This study presents a robust approach to predicting solar irradiation in the challenging climatic conditions of Tomsk using LASSO regression, with a particular emphasis on interpretability and climatic variability. Two distinct models were developed: Model 1, integrating specific humidity at 2 meters, and Model 2, excluding this variable to assess the impact of a wider range of meteorological factors. The comprehensive meteorological dataset from NASA’s POWER database underpinned the analysis. The models' efficacy was demonstrated by impressive R-squared values: 0.843 for Model 1 and 0.813 for Model 2, indicating a substantial proportion of variance in solar irradiation was captured. Notably, Model 1's RMSE of 0.0353 and Model 2's RMSE of 0.0386 affirm the precision of the predictions. The study advances the predictive modeling of solar power output, offering valuable contributions to renewable energy forecasting literature and operational practices by providing a methodological framework that is both accurate and comprehensible, even amidst the complexities of extreme weather patterns.
Keywords
Solar Irradiation ForecastingLASSO Regression AnalysisClimatic Variability in EnergyModelling Renewable Energy Prediction in SiberiaMeteorological Data Analytics in PV Systems
References
"Transforming our world: the 2030 Agenda for Sustainable Development," UN Doc. A/RES/70/1, Sept. 25, 2015.
S. Krumdieck, Transition Engineering: Building a Sustainable Future. Taylor & Francis, 2020.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
·
This work is licensed under a
Creative Commons Attribution-ShareAlike 4.0 International License
