IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i11p2582-d1402892.html
   My bibliography  Save this article

Short-Term Forecast of Photovoltaic Solar Energy Production Using LSTM

Author

Listed:
  • Filipe D. Campos

    (Department of Electrical Engineering, Institute of Engineering—Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal)

  • Tiago C. Sousa

    (Department of Electrical Engineering, Institute of Engineering—Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal)

  • Ramiro S. Barbosa

    (Department of Electrical Engineering, Institute of Engineering—Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal
    GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, ISEP/IPP, 4249-015 Porto, Portugal)

Abstract

In recent times, renewable energy sources have gained considerable vitality due to their inexhaustible resources and the detrimental effects of fossil fuels, such as the impact of greenhouse gases on the planet. This article aims to be a supportive tool for the development of research in the field of artificial intelligence (AI), as it presents a solution for predicting photovoltaic energy production. The basis of the AI models is provided from two data sets, one for generated electrical power and another for meteorological data, related to the year 2017, which are freely available on the Energias de Portugal (EDP) Open Project website. The implemented AI models rely on long short-term memory (LSTM) neural networks, providing a forecast value for electrical energy with a 60-min horizon based on meteorological variables. The performance of the models is evaluated using the performance indicators MAE, RMSE, and R 2 , for which favorable results were obtained, with particular emphasis on forecasts for the spring and summer seasons.

