Marco general para la extracción de información y estimación de radiación solar diaria


Authors

DOI:

https://doi.org/10.22517/23447214.24744

Keywords:

Machine learning, Solar Energy, Principal Component Analysis

Abstract

This article presents a comparative study resulting from the design and simulation of a system of prediction of climatic conditions using machine learning models, in which the results obtained by using a database of environmental conditions are compared with another database generated from the treatment of the data through the analysis by principal components. In the first phase of the study, metadata is generated through the subspaces created with the analysis by principal components, a second phase consists of developing a system of prediction of climatic conditions using several machine learning models, which will use as a resource the original data and metadata generated in the first phase of the study , in the final phase of the study, both results are compared with the aim of observing the behavior of solar radiation inference systems. The proposed data processing strategy allows to extract information from environmental databases facilitating interpretation and observation as a time series of data, additionally, it is possible to build an experimental frame of reference for the inference of solar radiation using different supervised learning techniques on the databases generated.

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Author Biography

Diego Fernando Muñoz Torres, Fundacion Universitaria Tecnologico Comfenalco

My name is Diego Fernando Muñoz Torres, I am an electrical engineer at the Universidad Tecnologica de Pereira, I like the topics related to power electronics, computer science and digital control. I currently work in Cartagena de Indias Colombia, as a plant research teacher, in the Electronic Engineering program.

References

M. Viviana and O. L. Castillo, "Colombian energy planning -Neither for energy, nor for Colombia," Energy Policy, vol. 129, pp. 1132-1142, 2019, doi: https://doi.org/10.1016/j.enpol.2019.03.025.

D. Silva Herranand T. Nakata, "Design of decentralized energy systems for rural electrification in developing countries considering regional disparity," Appl. Energy, vol. 91, no. 1, pp. 130-145, 2012, doi: https://doi.org/10.1016/j.apenergy.2011.09.022.

BP p.l.c., "Statistical Review of World Energy," London, 2018. [Online]. Available: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html.

C. Washburn and M. Pablo-Romero, "Measures to promote renewable energies for electricity generation in Latin American countries," Energy Policy, pp. 212-222, 2019, doi: https://doi.org/10.1016/j.enpol.2018.12.059.

S. E. Hosseini and M. A. Wahid, "Hydrogen production from renewable and sustainable energy resources: Promising green energy carrier forclean development," Renew. Sustain. Energy Rev., vol. 57, pp. 850-866, 2016, doi: https://doi.org/10.1016/j.rser.2015.12.112.

N. Abdelhafidi, N. E. I. Bachari, and Z. Abdelhafidi, "Estimation of solar radiation using stepwise multiple linear regression with principal component analysis in Algeria," Meteorol. Atmos. Phys., vol. 133, no. 2, pp. 205-216, 2021, doi: https://doi.org/10.1007/s00703-020-00739-0.

K. Bouchouicha, N. Bailek, M. E.-S. Mahmoud, J. A. Alonso, A. Slimani, and A. Djaafari, "Estimation of MonthlyAverage Daily Global Solar Radiation Using Meteorological-Based Models in Adrar, Algeria," Appl. Sol. Energy, vol. 54, no. 6, pp. 448-455, 2018, doi: https://doi.org/10.3103/S0003701X1806004X.

X. Zhang and Z. Wei, "A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting," Sustainability , vol. 11, no. 15. 2019, doi: https://doi.org/10.3390/su11154138.

J. Xue, T. L. Yip, B. Wu, C. Wu, and P. H. A. J. M. van Gelder, "A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways: An application to a case in China," Renew. Energy, vol. 172, pp. 897-917, 2021, doi: https://doi.org/10.1016/j.renene.2021.03.084.

K. Bouchouicha, M. A. Hassan, N. Bailek, and N. Aoun, "Estimating the global solar irradiation and optimizing the error estimates under Algerian desert climate," Renew. Energy, vol. 139, pp. 844-858, 2019, doi: https://doi.org/10.1016/j.renene.2019.02.071.

H. B. Tolabi, S.B. M. Ayob, M. H. Moradi, and M. Shakarmi, "New technique for estimating the monthly average daily global solar radiation using bees algorithm and empirical equations," Environ. Prog. Sustain. Energy, vol. 33, no. 3, pp. 1042-1050, Oct. 2014, doi: https://doi.org/10.1002/ep.11858.

U. Waqas, M. F. Ahmed, F. G. Awan, and Z. Hussain, "A Dimensionality Reduction-Based Approach to Select a Suitable Interpolator for the Mapping of Solar Irradiation Across Pakistan," MAPAN, 2021, doi: https://doi.org/10.1007/s12647-021-00435-3.

F. Li et al., "Novel models to estimate hourly diffuse radiation fraction for global radiation based on weather type classification," Renew. Energy, vol. 157, pp. 1222-1232, 2020, doi: https://doi.org/10.1016/j.renene.2020.05.080.

