Intelligent prediction of capacity margin in different time periods based on wavelet analysis

Yaohui Sun; Chenguang Yang; Xiaojuan Chen; Zhi Fang

doi:10.1117/12.3024234

13 May 2024 Intelligent prediction of capacity margin in different time periods based on wavelet analysis

Yaohui Sun, Chenguang Yang, Xiaojuan Chen, Zhi Fang

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131599L (2024) https://doi.org/10.1117/12.3024234
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

Begin In order to understand the intelligent prediction of capacity margin in different time periods, the author proposes research on intelligent prediction of capacity margin in different time periods based on wavelet analysis. The author first uses wavelet decomposition and neural networks as tools to predict electricity prices in different time periods. The changes in the electricity price sequence during different time periods are relatively single, which is conducive to the learning and training of neural networks, thereby improving prediction accuracy. Secondly, compare the predicted results of time slot capacity margin based on wavelet analysis technology with the actual values. Finally, the experimental results indicate that the average relative percentage error of short-term electricity price prediction can reach 11.40%. The intelligent Jun page measurement method for capacity margin based on wavelet analysis proposed by the author can effectively improve the prediction accuracy of power grid capacity margin and has strong practicality and effectiveness.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yaohui Sun, Chenguang Yang, Xiaojuan Chen, and Zhi Fang "Intelligent prediction of capacity margin in different time periods based on wavelet analysis", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131599L (13 May 2024); https://doi.org/10.1117/12.3024234

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KEYWORDS

Wavelets

Neural networks

Power consumption

Analytical research

Education and training

Wavelet transforms

Signal processing

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