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Deep Neural Networks for Global Horizontal Irradiation Forecasting: A Comparative Study

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Smart Cities ( ICSC-CITIES 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1706))

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Abstract

In order to contribute to the integration of photovoltaic renewable energy into power system, this paper addresses the problem of forecasting solar irradiance or Global Horizontal Irradiation (GHI). The collection, adjustment and processing of meteorological data used as input is carried out, in addition various Deep Neural Networks (DNN) models are implemented and analyzed, among which are the Artificial Neural Networks (ANN) of type as Transformer, LSTM, GRU, and mixed between Convolutional ANN (CNN)-LSTM, and CNN-GRU. These ANN variants are implemented, and a comparative study are made. Finally, the results obtained show that the ANN transformer has less error in the GHI forecasting.

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References

  1. Morales Ramírez, D., Luyando Cuevas, J.R.: Analisis del Consumo de Energia Electrica Residencial en el Area Metropolitana de Monterrey, vol. 31, no. 62, Art. n.o 62

    Google Scholar 

  2. REN21, Renewables Global Status Report, Paris, France, 22 March 2022. https://www.ren21.net/reports/global-status-report/. Accessed 22 Mar 2022

  3. Demanda de energía eléctrica en tiempo real, estructura de generación y emisiones de CO2, REE, 22 March 2022. https://demanda.ree.es/visiona/seleccionar-sistema. Accessed 22 Mar 2022

  4. Robledo Quintero, Á.: Predicción de la radiación global utilizando redes de neuronas artificiales, July 2018). https://e-archivo.uc3m.es/handle/10016/29002. Accessed 22 Mar 2022

  5. de Santos Jr, D.S.O., et al.: Solar irradiance forecasting using dynamic ensemble selection. Appl. Sci. 12(7), 3510 (2022)

    Google Scholar 

  6. Aliberti, A., Fucini, D., Bottaccioli, L., Macii, E., Acquaviva, A., Patti, E.: Comparative analysis of neural networks techniques to forecast global horizontal irradiance. IEEE Access 9, 122829–122846 (2021). https://doi.org/10.1109/ACCESS.2021.3110167

  7. Géron, A.: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, Inc., Sebastopol (2019)

    Google Scholar 

  8. Liu, C.-H., Gu, J.-C., Yang, M.-T.: A simplified LSTM neural networks for one day-ahead solar power forecasting. IEEE Access 9, 17174–17195 (2021). https://doi.org/10.1109/ACCESS.2021.3053638

  9. Aslam, M., Lee, S.J., Khang, S.H., Hong, S.: Two-stage attention over LSTM with Bayesian optimization for day-ahead solar power forecasting. IEEE Access 9, 107387–107398 (2021). https://doi.org/10.1109/ACCESS.2021.3100105

  10. Yu, Y., Cao, J., Zhu, J.: An LSTM short-term solar irradiance forecasting under complicated weather conditions. IEEE Access 7, 145651–145666 (2019). https://doi.org/10.1109/ACCESS.2019.2946057

  11. Konstantinou, M., Peratikou, S., Charalambides, A.G.: Solar photovoltaic forecasting of power output using LSTM networks. Atmosphere 12(1), Art. n.o 1 (2021). https://doi.org/10.3390/atmos12010124

  12. Jalali, S.M.J., Ahmadian, S., Kavousi-Fard, A., Khosravi, A., Nahavandi, S.: Automated Deep CNN-LSTM architecture design for solar irradiance forecasting. IEEE Trans. Syst. Man Cybern. Syst. 52(1), Art. n.o 1 (2022). https://doi.org/10.1109/TSMC.2021.3093519

  13. Wang, F., Yu, Y., Zhang, Z., Li, J., Zhen, Z., Li, K.: Wavelet decomposition and convolutional LSTM networks based improved deep learning model for solar irradiance forecasting. Appl. Sci. 8(8), Art. n.o 8 (2018). https://doi.org/10.3390/app8081286

  14. Prado-Rujas, I.-I., García-Dopico, A., Serrano, E., Pérez, M.S.: A flexible and robust deep learning-based system for solar irradiance forecasting. IEEE Access 9, 12348–12361(2021). https://doi.org/10.1109/ACCESS.2021.3051839

