Derechos de autor 2020 Investigación e Innovación en Ingenierías
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Sistema adaptativo de inferencia neuro-difusa (ANFIS) para la estimación de la radiación solar global
Corresponding Author(s) : Diana Carolina Bastos Guerrero
Investigación e Innovación en Ingenierías,
Vol. 9 Núm. 1 (2021): Enero-Junio
Resumen
Objetivo: Ajustar un sistema adaptativo de inferencia neuro-difusa (ANFIS) para la estimación de la radiación solar global en la ciudad de Cúcuta. Metodología: A partir de registros históricos del Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM) se obtuvieron las variables: temperatura ambiente, brillo solar, velocidad del viento, humedad relativa, hora de medición y radiación solar global entre los años 2005 y 2015. Usando la herramienta Fuzzy Logic Toolbox de Matlab y los datos del IDEAM se crearon redes ANFIS con diferentes configuraciones. Después de comparar los errores estadísticos, se escogió el modelo ANFIS que permitió minimizar el error cuadrático medio (RMSE). Resultados: Los errores estadísticos del modelo son: R2 = 0.9115, RMSE = 124,23 Wh/m2 y MAPE = 27,8 %, evidenciando una alta precisión para la estimación de la radiación solar global en el territorio seleccionado. Conclusiones: La red ANFIS propuesta es un modelo basado en inteligencia artificial con precisión suficiente para ser usado en el diseño de sistemas fotovoltaicos en la región, que no cuenta con una amplia red de piranómetros para medir el recurso solar.
Palabras clave
Descargar cita
Endnote/Zotero/Mendeley (RIS)BibTeX
- REN21, “Renewables 2020 Global Status Report,” 2020.
- D. Henao León, A. C. Báez Alarcón, and J. B. Pedroza Rojas, “Metodología para determinar la viabilidad de generación de energía eléctrica por medio del recurso eólico,” Investig. e Innovación en Ing., vol. 6, no. 2, pp. 6–15, 2018.
- S. Sobri, S. Koohi-Kamali, and N. A. Rahim, “Solar photovoltaic generation forecasting methods: A review,” Energy Convers. Manag., vol. 156, pp. 459–497, 2018.
- Instituto de Hidrología Meteorología y Estudios Ambientales (IDEAM), “Atlas de Radiación Solar, Ultravioleta y Ozono de Colombia,” 2015. [Online]. Available: http://atlas.ideam.gov.co/visorAtlasRadiacion.html.
- W. Contreras, M. G. Galban, and S. B. Sepúlveda, “Análisis estadístico de la radiación solar en la ciudad de Cúcuta,” Entre Cienc. e Ing., vol. 12, no. 23, p. 16, 2018.
- E. Noriega-Angarita, V. Sousa-Santos, M. Quintero-Duran, and C. Gil-Arrieta, “Solar radiation prediction for dimensioning photovoltaic systems using artificial neural networks,” Int. J. Eng. Technol., vol. 8, no. 4, pp. 1817–1825, 2016.
- L. Olatomiwa, S. Mekhilef, S. Shamshirband, and D. Petković, “Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria,” Renew. Sustain. Energy Rev., vol. 51, pp. 1784–1791, 2015.
- R. Kumar, R. K. Aggarwal, and J. D. Sharma, “Comparison of regression and artificial neural network models for estimation of global solar radiations,” Renew. Sustain. Energy Rev., vol. 52, pp. 1294–1299, 2015.
- J. I. Roa Mora, “Análisis de las Alternativas del Diseño de un Sistema Difuso para la Predicción del Precio del Bitcoin (BTC),” Investig. e Innovación en Ing., vol. 7, no. 1, pp. 34–46, 2019.
- K. Mohammadi, S. Shamshirband, A. Kamsin, P. C. Lai, and Z. Mansor, “Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure,” Renew. Sustain. Energy Rev., vol. 63, pp. 423–434, 2016.
- L. M. Halabi, S. Mekhilef, and M. Hossain, “Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation,” Appl. Energy, vol. 213, pp. 247–261, 2018.
