Comparative Analysis of LSTM-Based PV Power Forecasting Models with Climate-Adaptive Feature Selection in Abuja, Nigeria

David Akpuluma, Wolf-Gerrit Früh, Neda Firoz, James Abam, Mohammed Umar Bello, Comfort Williams and Ambrose Onne Okpu

References:

1. C. Etukudor, B. Couraud, V. Robu, W. Fruh, D. Flynn, and C. Okereke, “Automated negotiation for peer-to-peer electricity trading in local energy markets,” Energies, vol. 13, no. 4, 2020. [Online]. Available: https://doi.org/10.3390/en13040920.
2. S. M. Kadiri, A. O. Bagre, M. B. Camara, B. Dakyo, and Y. Coulibaly, “Electrical power distribution status in west assessment and perspective overview,” in Proc. IEEE Int. Conf. Renewable Energy Res. Appl. (ICRERA), 2019. [Online]. Available: https://doi.org/10.1109/ICRERA47325.2019.8997112.
3. W. Tsai, C. Tu, C. Hong, and W. Lin, “A review of state-of-the-art and short-term forecasting models for solar PV power generation,” Energies, vol. 16, no. 14, 2023. [Online]. Available: https://doi.org/10.3390/en16145436.
4. K. Tu, H. Xiong, and F. Chen, “Comprehensive prediction of generation power in photovoltaic microgrid,” in Proc. IEEE Joint Int. Inf. Technol. Artif. Intell. Conf., 2019. [Online]. Available: https://doi.org/10.1109/ITAIC.2019.8785746.
5. O. T. Ibitoye, O. S. Agunbiade, T. W. Ilemobola, A. B. Oluwadare, P. C. Ofodu, K. O. Lawal, and J. O. Dada, “Nigeria electricity grid and the potentials of renewable energy integration: A concise review,” in Proc. IEEE 7th Int. Energy Conf. (ENERGYCON), 2022, pp. 1-4. [Online]. Available: https://doi.org/10.1109/ENERGYCON53164.2022.9830349.
6. Y. N. Chanchangi, F. Adu, A. Ghosh, S. Sundaram, and T. K. Mallick, “Nigeria’s energy review: Focusing on solar energy potential and penetration,” Environ. Dev. Sustain., vol. 25, no. 7, pp. 5755-5796, 2023. [Online]. Available: https://doi.org/10.1007/s10668-022-02308-4.
7. C. K. Rao, S. Sahoo, and F. F. Yanine, “Forecasting electric power generation in a photovoltaic power system for smart energy management,” in Proc. Int. Conf. Intell. Controller Comput. Smart Power (ICICCSP), 2022. [Online]. Available: https://doi.org/10.1109/ICICCSP53532.2022.9862396.
8. M. N. Akhter et al., “An hour-ahead PV power forecasting method based on an RNN-LSTM model for three different PV plants,” Energies, vol. 15, p. 2243, 2022. [Online]. Available: https://doi.org/10.3390/EN15062243.
9. Y. Wang, Y. Shen, S. Mao, X. Chen, and H. Zou, “LASSO and LSTM integrated temporal model for short-term solar intensity forecasting,” IEEE Internet Things J., vol. 6, pp. 2933-2944, 2019. [Online]. Available: https://doi.org/10.1109/JIOT.2018.2877510.
10. F. Jin, “Photovoltaic power generation prediction based on k-means clustering analysis and GRO-CNN-LSTM attention,” Math. Model. Algorithm Appl., vol. 2, no. 1, pp. 61-68, 2024. [Online]. Available: https://doi.org/10.54097/qdcrpc46.
11. R. Kabir, A. Elmouatamid, H. Elkhoukhi, and P. W. Pong, “Photovoltaic power forecasting using neural networks for short and medium-term dependencies,” in Proc. IEEE Texas Power Energy Conf. (TPEC), 2024, pp. 1-6. [Online]. Available: https://doi.org/10.1109/TPEC60005.2024.10472207.
12. T. Magesh et al., “Prediction of solar power irradiance using machine learning and deep learning for smart grid integration,” in Proc. Int. Conf. Recent Adv. Electr. Electron. Ubiquitous Commun. Comput. Intell. (RAEEUCCI), 2024, pp. 1-6. [Online]. Available: https://doi.org/10.1109/RAEEUCCI61380.2024.10547750.
13. C. K. Rodriguez-Leguizamon et al., “PV power generation forecasting based on XGBoost and LSTM models,” in Proc. IEEE Workshop Power Electron. Power Qual. Appl. (PEPQA), 2023, pp. 1-6. [Online]. Available: https://doi.org/10.1109/PEPQA59611.2023.10325757.
14. H. He, R. Hu, Y. Zhang, Y. Zhang, and R. Jiao, “A power forecasting approach for PV plant based on irradiance index and LSTM,” in Proc. 37th Chin. Control Conf. (CCC), 2018, pp. 9404-9409. [Online]. Available: https://doi.org/10.23919/ChiCC.2018.8483960.
15. T. Limouni et al., “Univariate and multivariate LSTM models for one step and multistep PV power forecasting,” Int. J. Renew. Energy Dev., vol. 11, no. 3, pp. 815-824, 2022. [Online]. Available: https://doi.org/10.14710/ijred.2022.43953.
16. R. Ahmed et al., “Computationally expedient photovoltaic power forecasting: A LSTM ensemble method augmented with adaptive weighting and data segmentation technique,” Energy Convers. Manage., vol. 258, p. 115563, 2022. [Online]. Available: https://doi.org/10.1016/j.enconman.2022.115563.
17. H. Hersbach et al., “ERA5 hourly data on single levels from 1940 to present,” Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2023. [Online]. Available: https://doi.org/10.24381/cds.adbb2d47.
18. M. AlKandari and I. Ahmad, “Solar power generation forecasting using ensemble approach based on deep learning and statistical methods,” Appl. Comput. Informat., vol. 20, no. 3/4, pp. 231-250, 2024. [Online]. Available: https://doi.org/10.1016/j.aci.2019.11.002.
19. A. M. Hayajneh, F. Alasali, A. Salama, and W. Holderbaum, “Intelligent solar forecasts: Modern machine learning models and TinyML role for improved solar energy yield predictions,” IEEE Access, vol. 12, pp. 10846-10864, 2024. [Online]. Available: https://doi.org/10.1109/ACCESS.2024.3354703.