A Novel Wavelet Neural Network Load Forecasting Algorithm with Adaptive Momentum Factor

Power load forecasting is an important component of modern power system project which is the foundation of economic operation. In order to effectively improve the precision of short period load forecasting, traditional neural network has slow convergence and easily falling into local optimum in the...

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Vydáno v:	IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (Online) s. 1673 - 1678
Hlavní autoři:	Zhang, Jin, Yan, Shuilin, Liu, Yang, Zhu, Weixiang, Zhao, Zhongwei
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 12.03.2021
Témata:	Convergence Load forecasting load forecasting algorithm Load modeling Neural networks power system Power system stability Prediction algorithms Predictive models Self-adaptive momentum factor short-term load forecasting wavelet transform
ISSN:	2689-6621
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Power load forecasting is an important component of modern power system project which is the foundation of economic operation. In order to effectively improve the precision of short period load forecasting, traditional neural network has slow convergence and easily falling into local optimum in the practical application. This paper proposes a novel neural network load forecasting algorithm with wavelet neural network and self-adaptive momentum factor. This algorithm combines the characteristics in the wavelet transform's time domain and frequency domain. By adding an adaptive momentum factor, the new algorithm has better stability and higher convergence rate. By comparing three different prediction models, the simulation results show that the improved algorithm model has higher convergence accuracy.
ISSN:	2689-6621
DOI:	10.1109/IAEAC50856.2021.9390726