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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Vydané v:IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (Online) s. 1673 - 1678
Hlavní autori: Zhang, Jin, Yan, Shuilin, Liu, Yang, Zhu, Weixiang, Zhao, Zhongwei
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 12.03.2021
Predmet:
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
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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