- D. Jain and M. Lalwani, “Prediction of irradiation: A comparative study of ANFIS,” International Conference on Circuit, Power and Computing Technologies (ICCPCT), 2017, pp. 1–6.
- A. Khosravi, R. N. N. Koury, L. Machado, and J. J. G. Pabon, “Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms,” J. Clean. Prod., vol. 176, pp. 63–75, 2018.
- K. Mohammadi, S. Shamshirband, C. W. Tong, K. A. Alam, and D. Petković, “Potential of adaptive neuro-fuzzy system for prediction of daily global solar radiation by day of the year,” Energy Convers. Manag., vol. 93, pp. 406–413, 2015.
- A. Mohan, P. Kuchhal, and M. G. Sharma, “Prediction Models for Global Solar Radiations, Diffused Radiations and Direct Solar Radiations Using ANFIS,” International Conference on Intelligent Communication, Control and Devices, 2017, pp. 27–37.
- A. Muhammad, M. S. Gaya, RAliyu, R.A. Abdulkadir, I.D. Umar, L. A. Yusuf, et al., “Forecasting of global solar radiation using ANFIS and ARMAX techniques,” IOP Conference Series: Materials Science and Engineering, 2018, vol. 303, pp. 1–6.
- L. Zou, L. Wang, L. Xia, A. Lin, B. Hu, and H. Zhu, “Prediction and comparison of solar radiation using improved empirical models and Adaptive Neuro-Fuzzy Inference Systems,” Renew. Energy, vol. 106, pp. 343–353, 2017.
- S. Mohanty, “ANFIS based Prediction of Monthly Average Global Solar Radiation over Bhubaneswar (State of Odisha),” Int. J. Ethics Eng. Manag. Educ., vol. 1, no. 5, pp. 97–101, 2014.
- T. R. Sumithira and A. Nirmal Kumar, “Prediction of monthly global solar radiation using adaptive neuro fuzzy inference system (ANFIS) technique over the state of Tamilnadu (India): A comparative study,” Appl. Sol. Energy, vol. 48, pp. 140–145, 2012.
- A. Mellit, S. A. Kalogirou, S. Shaari, H. Salhi, and A. Hadj Arab, “Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: Application for sizing a stand-alone PV system,” Renew. Energy, vol. 33, no. 7, pp. 1570–1590, 2008.
- J. S. R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. Syst. Man Cybern., vol. 23, no. 3, pp. 665–685, 1993.
- A. M. Abdulshahed, A. P. Longstaff, and S. Fletcher, “The application of ANFIS prediction models for thermal error compensation on CNC machine tools,” Appl. Soft Comput., vol. 27, pp. 158–168, 2015.
- A. Abraham, “Adaptation of Fuzzy Inference System Using Neural Learning,” Fuzzy Systems Engineering, vol. 181, N. Nedjah and L. Macedo Mourelle, Eds. Springer, Berlin, Heidelberg, 2005, pp. 53–83.
- Instituto de Hidrología Meteorología y Estudios Ambientales de Colombia (IDEAM), “Solicitud de Información,” 2014. [Online]. Available: http://www.ideam.gov.co/solicitud-de-informacion.
- V. H. Quej, J. Almorox, J. A. Arnaldo, and L. Saito, “ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment,” J. Atmos. Solar-Terrestrial Phys., vol. 155, pp. 62–70, 2017.
- H. B. Yıldırım, Ö. Çelik, A. Teke, and B. Barutçu, “Estimating daily Global solar radiation with graphical user interface in Eastern Mediterranean region of Turkey,” Renew. Sustain. Energy Rev., vol. 82, pp. 1528–1537, 2018.
- U.K. Das, K. Soon Tey, M. Seyedmahmoudian, S. Mekhilef, M.Y. Idna Idris, W.V. Deventer, et al., “Forecasting of photovoltaic power generation and model optimization: A review,” Renew. Sustain. Energy Rev., vol. 81, pp. 912–928, 2018.
- N. Premalatha and A. Valan Arasu, “Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms,” J. Appl. Res. Technol., vol. 14, no. 3, pp. 206–214, 2016.
- K. Chiteka and C. C. Enweremadu, “Prediction of global horizontal solar irradiance in Zimbabwe using artificial neural networks,” J. Clean. Prod., vol. 135, pp. 701–711, 2016.
