Aplicaciones y Desafíos de la Inteligencia Artificial en la Predicción de la Radiación Solar: Una Revisión Sistemática
DOI:
https://doi.org/10.55761/abclima.v36i21.18872Palabras clave:
Aprendizaje automático, Aprendizaje profundo, Modelo de predicción, Redes neuronales artificialesResumen
La previsión precisa de la radiación solar es esencial para diversos ámbitos, como la energía solar y la agricultura. La Inteligencia Artificial (IA) ha demostrado ser una poderosa herramienta para mejorar estas previsiones. Este estudio analizó diversas investigaciones que empleanIA para predecir la radiación solar. Las técnicas más comunes incluyen redes neuronales, máquinas de vectores de soporte y aprendizaje profundo. Estas técnicas son capaces de identificar patrones complejos en los datos y relacionar la radiación solar con factores como la temperatura, la humedad y la nubosidad. Los modelos de IA son entrenadoscon grandes conjuntos de datos meteorológicos y de radiación solar, lo que les permite generar predicciones más precisas. Las métricas empleadas para evaluar el desempeño de estos modelos incluyen el error cuadrático medio (RMSE), el coeficiente de determinación (R²) y el error porcentual medio absoluto (MAPE). La revisión demuestra que la IA posee un altopotencial para optimizar la predicción de la radiación solar. Predicciones más precisas pueden optimizar la producción de energía solar, mejorar la gestión del riego y contribuir a otros procesos que dependen de la radiación solar.
Descargas
Citas
ADEDEJI, P. A. et al. Evolutionary-based neurofuzzy model with wavelet decomposition for global horizontal irradiance medium-term prediction. Journal of Ambient Intelligence and Humanized Computing, 14 jan. 2022. DOI: https://doi.org/10.1007/s12652-021-03639-2
AL-ROUSAN, N.; AL-NAJJAR, H. A Comparative Assessment of Time Series Forecasting Using NARX and SARIMA to Predict Hourly, Daily, and Monthly Global Solar Radiation Based on Short-Term Dataset. Arabian Journal for Science and Engineering, 4 maio. 2021. DOI: https://doi.org/10.1007/s13369-021-05669-6
ALKAHTANI, H.; ALDHYANI, T. H. H.; ALSUBARI, S. N. Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems. Sustainability, v. 15, n. 8, p. 6973, 21 abr. 2023. DOI: https://doi.org/10.3390/su15086973
ALASSERY, F. et al. An artificial intelligence-based solar radiation prophesy model for green energy utilization in energy management system. Sustainable Energy Technologies and Assessments, v. 52, p. 102060, ago. 2022. DOI: https://doi.org/10.1016/j.seta.2022.102060
ALIZAMIR, M. et al. A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions. Energy, v. 197, p. 117239, abr. 2020. DOI: https://doi.org/10.1016/j.energy.2020.117239
ALIZAMIR, M. et al. Improving the accuracy of daily solar radiation prediction by climatic data using an efficient hybrid deep learning model: Long short-term memory (LSTM) network coupled with wavelet transform. Engineering Applications of Artificial Intelligence, v. 123, p. 106199, ago. 2023. DOI: https://doi.org/10.1016/j.engappai.2023.106199
CHEN, Y. et al. Proactively selection of input variables based on information gain factors for deep learning models in short-term solar irradiance forecasting. Energy, v. 284, p. 129261, 1 dez. 2023. DOI: https://doi.org/10.1016/j.energy.2023.129261
FAN, J. et al. Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China. Energy Conversion and Management, v. 164, p. 102–111, 15 maio 2018. DOI: https://doi.org/10.1016/j.enconman.2018.02.087
FENG, Y. et al. Comparison of artificial intelligence and empirical models for estimation of daily diffuse solar radiation in North China Plain. v. 42, n. 21, p. 14418–14428, 1 maio 2017. DOI: https://doi.org/10.1016/j.ijhydene.2017.04.084
GÜREL, A. E. et al. A state of art review on estimation of solar radiation with various models. Heliyon, p. e13167, jan. 2023. DOI: https://doi.org/10.1016/j.heliyon.2023.e13167
HEDAR, Abdel-Rahman et al. Hybrid Machine Learning for Solar Radiation Prediction in Reduced Feature Spaces. Energies, v. 14, n. 23, p. 7970, 1 jan. 2021. DOI: https://doi.org/10.3390/en14237970
HUANG, J.; LIU, H. A hybrid decomposition-boosting model for short-term multi-step solar radiation forecasting with NARX neural network. Journal of Central South University, v. 28, n. 2, p. 507–526, 1 fev. 2021. DOI: https://doi.org/10.1007/s11771-021-4618-9
JAFRI, N.; T., M. ; AHAD, A. The role of artificial intelligence in solar harvesting, storage, and conversion. Disponível em: <https://www.sciencedirect.com/science/article/abs/pii/B9780323906012000106>. Acesso em: 21 jul. 2023. DOI: https://doi.org/10.1016/B978-0-323-90601-2.00010-6
AIMEUR, K.; SAOUD, L. S.; GHORBANI, R. Short-Term Solar Irradiance Forecasting and Photovoltaic System Management Using Octonion Neural Networks. Applied Solar Energy, v. 56, n. 3, p. 219–226, maio 2020. DOI: https://doi.org/10.3103/S0003701X20030020
KHOSRAVI, A. et al. Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms. Journal of Cleaner Production, v. 176, p. 63–75, mar. 2018 a. DOI: https://doi.org/10.1016/j.jclepro.2017.12.065
KHOSRAVI, A. et al. Comparison of artificial intelligence methods in estimation of daily global solar radiation. Journal of Cleaner Production, v. 194, p. 342–358, set. 2018 b. DOI: https://doi.org/10.1016/j.jclepro.2018.05.147
KHOSRAVI, A. et al. Energy modeling of a solar dish/Stirling by artificial intelligence approach. Energy Conversion and Management, v. 199, p. 112021, 1 nov. 2019. DOI: https://doi.org/10.1016/j.enconman.2019.112021
KOSOVIC, I. N.; MASTELIC, T.; IVANKOVIC, D. Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis. Journal of Cleaner Production, v. 266, p. 121489, set. 2020. DOI: https://doi.org/10.1016/j.jclepro.2020.121489
LIMA, M. A. F.B. et al. Improving solar forecasting using Deep Learning and Portfolio Theory integration. Energy, v. 195, p. 117016, mar. 2020. DOI: https://doi.org/10.1016/j.energy.2020.117016
LOU, S. et al. Prediction of diffuse solar irradiance using machine learning and multivariable regression. Applied Energy, v. 181, p. 367–374, nov. 2016. DOI: https://doi.org/10.1016/j.apenergy.2016.08.093
MALIK, P. et al. A Review on ANN Based Model for Solar Radiation and Wind Speed Prediction with Real-Time Data. Archives of Computational Methods in Engineering, v. 29, n. 5, p. 3183–3201, 6 jan. 2022. DOI: https://doi.org/10.1007/s11831-021-09687-3
MCCANDLESS, T.C.; HAUPT, S.E.; YOUNG, G.S. A model tree approach to forecasting solar irradiance variability. Solar Energy, v. 120, p. 514–524, out. 2015. DOI: https://doi.org/10.1016/j.solener.2015.07.020
MEHDIZADEH, S.; BEHMANESH, J.; KHALILI, K. Comparison of artificial intelligence methods and empirical equations to estimate daily solar radiation. Journal of Atmospheric and Solar-Terrestrial Physics, v. 146, p. 215–227, ago. 2016. DOI: https://doi.org/10.1016/j.jastp.2016.06.006
ALIZAMIR, M. et al. A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique. Sustainability, v. 15, n. 14, p. 11275–11275, 19 jul. 2023. DOI: https://doi.org/10.3390/su151411275
MOHAMMADI, B. et al. Application of ERA-Interim, empirical models, and an artificial intelligence-based model for estimating daily solar radiation. Ain Shams Engineering Journal, v. 13, n. 1, p. 101498, 1 jan. 2022. DOI: https://doi.org/10.1016/j.asej.2021.05.012
NAWAB, F. et al. Solar irradiation prediction using empirical and artificial intelligence methods: A comparative review. Heliyon, v. 9, n. 6, p. e17038, 1 jun. 2023. DOI: https://doi.org/10.1016/j.heliyon.2023.e17038
NESHAT, M. et al. Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy. Energy, v. 278, p. 127701, 1 set. 2023. DOI: https://doi.org/10.1016/j.energy.2023.127701
OLABI, A.G. et al. Application of artificial intelligence for prediction, optimization, and control of thermal energy storage systems. Thermal Science and Engineering Progress, p. 101730, fev. 2023. DOI: https://doi.org/10.1016/j.tsep.2023.101730
OLATOMIWA, Lanre et al. A support vector machine–firefly algorithm-based model for global solar radiation prediction. Solar Energy, v. 115, p. 632–644, maio 2015. DOI: https://doi.org/10.1016/j.solener.2015.03.015
RAJASUNDRAPANDIYANLEEBANON, T. et al. Solar Energy Forecasting Using Machine Learning and Deep Learning Techniques. Archives of Computational Methods in Engineering, 24 fev. 2023. DOI: https://doi.org/10.1007/s11831-023-09893-1
SAYED, E. T. et al. Application of artificial intelligence techniques for modeling, optimizing, and controlling desalination systems powered by renewable energy resources. Journal of Cleaner Production, v. 413, p. 137486, 10 ago. 2023. DOI: https://doi.org/10.1016/j.jclepro.2023.137486
MOUSAVI, S. M.; MOSTAFAVI, E. S.; JIAO, P. Next generation prediction model for daily solar radiation on horizontal surface using a hybrid neural network and simulated annealing method. Energy Conversion and Management, v. 153, p. 671–682, 1 dez. 2017. DOI: https://doi.org/10.1016/j.enconman.2017.09.040
SENGUPTA, M. et al. The National Solar Radiation Data Base (NSRDB). Renewable and Sustainable Energy Reviews, v. 89, p. 51-60, 2018. DOI: https://doi.org/10.1016/j.rser.2018.03.003
GESHNIGANI, F. S. et al. Daily solar radiation estimation in Belleville station, Illinois, using ensemble artificial intelligence approaches. Engineering Applications of Artificial Intelligence, v. 120, p. 105839, abr. 2023. DOI: https://doi.org/10.1016/j.engappai.2023.105839
SUDHARSHAN, K. et al. Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction. Energies, v. 15, n. 17, p. 6267, 1 jan. 2022. DOI: https://doi.org/10.3390/en15176267
TAOM, H. et al.Global solar radiation prediction over North Dakota using air temperature: Development of novel hybrid intelligence model. Energy Reports, v. 7, p. 136–157, nov. 2021. DOI: https://doi.org/10.1016/j.egyr.2020.11.033
VEISI, O.; SHAKIBAMANESH, A.; RAHBAR, M. Using intelligent multi-objective optimization and artificial neural networking to achieve maximum solar radiation with minimum volume in the archetype urban block. Sustainable Cities and Society, v. 86, p. 104101, 1 nov. 2022. DOI: https://doi.org/10.1016/j.scs.2022.104101
WANG, Z. et al. Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition. v. 11, n. 1, p. 68–68, 1 jan. 2018. DOI: https://doi.org/10.3390/en11010068
WANG, Z. et al. Taxonomy research of artificial intelligence for deterministic solar power forecasting. Energy Conversion and Management, v. 214, p. 112909, 15 jun. 2020. DOI: https://doi.org/10.1016/j.enconman.2020.112909
YÜZER, E. Ö; BOZKURT, A. Deep learning model for regional solar radiation estimation using satellite images. Ain Shams Engineering Journal, p. 102057, dez. 2022. DOI: https://doi.org/10.1016/j.asej.2022.102057
ZAIM, S. et al. Using artificial intelligence for global solar radiation modeling from meteorological variables. Renewable Energy, v. 215, p. 118904, 1 out. 2023. DOI: https://doi.org/10.1016/j.renene.2023.118904
ZHOU, Y. Artificial intelligence in renewable systems for transformation towards intelligent buildings. Energy and AI, v. 10, p. 100182, nov. 2022. DOI: https://doi.org/10.1016/j.egyai.2022.100182
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
A aprovação dos artigos implica a aceitação imediata e sem ônus de que a Revista Brasileira de Climatologia terá exclusividade na primeira publicação do artigo. Os autores continuarão, não obstante, a deter os direitos autorais. Os autores autorizam também que seus artigos sejam disponibilizados em todos os indexadores aos quais a revista está vinculada.
Os autores mantém seus direitos de publicação sem restrições
A Comissão Editorial não se responsabiliza pelos conceitos ou afirmações expressos nos trabalhos publicados, que são de inteira responsabilidade dos autores.
A Revista Brasileira de Climatologia oferece acesso livre imediato ao seu conteúdo, seguindo o entendimento de que disponibilizar gratuitamente o conhecimento científico ao público proporciona maior democratização do conhecimento e tende a produzir maior impacto dos artigos publicados. Os artigos publicados na revista são disponibilizados segundo a Licença Creative Commons CC-BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/). Segundo essa licença é permitido acessar, distribuir e reutilizar os artigos para fins não comerciais desde que citados os autores e a fonte. Ao submeter artigos à Revista Brasileira de Climatologia, os autores concordam em tornar seus textos legalmente disponíveis segundo essa licença
