IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i14p2662-d247553.html
   My bibliography  Save this article

An ANFIS-Based Modeling Comparison Study for Photovoltaic Power at Different Geographical Places in Mexico

Author

Listed:
  • Nun Pitalúa-Díaz

    (Departamento de Ingeniería Industrial, Departamento de Ingeniería Química y Metalurgia, Departamento de Investigación en Física, Departamento de Investigación en Polímeros y Materiales, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro. Hermosillo 83000, Sonora C.P., Mexico)

  • Fernando Arellano-Valmaña

    (Facultad de Ingeniería, Universidad Autónoma del Carmen, Calle 56 No. 4 Esq. Avenida Concordia Col. Benito Juárez C.P. 24180 Cd. Del Carmen, Campeche, Mexico)

  • Jose A. Ruz-Hernandez

    (Facultad de Ingeniería, Universidad Autónoma del Carmen, Calle 56 No. 4 Esq. Avenida Concordia Col. Benito Juárez C.P. 24180 Cd. Del Carmen, Campeche, Mexico)

  • Yasuhiro Matsumoto

    (Departamento de Ingeniería Eléctrica, Centro de Investigación y de Estudios Avanzados del IPN, Av. Instituto Politécnico Nacional 2508, La Laguna Ticoman, C.P. 07360 Ciudad de México, CDMX, Mexico)

  • Hussain Alazki

    (Facultad de Ingeniería, Universidad Autónoma del Carmen, Calle 56 No. 4 Esq. Avenida Concordia Col. Benito Juárez C.P. 24180 Cd. Del Carmen, Campeche, Mexico)

  • Enrique J. Herrera-López

    (Biotecnología Industrial, Sublínea Bioelectrónica, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco A.C., Camino Arenero 1227, Col. El Bajío del Arenal, C.P. 45019 Zapopan Jalisco, Mexico)

  • Jesús Fernando Hinojosa-Palafox

    (Departamento de Ingeniería Industrial, Departamento de Ingeniería Química y Metalurgia, Departamento de Investigación en Física, Departamento de Investigación en Polímeros y Materiales, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro. Hermosillo 83000, Sonora C.P., Mexico)

  • A. García-Juárez

    (Departamento de Ingeniería Industrial, Departamento de Ingeniería Química y Metalurgia, Departamento de Investigación en Física, Departamento de Investigación en Polímeros y Materiales, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro. Hermosillo 83000, Sonora C.P., Mexico)

  • Ricardo Arturo Pérez-Enciso

    (Departamento de Ingeniería Industrial, Departamento de Ingeniería Química y Metalurgia, Departamento de Investigación en Física, Departamento de Investigación en Polímeros y Materiales, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro. Hermosillo 83000, Sonora C.P., Mexico)

  • Enrique Fernando Velázquez-Contreras

    (Departamento de Ingeniería Industrial, Departamento de Ingeniería Química y Metalurgia, Departamento de Investigación en Física, Departamento de Investigación en Polímeros y Materiales, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro. Hermosillo 83000, Sonora C.P., Mexico)

Abstract

In this manuscript, distinct approaches were used in order to obtain the best electrical power estimation from photovoltaic systems located at different selected places in Mexico. Multiple Linear Regression (MLR) and Gradient Descent Optimization (GDO) were applied as statistical methods and they were compared against an Adaptive Neuro-Fuzzy Inference System (ANFIS) as an intelligent technique. The data gathered involved solar radiation, outside temperature, wind speed, daylight hour and photovoltaic power; collected from on-site real-time measurements at Mexico City and Hermosillo City, Sonora State. According to our results, all three methods achieved satisfactory performances, since low values were obtained for the convergence error. The GDO improved the MLR results, minimizing the overall error percentage value from 7.2% to 6.9% for Sonora and from 2.0% to 1.9% for Mexico City; nonetheless, ANFIS overcomes both statistical methods, achieving a 5.8% error percentage value for Sonora and 1.6% for Mexico City. The results demonstrated an improvement by applying intelligent systems against statistical techniques achieving a lesser mean average error.

