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

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  • 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
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    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. Rodríguez, Eduardo & Droguett, Enrique López & Cardemil, José M. & Starke, Allan R. & Cornejo-Ponce, Lorena, 2024. "Enhancing the estimation of direct normal irradiance for six climate zones through machine learning models," Renewable Energy, Elsevier, vol. 231(C).
    6. 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.

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