy for achieving decarbonisation in the state of Victoria, Australia's electricity sector?," Energy Policy, Elsevier, vol. 190(C).
    8. Wang, Lin & Hu, Huanling & Ai, Xue-Yi & Liu, Hua, 2018. "Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm," Energy, Elsevier, vol. 153(C), pages 801-815.
    9. Yadav, Amit Kumar & Sharma, Vikrant & Malik, Hasmat & Chandel, S.S., 2018. "Daily array yield prediction of grid-interactive photovoltaic plant using relief attribute evaluator based Radial Basis Function Neural Network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2115-2127.
    10. Chao-Rong Chen & Faouzi Brice Ouedraogo & Yu-Ming Chang & Devita Ayu Larasati & Shih-Wei Tan, 2021. "Hour-Ahead Photovoltaic Output Forecasting Using Wavelet-ANFIS," Mathematics, MDPI, vol. 9(19), pages 1-14, October.
    11. Karabacak, Kerim & Cetin, Numan, 2014. "Artificial neural networks for controlling wind–PV power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 804-827.
    12. Limouni, Tariq & Yaagoubi, Reda & Bouziane, Khalid & Guissi, Khalid & Baali, El Houssain, 2023. "Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model," Renewable Energy, Elsevier, vol. 205(C), pages 1010-1024.
    13. Wang, Guochang & Su, Yan & Shu, Lianjie, 2016. "One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models," Renewable Energy, Elsevier, vol. 96(PA), pages 469-478.
    14. Martina Radicioni & Valentina Lucaferri & Francesco De Lia & Antonino Laudani & Roberto Lo Presti & Gabriele Maria Lozito & Francesco Riganti Fulginei & Riccardo Schioppo & Mario Tucci, 2021. "Power Forecasting of a Photovoltaic Plant Located in ENEA Casaccia Research Center," Energies, MDPI, vol. 14(3), pages 1-22, January.
    15. Samu, Remember & Calais, Martina & Shafiullah, G.M. & Moghbel, Moayed & Shoeb, Md Asaduzzaman & Nouri, Bijan & Blum, Niklas, 2021. "Applications for solar irradiance nowcasting in the control of microgrids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    16. Mason, I.G. & Page, S.C. & Williamson, A.G., 2013. "Security of supply, energy spillage control and peaking options within a 100% renewable electricity system for New Zealand," Energy Policy, Elsevier, vol. 60(C), pages 324-333.
    17. Fuso Nerini, Francesco & Dargaville, Roger & Howells, Mark & Bazilian, Morgan, 2015. "Estimating the cost of energy access: The case of the village of Suro Craic in Timor Leste," Energy, Elsevier, vol. 79(C), pages 385-397.
    18. Alfredo Nespoli & Emanuele Ogliari & Sonia Leva & Alessandro Massi Pavan & Adel Mellit & Vanni Lughi & Alberto Dolara, 2019. "Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques," Energies, MDPI, vol. 12(9), pages 1-15, April.
    19. WenBo Xiao & Gina Nazario & HuaMing Wu & HuaMing Zhang & Feng Cheng, 2017. "A neural network based computational model to predict the output power of different types of photovoltaic cells," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-8, September.
    20. Nguyen, Thi Ngoc & Müsgens, Felix, 2022. "What drives the accuracy of PV output forecasts?," Applied Energy, Elsevier, vol. 323(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:11:y:2018:i:10:p:2714-:d:174952. 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.