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Wind Power Generation Prediction Based on LSTM

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Lixin CuiAuthors Info & Claims

ICMAI '19: Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence

Pages 85 - 89

https://doi.org/10.1145/3325730.3325735

Published: 12 April 2019 Publication History

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Abstract

In recent years, with the increasing proportion of wind power generation, the impact of wind power generation on grid security is also growing. This makes the prediction accuracy of wind power generation higher and higher. This paper utilizes the LSTM model of the deep learning domain to predict wind power generation. Besides, Auto Encoder is employed to reduce the data dimension, improve the generalization ability of the model, and shorten the training time. Simulation experiments show that the LSTM model has better prediction accuracy than other machine learning model such as SVM.

References

[1]

FAN, H., CHEN, C., & JIN, Y. (2013). Research on Short-term Wind Power Prediction. Journal of Shanghai University of Electric Power, 1, 012.

Google Scholar

[2]

Billinton, R., Chen, H., & Ghajar, R. (1996). A sequential simulation technique for adequacy evaluation of generating systems including wind energy. IEEE Transactions on Energy Conversion, 11(4), 728--734.

Crossref

Google Scholar

[3]

TIAN, B., PIAO, Z., & WANG, H. (2016). Short Term Prodiction of Wind Power Based on Time Series Modeling.

Google Scholar

[4]

NAN, X., LI, Q., & QIU, D. (2013). Analysis and forecast of wind power fluctuation based on symbolized time series theory. Electric Power, 46(6), 75--79.

Google Scholar

[5]

Billinton, R., Chen, H., & Ghajar, R. (1996). Time-series models for reliability evaluation of power systems including wind energy. Microelectronics Reliability, 36(9), 1253--1261.

Crossref

Google Scholar

[6]

Pai, P. F., & Hong, W. C. (2005). Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electric Power Systems Research, 74(3), 417--425.

Crossref

Google Scholar

[7]

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.

Google Scholar

[8]

Xiao, Y. S., Wang, W. Q., & Huo, X. P. (2007). Study on the time-series wind speed forecasting of the wind farm based on neural networks. Energy Conservation Technology, 2, 2.

Google Scholar

[9]

XueFeng, X., & Guo-Dong, Z. (2016). A survey on deep learning for natural language processing. Acta Automatica Sinica, 42(10), 1445--1465.

Google Scholar

[10]

Deng, L., Hinton, G., & Kingsbury, B. (2013, May). New types of deep neural network learning for speech recognition and related applications: An overview. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 8599--8603). IEEE.

Google Scholar

[11]

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735--1780.

Digital Library

Google Scholar

[12]

André Gensler, Janosch Henze, Bernhard Sick, and Nils Raabe, "Deep learning for solar power forecasting - an approach using AutoEncoder and LSTM neural networks," 2016 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2858--2865, 2016.

Google Scholar

[13]

A. Gensler, J. Henze, N. Raabe, and V. Pankraz, "GermanWindFarmData Set, 2016. {Online}. Available: http://ies-research.de/Software.

Google Scholar

Cited By

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Wang DXu MGuangming ZLuo FGao JChen Y(2025)Enhancing wind power forecasting accuracy through LSTM with adaptive wind speed calibration (C-LSTM)Scientific Reports10.1038/s41598-025-89398-y15:1Online publication date: 13-Feb-2025
https://doi.org/10.1038/s41598-025-89398-y
Widodo DIksan N(2024)Machine learning-driven wind energy mapping enhanced by natural neighbor interpolationJournal of Energy Systems10.30521/jes.14996318:4(193-206)Online publication date: 31-Dec-2024
https://doi.org/10.30521/jes.1499631
Cakiroglu CDemir SHakan Ozdemir MLatif Aylak BSariisik GAbualigah L(2024)Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysisExpert Systems with Applications10.1016/j.eswa.2023.121464237(121464)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121464
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Index Terms

Wind Power Generation Prediction Based on LSTM
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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ICMAI '19: Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence

April 2019

232 pages

ISBN:9781450362580

DOI:10.1145/3325730

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Southwest Jiaotong University
Xihua University: Xihua University

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Association for Computing Machinery

New York, NY, United States

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Published: 12 April 2019

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ICMAI 2019: 2019 4th International Conference on Mathematics and Artificial Intelligence

April 12 - 15, 2019

Chegndu, China

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Wang DXu MGuangming ZLuo FGao JChen Y(2025)Enhancing wind power forecasting accuracy through LSTM with adaptive wind speed calibration (C-LSTM)Scientific Reports10.1038/s41598-025-89398-y15:1Online publication date: 13-Feb-2025
https://doi.org/10.1038/s41598-025-89398-y
Widodo DIksan N(2024)Machine learning-driven wind energy mapping enhanced by natural neighbor interpolationJournal of Energy Systems10.30521/jes.14996318:4(193-206)Online publication date: 31-Dec-2024
https://doi.org/10.30521/jes.1499631
Cakiroglu CDemir SHakan Ozdemir MLatif Aylak BSariisik GAbualigah L(2024)Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysisExpert Systems with Applications10.1016/j.eswa.2023.121464237(121464)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121464
Van Poecke ATabari HHellinckx P(2024)Unveiling the backbone of the renewable energy forecasting process: Exploring direct and indirect methods and their applicationsEnergy Reports10.1016/j.egyr.2023.12.03111(544-557)Online publication date: Jun-2024
https://doi.org/10.1016/j.egyr.2023.12.031
Liu BHu BWang ZChen G(2023)Multi-Turbine Wind Power Forecasting with Simplified Firework Algorithm Optimized GCN-LSTM2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG)10.1109/ETFG55873.2023.10407594(1-6)Online publication date: 3-Dec-2023
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Jiang ZWang HXu RYu XSong SYu X(2023)Wind Power Point Prediction Based on VMD-GWO-LSTM2023 3rd International Conference on Energy, Power and Electrical Engineering (EPEE)10.1109/EPEE59859.2023.10351962(365-370)Online publication date: 15-Sep-2023
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Kim HLee YOk CKim DBaek S(2023)Embedding Climate Dynamics and Prediction with Deep Learning for Wind Power Forecasting: Short-Term to Long-Term Perspective2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386862(3929-3935)Online publication date: 15-Dec-2023
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Zhang WHe YYang S(2023)A multi-step probability density prediction model based on gaussian approximation of quantiles for offshore wind powerRenewable Energy10.1016/j.renene.2022.11.111202(992-1011)Online publication date: Jan-2023
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Jabbar MRaoof S(2023)Applications of Machine Learning for Renewable Energy: Issues, Challenges, and Future DirectionsHandbook of Smart Energy Systems10.1007/978-3-030-97940-9_71(735-747)Online publication date: 5-Aug-2023
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Geetika Sharma Madan Lal Kanwal Preet Singh Attwal (2022)Comprehensive Review on Deep Learning Algorithms for Wind Power PredictionInternational Journal of Next-Generation Computing10.47164/ijngc.v13i4.631Online publication date: 18-Nov-2022
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References

Cited By

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Recommendations

Dual-Branch Long-Term Wind Power Forecasting Method Based on Error Power Prediction

TISE-LSTM: A LSTM model for precipitation nowcasting with temporal interactions and spatial extract blocks

Short-term wind power forecasting based on multivariate/multi-step LSTM with temporal feature attention mechanism

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