IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i22p8732-d978572.html
   My bibliography  Save this article

Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study

Author

Listed:
  • L. Cabezón

    (Bluetab, IBM Company, 28020 Madrid, Spain)

  • L. G. B. Ruiz

    (Department of Software Engineering, University of Granada, 18071 Granada, Spain)

  • D. Criado-Ramón

    (Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain)

  • E. J. Gago

    (Engineering Construction and Project Management, School of Civil Engineering, University of Granada, 18071 Granada, Spain)

  • M. C. Pegalajar

    (Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain)

Abstract

Photovoltaic solar energy is booming due to the continuous improvement in photovoltaic panel efficiency along with a downward trend in production costs. In addition, the European Union is committed to easing the implementation of renewable energy in many companies in order to obtain funding to install their own panels. Nonetheless, the nature of solar energy is intermittent and uncontrollable. This leads us to an uncertain scenario which may cause instability in photovoltaic systems. This research addresses this problem by implementing intelligent models to predict the production of solar energy. Real data from a solar farm in Scotland was utilized in this study. Finally, the models were able to accurately predict the energy to be produced in the next hour using historical information as predictor variables.

Suggested Citation

  • L. Cabezón & L. G. B. Ruiz & D. Criado-Ramón & E. J. Gago & M. C. Pegalajar, 2022. "Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study," Energies, MDPI, vol. 15(22), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8732-:d:978572
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/22/8732/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/22/8732/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dudek, Grzegorz, 2016. "Multilayer perceptron for GEFCom2014 probabilistic electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1057-1060.
    2. Y. J. Zhang & T. Ideue & M. Onga & F. Qin & R. Suzuki & A. Zak & R. Tenne & J. H. Smet & Y. Iwasa, 2019. "Enhanced intrinsic photovoltaic effect in tungsten disulfide nanotubes," Nature, Nature, vol. 570(7761), pages 349-353, June.
    3. John F. Geisz & Ryan M. France & Kevin L. Schulte & Myles A. Steiner & Andrew G. Norman & Harvey L. Guthrey & Matthew R. Young & Tao Song & Thomas Moriarty, 2020. "Six-junction III–V solar cells with 47.1% conversion efficiency under 143 Suns concentration," Nature Energy, Nature, vol. 5(4), pages 326-335, April.
    4. J. R. S. Iruela & L. G. B. Ruiz & M. I. Capel & M. C. Pegalajar, 2021. "A TensorFlow Approach to Data Analysis for Time Series Forecasting in the Energy-Efficiency Realm," Energies, MDPI, vol. 14(13), pages 1-22, July.
    5. Francisco Javier Duque-Pintor & Manuel Jesús Fernández-Gómez & Alicia Troncoso & Francisco Martínez-Álvarez, 2016. "A New Methodology Based on Imbalanced Classification for Predicting Outliers in Electricity Demand Time Series," Energies, MDPI, vol. 9(9), pages 1-10, September.
    6. Nassar, Nedal T. & Wilburn, David R. & Goonan, Thomas G., 2016. "Byproduct metal requirements for U.S. wind and solar photovoltaic electricity generation up to the year 2040 under various Clean Power Plan scenarios," Applied Energy, Elsevier, vol. 183(C), pages 1209-1226.
    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. Domenico Palladino & Nicolandrea Calabrese, 2023. "Energy Planning of Renewable Energy Sources in an Italian Context: Energy Forecasting Analysis of Photovoltaic Systems in the Residential Sector," Energies, MDPI, vol. 16(7), pages 1-28, March.
    2. Mateusz Sumorek & Adam Idzkowski, 2023. "Time Series Forecasting for Energy Production in Stand-Alone and Tracking Photovoltaic Systems Based on Historical Measurement Data," Energies, MDPI, vol. 16(17), pages 1-23, September.
    3. L. G. B. Ruiz & M. C. Pegalajar, 2023. "Advances in Energy Efficiency through Neural-Network-Based Models," Energies, MDPI, vol. 16(5), pages 1-3, 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. J. R. S. Iruela & L. G. B. Ruiz & M. I. Capel & M. C. Pegalajar, 2021. "A TensorFlow Approach to Data Analysis for Time Series Forecasting in the Energy-Efficiency Realm," Energies, MDPI, vol. 14(13), pages 1-22, July.
