Abstract
In order to reduce the prediction error of short-term wind power, an EMD-LSTM based short-term wind power combination prediction model is proposed. First, the EMD algorithm is used to decompose the original wind power data into several eigenmode function components and trend components, and then each component is combined with NWP (Numerical weather prediction) wind resource data to establish a long and short term memory network for regression prediction. Finally, the prediction results are compared with the actual wind power to verify the prediction performance of the proposed model. The experimental results can reasonably predict the wind power with high prediction accuracy. At the same time, an error analysis method based on wind power model is proposed. By decoupling method, we find the cause of power prediction error, that is, the error caused by each link. This method can effectively excavate the causes of errors in the prediction model and the proportion of errors in each link, and provide accurate support for further improvement of wind power prediction errors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, S., Yu, J., Wang, H.: Damages detection of aeroengine blades via deep learning algorithms. IEEE Trans. Instrum. Meas. 72, 1–11 (2023)
Zhu, K., Fu, Q.: Short-term photovoltaic power prediction based on EEMD-Kmeans-ALO-LSTM. Chin. J. Power Sources 47(01), 103–107 (2023). (in chinese)
Tascikaraoglu, A., Uzunoglu, M.: A review of combined approaches for prediction of short-term wind speed and power. Renew. Sustain. Energy Rev. 34(6), 243–254 (2014)
Zhou, N., Liao, J.Q., Wang, Q.G., et al.: Analysis and prospect of deep learning application in smart grid. Automat. Electric Power Syst. 43(4), 180–191 (2019). (in chinese)
Liu, Y., Liu, L.Q.: Wind power prediction based on LSTM-CNN opti-mization. Sci. J. Intell. Syst. Res. 3(4), 277–285 (2021)
Zhou, X.L., Tong, X.Y.: Ultra-short term wind power combination prediction based on CEEMD-SBO-LSSVR. Power Syst. Technol. 45(3), 855–864 (2021). (in chinese)
Zhu, Q.M., Li, H.Y., Wang, Z.Q., Chen, J.F., Wang, B.: Ultra-short term power prediction of wind farm based on long short term memory network. Power Syst. Technol. 41(12), 3797–3802 (2017). (in chinese)
Li, Y., Peng, C.H., Fu, Y., Shu, H.J.: Research on short-term prediction of wind power based on CNN-LSTM network model. J. East China Jiaotong Univ. 37(4), 109–115 (2019). (in chinese)
Gu, X.J., Chen, H.L., Sun, Z.X., et al.: Short-term wind power prediction based on VMD-RL-LSTM. Comput. Simulat. 40(04), 89–93+309 (2023). (in Chinese)
Gao, Y., Li, J.: Price prediction of international carbon financial market based on EMD-PSO-SVM error correction model. Chin. Populat. Resources Environ. 24(06), 163–170 (2014). (in chinese)
Dong, S., Zhou, T., Li, W., et al.: Economic evaluation of energy storage power station in distribution network. In: International Conference on Power and Energy Applications (ICPEA), pp.393–397 (2022)
Zhao, X., Qin, Y., Fu, H., Jia, L., Zhang, X.: Blind source extraction based on EMD and temporal correlation for rolling element bearing fault diagnosis. Smart Resilient Transp. 3(1), 52–65 (2021)
Han, L., Zhang, R.C.: Wind Power Prediction and Consumption Strategies. Science Press, Bei**g (2020). (in chinese)
Malhotra, P., Ramakrishnan, A., Anand, G., et al.: LSTM-based encoder-decoder for multi-sensor anomaly detection. ICDM 1(17), 413–422 (2016)
Acknowledgment
The authors gratefully acknowledge the support of science and technology project of State Grid Hunan Electric Power Company (5216A522000Z) and Natural Science Foundation General Project of Hunan (2023JJ30024).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Bei**g Paike Culture Commu. Co., Ltd.
About this paper
Cite this paper
Sun, J., Shen, Y., **, H., Wu, H., Li, S. (2024). Error Location Analysis of Wind Power Prediction Based on EMD-LSTM. In: Cai, C., Qu, X., Mai, R., Zhang, P., Chai, W., Wu, S. (eds) The Proceedings of 2023 International Conference on Wireless Power Transfer (ICWPT2023). ICWPT 2023. Lecture Notes in Electrical Engineering, vol 1160. Springer, Singapore. https://doi.org/10.1007/978-981-97-0865-9_45
Download citation
DOI: https://doi.org/10.1007/978-981-97-0865-9_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0864-2
Online ISBN: 978-981-97-0865-9
eBook Packages: EngineeringEngineering (R0)