- A. Qazi, H. Fayaz, A. Wadi, R. G. Raj, N. A. Rahim, and W. A. Khan, “The artificial neural network for solar radiation prediction and designing solar systems: A systematic literature review,” J. Clean. Prod., vol. 104, pp. 1–12, 2015.
- Ö. Çelik, A. Teke, and H. B. Yildirim, “The optimized artificial neural network model with Levenberg-Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean Region of Turkey,” J. Clean. Prod., vol. 116, pp. 1–12, 2016.
- H. K. Yadav, Y. Pal, and M. M. Tripathi, “Short-Term PV Power Forecasting Using Adaptive Neuro-Fuzzy Inference System,” IEEE Power India International Conference, (PIICON), 2018, pp. 1–6.
Referencias
REN21, “Renewables 2020 Global Status Report,” 2020.
D. Henao León, A. C. Báez Alarcón, and J. B. Pedroza Rojas, “Metodología para determinar la viabilidad de generación de energía eléctrica por medio del recurso eólico,” Investig. e Innovación en Ing., vol. 6, no. 2, pp. 6–15, 2018.
S. Sobri, S. Koohi-Kamali, and N. A. Rahim, “Solar photovoltaic generation forecasting methods: A review,” Energy Convers. Manag., vol. 156, pp. 459–497, 2018.
Instituto de Hidrología Meteorología y Estudios Ambientales (IDEAM), “Atlas de Radiación Solar, Ultravioleta y Ozono de Colombia,” 2015. [Online]. Available: http://atlas.ideam.gov.co/visorAtlasRadiacion.html.
W. Contreras, M. G. Galban, and S. B. Sepúlveda, “Análisis estadístico de la radiación solar en la ciudad de Cúcuta,” Entre Cienc. e Ing., vol. 12, no. 23, p. 16, 2018.
E. Noriega-Angarita, V. Sousa-Santos, M. Quintero-Duran, and C. Gil-Arrieta, “Solar radiation prediction for dimensioning photovoltaic systems using artificial neural networks,” Int. J. Eng. Technol., vol. 8, no. 4, pp. 1817–1825, 2016.
L. Olatomiwa, S. Mekhilef, S. Shamshirband, and D. Petković, “Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria,” Renew. Sustain. Energy Rev., vol. 51, pp. 1784–1791, 2015.
R. Kumar, R. K. Aggarwal, and J. D. Sharma, “Comparison of regression and artificial neural network models for estimation of global solar radiations,” Renew. Sustain. Energy Rev., vol. 52, pp. 1294–1299, 2015.
J. I. Roa Mora, “Análisis de las Alternativas del Diseño de un Sistema Difuso para la Predicción del Precio del Bitcoin (BTC),” Investig. e Innovación en Ing., vol. 7, no. 1, pp. 34–46, 2019.
K. Mohammadi, S. Shamshirband, A. Kamsin, P. C. Lai, and Z. Mansor, “Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure,” Renew. Sustain. Energy Rev., vol. 63, pp. 423–434, 2016.
L. M. Halabi, S. Mekhilef, and M. Hossain, “Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation,” Appl. Energy, vol. 213, pp. 247–261, 2018.
D. Jain and M. Lalwani, “Prediction of irradiation: A comparative study of ANFIS,” International Conference on Circuit, Power and Computing Technologies (ICCPCT), 2017, pp. 1–6.
A. Khosravi, R. N. N. Koury, L. Machado, and J. J. G. Pabon, “Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms,” J. Clean. Prod., vol. 176, pp. 63–75, 2018.
K. Mohammadi, S. Shamshirband, C. W. Tong, K. A. Alam, and D. Petković, “Potential of adaptive neuro-fuzzy system for prediction of daily global solar radiation by day of the year,” Energy Convers. Manag., vol. 93, pp. 406–413, 2015.
A. Mohan, P. Kuchhal, and M. G. Sharma, “Prediction Models for Global Solar Radiations, Diffused Radiations and Direct Solar Radiations Using ANFIS,” International Conference on Intelligent Communication, Control and Devices, 2017, pp. 27–37.
