Impact of feature selection on the prediction of global horizontal irradiation under ouarzazate city climate

Salma Benchikh; Tarik Jarou; Lamrani  Roa; Nasri  Elmehdi

doi:10.56294/dm2024363

Authors

Benchikh Salma Advanced systems engineering laboratory. Ibn Tofail University, Kenitra, Morocco Author
Jarou Tarik Advanced systems engineering laboratory. Ibn Tofail University, Kenitra, Morocco Author
Lamrani Roa Advanced systems engineering laboratory. Ibn Tofail University, Kenitra, Morocco Author
Nasri Elmehdi Advanced systems engineering laboratory. Ibn Tofail University, Kenitra, Morocco Author

DOI:

https://doi.org/10.56294/dm2024363

Keywords:

Feature Selection, Machine Learning Algorithms, Solar Irradiation, GHI, Prediction

Abstract

Ensuring accurate forecasts of Global Horizontal Irradiance (GHI) stands as a pivotal aspect in optimizing the efficient utilization of solar energy resources. Machine learning techniques offer promising prospects for predicting global horizontal irradiance. However, within the realm of machine learning, the importance of feature selection cannot be overestimated, as it is crucial in determining performance and reliability of predictive models. To address this, a comprehensive machine learning algorithm has been developed, leveraging advanced feature importance techniques to forecast GHI data with precision. The proposed models draw upon historical data encompassing solar irradiance characteristics and environmental variables within the Ouarzazate region, Morocco, spanning from 1st January 2018, to 31 December 2018, with readings taken at 60-minute intervals. The findings underscore the profound impact of feature selection on enhancing the predictive capabilities of machine learning models for GHI forecasting. By identifying and prioritizing the most informative features, the models exhibit significantly enhanced accuracy metrics, thereby bolstering the reliability, efficiency, and practical applicability of GHI forecasts. This advancement not only holds promise for optimizing solar energy utilization but also contributes to the broader discourse on leveraging machine learning for renewable energy forecasting and sustainability initiatives

References

1. Boutahir, M.K., Farhaoui, Y., Azrour, M., Zeroual, I., El Allaoui, A.: Effect of feature selection on the prediction of direct normal irradiance. Big Data Mining and Analytics 5 (2022) https://doi.org/10.26599/bdma.2022.9020003 DOI: https://doi.org/10.26599/BDMA.2022.9020003

2. BENCHIKH, S., JAROU, T., BOUTAHIR, M.K., NASRI, E., LAMRANI, R.: Improving photovoltaic system performance with artificial neural network control. Data and Metadata 2 (2023) https://doi.org/10.56294/dm2023144 DOI: https://doi.org/10.56294/dm2023144

3. Boutahir, M.K., Hessane, A., Lasri, I., Benchikh, S., Farhaoui, Y., Azrour, M.: Dynamic threshold fine-tuning in anomaly severity classification for enhanced solar power optimization. Data and Metadata 2 (2023) https://doi.org/10.56294/ dm202394 DOI: https://doi.org/10.56294/dm202394

4. Ono, K., Kunii, M., Honda, Y.: The regional model-based mesoscale ensemble prediction system. Quarterly Journal of the Royal Meteorological Society 147 (2020) https://doi.org/10.1002/qj.3928 DOI: https://doi.org/10.1002/qj.3928

5. Liu, C., Li, M., Yu, Y., Wu, Z., Gong, H., Cheng, F.: A review of multitemporal and multispatial scales photovoltaic forecasting methods. IEEE Access 10 (2022) https://doi.org/10.1109/access.2022.3162206 DOI: https://doi.org/10.1109/ACCESS.2022.3162206

6. Zhou, Y., Liu, Y., Wang, D., Liu, X., Wang, Y.: A review on global solar radiation prediction with machine learning models in a comprehensive perspective. Energy Conversion and Management 235 (2021) https://doi.org/10.1002/qj.3928 DOI: https://doi.org/10.1016/j.enconman.2021.113960

7. Rodríguez, F., Azc´arate, I., Vadillo, J., Galarza, A.: Forecasting intra-hour solar photovoltaic energy by assembling wavelet based time-frequency analysis with deep learning neural networks. International Journal of Electrical Power amp; Energy Systems 137 (2022) https://doi.org/10.1016/j.ijepes.2021.107777 DOI: https://doi.org/10.1016/j.ijepes.2021.107777

8. Solano, E.S., Dehghanian, P., Affonso, C.M.: Solar radiation forecasting using machine learning and ensemble feature selection. Energies 15 (2022) https://doi.org/10.3390/en15197049 DOI: https://doi.org/10.3390/en15197049

9. Trang, T.-T., Ma, T., Do, T.-N.: Lorap: Local deep neural network for solar radiation prediction. Communications in Computer and Information Science (2023) https://doi.org/10.1007/978-981-99-8296-726 DOI: https://doi.org/10.1007/978-981-99-8296-7_26

