A Survey on Machine Learning Techniques for The Prediction of Solar Power Production
Abstract
Renewable energy sources are needed globally to support the available non-renewable energy sources our day-to-day living. There is high demand for renewable energy sources in both the developed and developing economies. Solar power is a good example of renewable energy source and people are currently embracing it globally for both domestic and industrial uses. Generally, these energy sources are meant to support the hydro, thermal and other energy sources that are available in different countries of the world. With the popularity of solar energy for both domestic and industrial usage, it can be argued that the estimation of the production level of such energy source is necessary so as to achieve proper planning and management. Due to the fact that the availability of the solar energy power depends largely on a number of environmental and weather conditions, predicting its production or generation can be very important. This study surveyed different works in the area of using machine learning techniques for solar power production prediction. The articles sourced were from notable research repositories. This study focuses on articles that were published between 2013 and 2023 on the subject matter. Different types of machine learning (ML) algorithms that have been used to build models from solar energy datasets are reported in this study. It is believed that the work can provide better insights for the researchers working in the problem area. Thus, the insights in this study can lead to building of improved machine learning-based models for solar power forecasting
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References
S. Ogunjo, O. Aderonke, and B. Rabiu, “Machine learning prediction of solar energy potential in Nigeria,” 2022, p. 030002, doi: 10.1063/5.0099501.
K. Anuradha, D. Erlapally, G. Karuna, V. Srilakshmi, and K. Adilakshmi, “Analysis Of Solar Power Generation Forecasting Using Machine Learning Techniques,” E3S Web Conf., vol. 309, p. 01163, Oct. 2021, doi: 10.1051/e3sconf/202130901163.
B. Carrera and K. Kim, “Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data,” Sensors, vol. 20, no. 11, p. 3129, Jun. 2020, doi: 10.3390/s20113129.
R. H. Charlier, “Coastal Planning and Management,” Int. J. Environ. Stud., vol. 66, no. 6, pp. 800–800, 2009, doi: 10.1080/00207230600836518.
R. Aler, R. Martín, J. M. Valls, and I. M. Galván, “A Study of Machine Learning Techniques for Daily Solar Energy Forecasting Using Numerical Weather Models,” 2015, pp. 269–278.
G. Chartrand et al., “Deep Learning: A Primer for Radiologists,” RadioGraphics, vol. 37, no. 7, pp. 2113–2131, Nov. 2017, doi: 10.1148/rg.2017170077.
D. P. Larson, L. Nonnenmacher, and C. F. M. Coimbra, “Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest,” Renew. Energy, vol. 91, pp. 11–20, Jun. 2016, doi: 10.1016/j.renene.2016.01.039.
A.-N. Sharkawy, M. Ali, H. Mousa, A. Ali, and G. Abdel-Jaber, “Machine Learning Method for Solar PV Output Power Prediction,” SVU-International J. Eng. Sci. Appl., vol. 3, no. 2, pp. 123–130, Dec. 2022, doi: 10.21608/svusrc.2022.157039.1066.
A. Alcañiz, D. Grzebyk, H. Ziar, and O. Isabella, “Trends and gaps in photovoltaic power forecasting with machine learning,” Energy Reports, vol. 9, pp. 447–471, Dec. 2023, doi: 10.1016/j.egyr.2022.11.208.
A. Abraham Iorkaa, M. Barma, and H. GAYA Muazu, “Machine Learning Techniques, methods and Algorithms: Conceptual and Practical Insights,” Int. J. Eng. Res. Appl. www.ijera.com, vol. 11, no. 8, pp. 55–64, 2021, doi: 10.9790/9622-1108025564.
G. Cerulli, “The Basics of Machine Learning,” pp. 1–17, 2023, doi: 10.1007/978-3-031-41337-7_1.
N. E. Benti, M. D. Chaka, and A. G. Semie, “Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects,” Sustainability, vol. 15, no. 9, p. 7087, Apr. 2023, doi: 10.3390/su15097087.
A. Balal, Y. Pakzad Jafarabadi, A. Demir, M. Igene, M. Giesselmann, and S. Bayne, “Forecasting Solar Power Generation Utilizing Machine Learning Models in Lubbock,” Emerg. Sci. J., vol. 7, no. 4, pp. 1052–1062, Jul. 2023, doi: 10.28991/ESJ-2023-07-04-02.
