Details of Research Outputs

TitleVery-Short-Term Solar Forecasting with Long Short-Term Memory (LSTM) Network
Author (Name in English or Pinyin)
Lin, Y.1,2; Duan, D.2,3; Hong, X.1,2; Cheng, X.4; Yang, L.5; Cui, S.2,6,7
Date Issued2020-05-29
Conference Name2020 Asia Energy and Electrical Engineering Symposium, AEEES 2020
Source Publication2020 Asia Energy and Electrical Engineering Symposium, AEEES 2020
Conference PlaceChengdu, China, China
DOI10.1109/AEEES48850.2020.9121512
Education discipline科技类
Published range国外学术期刊
Volume Issue Pages页: 963-967
References
[1] Xu G, Yu W, Griffith D, et al, "Toward integrating distributed energy resources and storage devices in smart grid", IEEE Internet of Things Journal, 2017, 4 (1): 192-204.
[2] Wilczak J, Finley C, Freedman J, et al., "The wind forecast improvement project (WFIP): A public-private partnership addressing wind energy forecast need", Bull. Amer. Meteorol. Soc., vol. 96, no. 10, pp. 1699-1718, 2015.
[3] L. K. Gan, J. K. H. Shek, M. A. Mueller, "Hybrid wind-photovoltaicdiesel-battery system sizing tool development using empirical approach life-cycle cost and performance analysis: A case study in Scotland", Energy Conversion and Management, vol. 106, pp. 479-494, 2015.
[4] Raza, Muhammad Qamar, Mithulananthan Nadarajah, and Chandima Ekanayake, "On recent advances in PV output power forecast", Solar Energy, vol. 136, pp. 125-144, Oct. 2016.
[5] M. G. De Giorgi, P. M. Congedo, M. Malvoni, "Photovoltaic power forecasting using statistical methods: impact of weather data", IET Sci. Meas. Technol., vol. 8, no. 3, pp. 90-97, May 2014.
[6] Y. Chu, B. Urquhart, S. M. Gohari, H. T. Pedro, J. Kleissl, C. F. Coimbra, "Short-term reforecasting of power output from a 48 mwe solar PV plant", Solar Energy, vol. 112, pp. 68-77, 2015.
[7] E. K. Hart, E. D. Stoutenburg, M. Z. Jacobson, "The potential of intermittent renewables to meet electric power demand: Current methods and emerging analytical techniques", Proc. IEEE, vol. 100, no. 2, pp. 322-334, Feb. 2012.
[8] M. Lave, J. Kleissl, J. S. Stein, "A wavelet-based variability model (WVM) for solar PV power plants", IEEE Trans. Sustain. Energy, vol. 4, no. 2, pp. 501-509, Apr. 2013.
[9] W. Chow, B. Urquhart, M. Lave, A. Dominguez, J. Kleissl, J. Shields, and B. Washom, "Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed", Solar Energy, vol. 85, no. 11, pp. 2881-2893, Nov. 2011.
[10] L. A. Fernandez-Jimenez et al., "Short-term power forecasting system for photovoltaic plants", Renew. Energy, vol. 44, pp. 311-317, 2012.
[11] Z. Zhen et al., "SVM based cloud classification model using total sky images for PV power forecasting", 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1-5, 2015.
[12] C.-L. Fua, H.-Y. Cheng, "Predicting solar irradiance with all-sky image features via regression", Solar Energy, vol. 97, pp. 537-550, November 2013.
[13] S. Cros, O. Liandrat, N. Sebastien, N. Schmutz, "Extracting cloud motion vectors from satellite images for solar power forecasting", Geoscience and Remote Sensing Symposium (IGARSS) 2014 IEEE International, pp. 4123-4126, 2014.
[14] R. Marquez, C. F. M. Coimbra, "Intra-hour DNI forecasting based on cloud tracking image analysis", Solar Energy, vol. 91, no. 0, pp. 327-336, 2013.