Suggested Citation

  • Nun Pitalúa-Díaz & Fernando Arellano-Valmaña & Jose A. Ruz-Hernandez & Yasuhiro Matsumoto & Hussain Alazki & Enrique J. Herrera-López & Jesús Fernando Hinojosa-Palafox & A. García-Juárez & Ricardo Art, 2019. "An ANFIS-Based Modeling Comparison Study for Photovoltaic Power at Different Geographical Places in Mexico," Energies, MDPI, vol. 12(14), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2662-:d:247553
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/14/2662/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/14/2662/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmed Bilal Awan & Muhammad Zubair & Praveen R. P. & Ahmed G. Abokhalil, 2018. "Solar Energy Resource Analysis and Evaluation of Photovoltaic System Performance in Various Regions of Saudi Arabia," Sustainability, MDPI, vol. 10(4), pages 1-27, April.
    2. Alberto-Jesus Perea-Moreno & Quetzalcoatl Hernandez-Escobedo & Javier Garrido & Joel Donaldo Verdugo-Diaz, 2018. "Stand-Alone Photovoltaic System Assessment in Warmer Urban Areas in Mexico," Energies, MDPI, vol. 11(2), pages 1-13, January.
    3. Aggarwal, S.K. & Saini, L.M., 2014. "Solar energy prediction using linear and non-linear regularization models: A study on AMS (American Meteorological Society) 2013–14 Solar Energy Prediction Contest," Energy, Elsevier, vol. 78(C), pages 247-256.
    4. A. Bassam & O. May Tzuc & M. Escalante Soberanis & L. J. Ricalde & B. Cruz, 2017. "Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System," Sustainability, MDPI, vol. 9(8), pages 1-16, August.
    5. Chen, Serena H. & Jakeman, Anthony J. & Norton, John P., 2008. "Artificial Intelligence techniques: An introduction to their use for modelling environmental systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 379-400.
    6. Zahedi, A., 2006. "Solar photovoltaic (PV) energy; latest developments in the building integrated and hybrid PV systems," Renewable Energy, Elsevier, vol. 31(5), pages 711-718.
    7. Young Seok Song & Moo Jong Park, 2018. "A Study on Estimation Equation for Damage and Recovery Costs Considering Human Losses Focused on Natural Disasters in the Republic of Korea," Sustainability, MDPI, vol. 10(9), pages 1-16, August.
    8. Siddhartha Verma & Alena Bartosova & Momcilo Markus & Richard Cooke & Myoung-Jin Um & Daeryong Park, 2018. "Quantifying the Role of Large Floods in Riverine Nutrient Loadings Using Linear Regression and Analysis of Covariance," Sustainability, MDPI, vol. 10(8), pages 1-19, August.
    9. Kashyap, Yashwant & Bansal, Ankit & Sao, Anil K., 2015. "Solar radiation forecasting with multiple parameters neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 825-835.
    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. Nawal Rai & Amel Abbadi & Fethia Hamidia & Nadia Douifi & Bdereddin Abdul Samad & Khalid Yahya, 2023. "Biogeography-Based Teaching Learning-Based Optimization Algorithm for Identifying One-Diode, Two-Diode and Three-Diode Models of Photovoltaic Cell and Module," Mathematics, MDPI, vol. 11(8), pages 1-30, April.
    2. Yousri, Dalia & Thanikanti, Sudhakar Babu & Allam, Dalia & Ramachandaramurthy, Vigna K. & Eteiba, M.B., 2020. "Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models’ parameters," Energy, Elsevier, vol. 195(C).
    3. Ajith Gopi & Prabhakar Sharma & Kumarasamy Sudhakar & Wai Keng Ngui & Irina Kirpichnikova & Erdem Cuce, 2022. "Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques," Sustainability, MDPI, vol. 15(1), pages 1-28, December.
    4. Kaloop, Mosbeh R. & Bardhan, Abidhan & Kardani, Navid & Samui, Pijush & Hu, Jong Wan & Ramzy, Ahmed, 2021. "Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(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. Voyant, Cyril & Motte, Fabrice & Notton, Gilles & Fouilloy, Alexis & Nivet, Marie-Laure & Duchaud, Jean-Laurent, 2018. "Prediction intervals for global solar irradiation forecasting using regression trees methods," Renewable Energy, Elsevier, vol. 126(C), pages 332-340.
