Acknowledgement
본 논문은 전남대학교 학술연구비(과제번호: 2021-2176) 지원에 의하여 연구되었음.
References
- H. Kim, G. Yang, C. Nam, S. Jeong and S. P. Jung,"Solar photovoltaic industry in Korea: cur rent status and perspectives,"J. of Korean Society of Environmental Engineers, vol. 45, no. 2, Fe b. 2023, pp. 107-119. https://doi.org/10.4491/KSEE.2023.45.2.107
- M. Kim, S. Jung, J. Kim, H. Lee, B. Kim and S. Kim,"A Study on Artificial Neural Network-based Solar Radiation Forecasting for Efficient Solar Photovoltaic System,"J. of Korean Institute of Intelligent Systems, vol. 29, no. 6, Dec. 2019, pp. 501-506. https://doi.org/10.5391/JKIIS.2019.29.6.501
- S.-M. Lee, J.-A. Noh, S.-J. Kang and J.-W. Park,"A Study on Machine Learning Models and Trends for Forecasting of Photovoltaic Power Generation,"Conf. The Korean Institute of Electrical Engineers, Oct. 2021, pp. 169-170.
- S.-R. Jung, J. Koh and S.-K. Lee, "Recurrent Network based Prediction System of Agricultural Photovoltaic Power Generation," J. of the Korea institute of Electronic Communication, vol. 17, no. 5, 2022, pp. 825-832.
- M. A. Bou-Rabee, M. Y. Naz, I. E. Albalaa and S. A. Sulaiman,"BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones," Energies, vol. 15, no. 6, 2022, 2226.
- S.-R. Jung, K.-W. Park and S.-K. Lee, "Intelligent Prediction System for Diagnosis of Agricultural Photovoltaic Power Generation," J. of the Korea institute of Electronic Communication, vol. 16, no. 5, 2021, pp. 859-866.
- M.-Y. Kang, "Renewable Energy Generation Prediction Model using Meteorological Big Data,"J. of the Korea institute of Electronic Communication, vol. 18, no. 1, 2023, pp. 39-44.
- Y. Zhou, Y. Li, D. wang, and Y. Liu"A multi-step ahead global solar radiation prediction method using an attention-based transformer model with an interpretable mechanism,"Int. J. of Hydrogen Energy, vol. 48, no. 40, May, 2023, pp. 15317-15330.
- A. Heidari and D. Khovalyg,"Short-term energy use prediction of solar-assisted water heating system: Application case of combined attention-based LSTM and time-series decomposition,"Solar Energy vol. 207, no 1, Sept., 2020, pp. 626-639. https://doi.org/10.1016/j.solener.2020.07.008
- A. Graves, "Long short-term memory,"Supervised sequence labelling with recurrent neural networks, Berlin : Springer, 2012, pp. 37-45.
- T. Peng, C. Zhang, J. Zhou and M. S. Nazir, "An integrated framework of Bi-directional lo ng-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting,"Energy vol. 221, Apr., 2021. pp. 119887.
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomesz and I. Polosukhin,"Attention is all you need," Advances in neural information processing systems 30, 2017.
- N. Rahimi, S. Park, W. Choi, B. Oh, S. Kim, Y. Cho and D. Lee, "A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms."J. of Electrical Engineering & Technology, Jan., 2023, pp. 719-733.