[15] M. Cervantes, H. Krishnaswami, W. Richardson, R. Vega, "Utilization of Low Cost Sky-Imaging Technology for Irradiance Forecasting of Distributed Solar Generation", 2016 IEEE, pp. 142-146, 2016.
[16] F. Wang et al., "Solar irradiance feature extraction and support vector machines based weather status pattern recognition model for short-term photovoltaic power forecasting", Energy Build., vol. 86, pp. 427-438, Jan. 2015.
[17] Y. Chu, H. T. Pedro, M. Li, C. F. Coimbra, "Real-time forecasting of solar irradiance ramps with smart image processing", Solar Energy, vol. 114, pp. 91-104, 2015.
[18] N. Al-Messabi, Y. Li, I. El-Amin, C. Goh, "Forecasting of photovoltaic power yield using dynamic neural networks", Proc. Int. Joint Conf. Neural Netw., pp. 1-5, 2012-Jun.
[19] F. Golestaneh, P. Pinson, H. B. Gooi, "Very short-term nonparametric probabilistic forecasting of renewable energy generation with application to solar energy", IEEE Trans. Power Syst., vol. 31, no. 5, pp. 3850-3863, Sep. 2016.
[20] Y. Lin, D. Duan, X. Hong, X. Han, X. Cheng, L. Yang, and S. Cui, "Transfer Learning on the Feature Extractions of Sky Images for Solar Power Production, " in Proceedings of 2019 IEEE PES General Meeting, Atlanta, GA, August 4-8, 2019.
[21] Takahashi M, Mori H, "A hybrid intelligent system approach to fore-casting of PV generation output", [J]. Journal of International Council on Electrical Engineering, 2013, 3 (4): 295-299.
[22] Alzahrani, P. Shamsi, C. Dagli, M. Ferdowsi, "Solar irradiance forecasting using deep neural networks", Procedia Computer Science, vol. 114, pp. 304-313, 2017.
[23] S. Hochreiter and J. Schmidhuber, "Long short-term memory", Neural computation, 9 (8):1735-1780, 1997.
[24] Graves, "Sequence transduction with recurrent neural networks", arXiv, pp. 1211. 3711, 2012.
[25] R. Jozefowicz, W. Zaremba, I. Sutskever, "An empirical exploration of recurrent network architectures", Proc. 32nd Int. Conf. Mach. Learn. (ICML), pp. 2342-2350, 2015.
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
Identifierhttps://irepository.cuhk.edu.cn/handle/3EPUXD0A/1613
CollectionSchool of Science and Engineering
Corresponding AuthorCui, S.
Affiliation
1.School of Information Science and Engineering, Xiamen University, Xiamen, China
2.Shenzhen Research Institute of Big Data (SRIBD), Shenzhen, China
3.Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY, United States
4.School of Electronics Engineering and Computer Science, Peking University, Beijing, China
5.Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, United States
6.Future Network of Intelligence Institute (FNii), Chinese University of Hong Kong, Shenzhen, China
7.Department of Electrical and Computer Engineering, University of California, Davis, CA, United States
First Author AffilicationShenzhen Research Institute of Big Data
Corresponding Author AffilicationShenzhen Research Institute of Big Data;  Future Network of Intelligence Institute
Recommended Citation
GB/T 7714
Lin, Y.,Duan, D.,Hong, X.et al. Very-Short-Term Solar Forecasting with Long Short-Term Memory (LSTM) Network[C],2020.
Files in This Item:
There are no files associated with this item.
Related Services
Usage statistics
Google Scholar
Similar articles in Google Scholar
[Lin, Y.]'s Articles
[Duan, D.]'s Articles
[Hong, X.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Lin, Y.]'s Articles
[Duan, D.]'s Articles
[Hong, X.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Lin, Y.]'s Articles
[Duan, D.]'s Articles
[Hong, X.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.