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Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Research Article

Decentralized Wind Power Forecasting Method Based on Informer

Author(s): Yuting Yan, Wei Li*, Shi Su, Hao Bai, Yang Yang, Shuhui Pan, Zhiyong Yuan, Jun Chen and Qindao Zhao

Volume 15, Issue 8, 2022

Published on: 26 September, 2022

Page: [679 - 687] Pages: 9

DOI: 10.2174/2352096515666220818122603

Price: $65

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Abstract

Background: Improving the accuracy of decentralized wind power forecasting has become an important means to ensure the safe and stable operation of the power grid. Traditional prediction methods cannot meet the requirements of precise wind power prediction for long time series.

Methods: A decentralized wind power prediction model based on Informer is established. Introducing the model's sparse self-attention mechanism, self-attention distillation mechanism and The generative decoder model realizes the long-term sequence power prediction of decentralized wind power.

Results: The results of the proposed model are analyzed by comparing it with traditional neural network models such as LSTM and feed-forward neural networks. The root mean square error (RMSE) can be reduced by up to 6.3%.

Conclusion: It is proved that the Informer model prediction has a lower prediction error and performs better in long-term sequence power pre-tasks.

Keywords: Decentralized wind, power, wind power, time series, forecast, informer

Graphical Abstract

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