10. Huang, N., Li, R., Lin, L., Yu, Z., Cai, G.: Low redundancy feature selection of short term solar irradiance prediction using conditional mutual information and gauss process regression. Sustainability 10 (2018) https://doi.org/10.3390/SU10082889 DOI: https://doi.org/10.3390/su10082889

11. Takamatsu, T., Ohtake, H., Oozeki, T., Nakaegawa, T., Honda, Y., Kazumori, M.: Regional solar irradiance forecast for kanto region by support vector regression using forecast of meso-ensemble prediction system. Energies 14 (2021) https://doi.org/10.3390/en14113245 DOI: https://doi.org/10.3390/en14113245

12. O. Santos, D.S., al.: Solar irradiance forecasting using dynamic ensemble selection. Applied Sciences 12(7) (2022) https://doi.org/10.3390/app12073510 DOI: https://doi.org/10.3390/app12073510

13. Castangia, M., Aliberti, A., Bottaccioli, L., Macii, E., Patti, E.: A compound of feature selection techniques to improve solar radiation forecasting. Expert Systems With Applications 178 (2021) https://doi.org/10.1016/J.ESWA.2021.114979 DOI: https://doi.org/10.1016/j.eswa.2021.114979

14. Puga-Gil, D., Astray, G., Barreiro, E.W., G´alvez, J.F., ., J.C.M.: Global solar irradiation modelling and prediction using machine learning models for their potential use in renewable energy applications. Mathematics 10 (2022) https://doi.org/10.3390/math10244746 DOI: https://doi.org/10.3390/math10244746

15. Bamisile, O., Oluwasanmi, A., Ejiyi, C., Yimen, N., Obiora, S., Huang, Q.: Comparison of machine learning and deep learning algorithms for hourly global/diffuse solar radiation predictions. Int. J. Energy Res 46 (2021) https://doi.org/10.1002/er.65295 DOI: https://doi.org/10.1002/er.6529

16. Pombo, D.V., Bindner, H.W., Spataru, S.V., Sørensen, P.E., Bacher, P.I.: Increasing the accuracy of hourly multi-output solar power forecast with physics-informed machine learning. Sensors 2 22 (2022) https://doi.org/10.3390/ s22030749 DOI: https://doi.org/10.3390/s22030749

17. Cha, J., Kim, M.K., Lee, S., Kim, K.S.: Investigation of applicability of impact factors to estimate solar irradiance: Comparative analysis using machine learning algorithms. Applied Sciences 11 (2021) https://doi.org/10.3390/app11188533 DOI: https://doi.org/10.3390/app11188533

18. Gb´emou, S., Eynard, J., Thil, S., Guillot, E., Grieu, S.: A comparative study of machine learning-based methods for global horizontal irradiance forecasting. Energies, 14 (2021) https://doi.org/10.3390/en14113192 DOI: https://doi.org/10.3390/en14113192

19. Tovar, M., Robles, M., Rashid, F.: Pv power prediction, using cnn-lstm hybrid neural network model. case of study: Temixco-morelos, m´exico. Energies, 13 (2020) https://doi.org/10.3390/en13246512 DOI: https://doi.org/10.3390/en13246512

20. Wang, Y., Feng, B., Hua, Q.S., Sun, L.: Short-term solar power forecasting: A combined long short-term memory and gaussian process regression method. Sustainability, 13 (2021) https://doi.org/10.3390/su13073665 DOI: https://doi.org/10.3390/su13073665

21. Konstantinou, M., Peratikou, S., Charalambides, A.G.: Solar photovoltaic forecasting of power output using lstm networks. Atmosphere, 12 (2021) https://doi.org/10.3390/atmos12010124 DOI: https://doi.org/10.3390/atmos12010124

22. Perez-Astudillo, Bachour, D.: Dni, ghi and dhi ground measurements in doha, qatar. Energy Procedia, 49 (2014) https://doi.org/10.1016/j.egypro.2014.03.254 DOI: https://doi.org/10.1016/j.egypro.2014.03.254

23. Salter, S.H.: Cloud Albedo Enhancement for Solar Radiation Management. Springer, Boston. https://doi.org/10.1007/springerreference308778

24. Takamatsu, T., Ohtake, H., Oozeki, T.: Support vector quantile regression for the post-processing of meso-scale ensemble prediction system data in the kanto region: Solar power forecast reducing overestimation. Energies 15 (2022) https://doi.org/10.3390/en15041330 DOI: https://doi.org/10.3390/en15041330

25. Saha, P., al.: Novel multimodal emotion detection method using electroencephalogram and electrocardiogram signals,” biomedical signal processing and control 92 (2024) https://doi.org/10.1016/j.bspc.2024.106002 DOI: https://doi.org/10.1016/j.bspc.2024.106002

26. Shemirani, A.B., Lawaf, M.P.: Prediction of tensile strength of concrete using the machine learning methods. Asian Journal of Civil Engineering 92 (2023) https://doi.org/10.1007/s42107-023-00837-5 DOI: https://doi.org/10.1007/s42107-023-00837-5

27. Arai, K. (ed.): Intelligent Computing. Springer. https://doi.org/10.34133/icomputing DOI: https://doi.org/10.34133/icomputing