IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i22p8755-d979464.html
   My bibliography  Save this article

Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea

Author

Listed:
  • Yeji Lee

    (Department of Architectural Design and Engineering, Incheon national University, Incheon 22012, Republic of Korea)

  • Doosung Choi

    (Department of Building Equipment System and Fire Protection Engineering, Chungwoon University, Incheon 22100, Republic of Korea)

  • Yongho Jung

    (Department of Building Equipment System and Fire Protection Engineering, Chungwoon University, Incheon 22100, Republic of Korea)

  • Myeongjin Ko

    (Department of Building System Technology, Daelim University College, Anyang 13916, Republic of Korea)

Abstract

To increase the accuracy of photovoltaic (PV) power prediction, meteorological data measured at a plant’s target location are widely used. If observation data are missing, public data such as automated synoptic observing systems (ASOS) and automatic weather stations (AWS) operated by the government can be effectively utilized. However, if the public weather station is located far from the target location, uncertainty in the prediction is expected to increase owing to the difference in distance. To solve this problem, we propose a power output prediction process based on inverse distance weighting interpolation (IDW), a spatial statistical technique that can estimate the values of unsampled locations. By demonstrating the proposed process, we tried to improve the prediction of photovoltaic power in random locations without data. The forecasting accuracy depends on the power generation forecasting model and proven case, but when forecasting is based on IDW, it is up to 1.4 times more accurate than when using ASOS data. Therefore, if measured data at the target location are not available, it was confirmed that it is more advantageous to use data predicted by IDW as substitute data than public data such as ASOS.

Suggested Citation

  • Yeji Lee & Doosung Choi & Yongho Jung & Myeongjin Ko, 2022. "Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea," Energies, MDPI, vol. 15(22), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8755-:d:979464
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/22/8755/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/22/8755/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lima, Francisco J.L. & Martins, Fernando R. & Pereira, Enio B. & Lorenz, Elke & Heinemann, Detlev, 2016. "Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks," Renewable Energy, Elsevier, vol. 87(P1), pages 807-818.
    2. Jeong, D.I. & St-Hilaire, A. & Gratton, Y. & Bélanger, C. & Saad, C., 2017. "A guideline to select an estimation model of daily global solar radiation between geostatistical interpolation and stochastic simulation approaches," Renewable Energy, Elsevier, vol. 103(C), pages 70-80.
    3. Ping-Huan Kuo & Hsin-Chuan Chen & Chiou-Jye Huang, 2018. "Solar Radiation Estimation Algorithm and Field Verification in Taiwan," Energies, MDPI, vol. 11(6), pages 1-12, May.
    4. Kristijan Brecl & Marko Topič, 2018. "Photovoltaics (PV) System Energy Forecast on the Basis of the Local Weather Forecast: Problems, Uncertainties and Solutions," Energies, MDPI, vol. 11(5), pages 1-12, May.
    5. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    6. Claudio Monteiro & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado & Andres Muñoz-Jimenez & Pedro M. Lara-Santillan, 2013. "Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, November.
    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. 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).
    2. Dou, Weijing & Wang, Kai & Shan, Shuo & Li, Chenxi & Wang, Yiye & Zhang, Kanjian & Wei, Haikun & Sreeram, Victor, 2024. "Day-ahead Numerical Weather Prediction solar irradiance correction using a clustering method based on weather conditions," Applied Energy, Elsevier, vol. 365(C).
    3. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    4. Dennis Dreier & Mark Howells, 2019. "OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling," Energies, MDPI, vol. 12(7), pages 1-26, April.
    5. Lou, Siwei & Li, Danny H.W. & Lam, Joseph C., 2017. "CIE Standard Sky classification by accessible climatic indices," Renewable Energy, Elsevier, vol. 113(C), pages 347-356.
    6. Rohani, Abbas & Taki, Morteza & Abdollahpour, Masoumeh, 2018. "A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)," Renewable Energy, Elsevier, vol. 115(C), pages 411-422.
    7. Mahfuzur Rahman & Solaiman Chowdhury & Mohammad Shorfuzzaman & Mohammad Kamal Hossain & Mohammad Hammoudeh, 2023. "Peer-to-Peer Power Energy Trading in Blockchain Using Efficient Machine Learning Model," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    8. Jayesh Thaker & Robert Höller, 2022. "A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification," Energies, MDPI, vol. 15(8), pages 1-26, April.
    9. Tomislav Malvić & Josip Ivšinović & Josipa Velić & Jasenka Sremac & Uroš Barudžija, 2020. "Application of the Modified Shepard’s Method (MSM): A Case Study with the Interpolation of Neogene Reservoir Variables in Northern Croatia," Stats, MDPI, vol. 3(1), pages 1-16, March.
    10. Cervone, Guido & Clemente-Harding, Laura & Alessandrini, Stefano & Delle Monache, Luca, 2017. "Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble," Renewable Energy, Elsevier, vol. 108(C), pages 274-286.
    11. Heo, Jae & Jung, Jaehoon & Kim, Byungil & Han, SangUk, 2020. "Digital elevation model-based convolutional neural network modeling for searching of high solar energy regions," Applied Energy, Elsevier, vol. 262(C).
    12. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    13. Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
    14. Olubayo M. Babatunde & Josiah L. Munda & Yskandar Hamam, 2020. "Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation," Energies, MDPI, vol. 13(10), pages 1-18, May.
    15. Alberto Bocca & Luca Bergamasco & Matteo Fasano & Lorenzo Bottaccioli & Eliodoro Chiavazzo & Alberto Macii & Pietro Asinari, 2018. "Multiple-Regression Method for Fast Estimation of Solar Irradiation and Photovoltaic Energy Potentials over Europe and Africa," Energies, MDPI, vol. 11(12), pages 1-17, December.
    16. Akhter, Muhammad Naveed & Mekhilef, Saad & Mokhlis, Hazlie & Ali, Raza & Usama, Muhammad & Muhammad, Munir Azam & Khairuddin, Anis Salwa Mohd, 2022. "A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems," Applied Energy, Elsevier, vol. 307(C).
    17. Opoku, Richard & Obeng, George Y. & Adjei, Eunice A. & Davis, Francis & Akuffo, Fred O., 2020. "Integrated system efficiency in reducing redundancy and promoting residential renewable energy in countries without net-metering: A case study of a SHS in Ghana," Renewable Energy, Elsevier, vol. 155(C), pages 65-78.
    18. Na Sun & Nan Zhang & Shuai Zhang & Tian Peng & Wei Jiang & Jie Ji & Xiangmiao Hao, 2022. "An Integrated Framework Based on an Improved Gaussian Process Regression and Decomposition Technique for Hourly Solar Radiation Forecasting," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
    19. Ceballos, Juan Carlos & Porfirio, Anthony Carlos Silva & Oricchio, Patricio Alberto & Posse, Gabriela, 2022. "Characterization of the annual regime of surface solar irradiance over Argentine Pampean Region using GL1.2 satellite-based data," Renewable Energy, Elsevier, vol. 194(C), pages 526-537.
    20. Yuhao Zhang & Ting Li & Tianyi Ma & Dongsheng Yang & Xiaolong Sun, 2024. "Short-Term Photovoltaic Power Prediction Based on Extreme Learning Machine with Improved Dung Beetle Optimization Algorithm," Energies, MDPI, vol. 17(4), pages 1-24, February.

    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:15:y:2022:i:22:p:8755-:d:979464. 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.