IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i15p5693-d1206199.html
   My bibliography  Save this article

Recent Trends in Real-Time Photovoltaic Prediction Systems

Author

Listed:
  • Isaac Gallardo

    (ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain
    RBZ Robot Design S.L., C. Casas de Miravete 24A, 28031 Madrid, Spain)

  • Daniel Amor

    (RBZ Robot Design S.L., C. Casas de Miravete 24A, 28031 Madrid, Spain)

  • Álvaro Gutiérrez

    (ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain)

Abstract

Photovoltaic power forecasting is an important problem for renewable energy integration in the grid. The purpose of this review is to analyze current methods to predict photovoltaic power or solar irradiance, with the aim of summarizing them, identifying gaps and trends, and providing an overview of what has been achieved in recent years. A search on Web of Science was performed, obtaining 60 articles published from 2020 onwards. These articles were analyzed, gathering information about the forecasting methods used, the horizon, time step, and parameters. The most used forecasting methods are machine learning and deep learning based, especially artificial neural networks. Most of the articles make predictions for one hour or less ahead and predict power instead of irradiance, although both parameters are strongly correlated, and output power depends on received irradiance. Finally, they use weather variables as inputs, consisting mainly of irradiance, temperature, wind speed and humidity. Overall, there is a lack of hardware implementations for real-time predictions, being an important line of development in future decades with the use of embedded prediction systems at the photovoltaic installations.

Suggested Citation

  • Isaac Gallardo & Daniel Amor & Álvaro Gutiérrez, 2023. "Recent Trends in Real-Time Photovoltaic Prediction Systems," Energies, MDPI, vol. 16(15), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5693-:d:1206199
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/15/5693/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/15/5693/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Edna S. Solano & Payman Dehghanian & Carolina M. Affonso, 2022. "Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection," Energies, MDPI, vol. 15(19), pages 1-18, September.
    2. Mario Tovar & Miguel Robles & Felipe Rashid, 2020. "PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México," Energies, MDPI, vol. 13(24), pages 1-15, December.
    3. Sue Ellen Haupt & Tyler C. McCandless & Susan Dettling & Stefano Alessandrini & Jared A. Lee & Seth Linden & William Petzke & Thomas Brummet & Nhi Nguyen & Branko Kosović & Gerry Wiener & Tahani Hussa, 2020. "Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting," Energies, MDPI, vol. 13(8), pages 1-23, April.
    4. Veena Raj & Sam-Quarcoo Dotse & Mathew Sathyajith & M. I. Petra & Hayati Yassin, 2023. "Ensemble Machine Learning for Predicting the Power Output from Different Solar Photovoltaic Systems," Energies, MDPI, vol. 16(2), pages 1-15, January.
    5. Majid Hosseini & Satya Katragadda & Jessica Wojtkiewicz & Raju Gottumukkala & Anthony Maida & Terrence Lynn Chambers, 2020. "Direct Normal Irradiance Forecasting Using Multivariate Gated Recurrent Units," Energies, MDPI, vol. 13(15), pages 1-15, July.
    6. Spyros Theocharides & Marios Theristis & George Makrides & Marios Kynigos & Chrysovalantis Spanias & George E. Georghiou, 2021. "Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting," Energies, MDPI, vol. 14(4), pages 1-22, February.
    7. 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.
    8. Dong, Jin & Olama, Mohammed M. & Kuruganti, Teja & Melin, Alexander M. & Djouadi, Seddik M. & Zhang, Yichen & Xue, Yaosuo, 2020. "Novel stochastic methods to predict short-term solar radiation and photovoltaic power," Renewable Energy, Elsevier, vol. 145(C), pages 333-346.
    9. Hyung Keun Ahn & Neungsoo Park, 2021. "Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors," Energies, MDPI, vol. 14(2), pages 1-17, January.
    10. Kumari, Pratima & Toshniwal, Durga, 2021. "Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting," Applied Energy, Elsevier, vol. 295(C).
    11. Lauria, Davide & Mottola, Fabio & Proto, Daniela, 2022. "Caputo derivative applied to very short time photovoltaic power forecasting," Applied Energy, Elsevier, vol. 309(C).
    12. Walch, Alina & Castello, Roberto & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2020. "Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty," Applied Energy, Elsevier, vol. 262(C).
    13. Rai, Amit & Shrivastava, Ashish & Jana, Kartick C., 2023. "Differential attention net: Multi-directed differential attention based hybrid deep learning model for solar power forecasting," Energy, Elsevier, vol. 263(PC).
    14. Fateh Mehazzem & Maina André & Rudy Calif, 2022. "Efficient Output Photovoltaic Power Prediction Based on MPPT Fuzzy Logic Technique and Solar Spatio-Temporal Forecasting Approach in a Tropical Insular Region," Energies, MDPI, vol. 15(22), pages 1-21, November.
    15. Rong-Jong Wai & Pin-Xian Lai, 2022. "Design of Intelligent Solar PV Power Generation Forecasting Mechanism Combined with Weather Information under Lack of Real-Time Power Generation Data," Energies, MDPI, vol. 15(10), pages 1-30, May.
    16. Puah, Boon Keat & Chong, Lee Wai & Wong, Yee Wan & Begam, K.M. & Khan, Nafizah & Juman, Mohammed Ayoub & Rajkumar, Rajprasad Kumar, 2021. "A regression unsupervised incremental learning algorithm for solar irradiance prediction," Renewable Energy, Elsevier, vol. 164(C), pages 908-925.
    17. Rafati, Amir & Joorabian, Mahmood & Mashhour, Elaheh & Shaker, Hamid Reza, 2021. "High dimensional very short-term solar power forecasting based on a data-driven heuristic method," Energy, Elsevier, vol. 219(C).
    18. Aleksandar Dimovski & Matteo Moncecchi & Davide Falabretti & Marco Merlo, 2020. "PV Forecast for the Optimal Operation of the Medium Voltage Distribution Network: A Real-Life Implementation on a Large Scale Pilot," Energies, MDPI, vol. 13(20), pages 1-21, October.
    19. Simeunović, Jelena & Schubnel, Baptiste & Alet, Pierre-Jean & Carrillo, Rafael E. & Frossard, Pascal, 2022. "Interpretable temporal-spatial graph attention network for multi-site PV power forecasting," Applied Energy, Elsevier, vol. 327(C).
    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. Jinhwa Jeong & Dongkyu Lee & Young Tae Chae, 2023. "A Novel Approach for Day-Ahead Hourly Building-Integrated Photovoltaic Power Prediction by Using Feature Engineering and Simple Weather Forecasting Service," Energies, MDPI, vol. 16(22), pages 1-21, November.

    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. Acikgoz, Hakan, 2022. "A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting," Applied Energy, Elsevier, vol. 305(C).
    2. Gianfranco Di Lorenzo & Erika Stracqualursi & Leonardo Micheli & Salvatore Celozzi & Rodolfo Araneo, 2022. "Prognostic Methods for Photovoltaic Systems’ Underperformance and Degradation: Status, Perspectives, and Challenges," Energies, MDPI, vol. 15(17), pages 1-6, September.
    3. Putri Nor Liyana Mohamad Radzi & Muhammad Naveed Akhter & Saad Mekhilef & Noraisyah Mohamed Shah, 2023. "Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    4. Maria Krechowicz & Adam Krechowicz & Lech Lichołai & Artur Pawelec & Jerzy Zbigniew Piotrowski & Anna Stępień, 2022. "Reduction of the Risk of Inaccurate Prediction of Electricity Generation from PV Farms Using Machine Learning," Energies, MDPI, vol. 15(11), pages 1-21, May.
    5. Nunes Maciel, Joylan & Javier Gimenez Ledesma, Jorge & Hideo Ando Junior, Oswaldo, 2024. "Hybrid prediction method of solar irradiance applied to short-term photovoltaic energy generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    6. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & David Celeita & George Anders, 2023. "Deep and Machine Learning Models to Forecast Photovoltaic Power Generation," Energies, MDPI, vol. 16(10), pages 1-24, May.
    7. Sabadus, Andreea & Blaga, Robert & Hategan, Sergiu-Mihai & Calinoiu, Delia & Paulescu, Eugenia & Mares, Oana & Boata, Remus & Stefu, Nicoleta & Paulescu, Marius & Badescu, Viorel, 2024. "A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches," Renewable Energy, Elsevier, vol. 226(C).
    8. Li, Chen & Kies, Alexander & Zhou, Kai & Schlott, Markus & Sayed, Omar El & Bilousova, Mariia & Stöcker, Horst, 2024. "Optimal Power Flow in a highly renewable power system based on attention neural networks," Applied Energy, Elsevier, vol. 359(C).
    9. Stephan Schlüter & Fabian Menz & Milena Kojić & Petar Mitić & Aida Hanić, 2022. "A Novel Approach to Generate Hourly Photovoltaic Power Scenarios," Sustainability, MDPI, vol. 14(8), pages 1-16, April.
    10. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention," Applied Energy, Elsevier, vol. 321(C).
    11. Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
    12. Sibtain, Muhammad & Li, Xianshan & Saleem, Snoober & Ain, Qurat-ul- & Shi, Qiang & Li, Fei & Saeed, Muhammad & Majeed, Fatima & Shah, Syed Shoaib Ahmed & Saeed, Muhammad Hammad, 2022. "Multifaceted irradiance prediction by exploiting hybrid decomposition-entropy-Spatiotemporal attention based Sequence2Sequence models," Renewable Energy, Elsevier, vol. 196(C), pages 648-682.
    13. Nourani, Vahid & Sharghi, Elnaz & Behfar, Nazanin & Zhang, Yongqiang, 2022. "Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data," Applied Energy, Elsevier, vol. 315(C).
    14. Müller, Jonas & Trutnevyte, Evelina, 2020. "Spatial projections of solar PV installations at subnational level: Accuracy testing of regression models," Applied Energy, Elsevier, vol. 265(C).
    15. Gupta, Priya & Singh, Rhythm, 2023. "Combining simple and less time complex ML models with multivariate empirical mode decomposition to obtain accurate GHI forecast," Energy, Elsevier, vol. 263(PC).
    16. Dongkyu Lee & Jae-Weon Jeong & Guebin Choi, 2021. "Short Term Prediction of PV Power Output Generation Using Hierarchical Probabilistic Model," Energies, MDPI, vol. 14(10), pages 1-15, May.
    17. Eşlik, Ardan Hüseyin & Akarslan, Emre & Hocaoğlu, Fatih Onur, 2022. "Short-term solar radiation forecasting with a novel image processing-based deep learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 1490-1505.
    18. Cai Tao & Junjie Lu & Jianxun Lang & Xiaosheng Peng & Kai Cheng & Shanxu Duan, 2021. "Short-Term Forecasting of Photovoltaic Power Generation Based on Feature Selection and Bias Compensation–LSTM Network," Energies, MDPI, vol. 14(11), pages 1-16, May.
    19. Ajith, Meenu & Martínez-Ramón, Manel, 2021. "Deep learning based solar radiation micro forecast by fusion of infrared cloud images and radiation data," Applied Energy, Elsevier, vol. 294(C).
    20. Lonergan, Katherine Emma & Sansavini, Giovanni, 2022. "Business structure of electricity distribution system operator and effect on solar photovoltaic uptake: An empirical case study for Switzerland," Energy Policy, Elsevier, vol. 160(C).

    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:15:p:5693-:d:1206199. 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.