Time series analysis of solar radiation in the City of Recife/PE
DOI:
https://doi.org/10.33448/rsd-v9i9.6870Keywords:
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.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Jucarlos Rufino de Freitas
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.