IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i14p4256-d594399.html
   My bibliography  Save this article

A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation

Author

Listed:
  • Taeyoung Kim

    (Gwangju Institute of Science and Technology (GIST), School of Integrated Technology, Gwangju 61005, Korea)

  • Jinho Kim

    (Gwangju Institute of Science and Technology (GIST), School of Integrated Technology, Gwangju 61005, Korea)

Abstract

Rooftop photovoltaic (PV) systems are usually behind the meter and invisible to utilities and retailers and, thus, their power generation is not monitored. If a number of rooftop PV systems are installed, it transforms the net load pattern in power systems. Moreover, not only generation but also PV capacity information is invisible due to unauthorized PV installations, causing inaccuracies in regional PV generation forecasting. This study proposes a regional rooftop PV generation forecasting methodology by adding unauthorized PV capacity estimation. PV capacity estimation consists of two steps: detection of unauthorized PV generation and estimation capacity of detected PV. Finally, regional rooftop PV generation is predicted by considering unauthorized PV capacity through the support vector regression (SVR) and upscaling method. The results from a case study show that compared with estimation without unauthorized PV capacity, the proposed methodology reduces the normalized root mean square error (nRMSE) by 5.41% and the normalized mean absolute error (nMAE) by 2.95%, It can be concluded that regional rooftop PV generation forecasting accuracy is improved.

Suggested Citation

  • Taeyoung Kim & Jinho Kim, 2021. "A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation," Energies, MDPI, vol. 14(14), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4256-:d:594399
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/14/4256/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/14/4256/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. da Silva Fonseca Junior, Joao Gari & Oozeki, Takashi & Ohtake, Hideaki & Shimose, Ken-ichi & Takashima, Takumi & Ogimoto, Kazuhiko, 2014. "Regional forecasts and smoothing effect of photovoltaic power generation in Japan: An approach with principal component analysis," Renewable Energy, Elsevier, vol. 68(C), pages 403-413.
    2. Li, Kangping & Wang, Fei & Mi, Zengqiang & Fotuhi-Firuzabad, Mahmoud & Duić, Neven & Wang, Tieqiang, 2019. "Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Shaker, Hamid & Manfre, Daniel & Zareipour, Hamidreza, 2020. "Forecasting the aggregated output of a large fleet of small behind-the-meter solar photovoltaic sites," Renewable Energy, Elsevier, vol. 147(P1), pages 1861-1869.
    4. Fei Wang & Kangping Li & Xinkang Wang & Lihui Jiang & Jianguo Ren & Zengqiang Mi & Miadreza Shafie-khah & João P. S. Catalão, 2018. "A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features," Energies, MDPI, vol. 11(7), pages 1-19, July.
    5. Lei Fu & Yiling Yang & Xiaolong Yao & Xufen Jiao & Tiantian Zhu, 2019. "A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion," Energies, MDPI, vol. 12(20), pages 1-23, October.
    6. Alessandro Niccolai & Alberto Dolara & Emanuele Ogliari, 2021. "Hybrid PV Power Forecasting Methods: A Comparison of Different Approaches," Energies, MDPI, vol. 14(2), pages 1-18, January.
    7. Koster, Daniel & Minette, Frank & Braun, Christian & O'Nagy, Oliver, 2019. "Short-term and regionalized photovoltaic power forecasting, enhanced by reference systems, on the example of Luxembourg," Renewable Energy, Elsevier, vol. 132(C), pages 455-470.
    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. Perera, Maneesha & De Hoog, Julian & Bandara, Kasun & Senanayake, Damith & Halgamuge, Saman, 2024. "Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data," Applied Energy, Elsevier, vol. 361(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. Dengchang Ma & Rongyi Xie & Guobing Pan & Zongxu Zuo & Lidong Chu & Jing Ouyang, 2023. "Photovoltaic Power Output Prediction Based on TabNet for Regional Distributed Photovoltaic Stations Group," Energies, MDPI, vol. 16(15), pages 1-22, July.
    2. Keda Pan & Changhong Xie & Chun Sing Lai & Dongxiao Wang & Loi Lei Lai, 2020. "Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems," Forecasting, MDPI, vol. 2(4), pages 1-18, November.
    3. Perera, Maneesha & De Hoog, Julian & Bandara, Kasun & Senanayake, Damith & Halgamuge, Saman, 2024. "Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data," Applied Energy, Elsevier, vol. 361(C).
    4. Erdener, Burcin Cakir & Feng, Cong & Doubleday, Kate & Florita, Anthony & Hodge, Bri-Mathias, 2022. "A review of behind-the-meter solar forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    5. Yuan-Kang Wu & Yi-Hui Lai & Cheng-Liang Huang & Nguyen Thi Bich Phuong & Wen-Shan Tan, 2022. "Artificial Intelligence Applications in Estimating Invisible Solar Power Generation," Energies, MDPI, vol. 15(4), pages 1-18, February.
    6. Ji-Won Cha & Sung-Kwan Joo, 2021. "Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)," Energies, MDPI, vol. 14(21), pages 1-19, October.
    7. Pierro, Marco & Gentili, Damiano & Liolli, Fabio Romano & Cornaro, Cristina & Moser, David & Betti, Alessandro & Moschella, Michela & Collino, Elena & Ronzio, Dario & van der Meer, Dennis, 2022. "Progress in regional PV power forecasting: A sensitivity analysis on the Italian case study," Renewable Energy, Elsevier, vol. 189(C), pages 983-996.
    8. 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).
    9. Bartosz Wachnik & Zbigniew Chyba, 2021. "Key Growth Factors and Limitations of Photovoltaic Companies in Poland and the Phenomenon of Technology Entrepreneurship under Conditions of Information Asymmetry," Energies, MDPI, vol. 14(24), pages 1-16, December.
    10. 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).
    11. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    12. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    13. Hongchao Zhang & Tengteng Zhu, 2022. "Stacking Model for Photovoltaic-Power-Generation Prediction," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    14. Varone, Alberto & Heilmann, Zeno & Porruvecchio, Guido & Romanino, Alessandro, 2024. "Solar parking lot management: An IoT platform for smart charging EV fleets, using real-time data and production forecasts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    15. 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).
    16. Liu, Jia & Zeng, Peter Pingliang & Xing, Hao & Li, Yalou & Wu, Qiuwei, 2020. "Hierarchical duality-based planning of transmission networks coordinating active distribution network operation," Energy, Elsevier, vol. 213(C).
    17. Astriani, Yuli & Shafiullah, GM & Shahnia, Farhad, 2021. "Incentive determination of a demand response program for microgrids," Applied Energy, Elsevier, vol. 292(C).
    18. Lai, Wenzhe & Zhen, Zhao & Wang, Fei & Fu, Wenjie & Wang, Junlong & Zhang, Xudong & Ren, Hui, 2024. "Sub-region division based short-term regional distributed PV power forecasting method considering spatio-temporal correlations," Energy, Elsevier, vol. 288(C).
    19. Lind, Leandro & Chaves-Ávila, José Pablo & Valarezo, Orlando & Sanjab, Anibal & Olmos, Luis, 2024. "Baseline methods for distributed flexibility in power systems considering resource, market, and product characteristics," Utilities Policy, Elsevier, vol. 86(C).
    20. Huang, Sen & Ye, Yunyang & Wu, Di & Zuo, Wangda, 2021. "An assessment of power flexibility from commercial building cooling systems in the United States," Energy, Elsevier, vol. 221(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:14:p:4256-:d:594399. 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.