EVALUATION OF MACHINE LEARNING MODELS IN SOLAR RADIATION PREDICTION FOR PHOTOVOLTAIC SYSTEM DESIGN
Keywords:
Solar radiation, machine learning techniquesAbstract
This research evaluates machine learning models in predicting solar radiation, crucial for designing photovoltaic systems. Accuracy in solar forecasting is key to mitigating climate change and meeting energy demand. Advanced machine learning techniques were applied, surpassing traditional models in precision and efficiency, including SARIMA, Random Forests, SVM, ANN, and LSTM, assessed with metrics such as accuracy, sensitivity, precision, NME, R², and execution time. After normalization, the SVM model achieved the highest overall score of 5.86. A photovoltaic system was sized using an SVM model with solar radiation data (2017-2020). Predictions calculated an average daily consumption of 4.89 kWh, a total daily energy of 109.88 kWh, and a solar panel area of 4.42 m². The system's peak power is 0.86 kWp, and the inverter power with a safety margin is 1.04 kW.
Keywords: Solar radiation, machine learning techniques.
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Aaronson, S. (2020). Por qué los Filósofos debieran Preocuparse de la Complejidad Computacional. 1, 6–50. https://orbitaci130.org/wp-content/uploads/2018/01/AaronsonIntegrado-1.pdf
Albistur Marin, F. X. (2014). La transición energética: un reto al desarrollo sostenible. Cuadernos Del Cendes, 31(86), 149–155. http://ve.scielo.org/scielo.php?script=sci_arttext&pid=S1012-25082014000200011&lng=es&nrm=iso&tlng=es
Boubaker, S., Benghanem, M., Mellit, A., Lefza, A., Kahouli, O., & Kolsi, L. (2021). Deep Neural Networks for Predicting Solar Radiation at Hail Region, Saudi Arabia. IEEE Access, 9, 36719–36729. https://doi.org/10.1109/ACCESS.2021.3062205
Casal, R. F., Bouzas, J. C., & Oviedo De La Fuente, M. (2021). Aprendizaje Estadístico (2021st ed., Vol. 1). https://rubenfcasal.github.io/aprendizaje_estadistico/aprendizaje_estadistico.pdf
Cuesta A., Constante J., & Jijón D. (2023). Vista de Modelos de Predicción de Radiación Solar y Temperatura Ambiente mediante Redes Neuronales Recurrentes. CENACE, 19, 2–9. https://revistaenergia.cenace.gob.ec/index.php/cenace/article/view/552/805
David Dalpiaz. (2020). Basics of Statistical Learning. https://statisticallearning.org/
David, T. M., Silva Rocha Rizol, P. M., Guerreiro Machado, M. A., & Buccieri, G. P. (2020). Future research tendencies for solar energy management using a bibliometric analysis, 2000–2019. Heliyon, 6(7), 1–5. https://doi.org/10.1016/J.HELIYON.2020.E04452
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 1, 4171–4186.
Fernández, L., & Gutiérrez, M. (2013). Bienestar Social, Económico y Ambiental para las Presentes y Futuras Generaciones. 24. https://doi.org/10.4067/S0718-07642013000200013
Hastie, T., Tibshirani, R., & Friedman, J. (2008). The Elements of Statistical Learning Data Mining, Inference, and Prediction (Vol. 2). https://hastie.su.domains/Papers/ESLII.pdf
Hyndman, R. J., & A. G. (2018). Forecasting: Principles and Practice. OTexts. (Vol. 2). https://otexts.com/fpp2/index.html
Inman, R. H., Pedro, H. T. C., & Coimbra, C. F. M. (2013). Solar forecasting methods for renewable energy integration. Progress in Energy and Combustion Science, 39(6), 535–576. https://doi.org/10.1016/J.PECS.2013.06.002
James Gareth, Witten Daniela, Hastie Trevor, & Tibshirani Robert. (2013). An Introduction to Statistical Learning (Casella G., Vol. 4). http://www.springer.com/series/417
Kumar, K. P., & Saravanan, B. (2017). Recent techniques to model uncertainties in power generation from renewable energy sources and loads in microgrids – A review. Renewable and Sustainable Energy Reviews, 71, 348–358. https://doi.org/10.1016/J.RSER.2016.12.063
Lauret, P., Voyant, C., Soubdhan, T., David, M., Poggi, P., & Soubdhan GRER, T. (2015). A benchmarking of machine learning techniques for solar radiation forecasting in an insular context A benchmarking of machine learning techniques for solar radiation forecasting in an insular context A benchmarking of machine learning techniques for solar radiation forecasting in an insular context. HAL Open Science, 112, 446–457. https://doi.org/10.1016/j.solener.2014.12.014ï
Leo Breiman. (2001). Random Forests. 1, 5–32. https://chatgpt.com/c/5a0c2568-8fef-45ec-8b03-79659dfc2325
Lewis, N. S., & Nocera, D. G. (2006). Powering the planet: chemical challenges in solar energy utilization. Proceedings of the National Academy of Sciences of the United States of America, 103(43), 15729–15735. https://doi.org/10.1073/PNAS.0603395103
Mateus, S. P., González, N., & Branch, J. W. (2014). Aplicación de Redes Neuronales Artificiales en Entornos Virtuales Inteligentes. Información Tecnológica, 25(5), 103–112. https://doi.org/10.4067/S0718-07642014000500015
Obando E., Carvajal S., & Pineda J. (2019). Solar Radiatiation Prediction Using Machine Learning Techniques: A Review. IEEE LATIN AMERICA TRANSACTIONS, 17, 2–8. https://latamt.ieeer9.org/index.php/transactions/article/view/261
Poullikkas, A. (2010). Technology and market future prospects of photovoltaic systems Internationa Journal of energy and enviroment Technology and market future prospects of photovoltaic systems. Journal Homepage: www.ijee.ieefoundation.org ISSN, 1(4), 2076–2909. www.IJEE.IEEFoundation.org
Quispe Huamán, L., Sotomayor Alzamora, G., Quispe Huamán, L., & Sotomayor Alzamora, G. (2022). Determinación y análisis temporal de la radiación solar global en el Altiplano de Puno. Ingeniare. Revista Chilena de Ingeniería, 30(1), 69–81. https://doi.org/10.4067/S0718-33052022000100069
Rurales, V., De Vehículos, C., Basado, E., Un, E., Multipropósito, E., & Villarreal, J. G. (2024). Diseño y Evaluación de un Sistema Fotovoltaico Aislado para Iluminación en Vías Rurales y Carga de Vehículos Eléctricos Basado En Un Enfoque Multipropósito. Revista Técnica Energía, 20(2), 47–57. https://doi.org/10.37116/REVISTAENERGIA.V20.N2.2024.614
Shiva Kumar, B., & Sudhakar, K. (2015). Performance evaluation of 10 MW grid connected solar photovoltaic power plant in India. Energy Reports, 1, 184–192. https://doi.org/10.1016/J.EGYR.2015.10.001
Simon Philipps. (2024). Photovoltaics Report Fraunhofer ISE. 1, 10–40. https://www.ise.fraunhofer.de/en/publications/studies/photovoltaics-report.html
Suárez Ruth, & Ladino Iván. (2022). Redes neuronales aplicadas al control estadístico de procesos con cartas de control EWMA. 27, 72–88. https://doi.org/10.14483/22487638.18623
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