CUED Publications database

Electricity load demand time series forecasting with Empirical Mode Decomposition based Random Vector Functional Link network

Qiu, X and Suganthan, PN and Amaratunga, GAJ (2017) Electricity load demand time series forecasting with Empirical Mode Decomposition based Random Vector Functional Link network. In: UNSPECIFIED pp. 1394-1399..

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Abstract

© 2016 IEEE. Short-term electricity load demand forecasting is a critical process in the management of modern power system. An ensemble method composed of Empirical Mode Decomposition (EMD) and Random Vector Functional Link network (RVFL) is presented in this paper. Due to the randomly generated weights between input and hidden layers and the close form solution for parameter tuning, RVFL network is a universal approximator with the advantages of fast training. By introducing ensemble approach via EMD into RVFL network, the performance can be significantly improved. Five electricity load demand datasets from Australian Energy Market Operator (AEMO) were used to evaluate the performance of the proposed method. The attractiveness of the proposed EMD based RVFL network can be demonstrated by the comparison with six benchmark methods.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div B > Electronics, Power & Energy Conversion
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:05
Last Modified: 05 Sep 2017 01:51
DOI: