Time series analysis of solar radiation in the City of Recife/PE

Authors

DOI:

https://doi.org/10.33448/rsd-v9i9.6870

Keywords:

Solar radiation; Climate change; Modeling; Forecast.

Abstract

Objective: analyze and describe the best fit for time serie of solar radiation in the city of Recife/PE, applying the ARMA and ARMAX models, in addition to forecasting radiation levels for the next six years. Method: used climate series data of the National Institute of Meteorology (INMET), available from January 2009 to December 2018. The comparison of the models was performed by Akaike's information criteria. Results: the series of solar radiation presents a high variability of the monthly averages, indicating the presence of seasonality and a strong negative asymmetry. The results of the error statistics show the accuracy of the ARMAX model (2.1), with a percentage error close to 18.68%, comparing the observed and adjusted series, in addition, it was possible to identify that the forecast manages to capture the existence seasonality. Conclusion: ARMAX model was adequate to describe a solar radiation including exogenous variables, being a tool capable of assisting in public health policies in the fight against skin cancer and interventions.

References

Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705.

Alsharif, M. H., Younes, M. K., & Kim, J. (2019). Time series ARIMA model for prediction of daily and monthly average global solar radiation: The case study of Seoul, South Korea. Symmetry, 11(2), 240. https://doi.org/10.3390/sym11020240.

Baierle, E., Martins, M. T. F., Rodrigues, M. B., & Blass, L. (2019). Estudo da curva de radiação solar no município de Bagé utilizando métodos numéricos. Anais do Salão Internacional de Ensino, Pesquisa e Extensão, 10(2).

Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons, p. 709.

da Silva, C. A. G. (2019). Análise da previsão do preço do etanol hidratado no estado de São Paulo: uma aplicação do modelo arima/Analysis of the forecasting of the hydrated ethanol price in the state of São Paulo: an application of the arima model Brazilian Journal of Development, 5(10), 17763-17778. https://doi.org/10.34117/bjdv5n10-045.

da Silva Tabosa, F. J., Costa, E. M., do Amaral Filho, J., Neto, N. T., Araújo, J. A., & dos Santos, C. P. B. (2019). Análise da demanda por energia elétrica no meio rural do Brasil. Planejamento e Políticas Públicas, (52).

de Souza, S. R., Maciel, M. D. N. M., de Assis Oliveira, F., & de Almeida Jesuíno, S. (2011). Dinâmica da paisagem na bacia hidrográfica do Rio Apeú, nordeste do Pará, Brasil. Revista Acadêmica Ciência Animal, 9(2), 141-150. https://dx.doi.org/10.73213/cienciaanimal.v9i2.11756.

de Souza, A., Ihaddadene, R., Haddadene, N., Oguntunde, P., Pavao, H. P. H., Fernandes, W., de Oliveira-Júnior, J. F., Soares, D. G., Pobocikova, I., Abreu, M. C., & dos Santos, C. M. (2019). Modeling of the Global Solar Radiation Series as a Function of Probability Distribution. Open Science Journal of Statistics and Application, 6(3), 35.

Gómez, J. M., Carlesso, F., Vieira, L. E., & Da Silva, L. (2018). A irradiância solar: conceitos básicos. Revista Brasileira de Ensino de Física, 40(3). https://doi.org/10.1590/1806-9126-rbef-2017-0342.

Gujarati, D. N.; Porter, D. C. (2011). Econometria Básica-5. Amgh Editora, p. 918.

Handoyo, S., Efendi, A., Jie, F., & Widodo, A. (2017). Implementation of particle swarm optimization (PSO) algorithm for estimating parameter of arma model via maximum likelihood method. Far East Journal of Mathematical Sciences, 102(7), 1337-1363. http://dx.doi.org/10.17654/MS102071337.

Hopkins, W. G. Correlation coefficient: a new view of statistics. Recuperado de www.sportsci. org/resource/stats/correl.html, 2000.

Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American statistical Association, Taylor & Francis Group, 47(260), 583-621. https://doi.org/10.2307/2280779.

Mo, J. Y., & Jeon, W. (2017). How does energy storage increase the efficiency of an electricity market with integrated wind and solar power generation? A case study of Korea. Sustainability, 9(10), 1797. https://doi.org/10.3390/su9101797.

Morettin, P. A., & Toloi, C. (2004). Análise de Series Temporais. São Paulo: Edgard Blucher LTDA, p. 535.

Peng, B. I., Xiao-Ming, S. H. I., & Qi-Yong, L. I. U. (2020). Climate change and population health research in China: Knowledge gaps and further directions. Advances in Climate Change Research.

Provenza, M. M., da Serra Costa, J. F., & de Carvalho Silva, L. (2018). Análise de dados e previsão de séries temporais do homicídio doloso no Estado do Rio de Janeiro entre 2001 e 2016. Produção em Foco, 8(2). https://doi.org/10.14521/P2237-5163.2018.0015.0001.

Rocha, C. A. (2017). Índice de qualidade ambiental de áreas utilizadas para a prática de atividades físicas e lazer na cidade de Fortaleza, CE.

Santos, D. A. D. S., Azevedo, P. V. D., Olinda, R. A. D., Santos, C. A. C. D., Souza, A. D., Sette, D. M., & Souza, P. M. D. (2017). A relação das variáveis climáticas na prevalência de infecção respiratória aguda em crianças menores de dois anos em Rondonópolis-MT, Brasil. Ciência & saúde coletiva, 22, 3711-3722. https://doi.org/10.1590/1413-812320172211.28322015.

Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591-611, 1965. https://doi.org/10.2307/2333709.

Spokas, K., & Forcella, F. (2006). Estimating hourly incoming solar radiation from limited meteorological data. Weed science, 54(1), 182-189. https://doi.org/10.1614/WS-05-098R.1.

Sobral, M. F. F., Duarte, G. B., da Penha Sobral, A. I. G., Marinho, M. L. M., & de Souza Melo, A. (2020). Association between climate variables and global transmission oF SARS-CoV-2. Science of The Total Environment, 729, 138997.

Souza, A. D., Andrade, F. A., Oguntunde, P. E., Arsić, M., & Silva, D. A. (2018). Climate indicators and the impact on morbidity and mortality of acute respiratory infections. Advanced Studies in Medical Sciences, 6(1), 5-20.

Tawatsupa, B., Lim, L. L., Kjellstrom, T., Seubsman, S. A., Sleigh, A., & Thai Cohort Study Team. (2012). Association between occupational heat stress and kidney disease among 37 816 workers in the Thai Cohort Study (TCS). Journal of epidemiology, 22(3), 251-260. https://doi.org/10.2188/jea.JE20110082.

Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30(1), 79-82.

Published

14/08/2020

How to Cite

FREITAS, J. R. de; PESSOA, R. V. S. .; PEREIRA, M. M. de A. .; SANTANA, L. I. T. de .; SILVA, J. M. da .; CUNHA FILHO, M. . Time series analysis of solar radiation in the City of Recife/PE. Research, Society and Development, [S. l.], v. 9, n. 9, p. e131996870, 2020. DOI: 10.33448/rsd-v9i9.6870. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/6870. Acesso em: 19 nov. 2024.

Issue

Section

Health Sciences