Resumen
El distrito de San Jerónimo de Apurimac presenta una elevada radiación solar que afecta las actividades agrícolas, ganaderas y mineras. Si se tiene un pronóstico confiable, es posible aumentar la producción y el nivel socioeconómico. En este estudio se elaboraron y compararon modelos de media móvil autoregresiva (Autoregressive Integrated Moving Average, ARIMA) de la radiación solar global (RSG) en el distrito de San Jerónimo con los datos de las estaciones meteorológicas de la Universidad Nacional José María Arguedas (UNAJMA) y de la Administración Nacional de Aeronáutica y del Espacio de Estados Unidos (NASA). Se hizo un estudio experimental aplicado de enfoque cuantitativo y nivel descriptivo propositivo con una muestra de 1.037 datos históricos de RSG registrados desde el 1 de enero de 2022 hasta el 2 de noviembre de 2024 mediante observación con un sensor de luz en la estación terrestre y técnicas documentales de datos recolectados del satélite geoestacionario operacional ambiental (Geostationary Operational Environmental Satellite, GOES) en la estación espacial (POWER, 2024). Se aplicaron pruebas de normalidad gráfica, la Q-Q normal, la de Dickey-Fuller, pruebas de estacionariedad, la de Levene y la de D’Agostino, gráficas de autocorrelación simple (autocorrelation function, ACF) y parcial para el cálculo de retrasos, y el criterio de información de Akaike (Akaike information criterion, AIC) para la elección del mejor modelo. Se obtuvieron modelos predictivos de RSG, ARIMA (3,0,2) y (5,0,4) precisos en las estaciones NASA y UNAJMA. Se obtuvieron modelos ARIMA(3,0,2) para la estación NASA y ARIMA(5,0,4) para la estación UNAJMA, con valores de error absoluto medio (mean absolute error, MAE), error porcentual absoluto medio (mean absolute percentage error, MAPE) y la raíz del error cuadrático medio (root mean squared error, RMSE) de 0,290; 5,211% y 0,384, respectivamente, en el primer caso; y de 0,610; 10,623% y 0,702, en el segundo. Todos estos permitieron pronosticar la RSG en el distrito de San Jerónimo desde el 3 hasta el 12 de noviembre de 2024.
Referencias
Ahmed, R., Sreeram, V., Mishra, Y., Arif, M. D. (2020). A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization. In Renewable and Sustainable Energy Reviews (Vol. 124). Elsevier Ltd. https://doi.org/10.1016/j.rser.2020.109792
Aliberti, A., Fucini, D., Bottaccioli, L., Macii, E., Acquaviva, A., Patti, E. (2021). Comparative Analysis of Neural Networks Techniques to Forecast Global Horizontal Irradiance. IEEE Access, 9, 122829-122846. https://doi.org/10.1109/ACCESS.2021.3110167
Atique, S., Noureen, S., Roy, V., Bayne, S., MacFie, J. (2020). Time series forecasting of total daily solar energy generation: A comparative analysis between ARIMA and machine learning techniques. IEEE Green Technologies Conference, 2020-April, 175-180. https://doi.org/10.1109/GreenTech46478.2020.9289796
Basmadjian, R., Shaafieyoun, A., Julka, S. (2021). Day-ahead forecasting of the percentage of renewables based on time-series statistical methods. Energies, 14(21), 7443. https://doi.org/10.3390/en14217443
Blal, M., Khelifi, S., Dabou, R., Sahouane, N., Slimani, A., Rouabhia, A., Ziane, A., Neçaibia, A., Bouraiou, A., Tidjar, B. (2020). A prediction model for estimating global solar radiation and evaluation meteorological effect on solar radiation potential under several weather conditions at the surface of Adrar environment. Measurement: Journal of the International Measurement Confederation, 152, 107348. https://doi.org/10.1016/j.measurement.2019.107348
