基于并行预测方法的混合小波神经网络电力负荷预测

N. Sovann, P. Nallagownden, Z. Baharudin
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引用次数: 3

摘要

本文提出了一种新的混合负荷预测模型,提高了1 ~ 24小时负荷预测的准确性和鲁棒性。它由小波变换和基于并行预测方法的神经网络组成,称为“PWNN”。采用小波变换将原负荷序列分解为多个不同频率的负荷子序列。然后,采用神经网络并行预测方法对各负荷子序列进行预测。利用小波反变换可以得到负荷预测结果。结果表明,与其他模型相比,PWNN在负荷预测方面的准确性和鲁棒性都有显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Electricity load forecasting using hybrid wavelet neural network based on parallel prediction method
This paper presents a new hybrid load forecast model to improve the accuracy and robustness of load profile forecasting (1–24 hours ahead). It comprises of Wavelet transform and Neural network based on parallel prediction method, which is called "PWNN". Wavelet transform is used to decompose the original load series into multiple load sub-series with different frequencies. Then, neural network is used to predict each load sub-series using parallel prediction method. The load forecast can be obtained by inverse wavelet transform. The results indicate that PWNN has a significant improvement of accuracy and robustness in load forecasting over other models used for comparison in this study.
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