  15. Khan, W., Walker, S., Zeiler, W.: Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach. Energy 240, 122812 (2022). https://doi.org/10.1016/j.energy.2021.122812

  16. Abdel-Nasser, M., Mahmoud, K., Lehtonen, M.: HIFA: promising heterogeneous solar irradiance forecasting approach based on kernel mapping. IEEE Access 9, 144906–144915 (2021). https://doi.org/10.1109/ACCESS.2021.3122826

  17. Katrompas, A., Ntakouris, T., Metsis, V.: Recurrence and self-attention vs the transformer for time-series classification: a comparative study. In: International Conference on Artificial Intelligence in Medicine, 2022, pp. 99–109 (2022)

    Google Scholar 

  18. Cristian Arbelaez-Duque, A.D.: Solar Forecasting Elec Eng UdeA. Medellin, 25 July 2022. https://github.com/cdarbelaez/SolarForecastingElecEngUdeA. Accessed 23 Sep 2022

  19. Romero Rodríguez, J.M.: Modelo para predicción de potencia de paneles fotovoltaicos utilizando técnicas de clasificación no supervisada y redes neuronales artificiales (2020). https://manglar.uninorte.edu.co/handle/10584/9049. Accessed 22 Mar 2022

  20. Sharda, S., Singh, M., Sharma, K.: RSAM: robust self-attention based multi-horizon model for solar irradiance forecasting. IEEE Trans. Sustain. Energy 12(2), 1394–1405 (2021). https://doi.org/10.1109/TSTE.2020.3046098

  21. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017). https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf

  22. Huang, X., Zhang, C., Li, Q., Tai, Y., Gao, B., Shi, J.: A comparison of hour-ahead solar irradiance forecasting models based on LSTM network. Math. Probl. Eng. 2020, e4251517 (2020). https://doi.org/10.1155/2020/4251517

  23. Ashfaq, Q., Ulasyar, A., Zad, H.S., Nisar, S., Khattak, A., Imran, K.: Multi-step forecasting of global horizontal irradiance using long short-term memory network for solving economic dispatch problem. In: 2021 International Conference on Innovative Computing (ICIC), pp. 1–9 (2021). https://doi.org/10.1109/ICIC53490.2021.9693031

  24. Vakitbilir, N., Hilal, A., Direkoğlu, C.: Hybrid deep learning models for multivariate forecasting of global horizontal irradiation. Neural Comput. Appl. ene. (2022). https://doi.org/10.1007/s00521-022-06907-0

  25. GeoSUN Africa: Kenya - Solar Radiation Measurement Data, EnergyData.info (2020). https://energydata.info/dataset/kenya-solar-radiation-measurement-data. Accessed 23 Sep 2022

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Funding

This research was funded by the Colombia Scientific Program within the framework of the so-called Ecosistema Científico (Contract No. FP44842-218-2018).

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Authors and Affiliations

  1. Universidad de Antioquia, Calle 67 #53-108, CP 050010, Medellín, Colombia

    Cristian Arbeláez-Duque, Alejandro Duque-Ciro, Walter Villa-Acevedo & Álvaro Jaramillo-Duque

Authors
  1. Cristian Arbeláez-Duque
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  2. Alejandro Duque-Ciro
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  3. Walter Villa-Acevedo
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  4. Álvaro Jaramillo-Duque
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Corresponding author

Correspondence to Álvaro Jaramillo-Duque .

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Editors and Affiliations

  1. Universidad de la República, Montevideo, Uruguay

    Sergio Nesmachnow

  2. Universidad de Valladolid, Soria, Soria, Spain

    Luis Hernández Callejo

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Arbeláez-Duque, C., Duque-Ciro, A., Villa-Acevedo, W., Jaramillo-Duque, Á. (2023). Deep Neural Networks for Global Horizontal Irradiation Forecasting: A Comparative Study. In: Nesmachnow, S., Hernández Callejo, L. (eds) Smart Cities. ICSC-CITIES 2022. Communications in Computer and Information Science, vol 1706. Springer, Cham. https://doi.org/10.1007/978-3-031-28454-0_6

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  • DOI: https://doi.org/10.1007/978-3-031-28454-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28453-3

  • Online ISBN: 978-3-031-28454-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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