    2. Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
    3. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2015. "Application of rapid miner in ANN based prediction of solar radiation for assessment of solar energy resource potential of 76 sites in Northwestern India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1093-1106.
    4. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    5. Gong, Jian-zhou & Liu, Yan-sui & Xia, Bei-cheng & Zhao, Guan-wei, 2009. "Urban ecological security assessment and forecasting, based on a cellular automata model: A case study of Guangzhou, China," Ecological Modelling, Elsevier, vol. 220(24), pages 3612-3620.
    6. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
    7. Awan, Ahmed Bilal & Zubair, Muhammad & Chandra Mouli, Kotturu V.V., 2020. "Design, optimization and performance comparison of solar tower and photovoltaic power plants," Energy, Elsevier, vol. 199(C).
    8. Le Cam, M. & Daoud, A. & Zmeureanu, R., 2016. "Forecasting electric demand of supply fan using data mining techniques," Energy, Elsevier, vol. 101(C), pages 541-557.
    9. Amjad Ali, 2023. "Transforming Saudi Arabia’s Energy Landscape towards a Sustainable Future: Progress of Solar Photovoltaic Energy Deployment," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    10. Zeng, Ziqiang & Nasri, Ehsan & Chini, Abdol & Ries, Robert & Xu, Jiuping, 2015. "A multiple objective decision making model for energy generation portfolio under fuzzy uncertainty: Case study of large scale investor-owned utilities in Florida," Renewable Energy, Elsevier, vol. 75(C), pages 224-242.
    11. Tuan-Viet Hoang & Pouya Ifaei & Kijeon Nam & Jouan Rashidi & Soonho Hwangbo & Jong-Min Oh & ChangKyoo Yoo, 2018. "Optimal Management of a Hybrid Renewable Energy System Coupled with a Membrane Bioreactor Using Enviro-Economic and Power Pinch Analyses for Sustainable Climate Change Adaption," Sustainability, MDPI, vol. 11(1), pages 1-22, December.
    12. Kichul Jung & Myoung-Jin Um & Momcilo Markus & Daeryong Park, 2020. "Comparison of Long Short-Term Memory and Weighted Regressions on Time, Discharge, and Season Models for Nitrate-N Load Estimation," Sustainability, MDPI, vol. 12(15), pages 1-24, July.
    13. Ngoc-Lan Huynh, Anh & Deo, Ravinesh C. & Ali, Mumtaz & Abdulla, Shahab & Raj, Nawin, 2021. "Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition," Applied Energy, Elsevier, vol. 298(C).
    14. 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.
    15. Bo Gao & Chunsheng Wang & Yukun Hu & C. K. Tan & Paul Alun Roach & Liz Varga, 2018. "Function Value-Based Multi-Objective Optimisation of Reheating Furnace Operations Using Hooke-Jeeves Algorithm," Energies, MDPI, vol. 11(9), pages 1-18, September.
    16. Yahya Z. Alharthi, 2023. "Performance Analysis Using Multi-Year Parameters for a Grid-Connected Wind Power System," Energies, MDPI, vol. 16(5), pages 1-20, February.
    17. Quetzalcoatl Hernandez-Escobedo & Alida Ramirez-Jimenez & Jesús Manuel Dorador-Gonzalez & Miguel-Angel Perea-Moreno & Alberto-Jesus Perea-Moreno, 2020. "Sustainable Solar Energy in Mexican Universities. Case Study: The National School of Higher Studies Juriquilla (UNAM)," Sustainability, MDPI, vol. 12(8), pages 1-22, April.
    18. Hocaoglu, Fatih Onur & Serttas, Fatih, 2017. "A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 635-643.
    19. Wang, Lunche & Kisi, Ozgur & Zounemat-Kermani, Mohammad & Hu, Bo & Gong, Wei, 2016. "Modeling and comparison of hourly photosynthetically active radiation in different ecosystems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 436-453.
    20. Sampaio, Priscila Gonçalves Vasconcelos & González, Mario Orestes Aguirre, 2017. "Photovoltaic solar energy: Conceptual framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 590-601.

    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:12:y:2019:i:14:p:2662-:d:247553. 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.