A. Muhammad, M. S. Gaya, RAliyu, R.A. Abdulkadir, I.D. Umar, L. A. Yusuf, et al., “Forecasting of global solar radiation using ANFIS and ARMAX techniques,” IOP Conference Series: Materials Science and Engineering, 2018, vol. 303, pp. 1–6.
L. Zou, L. Wang, L. Xia, A. Lin, B. Hu, and H. Zhu, “Prediction and comparison of solar radiation using improved empirical models and Adaptive Neuro-Fuzzy Inference Systems,” Renew. Energy, vol. 106, pp. 343–353, 2017.
S. Mohanty, “ANFIS based Prediction of Monthly Average Global Solar Radiation over Bhubaneswar (State of Odisha),” Int. J. Ethics Eng. Manag. Educ., vol. 1, no. 5, pp. 97–101, 2014.
T. R. Sumithira and A. Nirmal Kumar, “Prediction of monthly global solar radiation using adaptive neuro fuzzy inference system (ANFIS) technique over the state of Tamilnadu (India): A comparative study,” Appl. Sol. Energy, vol. 48, pp. 140–145, 2012.
A. Mellit, S. A. Kalogirou, S. Shaari, H. Salhi, and A. Hadj Arab, “Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: Application for sizing a stand-alone PV system,” Renew. Energy, vol. 33, no. 7, pp. 1570–1590, 2008.
J. S. R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. Syst. Man Cybern., vol. 23, no. 3, pp. 665–685, 1993.
A. M. Abdulshahed, A. P. Longstaff, and S. Fletcher, “The application of ANFIS prediction models for thermal error compensation on CNC machine tools,” Appl. Soft Comput., vol. 27, pp. 158–168, 2015.
A. Abraham, “Adaptation of Fuzzy Inference System Using Neural Learning,” Fuzzy Systems Engineering, vol. 181, N. Nedjah and L. Macedo Mourelle, Eds. Springer, Berlin, Heidelberg, 2005, pp. 53–83.
Instituto de Hidrología Meteorología y Estudios Ambientales de Colombia (IDEAM), “Solicitud de Información,” 2014. [Online]. Available: http://www.ideam.gov.co/solicitud-de-informacion.
V. H. Quej, J. Almorox, J. A. Arnaldo, and L. Saito, “ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment,” J. Atmos. Solar-Terrestrial Phys., vol. 155, pp. 62–70, 2017.
H. B. Yıldırım, Ö. Çelik, A. Teke, and B. Barutçu, “Estimating daily Global solar radiation with graphical user interface in Eastern Mediterranean region of Turkey,” Renew. Sustain. Energy Rev., vol. 82, pp. 1528–1537, 2018.
U.K. Das, K. Soon Tey, M. Seyedmahmoudian, S. Mekhilef, M.Y. Idna Idris, W.V. Deventer, et al., “Forecasting of photovoltaic power generation and model optimization: A review,” Renew. Sustain. Energy Rev., vol. 81, pp. 912–928, 2018.
N. Premalatha and A. Valan Arasu, “Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms,” J. Appl. Res. Technol., vol. 14, no. 3, pp. 206–214, 2016.
K. Chiteka and C. C. Enweremadu, “Prediction of global horizontal solar irradiance in Zimbabwe using artificial neural networks,” J. Clean. Prod., vol. 135, pp. 701–711, 2016.
A. Qazi, H. Fayaz, A. Wadi, R. G. Raj, N. A. Rahim, and W. A. Khan, “The artificial neural network for solar radiation prediction and designing solar systems: A systematic literature review,” J. Clean. Prod., vol. 104, pp. 1–12, 2015.
Ö. Çelik, A. Teke, and H. B. Yildirim, “The optimized artificial neural network model with Levenberg-Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean Region of Turkey,” J. Clean. Prod., vol. 116, pp. 1–12, 2016.
H. K. Yadav, Y. Pal, and M. M. Tripathi, “Short-Term PV Power Forecasting Using Adaptive Neuro-Fuzzy Inference System,” IEEE Power India International Conference, (PIICON), 2018, pp. 1–6.