Borda-Luna, B. E. & Lahura-Albujar, N. E. (2022). Riesgos a exposición de radiación solar a trabajadores de limpieza pública, Lima (Perú). Yotantsipanko, 2(2). https://doi.org/10.54288/yotantsipanko.v2i2.22
Boubaker, S., Kamel, S., Kolsi, L., Kahouli, O. (2021). Forecasting of One-Day-Ahead Global Horizontal Irradiation Using Block-Oriented Models Combined with a Swarm Intelligence Approach. Natural Resources Research, 30(1), 1-26. https://doi.org/10.1007/s11053-020-09761-w
Box, G. E. P. & Pierce, D. A. (1970). Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models. Journal of the American Statistical Association, 65 (332), 1509-1526. https://doi.org/10.2307/2284333
Chandra, R. P. & Sahu, B. P. (2024). Time Series Modeling and Forecasting of Finger Millet Cultivation Area, Production and Productivity in Chhattisgarh, India: The Box Jenkins Methodology. Asian Research Journal of Agriculture, 17(4), 18-30. https://doi.org/10.9734/arja/2024/v17i4494
Dimri, T., Ahmad, S., Sharif, M. (2020). Time series analysis of climate variables using seasonal ARIMA approach. Journal of Earth System Science, 129(1), 149. https://doi.org/10.1007/s12040-020-01408-x
Fan, G.-F., Wei, H.-Z., Chen, M.-Y., Hong, W.-C. (2022). Photovoltaic Power Generation Forecasting Based on the ARIMA-BPNN-SVR Model. Global Journal of Energy Technology Research Updates, 9, 18-38. https://doi.org/10.15377/2409-5818.2022.09.2
Fara, L., Diaconu, A., Craciunescu, D., Fara, S. (2021). Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models. International Journal of Photoenergy, 2021, 6777488. https://doi.org/10.1155/2021/6777488
Garg, S., Agrawal, A., Goyal, S., Verma, K. (2020, December 16). Day ahead solar irradiance forecasting using different statistical techniques. 9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020. https://doi.org/10.1109/PEDES49360.2020.9379907
Guermoui, M., Melgani, F., Gairaa, K., & Mekhalfi, M. L. (2020). A comprehensive review of hybrid models for solar radiation forecasting. Journal of Cleaner Production, 258, 120357. https://doi.org/10.1016/j.jclepro.2020.120357
Hassan, M. A., Al-Ghussain, L., Ahmad, A. D., Abubaker, A. M., & Khalil, A. (2022). Aggregated independent forecasters of half-hourly global horizontal irradiance. Renewable Energy, 181, 365–383. https://doi.org/10.1016/j.renene.2021.09.060
Hwang, E. (2024). Improvement on forecasting of propagation of the COVID-19 pandemic through combining oscillations in ARIMA models. Forecasting, 6(1), 18–35. https://doi.org/10.3390/forecast6010002
Iqbal, M. (1983). The solar constant and spectral distribution. In An Introduction to Solar Radiation (pp. 43–58). Academic Press. https://doi.org/10.1016/B978-0-12-373750-2.50008-2
Ismail, M. T., Shah, N. Z. A., & Karim, S. A. A. (2021). Modeling solar radiation in peninsular Malaysia using ARIMA model. In Green Energy and Technology (pp. 53–71). Springer. https://doi.org/10.1007/978-981-15-9140-2_3
Jaihuni, M., Basak, J. K., Khan, F., Okyere, F. G., Arulmozhi, E., Bhujel, A., Park, J., Hyun, L. D., & Kim, H. T. (2020). A partially amended hybrid Bi-GRU–ARIMA model (PAHM) for predicting solar irradiance in short and very-short terms. Energies, 13(2), 435. https://doi.org/10.3390/en13020435
