IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i10p1542-d114208.html
   My bibliography  Save this article

An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window

Author

Listed:
  • Honglu Zhu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China
    School of Renewable Energy, North China Electric Power University, Changping District, Beijing 102206, China
    Beijing Key Laboratory of New and Renewable Energy, North China Electric Power University, Changping District, Beijing 102206, China)

  • Weiwei Lian

    (School of Renewable Energy, North China Electric Power University, Changping District, Beijing 102206, China)

  • Lingxing Lu

    (School of Renewable Energy, North China Electric Power University, Changping District, Beijing 102206, China)

  • Songyuan Dai

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China
    School of Renewable Energy, North China Electric Power University, Changping District, Beijing 102206, China
    Beijing Key Laboratory of New and Renewable Energy, North China Electric Power University, Changping District, Beijing 102206, China)

  • Yang Hu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China)

Abstract

Due to the large scale of grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economic operation of the electric power system. In the paper, by analyzing the influence of external ambient factors and the changing characteristics of PV modules with time, it is found that PV power generation is a nonlinear and time-varying process. This suggests that a certain single forecasting model is inadequate for representing actual generation characteristics, and it is difficult to obtain an accurate forecasting result. An adaptive back propagation (BP) neural network model adopting scrolling time window is proposed to solve the problem. Via an update of the training data of BP neural network with the scrolling time window, the forecasting model adapts to time and a changing external environment with the required modeling precision. Meanwhile, through evaluation of the forecasting performance in different time windows, an optimized time window can be determined to guarantee accuracy. Finally, using the actual operation data of a PV plant in Beijing, the approach is validated as being applicable for PV power forecasting and is able to effectively respond to the dynamic change of the PV power generation process. This improves the forecasting accuracy and also reduces computation complexity as compared with the conventional BP neural network algorithm.

Suggested Citation

  • Honglu Zhu & Weiwei Lian & Lingxing Lu & Songyuan Dai & Yang Hu, 2017. "An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window," Energies, MDPI, vol. 10(10), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1542-:d:114208
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/10/1542/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/10/1542/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ferlito, S. & Adinolfi, G. & Graditi, G., 2017. "Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production," Applied Energy, Elsevier, vol. 205(C), pages 116-129.
    2. Eltigani, Dalia & Masri, Syafrudin, 2015. "Challenges of integrating renewable energy sources to smart grids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 770-780.
    3. Graditi, G. & Ferlito, S. & Adinolfi, G., 2016. "Comparison of Photovoltaic plant power production prediction methods using a large measured dataset," Renewable Energy, Elsevier, vol. 90(C), pages 513-519.
    4. Antonello Rosato & Rosa Altilio & Rodolfo Araneo & Massimo Panella, 2017. "Prediction in Photovoltaic Power by Neural Networks," Energies, MDPI, vol. 10(7), pages 1-25, July.
    5. Eleonora Riva Sanseverino & Maria Luisa Di Silvestre & Gaetano Zizzo & Roberto Gallea & Ninh Nguyen Quang, 2013. "A Self-Adapting Approach for Forecast-Less Scheduling of Electrical Energy Storage Systems in a Liberalized Energy Market," Energies, MDPI, vol. 6(11), pages 1-22, November.
    6. Honglu Zhu & Xu Li & Qiao Sun & Ling Nie & Jianxi Yao & Gang Zhao, 2015. "A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks," Energies, MDPI, vol. 9(1), pages 1-15, December.
    7. Alberto Dolara & Francesco Grimaccia & Sonia Leva & Marco Mussetta & Emanuele Ogliari, 2015. "A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output," Energies, MDPI, vol. 8(2), pages 1-16, February.
    8. Vaz, A.G.R. & Elsinga, B. & van Sark, W.G.J.H.M. & Brito, M.C., 2016. "An artificial neural network to assess the impact of neighbouring photovoltaic systems in power forecasting in Utrecht, the Netherlands," Renewable Energy, Elsevier, vol. 85(C), pages 631-641.
    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. Gangqiang Li & Huaizhi Wang & Shengli Zhang & Jiantao Xin & Huichuan Liu, 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach," Energies, MDPI, vol. 12(13), pages 1-17, July.
    2. Spyros Theocharides & Marios Theristis & George Makrides & Marios Kynigos & Chrysovalantis Spanias & George E. Georghiou, 2021. "Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting," Energies, MDPI, vol. 14(4), pages 1-22, February.
    3. Yin, Wansi & Han, Yutong & Zhou, Hai & Ma, Ming & Li, Li & Zhu, Honglu, 2020. "A novel non-iterative correction method for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 159(C), pages 23-32.
    4. Lin Yang & Jikai Bi & Yanpeng Hao & Lupeng Nian & Zijun Zhou & Licheng Li & Yifan Liao & Fuzeng Zhang, 2018. "A Recognition Method of the Hydrophobicity Class of Composite Insulators Based on Features Optimization and Experimental Verification," Energies, MDPI, vol. 11(4), pages 1-13, March.
    5. Yu, Cao & Wang, Haizheng & Yao, Jianxi & Zhao, Jian & Sun, Qian & Zhu, Honglu, 2020. "A dynamic alarm threshold setting method for photovoltaic array and its application," Renewable Energy, Elsevier, vol. 158(C), pages 13-22.
    6. Muhammad Ahsan Zamee & Dongjun Won, 2020. "Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction," Energies, MDPI, vol. 13(23), pages 1-29, December.
    7. Nastaran Gholizadeh & Petr Musilek, 2021. "Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges," Energies, MDPI, vol. 14(12), pages 1-18, June.

