The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Time series forecasting of solar power generation for large-scale photovoltaic plants
Abstract
Abstract Accurate solar power forecasting is essential for grid-connected photovoltaic (PV) systems especially in case of fluctuating environmental conditions. The prediction of PV power output is critical to secure grid operation, scheduling and grid energy management. One of the key elements in PV output prediction is time series analysis especially in locations where the historical solar radiation measurements or other weather parameters have not been recorded. In this work, several time series prediction methods including the statistical methods and those based on artificial intelligence are introduced and compared rigorously for PV power output prediction. Moreover, the effect of prediction time horizon variation for all the algorithms is investigated. Hourly solar power forecasting is carried out to verify the effectiveness of different models. The data utilized in the current work comprises 3640 h of operation data taken from a 20 MW grid-connected PV station in China.
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Time Series Analysis: Forecasting and Control.
Anderson, O. D., Box, G. E. P., & Jenkins, G. M. (1978) · The Statistician · doi:10.2307/2988198 · Cited by 5,298
Time Series Analysis, Forecasting and Control.
Kendall, M. G., Box, G. E. P., & Jenkins, G. M. (1971) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2344246 · Cited by 2,771
Hybrid speech recognition with Deep Bidirectional LSTM
Graves, A., Jaitly, N., & Mohamed, A.-R. (2013) · 2013 IEEE Workshop on Automatic Speech Recognition and Understanding · doi:10.1109/asru.2013.6707742 · Cited by 1,736
Shi, J., Lee, W., Liu, Y., Yang, Y., & Wang, P. (2012) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2012.2190816 · Cited by 795
Accurate photovoltaic power forecasting models using deep LSTM-RNN
Abdel-Nasser, M., & Mahmoud, K. (2019) · Neural Computing and Applications · doi:10.1007/s00521-017-3225-z · Cited by 752
Chen, C., Duan, S., Cai, T., & Liu, B. (2011) · Solar Energy · doi:10.1016/j.solener.2011.08.027 · Cited by 625
Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks
Gensler, A., Henze, J., Sick, B., & Raabe, N. (2016) · 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · doi:10.1109/smc.2016.7844673 · Cited by 614
A Bayesian Analysis of the Minimum AIC Procedure
Akaike, H. (1998) · Springer Series in Statistics · doi:10.1007/978-1-4612-1694-0_21 · Cited by 560
Drought forecasting using stochastic models
Mishra, A. K., & Desai, V. R. (2005) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-005-0238-4 · Cited by 492
Cited by
A review and taxonomy of wind and solar energy forecasting methods based on deep learning
Alkhayat, G., & Mehmood, R. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100060 · Cited by 312
Distributed energy systems: A review of classification, technologies, applications, and policies
Nadeem, T. B., Siddiqui, M., Khalid, M., & Asif, M. (2023) · Energy Strategy Reviews · doi:10.1016/j.esr.2023.101096 · Cited by 267
Metadata
- Authors
- Sharadga, H., Hajimirza, S., & Balog, R. S.
- Venue
- Renewable Energy
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 150
- Pages
- 797-807
- ISSN
- 0960-1481 (ISSN-L)
- OpenAlex
- W2997684871
- Cited by
- 441
- References
- 24
- FWCI
- 13.01
- Type
- journal article
- Language
- en
- Open access
- closed
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