CUED Publications database

Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting

Qiu, X and Ren, Y and Suganthan, PN and Amaratunga, GAJ (2017) Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting. Applied Soft Computing Journal, 54. pp. 246-255. ISSN 1568-4946

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Abstract

© 2017 Elsevier B.V. Load demand forecasting is a critical process in the planning of electric utilities. An ensemble method composed of Empirical Mode Decomposition (EMD) algorithm and deep learning approach is presented in this work. For this purpose, the load demand series were first decomposed into several intrinsic mode functions (IMFs). Then a Deep Belief Network (DBN) including two restricted Boltzmann machines (RBMs) was used to model each of the extracted IMFs, so that the tendencies of these IMFs can be accurately predicted. Finally, the prediction results of all IMFs can be combined by either unbiased or weighted summation to obtain an aggregated output for load demand. The electricity load demand data sets from Australian Energy Market Operator (AEMO) are used to test the effectiveness of the proposed EMD-based DBN approach. Simulation results demonstrated attractiveness of the proposed method compared with nine forecasting methods.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div B > Electronics, Power & Energy Conversion
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:11
Last Modified: 07 Sep 2017 01:48
DOI: