Electricity load forecasting using hybrid wavelet neural network based on parallel prediction method

Sovann, N. and Nallagownden, P. and Baharudin, Z. (2017) Electricity load forecasting using hybrid wavelet neural network based on parallel prediction method. In: UNSPECIFIED.

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Abstract

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. © 2016 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 2; Conference of 6th International Conference on Intelligent and Advanced Systems, ICIAS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125970
Uncontrolled Keywords: Electric load forecasting; Electric power plant loads; Forecasting; Inverse problems; Neural networks, Different frequency; Electricity load forecasting; Inverse wavelet transforms; Load forecasting; Load profiles; Multiple loads; Prediction methods; Wavelet neural networks, Wavelet transforms
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 16:20
Last Modified: 09 Nov 2023 16:20
URI: https://khub.utp.edu.my/scholars/id/eprint/8984

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