The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Solar Power Prediction Based on Satellite Images and Support Vector Machine
Abstract
Penetration of solar energy into main grid has gradually increased in recent years due to a growing number of large-scale photovoltaic (PV) farms. The power output of these PV farms may fluctuate due to a wide variability of meteorological conditions, and, thus, we need to compensate for this effect in advance. In this paper, we propose a solar power prediction model based on various satellite images and a support vector machine (SVM) learning scheme. The motion vectors of clouds are forecasted by utilizing satellite images of atmospheric motion vectors (AMVs). We analyze 4 years' historical satellite images and utilize them to configure a large number of input and output data sets for the SVM learning. We compare the performance of the proposed SVM-based model, the conventional time-series model, and an artificial neural network (ANN) model in terms of prediction accuracy.
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Cited by
Dwivedi, Y. K., Hughes, L., Kar, A. K., Baabdullah, A. M., Grover, P., Abbas, R., Andreini, D., Abumoghli, I., Barlette, Y., Bunker, D., et al. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102456 · Cited by 761
Wang, H., Yi, H., Peng, J., Wang, G., Liu, Y., Jiang, H., & Liu, W. (2017) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.10.008 · Cited by 406
Energetics Systems and artificial intelligence: Applications of industry 4.0
Ahmad, T., Zhu, H., Zhang, D., Tariq, R., Bassam, A., Ullah, F., AlGhamdi, A. S., & Alshamrani, S. S. (2022) · Energy Reports · doi:10.1016/j.egyr.2021.11.256 · Cited by 369
Spatio-Temporal Graph Neural Networks for Multi-Site PV Power Forecasting
Simeunovic, J., Schubnel, B., Alet, P.-J., & Carrillo, R. E. (2022) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2021.3125200 · Cited by 268
Support Vector Machine-Based Islanding and Grid Fault Detection in Active Distribution Networks
Baghaee, H. R., Mlakic, D., Nikolovski, S., & Dragicevic, T. (2020) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2019.2916621 · Cited by 235
Metadata
- Authors
- Jang, H. S., Bae, K. Y., Park, H.-S., & Sung, D. K.
- Venue
- IEEE Transactions on Sustainable Energy
- Published
- 2016-07
- Volume
- 7
- Issue
- 3
- Pages
- 1255-1263
- ISSN
- 1949-3029 (ISSN-L)
- 1949-3037
- OpenAlex
- W2327473694
- Cited by
- 340
- References
- 2
- FWCI
- 17.70
- Type
- journal article
- Language
- en
- Open access
- closed
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- Jang, H. S., Bae, K. Y., Park, H.-S., & Sung, D. K. (2016). Solar Power Prediction Based on Satellite Images and Support Vector Machine. IEEE Transactions on Sustainable Energy. https://doi.org/10.1109/tste.2016.2535466
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