Z. Song etal., "General models for estimating daily and monthly mean daily diffuse solar radiation in China's subtropical monsoon climatic zone," Renew. Energy, vol. 145, pp. 318-332, 2020, doi: https://doi.org/10.1016/j.renene.2019.06.019.

J. Li, Z. Wang, X. Cheng, J. Shuai, C. Shuai, and J. Liu, "Has solar PV achieved the national poverty alleviation goals? Empirical evidence from the performances of 52 villages in rural China," Energy, vol. 201, p. 117631, 2020, doi: https://doi.org/10.1016/j.energy.2020.117631.

K. Ansari, S. K. Panda, and P. Jamjareegulgarn, "Singular spectrum analysis of GPS derived ionospheric TEC variations over Nepal during the low solar activity period," Acta Astronaut., vol. 169, pp. 216-223, 2020, doi: https://doi.org/10.1016/j.actaastro.2020.01.014.

U. Munawar and Z. Wang, "A Framework of Using Machine Learning Approaches for Short-Term Solar Power Forecasting," J. Electr. Eng. Technol., vol. 15, no. 2, pp. 561-569, 2020, doi: https://doi.org/10.1007/s42835-020-00346-4.

H. Bouzgou and C. A. Gueymard, "Fast short-term global solar irradiance forecasting with wrapper mutual information," Renew. Energy, vol. 133, pp. 1055-1065, 2019, doi: https://doi.org/10.1016/j.renene.2018.10.096.

P. Chung et al., "An intelligent control strategy for energy storage systems in solar power generation based on long-short-term power prediction," in 2020 8th International Electrical Engineering Congress (iEECON), 2020, pp. 1-4, doi: https://doi.org/10.1109/iEECON48109.2020.229485.

A. Bakdi, W. Bounoua, S. Mekhilef, and L. M. Halabi, "Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV," Energy, vol. 189, p. 116366, 2019, doi: https://doi.org/10.1016/j.energy.2019.116366

S. Bandong, E. Leksono, A. Purwarianti, and E. Joelianto, "Performance Ratio Estimation and Prediction of Solar Power Plants Using Machine Learning to Improve Energy Reliability," in 2019 6th International Conference on Instrumentation, Control, and Automation (ICA), 2019, pp. 36-41, doi: https://doi.org/10.1109/ICA.2019.8916687.

J. Zhang, Y. Chi, and L. Xiao, "Solar Power Generation Forecast Based on LSTM," in 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), 2018, pp. 869-872, doi:

https://doi.org/10.1109/ICSESS.2018.8663788.

I. Jolliffe, "Principal Component Analysis," in International Encyclopedia of Statistical Science, M. Lovric, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 1094-1096, doi: https://doi.org/10.1007/978-3-642-04898-2_455

C. M. Bishop, Pattern recognition and machine learning. New York : Springer, [2006] ©2006.

C. Bishop, "Machine learning and the learning machine with Dr. Christopher Bishop," Microsoft blog editor, 2018. https://www.microsoft.com/en-us/research/blog/machine-learning-and-the-learning-machine-with-dr-christopher-bishop/.

S. Balakrishnama and A. Ganapathiraju, "Institute For Signal And Information Processing Linear Discriminant Analysis-A Brief Tutorial."

A. J. Izenman, "Linear Discriminant Analysis," in Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning, New York, NY: Springer New York, 2008, pp. 237-280, doi: https://doi.org/10.1007/978-0-387-78189-1_8

Y. Qin, "A review of quadratic discriminant analysis for high-dimensional data," WIREs Comput. Stat. , vol. 10, no. 4, p. e1434, Jul. 2018, doi: https://doi.org/10.1002/wics.1434.

H. Zhang and J. Su, "Naive Bayesian Classifiers for Ranking," in Machine Learning: ECML 2004, 2004, pp. 501-512, doi: https://doi.org/10.1007/978-3-540-30115-8_46.

S. Suthaharan, "Support Vector Machine," in Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning, Boston, MA: Springer US, 2016, pp. 207-235, doi:https://doi.org/10.1007/978-1-4899-7641-3_9.

Z. Zhang, "Introduction to machine learning: k-nearest neighbors," Ann. Transl. Med., vol. 4, no. 11, p. 218, Jun. 2016, doi: https://doi.org/10.21037/atm.2016.03.37.

T. G. Dietterich, "Ensemble Methods in Machine Learning," in Multiple Classifier Systems, 2000, pp. 1-15, doi: https://doi.org/10.1007/3-540-45014-9_1.

"MERRA -www.soda-pro.com." http://www.soda-pro.com/web-services/meteo-data/merra (accessed Jun. 02, 2021).

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Published

2022-03-28

How to Cite

Muñoz Torres, D. F., Montoya Giraldo, O. D., & Sabach Matos, S. A. (2022). Marco general para la extracción de información y estimación de radiación solar diaria. Scientia Et Technica, 27(1), 61–70. https://doi.org/10.22517/23447214.24744

Issue

Vol. 27 No. 1 (2022): Enero-Marzo 2022

Section

Bioingeniería

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