A Proposed Model to Forecast Hourly Global Solar Irradiation Based on Satellite Derived Data, Deep Learning and Machine Learning Approaches

• Autorzy: Benamrou Badr , Ouardouz Mustapha , Allaouzi Imane , Ben Ahmed Mohamed

Identyfikatory

DOI

10.12911/22998993/119795

• Języki publikacji: EN

Abstrakty

EN

An accurate short-term global solar irradiation (GHI) forecast is essential for integrating the photovoltaic systems into the electricity grid by reducing some of the problems caused by the intermittency of solar energy, including rapid fluctuations in energy, management storage, and the high costs of electricity. In this paper, the authors proposed a new hybrid approach to forecast hourly GHI for the Al-Hoceima city, Morocco. For this purpose, a deep long short-term memory network is trained on a combination of the hourly GHI ground measurements from the meteorological station of Al-Hoceima and the satellite-derived GHI from the neighbouring pixels of the point of interest. Xgboost, Random Forest, and Recursive Feature Elimination with cross-validation were used to select the most relevant features, the lagged satellite-derived GHI around the point of interest, as input to the proposed model where the best forecasting model is selected using the Grid Search algorithm. The simulation and results showed that the proposed approach gives high performance and outperformed other benchmark approaches.

Słowa kluczowe

EN

solar energy; forecast; global solar irradiation; satellite-derived data; GHI; deep learning

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2020
• Tom: Vol. 21, nr 4
• Strony: 26--38
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Benamrou Badr

• Department of Mechanical Engineering, Faculty of Sciences and Techniques, University Abdelmalek Essaadi, Tangier, Morocco

autor

Ouardouz Mustapha

• Department of Mechanical Engineering, Faculty of Sciences and Techniques, University Abdelmalek Essaadi, Tangier, Morocco

autor

Allaouzi Imane

• Department of Computer Science, List laboratory, Faculty of Sciences and Techniques, University Abdelmalek Essaadi, Tangier, Morocco

autor

Ben Ahmed Mohamed

• Department of Computer Science, List laboratory, Faculty of Sciences and Techniques, University Abdelmalek Essaadi, Tangier, Morocco

Bibliografia

• 1. Alzahrani A.. Shamsi P.. Dagli C.. Ferdowsi M. 2017. Solar irradiance forecasting using deep neural networks. Procedia Computer Science. 114. 304–313.
• 2. Ameen B.. Balzter H.. Jarvis C. and Wheeler J.S. 2019. Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks. Energies. 12(1). 148.
• 3. Benali L.. Notton G.. Fouilloy A.. Voyant C.. and Dizene R. 2019. Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam. horizontal diffuse and global components. Renewable Energy. 132. 871–884.
• 4. Benmouiza K.. and Cheknane A. 2013. Forecasting hourly global solar radiation using hybrid kmeans and nonlinear autoregressive neural network models”. Energy Conversion and Management. 75. 561–569.
• 5. Breiman L. 2001. Random forests. Machine learning. 45. 5–32.
• 6. Chen T. and Guestrin C. 2016. XGBoost: A scalable tree boosting system. In: Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16. New York. NY. USA. ACM. 785–794.
• 7. Crisosto C.. Hofmann M.. Mubarak R.. Seckmeyer G.. 2018. One-Hour Prediction of the Global Solar Irradiance from All-Sky Images Using Artificial Neural Networks. Energies. 11. 2906.
• 8. Friedman J. 2001. Greedy boosting approximation: a gradient boosting machine. Ann. Stat. 29. 1189–1232.
• 9. Freund Y. and Schapire R. 1997. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55. 119–139.
• 10. Hochreiter S.. Schmidhuber J.. 1997. Long Short Memory. Neural Computation. 9. 1735–1780.
• 11. Huang X.. Shi J.. Gao B.. Tai Y.. Chen Z. and Zhang J. 2019. Forecasting Hourly Solar Irradiance Using Hybrid Wavelet Transformation and Elman Model in Smart Grid. IEEE Access. 7. 139909–139923.
• 12. Ji W. and Chan. C. K. 2011. Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN. Solar Energy. 85(5). 808–817.
• 13. Loutfi H. . Bernatchou A.. Tadili R. 2017. Generation of Horizontal Hourly Global Solar Radiation From Exogenous Variables Using an Artificial Neural Network in Fes (Morocco). international journal of renewable energy research. 7. No.3. 1097–1107.
• 14. Mazorra Aguiar L.. Pereira B. Lauret. P. Díaz. F. David. M. 2016. Combining solar irradiance measurements. satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting. Renewable Energy. 97. 599–610.
• 15. Pan C. and Tan J.. 2019. Day-Ahead Hourly Forecasting of Solar Generation Based on Cluster Analysis and Ensemble Model. IEEE Access. 7. 112921–112930.
• 16. Pavlovski A.. Kostylev V. 2011. Solar power forecasting performance towards industry standards. 1st International Workshop on the Integration of Solar Power into Power Systems Aarhus. Denmark.
• 17. Qu Z.. Oumbe A.. Blanc P.. Espinar B.. Gesell G.. Gschwind B.. Klüser L.. Lefèvre M.. Saboret L.. Schroedter-Homscheidt M.. and Wald. L. 2017. Fast radiative transfer parameterisation for assessing the surface solar irradiance: The Heliosat-4 method. Meteorol. Z.. 26. 33–57.
• 18. Schapire R.E.. 1990. The strength of weak learnability. Machine Learning. 2. 5. 197–227.
• 19. Smola A.J.. Scholkopf B.. 2004. A tutorial on support vector regression. Statistics and computing 14. 199–222.
• 20. Urraca. R.. Antonanzas. J.. Martinez. M.A.. Martinez-de-Pison. F.J.. Torres. F.A. 2016. Smart baseline models for solar irradiation forecasting. Energy Convers. Manag.. 108. 539–548.
• 21. Vapnik V.N.. 1995. The nature of statistical learning theory. New York: Springer.
• 22. Voyant C.. Notton G.. Kalogirou S.. Nivet M-L.. Paoli C.. Motte F.. Fouilloy A. 2016. Machine Learning methods for solar radiation forecasting: a review. Renew. Energy. 105. 569–582.
• 23. Xiangyun Q. and Yugang N. 2018. Hourly dayahead solar irradiance prediction using weather forecasts by lstm. Energy. 148. 461–468.
• 24. Yu Y.. Cao J. and Zhu J. 2019. An LSTM ShortTerm Solar Irradiance Forecasting Under Complicated Weather Conditions. IEEE Access. 7. 145651–145666.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).