Kim, D., Kim, Y., Kim, C., Kim, H., & Lee, Y. (2022). Development of short-term solar irradiance forecasting model using ARIMA and seasonal ARIMA in Daejeon. Journal of the Korean Solar Energy Society, 42(6), 105–114. https://doi.org/10.7836/kses.2022.42.6.105
Kontopoulou, V. I., Panagopoulos, A. D., Kakkos, I., & Matsopoulos, G. K. (2023). A review of ARIMA vs. machine learning approaches for time series forecasting in data-driven networks. Future Internet, 15(8), 255. https://doi.org/10.3390/fi15080255
Kurniadi, A. P., Aimon, H., Salim, Z., Ragimun, Sonjaya, A., Setiawan, S., Siagian, V., Nasution, L. Z., Nurhidajat, R., Mutaqin, & Sabtohadi, J. (2024). Analysis of existing and forecasting for coal and solar energy consumption on climate change in Asia Pacific: New evidence for sustainable development goals. International Journal of Energy Economics and Policy, 14(4), 352–359. https://doi.org/10.32479/ijeep.16187
Liu, Z. F., Luo, S. F., Tseng, M. L., Liu, H. M., Li, L., & Mashud, A. H. M. (2021). Short-term photovoltaic power prediction on modal reconstruction: A novel hybrid model approach. Sustainable Energy Technologies and Assessments, 45, 101048. https://doi.org/10.1016/j.seta.2021.101048
Makade, R. G., Chakrabarti, S., & Jamil, B. (2021). Development of global solar radiation models: A comprehensive review and statistical analysis for Indian regions. Journal of Cleaner Production, 293, 126208. https://doi.org/10.1016/j.jclepro.2021.126208
Mani, G., Joshi Kumar, V., & Stonier, A. A. (2022). Prediction and forecasting of air quality index in Chennai using regression and ARIMA time series models. Journal of Engineering Research (Kuwait), 10(2 A), 179–194. https://doi.org/10.36909/jer.10253
Mellit, A., Pavan, A. M., Ogliari, E., Leva, S., & Lughi, V. (2020). Advanced methods for photovoltaic output power forecasting: A review. Applied Sciences, 10(2), 487. https://doi.org/10.3390/app10020487
Melquíades, T. F., Diniz, F. F., Nunes, A. M. M., Martins, J. M., Santos Júnior, E. P., & Coelho Junior, L. M. (2022). Previsão da radiação solar global em João Pessoa, Paraíba, Brasil: Uma aplicação dos modelos da família ARIMA. Concilium, 22(3), 213–224. https://doi.org/10.53660/clm-197-214
Meng, H., Wu, L., Li, H., & Song, Y. (2023). Construction and research of ultra-short term prediction model of solar short wave irradiance suitable for Qinghai–Tibet Plateau. Atmosphere, 14(7), 1150. https://doi.org/10.3390/atmos14071150
Mirzabe, A. H., Hajiahmad, A., Keyhani, A., & Mirzaei, N. (2022). Approximation of daily solar radiation: A comprehensive review on employing of regression models. Renewable Energy Focus, 41, 143–159. https://doi.org/10.1016/j.ref.2022.02.003
Mohamed, M., Mahmood, F. E., Abd, M. A., Chandra, A., & Singh, B. (2022). Dynamic forecasting of solar energy microgrid systems using feature engineering. IEEE Transactions on Industry Applications, 58(6), 7857–7869. https://doi.org/10.1109/TIA.2022.3199182
Mughal, S. N., Sood, Y. R., & Jarial, R. K. (2022). Design and optimization of photovoltaic system with a week ahead power forecast using autoregressive artificial neural networks. Materials Today: Proceedings, 52, 834–841. https://doi.org/10.1016/j.matpr.2021.10.223
Mutombo, N. M. A., & Numbi, B. P. (2022). The development of ARIMA models for the clear sky beam and diffuse optical depths for HVAC systems design using RTSM: A case study of the Umlazi Township Area, South Africa. Sustainability, 14(6), 3662. https://doi.org/10.3390/su14063662
