IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i22p7630-d1282514.html
   My bibliography  Save this article

Refining the Selection of Historical Period in Analog Ensemble Technique

Author

Listed:
  • Federico E. del Pozo

    (Korea Institute of Energy Research, Daejeon 34129, Republic of Korea
    Energy Engineering, University of Science and Technology, Daejeon 34113, Republic of Korea
    Department of Science and Technology, Industrial Technology Development Institute, Taguig 1631, Philippines)

  • Chang Ki Kim

    (Korea Institute of Energy Research, Daejeon 34129, Republic of Korea
    Energy Engineering, University of Science and Technology, Daejeon 34113, Republic of Korea)

  • Hyun-Goo Kim

    (Korea Institute of Energy Research, Daejeon 34129, Republic of Korea
    Energy Engineering, University of Science and Technology, Daejeon 34113, Republic of Korea)

Abstract

A precise estimate of solar energy output is essential for its efficient integration into the power grid as solar energy becomes a more significant renewable energy source. Contrarily, the creation of solar energy involves fluctuation and uncertainty. The integration and operation of energy systems are complicated by the uncertainty in solar energy projection. As a post-processing technique to lower systematic and random errors in the operational meteorological forecast model, the analog ensemble algorithm will be introduced in this study. When determining the appropriate historical and predictive data required to use the approach, an optimization is conducted for the historical period in order to further maximize the capabilities of the analog ensemble. To determine statistical consistency and spread skill, the model is evaluated against both the raw forecast model and observations. The outcome lowers the uncertainty in the predicted data by demonstrating that statistical findings improve significantly even with 1-month historical data. Nevertheless, the optimization with a year’s worth of historical data demonstrates a notable decrease in the outcomes, limiting overestimation and lowering uncertainty. Specifically, analog ensemble algorithms calibrate analog forecasts that are equivalent to the latest target forecasts within a set of previous deterministic forecasts. Overall, we conclude that analog ensembles assuming a 1-year historical period offer a comprehensive method to minimizing uncertainty and that they should be carefully assessed given the specific forecasting aims and limits.

Suggested Citation

  • Federico E. del Pozo & Chang Ki Kim & Hyun-Goo Kim, 2023. "Refining the Selection of Historical Period in Analog Ensemble Technique," Energies, MDPI, vol. 16(22), pages 1-15, November.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7630-:d:1282514
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/22/7630/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/22/7630/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vanvyve, Emilie & Delle Monache, Luca & Monaghan, Andrew J. & Pinto, James O., 2015. "Wind resource estimates with an analog ensemble approach," Renewable Energy, Elsevier, vol. 74(C), pages 761-773.
    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. Liang, Yushi & Wu, Chunbing & Ji, Xiaodong & Zhang, Mulan & Li, Yiran & He, Jianjun & Qin, Zhiheng, 2022. "Estimation of the influences of spatiotemporal variations in air density on wind energy assessment in China based on deep neural network," Energy, Elsevier, vol. 239(PC).
    2. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
    3. Nedaei, Mojtaba & Assareh, Ehsanolah & Walsh, Philip R., 2018. "A comprehensive evaluation of the wind resource characteristics to investigate the short term penetration of regional wind power based on different probability statistical methods," Renewable Energy, Elsevier, vol. 128(PA), pages 362-374.
    4. Shahriari, M. & Cervone, G. & Clemente-Harding, L. & Delle Monache, L., 2020. "Using the analog ensemble method as a proxy measurement for wind power predictability," Renewable Energy, Elsevier, vol. 146(C), pages 789-801.
    5. Jiang, Haiyan & Wang, Jianzhou & Wu, Jie & Geng, Wei, 2017. "Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1199-1217.
    6. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Cervone, G., 2015. "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, Elsevier, vol. 157(C), pages 95-110.
    7. Javier Sanz Rodrigo & Roberto Aurelio Chávez Arroyo & Patrick Moriarty & Matthew Churchfield & Branko Kosović & Pierre‐Elouan Réthoré & Kurt Schaldemose Hansen & Andrea Hahmann & Jeffrey D. Mirocha & , 2017. "Mesoscale to microscale wind farm flow modeling and evaluation," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 6(2), March.
    8. Yang, Xiaolei & Milliren, Christopher & Kistner, Matt & Hogg, Christopher & Marr, Jeff & Shen, Lian & Sotiropoulos, Fotis, 2021. "High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm," Applied Energy, Elsevier, vol. 281(C).
    9. Zhang, Jie & Draxl, Caroline & Hopson, Thomas & Monache, Luca Delle & Vanvyve, Emilie & Hodge, Bri-Mathias, 2015. "Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods," Applied Energy, Elsevier, vol. 156(C), pages 528-541.
    10. Salcedo-Sanz, S. & García-Herrera, R. & Camacho-Gómez, C. & Aybar-Ruíz, A. & Alexandre, E., 2018. "Wind power field reconstruction from a reduced set of representative measuring points," Applied Energy, Elsevier, vol. 228(C), pages 1111-1121.
    11. Hao, Ying & Dong, Lei & Liao, Xiaozhong & Liang, Jun & Wang, Lijie & Wang, Bo, 2019. "A novel clustering algorithm based on mathematical morphology for wind power generation prediction," Renewable Energy, Elsevier, vol. 136(C), pages 572-585.

    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:16:y:2023:i:22:p:7630-:d:1282514. 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.