IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i16p6050-d1220053.html
   My bibliography  Save this article

Prediction of the Electricity Generation of a 60-kW Photovoltaic System with Intelligent Models ANFIS and Optimized ANFIS-PSO

Author

Listed:
  • Luis O. Lara-Cerecedo

    (Departamento de Ingeniería Química y Metalurgia, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro, Hermosillo 83000, Mexico)

  • Jesús F. Hinojosa

    (Departamento de Ingeniería Química y Metalurgia, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro, Hermosillo 83000, Mexico)

  • Nun Pitalúa-Díaz

    (Departamento de Ingeniería Industrial, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro, Hermosillo 83000, Mexico)

  • Yasuhiro Matsumoto

    (Departamento de Ingeniería Eléctrica, Centro de Investigación y de Estudios Avanzados del IPN, Av. Instituto Politécnico Nacional 2508, San Pedro Zacatenco, Ciudad de México 07360, Mexico)

  • Alvaro González-Angeles

    (Facultad de Ingeniería, Universidad Autónoma de Baja California, Blvd. Benito Juárez S/N, Mexicali 21280, Mexico)

Abstract

The development and constant improvement of accurate predictive models of electricity generation from photovoltaic systems provide valuable planning tools for designers, producers, and self-consumers. In this research, an adaptive neuro-fuzzy inference model (ANFIS) was developed, which is an intelligent hybrid model that integrates the ability to learn by itself provided by neural networks and the function of language expression, how fuzzy logic infers, and an ANFIS model optimized by the particle swarm algorithm, both with a predictive capacity of about eight months. The models were developed using the Matlab ® software and trained with four input variables (solar radiation, module temperature, ambient temperature, and wind speed) and the electrical power generated from a photovoltaic (PV) system as the output variable. The models’ predictions were compared with the experimental data of the system and evaluated with rigorous statistical metrics, obtaining results of RMSE = 1.79 kW, RMSPE = 3.075, MAE = 0.864 kW, and MAPE = 1.47% for ANFIS, and RMSE = 0.754 kW, RMSPE = 1.29, MAE = 0.325 kW, and MAPE = 0.556% for ANFIS-PSO, respectively. The evaluations indicate that both models have good predictive capacity. However, the PSO integration into the hybrid model allows for improving the predictive capability of the behavior of the photovoltaic system, which provides a better planning tool.

