Advancing Solar Irradiation Prediction in Extreme Climates: A LASSO Regression Analysis in Tomsk

David Akpuluma and Alexey Yurchenko

References:

1. "Transforming our world: the 2030 Agenda for Sustainable Development," UN Doc. A/RES/70/1, Sept. 25, 2015.
2. S. Krumdieck, Transition Engineering: Building a Sustainable Future. Taylor & Francis, 2020.
3. J. Stephenson, B. Barton, G. Carrington, A. Doering, R. Ford, D. Hopkins et al., "The energy cultures framework: exploring the role of norms, practices and material culture in shaping energy behaviour in New Zealand," Energy Research & Social Science, vol. 7, pp. 117-123, 2015, [Online]. Available: https://doi.org/10.1016/j.erss.2015.03.005.
4. B. Sovacool, M. Burke, L. Baker, C. Kotikalapudi, and H. Wlokas, "New frontiers and conceptual frameworks for energy justice," Energy Policy, vol. 105, pp. 677-691, 2017, [Online]. Available: https://doi.org/10.1016/j.enpol.2017.03.005.
5. D. McCollum, W. Zhou, C. Bertram, H. Boer, V. Bosetti, S. Busch et al., "Energy investment needs for fulfilling the Paris agreement and achieving the sustainable development goals," Nature Energy, vol. 3, no. 7, pp. 589-599, 2018, [Online]. Available: https://doi.org/10.1038/s41560-018-0179-z.
6. N. Tang, S. Mao, W. Yu, and R. Nelms, "Solar power generation forecasting with a lasso-based approach," IEEE Internet of Things Journal, vol. 5, no. 2, pp. 1090-1099, 2018, [Online]. Available: https://doi.org/10.1109/jiot.2018.2812155.
7. S. Pasari and A. Shah, "Time series auto-regressive integrated moving average model for renewable energy forecasting," Sustainable Production, Life Cycle Engineering and Management, pp. 71-77, 2020, [Online]. Available: https://doi.org/10.1007/978-3-030-44248-4_7.
8. C. Voyant, G. Notton, S. Kalogirou, M. Nivet, C. Paoli, F. Motte et al., "Machine learning methods for solar radiation forecasting: a review," Renewable Energy, vol. 105, pp. 569-582, 2017, [Online]. Available: https://doi.org/10.1016/j.renene.2016.12.095.
9. F. Niu and Z. O’Neill, "Recurrent neural network based deep learning for solar radiation prediction," Building Simulation Conference Proceedings, 2017, [Online]. Available: https://doi.org/10.26868/25222708.2017.507.
10. M. Sengupta, Y. Xie, A. Lopez, A. Habte, G. Maclaurin, and J. Shelby, "The national solar radiation data base (nsrdb)," Renewable and Sustainable Energy Reviews, vol. 89, pp. 51-60, 2018, [Online]. Available: https://doi.org/10.1016/j.rser.2018.03.003.
11. V. Sharma and S. Chandel, "Performance and degradation analysis for long term reliability of solar photovoltaic systems: a review," Renewable and Sustainable Energy Reviews, vol. 27, pp. 753-767, 2013, [Online]. Available: https://doi.org/10.1016/j.rser.2013.07.046.
12. S. Sobri, S. Koohi-Kamali, and N. Rahim, "Solar photovoltaic generation forecasting methods: a review," Energy Conversion and Management, vol. 156, pp. 459-497, 2018, [Online]. Available: https://doi.org/10.1016/j.enconman.2017.11.019.
13. R. Ahmed, V. Sreeram, Y. Mishra, and M. Arif, "A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization," Renewable and Sustainable Energy Reviews, vol. 124, p. 109792, 2020, [Online]. Available: https://doi.org/10.1016/j.rser.2020.109792.
14. C. Yen, H. Hsieh, K. Su, M. Yu, and J. Leu, "Solar power prediction via support vector machine and random forest," E3S Web of Conferences, vol. 69, p. 01004, 2018, [Online]. Available: https://doi.org/10.1051/e3sconf/20186901004.
15. Y. Kim and J. Kim, "Gradient lasso for feature selection," Twenty-First International Conference on Machine Learning - ICML '04, 2004, [Online]. Available: https://doi.org/10.1145/1015330.1015364.