Nugroho, M. V., Mardiansah, F., Pratama, T. O., Fikriyadi, Z. A., Dianti, D. V., & Prastawa, A. (2024a). A comparative study of the effect of weather conditions on solar irradiance forecasting using various methods in Yogyakarta, Indonesia. Journal of Physics: Conference Series, 2828(1), 012028. https://doi.org/10.1088/1742-6596/2828/1/012028
Nugroho, M. V., Prastawa, A., Mardiansah, F., Rezavidi, A., Fudholi, A., Odejobi, O. A., Alawode, K. O., & Lawal, M. O. (2024b). Efficient method for forecasting solar irradiance – A review. Fudma Journal of Sciences, 8(6), 285–298. https://doi.org/10.33003/fjs-2024-0806-2786
Palaloi, S. (2024). A stacked LSTM model for day-ahead solar irradiance forecasting under tropical seasons in Java-Bali. International Journal of Power Electronics and Drive Systems, 15(3), 1878–1891. https://doi.org/10.11591/ijpeds.v15.i3.pp1878-1891
POWER. (2024). POWER Data Access Viewer. NASA Langley Research Center. https://power.larc.nasa.gov/data-access-viewer/
Quispe, R., & Huamani, R. (2024). Autoregressive models for forecasting wind speed in the rural area of Socabaya, Peru, 2022. Revista Politecnica, 54(1), 7–14. https://doi.org/10.33333/rp.vol54n1.01
Quartey-Papafio, T. K., Javed, S. A., & Liu, S. (2021). Forecasting cocoa production of six major producers through ARIMA and grey models. Grey Systems, 11(3), 434–462. https://doi.org/10.1108/GS-04-2020-0050
Reynaldo, Q., & Verónica, J. (2024). Use of unrestricted minimization of a spectral function to estimate the visible zone in Matlab 19.0. Revista Politecnica, 54(1), 87–96. https://doi.org/10.33333/rp.vol54n1.09
Şahinli, M. A. (2020). Potato price forecasting with Holt-Winters and ARIMA methods: A case study. American Journal of Potato Research, 97(4), 336–346. https://doi.org/10.1007/s12230-020-09788-y
Salman, D., Direkoglu, C., Kusaf, M., & Fahrioglu, M. (2024). Hybrid deep learning models for time series forecasting of solar power. Neural Computing and Applications, 36(16), 9095–9112. https://doi.org/10.1007/s00521-024-09558-5
Santos, D. S. de O., de Mattos Neto, P. S. G., de Oliveira, J. F. L., Siqueira, H. V., Barchi, T. M., Lima, A. R., Madeiro, F., Dantas, D. A. P., Converti, A., Pereira, A. C., de Melo Filho, J. B., & Marinho, M. H. N. (2022). Solar irradiance forecasting using dynamic ensemble selection. Applied Sciences, 12(7), 3510. https://doi.org/10.3390/app12073510
Saxena, N., Kumar, R., Rao, Y. K. S. S., Mondloe, D. S., Dhapekar, N. K., Sharma, A., & Yadav, A. S. (2024). Hybrid KNN-SVM machine learning approach for solar power forecasting. Environmental Challenges, 14, 100838. https://doi.org/10.1016/j.envc.2024.100838
Sedai, A., Dhakal, R., Gautam, S., Dhamala, A., Bilbao, A., Wang, Q., Wigington, A., & Pol, S. (2023). Performance analysis of statistical, machine learning and deep learning models in long-term forecasting of solar power production. Forecasting, 5(1), 256–284. https://doi.org/10.3390/forecast5010014
Tarmanini, C., Sarma, N., Gezegin, C., & Ozgonenel, O. (2023). Short term load forecasting based on ARIMA and ANN approaches. Energy Reports, 9, 550–557. https://doi.org/10.1016/j.egyr.2023.01.060
Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Derechos de autor 2025 Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales