IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i2p351-d307345.html
   My bibliography  Save this article

Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model

Author

Listed:
  • Promphak Dawan

    (Department of Electrical Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand)

  • Kobsak Sriprapha

    (Solar energy technology laboratory, National Electronics, and Computer Technology Center, National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand)

  • Songkiate Kittisontirak

    (Solar energy technology laboratory, National Electronics, and Computer Technology Center, National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand)

  • Terapong Boonraksa

    (School of Electrical Engineering, Faculty of Engineering, Rajamangala University of Technology Rattanakosin, Nakhon Pathom 73170, Thailand)

  • Nitikorn Junhuathon

    (School of Electrical Engineering, Faculty of Engineering, Bangkok Thonburi University, Bankok 10170, Thailand)

  • Wisut Titiroongruang

    (Department of Electrical Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand)

  • Surasak Niemcharoen

    (Department of Electrical Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand)

Abstract

The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.

Suggested Citation

  • Promphak Dawan & Kobsak Sriprapha & Songkiate Kittisontirak & Terapong Boonraksa & Nitikorn Junhuathon & Wisut Titiroongruang & Surasak Niemcharoen, 2020. "Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model," Energies, MDPI, vol. 13(2), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:351-:d:307345
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/2/351/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/2/351/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Azhar Ahmed Mohammed & Zeyar Aung, 2016. "Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation," Energies, MDPI, vol. 9(12), pages 1-17, December.
    2. Jacobson, Mark Z. & Delucchi, Mark A., 2011. "Providing all global energy with wind, water, and solar power, Part I: Technologies, energy resources, quantities and areas of infrastructure, and materials," Energy Policy, Elsevier, vol. 39(3), pages 1154-1169, March.
    3. Claudio Monteiro & Tiago Santos & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado & M. Sonia Terreros-Olarte, 2013. "Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity," Energies, MDPI, vol. 6(5), pages 1-20, May.
    4. 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.
    5. Donghun Lee & Kwanho Kim, 2019. "Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information," Energies, MDPI, vol. 12(2), pages 1-22, January.
    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. Giorgio Guariso & Giuseppe Nunnari & Matteo Sangiorgio, 2020. "Multi-Step Solar Irradiance Forecasting and Domain Adaptation of Deep Neural Networks," Energies, MDPI, vol. 13(15), pages 1-18, August.
    2. Kelachukwu J. Iheanetu, 2022. "Solar Photovoltaic Power Forecasting: A Review," Sustainability, MDPI, vol. 14(24), pages 1-31, December.
    3. Tiago Silveira Gontijo & Marcelo Azevedo Costa, 2020. "Forecasting Hierarchical Time Series in Power Generation," Energies, MDPI, vol. 13(14), pages 1-17, July.
    4. 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.
    5. Nonthawat Khortsriwong & Promphak Boonraksa & Terapong Boonraksa & Thipwan Fangsuwannarak & Asada Boonsrirat & Watcharakorn Pinthurat & Boonruang Marungsri, 2023. "Performance of Deep Learning Techniques for Forecasting PV Power Generation: A Case Study on a 1.5 MWp Floating PV Power Plant," Energies, MDPI, vol. 16(5), pages 1-21, February.

    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. Takuji Matsumoto & Yuji Yamada, 2021. "Comprehensive and Comparative Analysis of GAM-Based PV Power Forecasting Models Using Multidimensional Tensor Product Splines against Machine Learning Techniques," Energies, MDPI, vol. 14(21), pages 1-22, November.
    3. 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.
    4. Luca Massidda & Marino Marrocu, 2018. "Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting," Energies, MDPI, vol. 11(7), pages 1-20, July.
    5. Evgeny Solomin & Shanmuga Priya Selvanathan & Sudhakar Kumarasamy & Anton Kovalyov & Ramyashree Maddappa Srinivasa, 2021. "The Comparison of Solar-Powered Hydrogen Closed-Cycle System Capacities for Selected Locations," Energies, MDPI, vol. 14(9), pages 1-18, May.
    6. 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.
    7. Arsani Alina & Stefan George, 2024. "Energy Transition and European Sub-Models. Restructuring EU Economy," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 86-101.
    8. Javier Borquez & Hector Chavez & Karina A. Barbosa & Marcela Jamett & Rodrigo Acuna, 2020. "A Simple Distribution Energy Tariff under the Penetration of DG," Energies, MDPI, vol. 13(8), pages 1-17, April.
    9. Altayib, Khalid & Dincer, Ibrahim, 2022. "Development of an integrated hydropower system with hydrogen and methanol production," Energy, Elsevier, vol. 240(C).
    10. David Gattie & Michael Hewitt, 2023. "National Security as a Value-Added Proposition for Advanced Nuclear Reactors: A U.S. Focus," Energies, MDPI, vol. 16(17), pages 1-26, August.
    11. 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.
    12. Javed, Muhammad Shahzad & Ma, Tao & Jurasz, Jakub & Canales, Fausto A. & Lin, Shaoquan & Ahmed, Salman & Zhang, Yijie, 2021. "Economic analysis and optimization of a renewable energy based power supply system with different energy storages for a remote island," Renewable Energy, Elsevier, vol. 164(C), pages 1376-1394.
    13. John Boland & Sleiman Farah & Lei Bai, 2022. "Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market: A Review," Energies, MDPI, vol. 15(1), pages 1-18, January.
    14. Krupa, Joel & Harvey, L.D. Danny, 2017. "Renewable electricity finance in the United States: A state-of-the-art review," Energy, Elsevier, vol. 135(C), pages 913-929.
    15. Maruf, Md. Nasimul Islam, 2021. "Open model-based analysis of a 100% renewable and sector-coupled energy system–The case of Germany in 2050," Applied Energy, Elsevier, vol. 288(C).
    16. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    17. Frate, Claudio Albuquerque & Brannstrom, Christian, 2017. "Stakeholder subjectivities regarding barriers and drivers to the introduction of utility-scale solar photovoltaic power in Brazil," Energy Policy, Elsevier, vol. 111(C), pages 346-352.
    18. Trowell, K.A. & Goroshin, S. & Frost, D.L. & Bergthorson, J.M., 2020. "Aluminum and its role as a recyclable, sustainable carrier of renewable energy," Applied Energy, Elsevier, vol. 275(C).
    19. Kevin Ummel & Charles Fant, 2014. "Planning for Large-Scale Wind and Solar Power in South Africa: Identifying Cost-Effective Deployment Strategies Through Spatiotemporal Modelling," WIDER Working Paper Series wp-2014-121, World Institute for Development Economic Research (UNU-WIDER).
    20. Peura, Pekka, 2013. "From Malthus to sustainable energy—Theoretical orientations to reforming the energy sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 309-327.

    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:13:y:2020:i:2:p:351-:d:307345. 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.