Suggested Citation

  • Filipe D. Campos & Tiago C. Sousa & Ramiro S. Barbosa, 2024. "Short-Term Forecast of Photovoltaic Solar Energy Production Using LSTM," Energies, MDPI, vol. 17(11), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2582-:d:1402892
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/11/2582/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/11/2582/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bo Pang & Erik Nijkamp & Ying Nian Wu, 2020. "Deep Learning With TensorFlow: A Review," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 227-248, April.
    2. Seung-Min Jung & Sungwoo Park & Seung-Won Jung & Eenjun Hwang, 2020. "Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities," Sustainability, MDPI, vol. 12(16), pages 1-20, August.
    3. Andi A. H. Lateko & Hong-Tzer Yang & Chao-Ming Huang, 2022. "Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method," Energies, MDPI, vol. 15(11), pages 1-21, June.
    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. Shahid Nawaz Khan & Syed Ali Abbas Kazmi & Abdullah Altamimi & Zafar A. Khan & Mohammed A. Alghassab, 2022. "Smart Distribution Mechanisms—Part I: From the Perspectives of Planning," Sustainability, MDPI, vol. 14(23), pages 1-109, December.
    2. Alexandros Menelaos Tzortzis & Sotiris Pelekis & Evangelos Spiliotis & Evangelos Karakolis & Spiros Mouzakitis & John Psarras & Dimitris Askounis, 2023. "Transfer Learning for Day-Ahead Load Forecasting: A Case Study on European National Electricity Demand Time Series," Mathematics, MDPI, vol. 12(1), pages 1-24, December.
    3. Seyed Mahdi Miraftabzadeh & Cristian Giovanni Colombo & Michela Longo & Federica Foiadelli, 2023. "A Day-Ahead Photovoltaic Power Prediction via Transfer Learning and Deep Neural Networks," Forecasting, MDPI, vol. 5(1), pages 1-16, February.
    4. Odin Foldvik Eikeland & Filippo Maria Bianchi & Harry Apostoleris & Morten Hansen & Yu-Cheng Chiou & Matteo Chiesa, 2021. "Predicting Energy Demand in Semi-Remote Arctic Locations," Energies, MDPI, vol. 14(4), pages 1-17, February.
    5. Magdalena Krystyna Wyrwicka & Ewa Więcek-Janka & Łukasz Brzeziński, 2023. "Transition to Sustainable Energy System for Smart Cities—Literature Review," Energies, MDPI, vol. 16(21), pages 1-26, October.
    6. Md. Tarek Hasan & Md. Al Emran Hossain & Md. Saddam Hossain Mukta & Arifa Akter & Mohiuddin Ahmed & Salekul Islam, 2023. "A Review on Deep-Learning-Based Cyberbullying Detection," Future Internet, MDPI, vol. 15(5), pages 1-47, May.
    7. Jabir, Brahim & Moutaouakil, Khalid El & Falih, Noureddine, 2023. "Developing an Efficient System with Mask R-CNN for Agricultural Applications," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 15(1), January.
    8. Chao-Ming Huang & Shin-Ju Chen & Sung-Pei Yang & Hsin-Jen Chen, 2023. "One-Day-Ahead Hourly Wind Power Forecasting Using Optimized Ensemble Prediction Methods," Energies, MDPI, vol. 16(6), pages 1-22, March.
    9. Tahir, Muhammad Faizan & Yousaf, Muhammad Zain & Tzes, Anthony & El Moursi, Mohamed Shawky & El-Fouly, Tarek H.M., 2024. "Enhanced solar photovoltaic power prediction using diverse machine learning algorithms with hyperparameter optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
    10. Firuz Kamalov & Hana Sulieman & Sherif Moussa & Jorge Avante Reyes & Murodbek Safaraliev, 2024. "Powering Electricity Forecasting with Transfer Learning," Energies, MDPI, vol. 17(3), pages 1-13, January.
    11. Hristo Ivanov Beloev & Stanislav Radikovich Saitov & Antonina Andreevna Filimonova & Natalia Dmitrievna Chichirova & Oleg Evgenievich Babikov & Iliya Krastev Iliev, 2024. "Prediction of Pipe Failure Rate in Heating Networks Using Machine Learning Methods," Energies, MDPI, vol. 17(14), pages 1-16, July.
    12. Xianbin Wang & Yuqi Zhao & Weifeng Li, 2023. "Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model," Sustainability, MDPI, vol. 15(3), pages 1-21, February.
    13. Herbert Amezquita & Cindy P. Guzman & Hugo Morais, 2024. "Forecasting Electric Vehicles’ Charging Behavior at Charging Stations: A Data Science-Based Approach," Energies, MDPI, vol. 17(14), pages 1-27, July.
    14. Peng Zhang & Huize Ren & Xiaobin Dong & Xuechao Wang & Mengxue Liu & Ying Zhang & Yufang Zhang & Jiuming Huang & Shuheng Dong & Ruiming Xiao, 2023. "Understanding and Applications of Tensors in Ecosystem Services: A Case Study of the Manas River Basin," Land, MDPI, vol. 12(2), pages 1-23, February.
    15. Wu, Xialai & Lin, Ling & Xie, Lei & Chen, Junghui & Shan, Lu, 2024. "Fast robust optimization of ORC based on an artificial neural network for waste heat recovery," Energy, Elsevier, vol. 301(C).
    16. Dae-Sung Lee & Sung-Yong Son, 2024. "Weighted Average Ensemble-Based PV Forecasting in a Limited Environment with Missing Data of PV Power," Sustainability, MDPI, vol. 16(10), pages 1-17, May.
    17. Li, Kangping & Li, Zhenghui & Huang, Chunyi & Ai, Qian, 2024. "Online transfer learning-based residential demand response potential forecasting for load aggregator," Applied Energy, Elsevier, vol. 358(C).
    18. Dorota Kamrowska-Załuska, 2021. "Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities," Land, MDPI, vol. 10(11), pages 1-19, November.
    19. Zachary K. Collier & Minji Kong & Olushola Soyoye & Kamal Chawla & Ann M. Aviles & Yasser Payne, 2024. "Deep Learning Imputation for Asymmetric and Incomplete Likert-Type Items," Journal of Educational and Behavioral Statistics, , vol. 49(2), pages 241-267, April.
    20. Baoyu Fan & Han Ma & Yue Liu & Xiaochen Yuan & Wei Ke, 2024. "KDTM: Multi-Stage Knowledge Distillation Transfer Model for Long-Tailed DGA Detection," Mathematics, MDPI, vol. 12(5), pages 1-19, February.

    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:17:y:2024:i:11:p:2582-:d:1402892. 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.