IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v293y2021ics0306261921003779.html
   My bibliography  Save this article

An adaptive short-term forecasting method for the energy yield of flat-plate solar collector systems

Author

Listed:
  • Unterberger, Viktor
  • Lichtenegger, Klaus
  • Kaisermayer, Valentin
  • Gölles, Markus
  • Horn, Martin

Abstract

The number of large-scale solar thermal installations has increased rapidly in Europe in recent years, with 70% of these systems operating with flat-plate solar collectors. Since these systems cannot be easily switched on and off but directly depend on the solar radiation, they have to be combined with other technologies or integrated in large energy systems. In order to most efficiently integrate and operate solar systems, it is of great importance to consider their expected energy yield to better schedule heat production, storage and distribution. To do so the availability of accurate forecasting methods for the future solar energy yield are essential. Currently available forecasting methods do not meet three important practical requirements: simple implementation, automatic adaption to seasonal changes and wide applicability. For these reasons, a simple and adaptive forecasting method is presented in this paper, which allows to accurately forecast the solar heat production of flat-plate collector systems considering weather forecasts. The method is based on a modified collector efficiency model where the parameters are continuously redetermined to specifically consider the influence of the time of the day. In order to show the wide applicability the method is extensively tested with measurement data of various flat-plate collector systems covering different applications (below 200°Celsius), sizes and orientations. The results show that the method can forecast the solar yield very accurately with a Mean Absolute Range Normalized Error (MARNE) of about 5% using real weather forecasts as inputs and outperforms common forecasting methods by being nearly twice as accurate.

Suggested Citation

  • Unterberger, Viktor & Lichtenegger, Klaus & Kaisermayer, Valentin & Gölles, Markus & Horn, Martin, 2021. "An adaptive short-term forecasting method for the energy yield of flat-plate solar collector systems," Applied Energy, Elsevier, vol. 293(C).
    • Handle: RePEc:eee:appene:v:293:y:2021:i:c:s0306261921003779
    DOI: 10.1016/j.apenergy.2021.116891
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921003779
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.116891?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    2. Yaïci, Wahiba & Entchev, Evgueniy, 2016. "Adaptive Neuro-Fuzzy Inference System modelling for performance prediction of solar thermal energy system," Renewable Energy, Elsevier, vol. 86(C), pages 302-315.
    3. Kalogirou, S.A. & Mathioulakis, E. & Belessiotis, V., 2014. "Artificial neural networks for the performance prediction of large solar systems," Renewable Energy, Elsevier, vol. 63(C), pages 90-97.
    4. Dyreson, Ana & Miller, Franklin, 2016. "Night sky cooling for concentrating solar power plants," Applied Energy, Elsevier, vol. 180(C), pages 276-286.
    5. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
    6. Nigitz, Thomas & Gölles, Markus, 2019. "A generally applicable, simple and adaptive forecasting method for the short-term heat load of consumers," Applied Energy, Elsevier, vol. 241(C), pages 73-81.
    7. Tian, Zhiyong & Perers, Bengt & Furbo, Simon & Fan, Jianhua, 2018. "Analysis and validation of a quasi-dynamic model for a solar collector field with flat plate collectors and parabolic trough collectors in series for district heating," Energy, Elsevier, vol. 142(C), pages 130-138.
    8. Moser, A. & Muschick, D. & Gölles, M. & Nageler, P. & Schranzhofer, H. & Mach, T. & Ribas Tugores, C. & Leusbrock, I. & Stark, S. & Lackner, F. & Hofer, A., 2020. "A MILP-based modular energy management system for urban multi-energy systems: Performance and sensitivity analysis," Applied Energy, Elsevier, vol. 261(C).
    9. Correa-Jullian, Camila & Cardemil, José Miguel & López Droguett, Enrique & Behzad, Masoud, 2020. "Assessment of Deep Learning techniques for Prognosis of solar thermal systems," Renewable Energy, Elsevier, vol. 145(C), pages 2178-2191.
    10. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
    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. Wang, Lu & Yuan, JianJuan & Qiao, Xu & Kong, Xiangfei, 2023. "Optimal rule based double predictive control for the management of thermal energy in a distributed clean heating system," Renewable Energy, Elsevier, vol. 215(C).
    2. Houben, Nikolaus & Cosic, Armin & Stadler, Michael & Mansoor, Muhammad & Zellinger, Michael & Auer, Hans & Ajanovic, Amela & Haas, Reinhard, 2023. "Optimal dispatch of a multi-energy system microgrid under uncertainty: A renewable energy community in Austria," Applied Energy, Elsevier, vol. 337(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. Rana, Mashud & Sethuvenkatraman, Subbu & Heidari, Rahmat & Hands, Stuart, 2022. "Solar thermal generation forecast via deep learning and application to buildings cooling system control," Renewable Energy, Elsevier, vol. 196(C), pages 694-706.
    2. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    3. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
    4. Croonenbroeck, Carsten & Stadtmann, Georg, 2019. "Renewable generation forecast studies – Review and good practice guidance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 312-322.
    5. Houben, Nikolaus & Cosic, Armin & Stadler, Michael & Mansoor, Muhammad & Zellinger, Michael & Auer, Hans & Ajanovic, Amela & Haas, Reinhard, 2023. "Optimal dispatch of a multi-energy system microgrid under uncertainty: A renewable energy community in Austria," Applied Energy, Elsevier, vol. 337(C).
    6. Mitrentsis, Georgios & Lens, Hendrik, 2022. "An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting," Applied Energy, Elsevier, vol. 309(C).
    7. Gu, Bo & Shen, Huiqiang & Lei, Xiaohui & Hu, Hao & Liu, Xinyu, 2021. "Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method," Applied Energy, Elsevier, vol. 299(C).
    8. Vakili, Masoud & Yahyaei, Masood & Ramsay, James & Aghajannezhad, Pouria & Paknezhad, Behnaz, 2021. "Adaptive neuro-fuzzy inference system modeling to predict the performance of graphene nanoplatelets nanofluid-based direct absorption solar collector based on experimental study," Renewable Energy, Elsevier, vol. 163(C), pages 807-824.
    9. Llinet Benavides Cesar & Rodrigo Amaro e Silva & Miguel Ángel Manso Callejo & Calimanut-Ionut Cira, 2022. "Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates," Energies, MDPI, vol. 15(12), pages 1-23, June.
    10. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    11. Nie, Yuhao & Li, Xiatong & Paletta, Quentin & Aragon, Max & Scott, Andea & Brandt, Adam, 2024. "Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    12. du Plessis, A.A. & Strauss, J.M. & Rix, A.J., 2021. "Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour," Applied Energy, Elsevier, vol. 285(C).
    13. Elena Collino & Dario Ronzio, 2021. "Exploitation of a New Short-Term Multimodel Photovoltaic Power Forecasting Method in the Very Short-Term Horizon to Derive a Multi-Time Scale Forecasting System," Energies, MDPI, vol. 14(3), pages 1-30, February.
    14. Wakui, Tetsuya & Sawada, Kento & Yokoyama, Ryohei & Aki, Hirohisa, 2019. "Predictive management for energy supply networks using photovoltaics, heat pumps, and battery by two-stage stochastic programming and rule-based control," Energy, Elsevier, vol. 179(C), pages 1302-1319.
    15. Heng, Shye Yunn & Asako, Yutaka & Suwa, Tohru & Nagasaka, Ken, 2019. "Transient thermal prediction methodology for parabolic trough solar collector tube using artificial neural network," Renewable Energy, Elsevier, vol. 131(C), pages 168-179.
    16. Athanasios Anagnostis & Serafeim Moustakidis & Elpiniki Papageorgiou & Dionysis Bochtis, 2022. "A Hybrid Bimodal LSTM Architecture for Cascading Thermal Energy Storage Modelling," Energies, MDPI, vol. 15(6), pages 1-24, March.
    17. Sajjad Ali & Imran Khan & Sadaqat Jan & Ghulam Hafeez, 2021. "An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid," Energies, MDPI, vol. 14(8), pages 1-29, April.
    18. Mostafa Esmaeili Shayan & Gholamhassan Najafi & Barat Ghobadian & Shiva Gorjian & Mohamed Mazlan & Mehdi Samami & Alireza Shabanzadeh, 2022. "Flexible Photovoltaic System on Non-Conventional Surfaces: A Techno-Economic Analysis," Sustainability, MDPI, vol. 14(6), pages 1-14, March.
    19. Wang, Yang & Zhang, Shanhong & Chow, David & Kuckelkorn, Jens M., 2021. "Evaluation and optimization of district energy network performance: Present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    20. Heo, SungKu & Byun, Jaewon & Ifaei, Pouya & Ko, Jaerak & Ha, Byeongmin & Hwangbo, Soonho & Yoo, ChangKyoo, 2024. "Towards mega-scale decarbonized industrial park (Mega-DIP): Generative AI-driven techno-economic and environmental assessment of renewable and sustainable energy utilization in petrochemical industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).

    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:eee:appene:v:293:y:2021:i:c:s0306261921003779. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.