IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v91y2015icp264-273.html
   My bibliography  Save this article

An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery

Author

Listed:
  • Linares-Rodriguez, Alvaro
  • Quesada-Ruiz, Samuel
  • Pozo-Vazquez, David
  • Tovar-Pescador, Joaquin

Abstract

A new evolutionary design of an ANN (artificial neural network) ensemble model is developed to generate hourly DNI (direct normal irradiance) estimates. The procedure combines a genetic algorithm for selecting the best inputs with an ANN ensemble method. The ensemble model was calibrated and evaluated using three years of Meteosat-9 images and data measured at 28 high-quality ground stations over an extensive area, mainly in Europe. The most valuable inputs for DNI estimation are shown to be the following: all Meteosat-9 channels except ch08 and ch11; relative air mass m, integral Rayleigh optical thickness δr, extraterrestrial global irradiance G0, beam clear-sky index Bcs, and the cosine of zenith angle θ. No additional atmospheric information such as turbidity, aerosol optical depth or water vapor content are required for the model. Ensemble estimates were nearly unbiased (MBE = 1.98%) and overall RMSE (root mean square error) was 24.29% across an independent spatial and temporal dataset. This represents an improvement of 35% over other common methods for estimating DNI. The estimates were reasonably reliable in all seasons, and were more accurate in clear-sky conditions.

Suggested Citation

  • Linares-Rodriguez, Alvaro & Quesada-Ruiz, Samuel & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2015. "An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery," Energy, Elsevier, vol. 91(C), pages 264-273.
    • Handle: RePEc:eee:energy:v:91:y:2015:i:c:p:264-273
    DOI: 10.1016/j.energy.2015.08.043
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544215011172
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2015.08.043?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. Kalogirou, Soteris A., 2004. "Optimization of solar systems using artificial neural-networks and genetic algorithms," Applied Energy, Elsevier, vol. 77(4), pages 383-405, April.
    2. Linares-Rodriguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2013. "An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images," Energy, Elsevier, vol. 61(C), pages 636-645.
    3. Mellit, Adel & Kalogirou, Soteris A., 2014. "MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives," Energy, Elsevier, vol. 70(C), pages 1-21.
    4. Mellit, Adel & Kalogirou, Soteris A. & Drif, Mahmoud, 2010. "Application of neural networks and genetic algorithms for sizing of photovoltaic systems," Renewable Energy, Elsevier, vol. 35(12), pages 2881-2893.
    5. Global Energy Assessment Writing Team,, 2012. "Global Energy Assessment," Cambridge Books, Cambridge University Press, number 9781107005198.
    6. Bertrand, Cédric & Vanderveken, Gilles & Journée, Michel, 2015. "Evaluation of decomposition models of various complexity to estimate the direct solar irradiance over Belgium," Renewable Energy, Elsevier, vol. 74(C), pages 618-626.
    7. Polo, J. & Antonanzas-Torres, F. & Vindel, J.M. & Ramirez, L., 2014. "Sensitivity of satellite-based methods for deriving solar radiation to different choice of aerosol input and models," Renewable Energy, Elsevier, vol. 68(C), pages 785-792.
    8. Global Energy Assessment Writing Team,, 2012. "Global Energy Assessment," Cambridge Books, Cambridge University Press, number 9780521182935.
    9. Gueymard, Christian A. & Ruiz-Arias, José Antonio, 2015. "Validation of direct normal irradiance predictions under arid conditions: A review of radiative models and their turbidity-dependent performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 379-396.
    10. Linares-Rodríguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vázquez, David & Tovar-Pescador, Joaquín, 2011. "Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks," Energy, Elsevier, vol. 36(8), pages 5356-5365.
    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. Oh, Myeongchan & Kim, Chang Ki & Kim, Boyoung & Yun, Changyeol & Kim, Jin-Young & Kang, Yongheack & Kim, Hyun-Goo, 2022. "Analysis of minute-scale variability for enhanced separation of direct and diffuse solar irradiance components using machine learning algorithms," Energy, Elsevier, vol. 241(C).
    2. Fu, Guoyin, 2018. "Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system," Energy, Elsevier, vol. 148(C), pages 269-282.
    3. Sholahudin, S. & Han, Hwataik, 2016. "Simplified dynamic neural network model to predict heating load of a building using Taguchi method," Energy, Elsevier, vol. 115(P3), pages 1672-1678.
    4. Hassan, Muhammed A. & Khalil, A. & Kaseb, S. & Kassem, M.A., 2017. "Exploring the potential of tree-based ensemble methods in solar radiation modeling," Applied Energy, Elsevier, vol. 203(C), pages 897-916.
    5. Xwégnon Ghislain Agoua & Robin Girard & Georges Kariniotakis, 2021. "Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy," Energies, MDPI, vol. 14(5), pages 1-15, March.

