IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v108y2017icp274-286.html
   My bibliography  Save this article

Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble

Author

Listed:
  • Cervone, Guido
  • Clemente-Harding, Laura
  • Alessandrini, Stefano
  • Delle Monache, Luca

Abstract

A methodology based on Artificial Neural Networks (ANN) and an Analog Ensemble (AnEn) is presented to generate 72 h deterministic and probabilistic forecasts of power generated by photovoltaic (PV) power plants using input from a numerical weather prediction model and computed astronomical variables. ANN and AnEn are used individually and in combination to generate forecasts for three solar power plants located in Italy. The computational scalability of the proposed solution is tested using synthetic data simulating 4450 PV power stations. The National Center for Atmospheric Research (NCAR) Yellowstone supercomputer is employed to test the parallel implementation of the proposed solution, ranging from one node (32 cores) to 4450 nodes (141,140 cores). Results show that a combined AnEn + ANN solution yields best results, and that the proposed solution is well suited for massive scale computation.

Suggested Citation

  • Cervone, Guido & Clemente-Harding, Laura & Alessandrini, Stefano & Delle Monache, Luca, 2017. "Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble," Renewable Energy, Elsevier, vol. 108(C), pages 274-286.
  • Handle: RePEc:eee:renene:v:108:y:2017:i:c:p:274-286
    DOI: 10.1016/j.renene.2017.02.052
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148117301386
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2017.02.052?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. Lima, Francisco J.L. & Martins, Fernando R. & Pereira, Enio B. & Lorenz, Elke & Heinemann, Detlev, 2016. "Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks," Renewable Energy, Elsevier, vol. 87(P1), pages 807-818.
    2. Jonas Muller & Marcus Hildmann & Andreas Ulbig & Goran Andersson, 2014. "Grid Integration Costs of Fluctuating Renewable Energy Sources," Papers 1407.7237, arXiv.org.
    3. Yang, Dazhi & Gu, Chaojun & Dong, Zibo & Jirutitijaroen, Panida & Chen, Nan & Walsh, Wilfred M., 2013. "Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging," Renewable Energy, Elsevier, vol. 60(C), pages 235-245.
    4. Arent, Doug & Pless, Jacquelyn & Mai, Trieu & Wiser, Ryan & Hand, Maureen & Baldwin, Sam & Heath, Garvin & Macknick, Jordan & Bazilian, Morgan & Schlosser, Adam & Denholm, Paul, 2014. "Implications of high renewable electricity penetration in the U.S. for water use, greenhouse gas emissions, land-use, and materials supply," Applied Energy, Elsevier, vol. 123(C), pages 368-377.
    5. Becker, Sarah & Frew, Bethany A. & Andresen, Gorm B. & Zeyer, Timo & Schramm, Stefan & Greiner, Martin & Jacobson, Mark Z., 2014. "Features of a fully renewable US electricity system: Optimized mixes of wind and solar PV and transmission grid extensions," Energy, Elsevier, vol. 72(C), pages 443-458.
    6. Kubik, M.L. & Coker, P.J. & Barlow, J.F., 2015. "Increasing thermal plant flexibility in a high renewables power system," Applied Energy, Elsevier, vol. 154(C), pages 102-111.
    7. McCandless, T.C. & Haupt, S.E. & Young, G.S., 2016. "A regime-dependent artificial neural network technique for short-range solar irradiance forecasting," Renewable Energy, Elsevier, vol. 89(C), pages 351-359.
    8. Eser, Patrick & Singh, Antriksh & Chokani, Ndaona & Abhari, Reza S., 2016. "Effect of increased renewables generation on operation of thermal power plants," Applied Energy, Elsevier, vol. 164(C), pages 723-732.
    9. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    10. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Nissen, J.N., 2015. "A novel application of an analog ensemble for short-term wind power forecasting," Renewable Energy, Elsevier, vol. 76(C), pages 768-781.
    11. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Cervone, G., 2015. "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, Elsevier, vol. 157(C), pages 95-110.
    12. Simone Sperati & Stefano Alessandrini & Pierre Pinson & George Kariniotakis, 2015. "The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation," Energies, MDPI, vol. 8(9), pages 1-26, September.
    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. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    2. Stefano Alessandrini & Tyler McCandless, 2020. "The Schaake Shuffle Technique to Combine Solar and Wind Power Probabilistic Forecasting," Energies, MDPI, vol. 13(10), pages 1-18, May.
