Research on Short-term Load Forecasting of Power System Based on Wavelet Denoising and Artificial Neural Network
Author: 
Zihang LiuAuthors Info & Claims
Pages 60 - 65
Abstract
Power system short-term load forecasting plays an important role in the reliable, safe and economic operation of power system. Power system load forecasting data is an important basis for power grid planning, scheduling, marketing and other departments. In order to fully mine the effective information in the load data of power system and carry out accurate short-term load forecasting, this paper proposes a Long Short-Term Memory (LSTM) model based on wavelet denoising to build a short-term load forecasting model. Wavelet denoising method is used for data preprocessing, so as to ensure the accuracy of the prediction model, while LSTM is used to achieve high-quality short-term load forecasting of the power system. The method proposed in this paper has the advantages of strong training and learning ability, fast convergence speed, high prediction accuracy and strong adaptability.
References
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Published In
October 2022
753 pages
ISBN:9781450397780
DOI:10.1145/3569966
Copyright © 2022 ACM.
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Association for Computing Machinery
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Published: 20 December 2022
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CSSE 2022
CSSE 2022: 2022 5th International Conference on Computer Science and Software Engineering
October 21 - 23, 2022
Guilin, China
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Overall Acceptance Rate 33 of 74 submissions, 45%
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