    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. YoungHyun Koo & Myeongchan Oh & Sung-Min Kim & Hyeong-Dong Park, 2020. "Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model," Energies, MDPI, vol. 13(2), pages 1-19, January.
    2. Mazorra Aguiar, L. & Polo, J. & Vindel, J.M. & Oliver, A., 2019. "Analysis of satellite derived solar irradiance in islands with site adaptation techniques for improving the uncertainty," Renewable Energy, Elsevier, vol. 135(C), pages 98-107.
    3. Wang, Lunche & Kisi, Ozgur & Zounemat-Kermani, Mohammad & Salazar, Germán Ariel & Zhu, Zhongmin & Gong, Wei, 2016. "Solar radiation prediction using different techniques: model evaluation and comparison," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 384-397.
    4. Wang, Lunche & Gong, Wei & Hu, Bo & Feng, Lan & Lin, Aiwen & Zhang, Ming, 2014. "Long-term variations of ultraviolet radiation in China from measurements and model reconstructions," Energy, Elsevier, vol. 78(C), pages 928-938.
    5. Anne-Maree Dowd & Michelle Rodriguez & Talia Jeanneret, 2015. "Social Science Insights for the BioCCS Industry," Energies, MDPI, vol. 8(5), pages 1-19, May.
    6. Fankhauser, Samuel & Jotzo, Frank, 2017. "Economic growth and development with low-carbon energy," LSE Research Online Documents on Economics 86850, London School of Economics and Political Science, LSE Library.
    7. Tilmann Rave, 2013. "Innovation Indicators on Global Climate Change – R&D Expenditure and Patents," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(15), pages 34-41, August.
    8. Daniel Moran & Richard Wood, 2014. "Convergence Between The Eora, Wiod, Exiobase, And Openeu'S Consumption-Based Carbon Accounts," Economic Systems Research, Taylor & Francis Journals, vol. 26(3), pages 245-261, September.
    9. Lykke E. Andersen & Luis Carlos Jemio, 2016. "Decentralization and poverty reduction in Bolivia: Challenges and opportunities," Development Research Working Paper Series 01/2016, Institute for Advanced Development Studies.
    10. Gueymard, Christian A. & Bright, Jamie M. & Lingfors, David & Habte, Aron & Sengupta, Manajit, 2019. "A posteriori clear-sky identification methods in solar irradiance time series: Review and preliminary validation using sky imagers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 412-427.
    11. Chen, Han & Huang, Ye & Shen, Huizhong & Chen, Yilin & Ru, Muye & Chen, Yuanchen & Lin, Nan & Su, Shu & Zhuo, Shaojie & Zhong, Qirui & Wang, Xilong & Liu, Junfeng & Li, Bengang & Tao, Shu, 2016. "Modeling temporal variations in global residential energy consumption and pollutant emissions," Applied Energy, Elsevier, vol. 184(C), pages 820-829.
    12. Inglesi-Lotz, Roula, 2017. "Social rate of return to R&D on various energy technologies: Where should we invest more? A study of G7 countries," Energy Policy, Elsevier, vol. 101(C), pages 521-525.
    13. Tom Mikunda & Tom Kober & Heleen de Coninck & Morgan Bazilian & Hilke R�sler & Bob van der Zwaan, 2014. "Designing policy for deployment of CCS in industry," Climate Policy, Taylor & Francis Journals, vol. 14(5), pages 665-676, September.
    14. Li, Yating & Fei, Yinxin & Zhang, Xiao-Bing & Qin, Ping, 2019. "Household appliance ownership and income inequality: Evidence from micro data in China," China Economic Review, Elsevier, vol. 56(C), pages 1-1.
    15. Xiaolun Wang & Xinlin Yao, 2020. "Fueling Pro-Environmental Behaviors with Gamification Design: Identifying Key Elements in Ant Forest with the Kano Model," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    16. Florian Knobloch & Hector Pollitt & Unnada Chewpreecha & Vassilis Daioglou & Jean-Francois Mercure, 2017. "Simulating the deep decarbonisation of residential heating for limiting global warming to 1.5C," Papers 1710.11019, arXiv.org, revised May 2018.
    17. He, Gang & Victor, David G., 2017. "Experiences and lessons from China’s success in providing electricity for all," Resources, Conservation & Recycling, Elsevier, vol. 122(C), pages 335-338.
    18. Jun Nakatani & Tamon Maruyama & Kosuke Fukuchi & Yuichi Moriguchi, 2015. "A Practical Approach to Screening Potential Environmental Hotspots of Different Impact Categories in Supply Chains," Sustainability, MDPI, vol. 7(9), pages 1-15, August.
    19. van der Zwaan, Bob & Kober, Tom & Calderon, Silvia & Clarke, Leon & Daenzer, Katie & Kitous, Alban & Labriet, Maryse & Lucena, André F.P. & Octaviano, Claudia & Di Sbroiavacca, Nicolas, 2016. "Energy technology roll-out for climate change mitigation: A multi-model study for Latin America," Energy Economics, Elsevier, vol. 56(C), pages 526-542.
    20. Fichter, Tobias & Soria, Rafael & Szklo, Alexandre & Schaeffer, Roberto & Lucena, Andre F.P., 2017. "Assessing the potential role of concentrated solar power (CSP) for the northeast power system of Brazil using a detailed power system model," Energy, Elsevier, vol. 121(C), pages 695-715.

    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:energy:v:91:y:2015:i:c:p:264-273. 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/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.