Reference
1. Ding, H., Zhou, D., Liu, G., and Zhou, P., ’Cost Reduction or
Electricity Penetration: Government R&D-Induced Pv Development and
Future Policy Schemes’, Renewable and Sustainable Energy Reviews, 2020,
124, p. 109752.
2. Mayer, M.J. and Gróf, G., ’Extensive Comparison of Physical Models
for Photovoltaic Power Forecasting’, Applied Energy, 2021, 283, p.
116239.
3. Mellit, A., Massi Pavan, A., Ogliari, E., Leva, S., and Lughi, V.,
’Advanced Methods for Photovoltaic Output Power Forecasting: A Review’,
Applied Sciences, 2020, 10, (2), p. 487.
4. Mohamad Radzi, P.N.L., Akhter, M.N., Mekhilef, S., and Mohamed Shah,
N., ’Review on the Application of Photovoltaic Forecasting Using Machine
Learning for Very Short-to Long-Term Forecasting’, Sustainability, 2023,
15, (4), p. 2942.
5. Ge, L., Du, T., Li, C., Li, Y., Yan, J., and Rafiq, M.U., ’Virtual
Collection for Distributed Photovoltaic Data: Challenges, Methodologies,
and Applications’, Energies, 2022, 15, (23), p. 8783.
6. Li, D.H., Cheung, G.H., and Lam, J.C., ’Analysis of the Operational
Performance and Efficiency Characteristic for Photovoltaic System in
Hong Kong’, Energy Conversion and Management, 2005, 46, (7-8), pp.
1107-1118.
7. Sun, W., Zhang, T., Tao, R., and Wang, A., ’Short-Term Photovoltaic
Power Prediction Modeling Based on Adaboost Algorithm and Elman’, in,
2020 10th International Conference on Power and Energy Systems (ICPES),
(IEEE, 2020)
8. Ma, X. and Zhang, X., ’A Short-Term Prediction Model to Forecast
Power of Photovoltaic Based on Mfa-Elman’, Energy Reports, 2022, 8, pp.
495-507.
9. Li, G., Xie, S., Wang, B., Xin, J., Li, Y., and Du, S., ’Photovoltaic
Power Forecasting with a Hybrid Deep Learning Approach’, IEEE Access,
2020, 8, pp. 175871-175880.