Solar Radiation Prediction Using Regression Methods
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8007-9_23
Reference15 articles.
1. Patel MD, Shah S, Sinha S, Gupta RK, Bharti SK, Singh VP (2021) Solar irradiation forecasting-comparative analysis of various methods. In: 2021 IEEE international conference on technology, research, and innovation for betterment of society (TRIBES), Raipur, India, pp 1–9. https://doi.org/10.1109/TRIBES52498.2021.9751626
2. Choi S, Hur J (2020) An ensemble learner-based bagging model using past output data for photovoltaic forecasting. Energies 13:1438. https://doi.org/10.3390/en13061438
3. Srivastava R, Tiwari AN, Giri V (2019) Solar radiation forecasting using MARS, CART, M5, and random forest model: a case study for India. Heliyon 5:e02692. https://doi.org/10.1016/j.heliyon.2019.e02692
4. Bacher P, Madsen H, Nielsen HH (2011) Online short-term solar power forecasting. https://doi.org/10.1016/j.solener.2009.05.016
5. Naik SS, Priyanka P, Behera S (2022) Comparison of machine learning methods for short-term solar irradiation forecast. In: 2022 2nd Odisha international conference on electrical power engineering, communication and computing technology (ODICON), Bhubaneswar, India, pp 1–5. https://doi.org/10.1109/ODICON54453.2022.10009941
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