    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. Nikoobakht, Ahmad & Aghaei, Jamshid & Mardaneh, Mohammad, 2017. "Securing highly penetrated wind energy systems using linearized transmission switching mechanism," Applied Energy, Elsevier, vol. 190(C), pages 1207-1220.
    5. Mikkola, Jani & Lund, Peter D., 2016. "Modeling flexibility and optimal use of existing power plants with large-scale variable renewable power schemes," Energy, Elsevier, vol. 112(C), pages 364-375.
    6. Tong Guo & Yajing Gao & Xiaojie Zhou & Yonggang Li & Jiaomin Liu, 2018. "Optimal Scheduling of Power System Incorporating the Flexibility of Thermal Units," Energies, MDPI, vol. 11(9), pages 1-17, August.
    7. Garðarsdóttir, Stefanía Ó. & Göransson, Lisa & Normann, Fredrik & Johnsson, Filip, 2018. "Improving the flexibility of coal-fired power generators: Impact on the composition of a cost-optimal electricity system," Applied Energy, Elsevier, vol. 209(C), pages 277-289.
    8. Yang, Dazhi & Sharma, Vishal & Ye, Zhen & Lim, Lihong Idris & Zhao, Lu & Aryaputera, Aloysius W., 2015. "Forecasting of global horizontal irradiance by exponential smoothing, using decompositions," Energy, Elsevier, vol. 81(C), pages 111-119.
    9. Sha Liu & Jiong Shen, 2022. "Modeling of Large-Scale Thermal Power Plants for Performance Prediction in Deep Peak Shaving," Energies, MDPI, vol. 15(9), pages 1-18, April.
    10. Luis Mazorra-Aguiar & Philippe Lauret & Mathieu David & Albert Oliver & Gustavo Montero, 2021. "Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency," Energies, MDPI, vol. 14(6), pages 1-26, March.
    11. Golestaneh, Faranak & Gooi, Hoay Beng & Pinson, Pierre, 2016. "Generation and evaluation of space–time trajectories of photovoltaic power," Applied Energy, Elsevier, vol. 176(C), pages 80-91.
    12. Saleh Abujarad & Mohd Wazir Mustafa & Jasrul Jamani Jamian & Abdirahman M. Abdilahi & Jeroen D. M. De Kooning & Jan Desmet & Lieven Vandevelde, 2020. "An Adjusted Weight Metric to Quantify Flexibility Available in Conventional Generators for Low Carbon Power Systems," Energies, MDPI, vol. 13(21), pages 1-19, October.
    13. Çam, Eren, 2020. "Optimal Dispatch of a Coal-Fired Power Plant with Integrated Thermal Energy Storage," EWI Working Papers 2020-5, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI), revised 10 Aug 2021.
    14. Safdarnejad, Seyed Mostafa & Hedengren, John D. & Powell, Kody M., 2018. "Performance comparison of low temperature and chemical absorption carbon capture processes in response to dynamic electricity demand and price profiles," Applied Energy, Elsevier, vol. 228(C), pages 577-592.
    15. Dou, Weijing & Wang, Kai & Shan, Shuo & Li, Chenxi & Wang, Yiye & Zhang, Kanjian & Wei, Haikun & Sreeram, Victor, 2024. "Day-ahead Numerical Weather Prediction solar irradiance correction using a clustering method based on weather conditions," Applied Energy, Elsevier, vol. 365(C).
    16. Arumugham, Dinesh Rajan & Rajendran, Parvathy, 2021. "Modelling global solar irradiance for any location on earth through regression analysis using high-resolution data," Renewable Energy, Elsevier, vol. 180(C), pages 1114-1123.
    17. Bartlett, Stuart & Dujardin, Jérôme & Kahl, Annelen & Kruyt, Bert & Manso, Pedro & Lehning, Michael, 2018. "Charting the course: A possible route to a fully renewable Swiss power system," Energy, Elsevier, vol. 163(C), pages 942-955.
    18. Cole, Wesley & Lewis, Haley & Sigrin, Ben & Margolis, Robert, 2016. "Interactions of rooftop PV deployment with the capacity expansion of the bulk power system," Applied Energy, Elsevier, vol. 168(C), pages 473-481.
    19. Pappa, Areti & Theodoropoulos, Ioannis & Galmarini, Stefano & Kioutsioukis, Ioannis, 2023. "Analog versus multi-model ensemble forecasting: A comparison for renewable energy resources," Renewable Energy, Elsevier, vol. 205(C), pages 563-573.
    20. Shahriari, M. & Cervone, G. & Clemente-Harding, L. & Delle Monache, L., 2020. "Using the analog ensemble method as a proxy measurement for wind power predictability," Renewable Energy, Elsevier, vol. 146(C), pages 789-801.

    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:renene:v:108:y:2017:i:c:p:274-286. 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.journals.elsevier.com/renewable-energy .

    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.