Suggested Citation

  • Luis O. Lara-Cerecedo & Jesús F. Hinojosa & Nun Pitalúa-Díaz & Yasuhiro Matsumoto & Alvaro González-Angeles, 2023. "Prediction of the Electricity Generation of a 60-kW Photovoltaic System with Intelligent Models ANFIS and Optimized ANFIS-PSO," Energies, MDPI, vol. 16(16), pages 1-26, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6050-:d:1220053
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/16/6050/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/16/6050/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wongchai Anupong & Muhsin Jaber Jweeg & Sameer Alani & Ibrahim H. Al-Kharsan & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq," Energies, MDPI, vol. 16(2), pages 1-14, January.
    2. Hai Tao & Ali Omran Al-Sulttani & Ameen Mohammed Salih Ameen & Zainab Hasan Ali & Nadhir Al-Ansari & Sinan Q. Salih & Reham R. Mostafa, 2020. "Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting," Complexity, Hindawi, vol. 2020, pages 1-22, October.
    3. Li, Jidong & Chen, Shijun & Wu, Yuqiang & Wang, Qinhui & Liu, Xing & Qi, Lijian & Lu, Xiuyuan & Gao, Lu, 2021. "How to make better use of intermittent and variable energy? A review of wind and photovoltaic power consumption in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    4. Paweł Piotrowski & Inajara Rutyna & Dariusz Baczyński & Marcin Kopyt, 2022. "Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors," Energies, MDPI, vol. 15(24), pages 1-38, December.
    5. Bengio, Yoshua & Lodi, Andrea & Prouvost, Antoine, 2021. "Machine learning for combinatorial optimization: A methodological tour d’horizon," European Journal of Operational Research, Elsevier, vol. 290(2), pages 405-421.
    6. Xu, Xiaofeng & Wei, Zhifei & Ji, Qiang & Wang, Chenglong & Gao, Guowei, 2019. "Global renewable energy development: Influencing factors, trend predictions and countermeasures," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    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. Feng Dong & Yuling Pan, 2020. "Evolution of Renewable Energy in BRI Countries: A Combined Econometric and Decomposition Approach," IJERPH, MDPI, vol. 17(22), pages 1-18, November.
    2. Gonçalves Rigueira Pinheiro Castro, Pedro Henrique & Filho, Delly Oliveira & Rosa, André Pereira & Navas Gracia, Luis Manuel & Almeida Silva, Thais Cristina, 2024. "Comparison of externalities of biogas and photovoltaic solar energy for energy planning," Energy Policy, Elsevier, vol. 188(C).
    3. Ding, Yihong & Tan, Qinliang & Shan, Zijing & Han, Jian & Zhang, Yimei, 2023. "A two-stage dispatching optimization strategy for hybrid renewable energy system with low-carbon and sustainability in ancillary service market," Renewable Energy, Elsevier, vol. 207(C), pages 647-659.
    4. Fuquan Zhao & Fanlong Bai & Xinglong Liu & Zongwei Liu, 2022. "A Review on Renewable Energy Transition under China’s Carbon Neutrality Target," Sustainability, MDPI, vol. 14(22), pages 1-27, November.
    5. Luo, Shihua & Hu, Weihao & Liu, Wen & Zhang, Zhenyuan & Bai, Chunguang & Huang, Qi & Chen, Zhe, 2022. "Study on the decarbonization in China's power sector under the background of carbon neutrality by 2060," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    6. Zhao, Zhonghao & Lee, Carman K.M. & Huo, Jiage, 2023. "EV charging station deployment on coupled transportation and power distribution networks via reinforcement learning," Energy, Elsevier, vol. 267(C).
    7. Sun, Yanshuo & Kirtonia, Sajeeb & Chen, Zhi-Long, 2021. "A survey of finished vehicle distribution and related problems from an optimization perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    8. Yijing Chu & Yingying Wang & Zucheng Zhang & Shengli Dai, 2022. "Decoupling of Economic Growth and Industrial Water Use in Hubei Province: From an Ecological–Economic Interaction Perspective," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    9. Liobikienė, Genovaitė & Miceikienė, Astrida, 2022. "The role of financial, social and informational mechanisms on willingness to use bioenergy," Renewable Energy, Elsevier, vol. 194(C), pages 21-27.
    10. Li, Mingjie & Hao, Jin-Kao & Wu, Qinghua, 2024. "A flow based formulation and a reinforcement learning based strategic oscillation for cross-dock door assignment," European Journal of Operational Research, Elsevier, vol. 312(2), pages 473-492.
    11. Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
    12. Yiyun Liu & Jun Wu & Jianjun Li & Jingjing Huang, 2023. "The Diffusion Rule of Demand-Oriented Biogas Supply in Distributed Renewable Energy System: An Evolutionary Game-Based Approach," Sustainability, MDPI, vol. 15(19), pages 1-16, September.
    13. Bootaki, Behrang & Zhang, Guoqing, 2024. "A location-production-routing problem for distributed manufacturing platforms: A neural genetic algorithm solution methodology," International Journal of Production Economics, Elsevier, vol. 275(C).
    14. Dogan, Eyup & Chishti, Muhammad Zubair & Karimi Alavijeh, Nooshin & Tzeremes, Panayiotis, 2022. "The roles of technology and Kyoto Protocol in energy transition towards COP26 targets: Evidence from the novel GMM-PVAR approach for G-7 countries," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    15. Ahmet Herekoğlu & Özgür Kabak, 2024. "Crew recovery optimization with deep learning and column generation for sustainable airline operation management," Annals of Operations Research, Springer, vol. 342(1), pages 399-427, November.
    16. Ankrah, Isaac & Lin, Boqiang, 2020. "Renewable energy development in Ghana: Beyond potentials and commitment," Energy, Elsevier, vol. 198(C).
    17. Zhang, Chuanguo & Yu, Xiaoxue & Zhou, Juncen, 2024. "China's embodied oil outflow in GVC participation: Patterns and drivers," Resources Policy, Elsevier, vol. 91(C).
    18. Jun Hao & Xiaolei Sun & Qianqian Feng, 2020. "A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 13(3), pages 1-25, January.
    19. Mohamed A. Ali & Ashraf Elsayed & Islam Elkabani & Mohammad Akrami & M. Elsayed Youssef & Gasser E. Hassan, 2023. "Optimizing Artificial Neural Networks for the Accurate Prediction of Global Solar Radiation: A Performance Comparison with Conventional Methods," Energies, MDPI, vol. 16(17), pages 1-30, August.
    20. Depraiter, Lisa & Goutte, Stephane, 2023. "The role and challenges of rare earths in the energy transition," Resources Policy, Elsevier, vol. 86(PB).

    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:16:p:6050-:d:1220053. 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.