    2. Ren, Kaipeng & Tang, Xu & Wang, Peng & Willerström, Jakob & Höök, Mikael, 2021. "Bridging energy and metal sustainability: Insights from China’s wind power development up to 2050," Energy, Elsevier, vol. 227(C).
    3. Grzegorz Marcjasz & Tomasz Serafin & Rafał Weron, 2018. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 11(9), pages 1-20, September.
    4. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1520-1532.
    5. Badr, Farouk & Radwan, Ali & Ahmed, Mahmoud & Hamed, Ahmed M., 2022. "An experimental study of the concentrator photovoltaic/thermoelectric generator performance using different passive cooling methods," Renewable Energy, Elsevier, vol. 185(C), pages 1078-1094.
    6. Manuel I. Capel, 2022. "Artificial Neuron-Based Model for a Hybrid Real-Time System: Induction Motor Case Study," Mathematics, MDPI, vol. 10(18), pages 1-30, September.
    7. Zhidan Luo & Wei Guo & Qingfu Liu & Yiuman Tse, 2023. "A hybrid prediction model with time‐varying gain tracking differentiator in Taylor expansion: Evidence from precious metals," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1138-1149, August.
    8. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    9. Liang, Yanan & Kleijn, René & Tukker, Arnold & van der Voet, Ester, 2022. "Material requirements for low-carbon energy technologies: A quantitative review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    10. Carlo Fezzi & Luca Mosetti, 2018. "Size matters: Estimation sample length and electricity price forecasting accuracy," DEM Working Papers 2018/10, Department of Economics and Management.
    11. Le Boulzec, Hugo & Delannoy, Louis & Andrieu, Baptiste & Verzier, François & Vidal, Olivier & Mathy, Sandrine, 2022. "Dynamic modeling of global fossil fuel infrastructure and materials needs: Overcoming a lack of available data," Applied Energy, Elsevier, vol. 326(C).
    12. Yan Sun & Shuting Xu & Zheqi Xu & Jiamin Tian & Mengmeng Bai & Zhiying Qi & Yue Niu & Hein Htet Aung & Xiaolu Xiong & Junfeng Han & Cuicui Lu & Jianbo Yin & Sheng Wang & Qing Chen & Reshef Tenne & All, 2022. "Mesoscopic sliding ferroelectricity enabled photovoltaic random access memory for material-level artificial vision system," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    13. Alessio Bosio & Gianluca Foti & Stefano Pasini & Donato Spoltore, 2023. "A Review on the Fundamental Properties of Sb 2 Se 3 -Based Thin Film Solar Cells," Energies, MDPI, vol. 16(19), pages 1-28, September.
    14. Shouheng Chen & Zihan Liang & Jinshui Miao & Xiang-Long Yu & Shuo Wang & Yule Zhang & Han Wang & Yun Wang & Chun Cheng & Gen Long & Taihong Wang & Lin Wang & Han Zhang & Xiaolong Chen, 2024. "Infrared optoelectronics in twisted black phosphorus," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    15. Luzia, Ruan & Rubio, Lihki & Velasquez, Carlos E., 2023. "Sensitivity analysis for forecasting Brazilian electricity demand using artificial neural networks and hybrid models based on Autoregressive Integrated Moving Average," Energy, Elsevier, vol. 274(C).
    16. André Månberger, 2021. "Reduced Use of Fossil Fuels can Reduce Supply of Critical Resources," Biophysical Economics and Resource Quality, Springer, vol. 6(2), pages 1-15, June.
    17. Yongheng Zhou & Xin Zhou & Xiang-Long Yu & Zihan Liang & Xiaoxu Zhao & Taihong Wang & Jinshui Miao & Xiaolong Chen, 2024. "Giant intrinsic photovoltaic effect in one-dimensional van der Waals grain boundaries," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    18. Song, Huiling & Wang, Chang & Sun, Kun & Geng, Hongjun & Zuo, Lyushui, 2023. "Material efficiency strategies across the industrial chain to secure indium availability for global carbon neutrality," Resources Policy, Elsevier, vol. 85(PB).
    19. Rao Fu & Kun Peng & Peng Wang & Honglin Zhong & Bin Chen & Pengfei Zhang & Yiyi Zhang & Dongyang Chen & Xi Liu & Kuishuang Feng & Jiashuo Li, 2023. "Tracing metal footprints via global renewable power value chains," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    20. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.

    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:15:y:2022:i:22:p:8732-:d:978572. 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.