C. Vennila et al., “Forecasting Solar Energy Production Using Machine Learning,” Int. J. Photoenergy, vol. 2022, pp. 1–7, Apr. 2022, doi: 10.1155/2022/7797488.
M. Y. ERTEN and H. AYDİLEK, “Solar Power Prediction using Regression Models,” Uluslararası Muhendis. Arastirma ve Gelistirme Derg., vol. 14, no. 3, pp. 1–1, Dec. 2022, doi: 10.29137/umagd.1100957.
M. Rupesh, J. Swathi Chandana, A. Aishwarya, C. Anusha, and B. Meghana, “Prediction of Solar Power Using Machine Learning Algorithm,” 2022, pp. 529–539.
B. Zazoum, “Solar photovoltaic power prediction using different machine learning methods,” Energy Reports, vol. 8, pp. 19–25, Apr. 2022, doi: 10.1016/j.egyr.2021.11.183.
M. Elsaraiti and A. Merabet, “Solar Power Forecasting Using Deep Learning Techniques,” IEEE Access, vol. 10, pp. 31692–31698, 2022, doi: 10.1109/ACCESS.2022.3160484.
S. P. C. Machina, S. S. Koduru, and S. Madichetty, “Solar Energy Forecasting Using Deep Learning Techniques,” in 2022 2nd International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), Jan. 2022, pp. 1–6, doi: 10.1109/PARC52418.2022.9726605.
C. S. H. Chuluunsaikhan Tserenpurev, Nasridinov Aziz, Choi Woo Seok, Choi Da Bin and K. Y. Myoung, “Predicting the Power Output of Solar Panels based on Weather and Air Pollution Features using Machine Learning, Journal of Korea Multimedia Society,” 2021.
P. R. Vishnu, C. S. Roy, and A. Srihari, “Solar Power Output Prediction using Machine Learning Techniques,” 2021.
T. R. Ayodele, A. S. O. Ogunjuyigbe, A. Amedu, and J. L. Munda, “Prediction of global solar irradiation using hybridized k-means and support vector regression algorithms,” Renew. Energy Focus, vol. 29, pp. 78–93, Jun. 2019, doi: 10.1016/j.ref.2019.03.003.
D. Su, E. Batzelis, and B. Pal, “Machine Learning Algorithms in Forecasting of Photovoltaic Power Generation,” in 2019 International Conference on Smart Energy Systems and Technologies (SEST), Sep. 2019, pp. 1–6, doi: 10.1109/SEST.2019.8849106.
D. Van Tai, “Solar photovoltaic power output forecasting using machine learning technique,” J. Phys. Conf. Ser., vol. 1327, no. 1, p. 012051, Oct. 2019, doi: 10.1088/1742-6596/1327/1/012051.
M. Abdel-Nasser and K. Mahmoud, “Accurate photovoltaic power forecasting models using deep LSTM-RNN,” Neural Comput. Appl., vol. 31, no. 7, pp. 2727–2740, Jul. 2019, doi: 10.1007/s00521-017-3225-z.
E. Isaksson and M. K. Conde, “Solar Power Forecasting with Machine Learning Techniques,” Kth R. Inst. Technol., p. 46, 2018.
J. Fan et al., “Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China,” Energy Convers. Manag., vol. 164, pp. 102–111, May 2018, doi: 10.1016/j.enconman.2018.02.087.
S. Leva, A. Dolara, F. Grimaccia, M. Mussetta, and E. Ogliari, “Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power,” Math. Comput. Simul., vol. 131, pp. 88–100, Jan. 2017, doi: 10.1016/j.matcom.2015.05.010.
C. Persson, P. Bacher, T. Shiga, and H. Madsen, “Multi-site solar power forecasting using gradient boosted regression trees,” Sol. Energy, vol. 150, pp. 423–436, Jul. 2017, doi: 10.1016/j.solener.2017.04.066.
M. R. Hossain, A. M. T. Oo, and A. B. M. S. Ali, “Hybrid Prediction Method for Solar Power Using Different Computational Intelligence Algorithms,” Smart Grid Renew. Energy, vol. 04, no. 01, pp. 76–87, 2013, doi: 10.4236/sgre.2013.41011.
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