    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. Emanuele Ogliari & Alessandro Niccolai & Sonia Leva & Riccardo E. Zich, 2018. "Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed," Energies, MDPI, vol. 11(6), pages 1-16, June.
    2. Trigo-González, Mauricio & Batlles, F.J. & Alonso-Montesinos, Joaquín & Ferrada, Pablo & del Sagrado, J. & Martínez-Durbán, M. & Cortés, Marcelo & Portillo, Carlos & Marzo, Aitor, 2019. "Hourly PV production estimation by means of an exportable multiple linear regression model," Renewable Energy, Elsevier, vol. 135(C), pages 303-312.
    3. Hugo Bezerra Menezes Leite & Hamidreza Zareipour, 2023. "Six Days Ahead Forecasting of Energy Production of Small Behind-the-Meter Solar Sites," Energies, MDPI, vol. 16(3), pages 1-14, February.
    4. Zheng, Lingwei & Liu, Zhaokun & Shen, Junnan & Wu, Chenxi, 2018. "Very short-term maximum Lyapunov exponent forecasting tool for distributed photovoltaic output," Applied Energy, Elsevier, vol. 229(C), pages 1128-1139.
    5. Michel Noussan & Roberta Roberto & Benedetto Nastasi, 2018. "Performance Indicators of Electricity Generation at Country Level—The Case of Italy," Energies, MDPI, vol. 11(3), pages 1-14, March.
    6. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
    7. Dong, Changgui & Sigrin, Benjamin, 2019. "Using willingness to pay to forecast the adoption of solar photovoltaics: A “parameterization + calibration” approach," Energy Policy, Elsevier, vol. 129(C), pages 100-110.
    8. Athanasios I. Salamanis & Georgia Xanthopoulou & Napoleon Bezas & Christos Timplalexis & Angelina D. Bintoudi & Lampros Zyglakis & Apostolos C. Tsolakis & Dimosthenis Ioannidis & Dionysios Kehagias & , 2020. "Benchmark Comparison of Analytical, Data-Based and Hybrid Models for Multi-Step Short-Term Photovoltaic Power Generation Forecasting," Energies, MDPI, vol. 13(22), pages 1-31, November.
    9. Ümmühan Başaran Filik & Tansu Filik & Ömer Nezih Gerek, 2018. "A Hysteresis Model for Fixed and Sun Tracking Solar PV Power Generation Systems," Energies, MDPI, vol. 11(3), pages 1-15, March.
    10. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    11. Wei Li & Hui Ren & Ping Chen & Yanyang Wang & Hailong Qi, 2020. "Key Operational Issues on the Integration of Large-Scale Solar Power Generation—A Literature Review," Energies, MDPI, vol. 13(22), pages 1-25, November.
    12. Federica Cucchiella & Idiano D’Adamo & Paolo Rosa, 2015. "Industrial Photovoltaic Systems: An Economic Analysis in Non-Subsidized Electricity Markets," Energies, MDPI, vol. 8(11), pages 1-16, November.
    13. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
    14. 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).
    15. 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.
    16. 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).
    17. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
    18. Elsinga, Boudewijn & van Sark, Wilfried G.J.H.M., 2017. "Short-term peer-to-peer solar forecasting in a network of photovoltaic systems," Applied Energy, Elsevier, vol. 206(C), pages 1464-1483.
    19. Mahmud, Nasif & Zahedi, A., 2016. "Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 582-595.
    20. Giovanni Artale & Antonio Cataliotti & Valentina Cosentino & Dario Di Cara & Riccardo Fiorelli & Salvatore Guaiana & Nicola Panzavecchia & Giovanni Tinè, 2019. "A New Coupling Solution for G3-PLC Employment in MV Smart Grids," Energies, MDPI, vol. 12(13), pages 1-23, June.

    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:10:y:2017:i:10:p:1542-:d:114208. 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.