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|Title:||Short-term electricity price forecasting with empirical mode decomposition based ensemble Kernel machines||Authors:||Qiu, Xueheng
Suganthan, Ponnuthurai Nagaratnam
Amaratunga, Gehan A. J.
|Keywords:||Kernel Ridge Regression
DRNTU::Engineering::Electrical and electronic engineering
Electricity Price Forecasting
|Issue Date:||2017||Source:||Qiu, X., Suganthan, P. N., & Amaratunga, G. A. J. (2017). Short-term electricity price forecasting with empirical mode decomposition based ensemble Kernel machines. Procedia Computer Science, 108, 1308-1317. doi:10.1016/j.procs.2017.05.055||Series/Report no.:||Procedia Computer Science||Abstract:||Short-term electricity price forecasting is a critical issue for the operation of both electricity markets and power systems. An ensemble method composed of Empirical Mode Decomposition (EMD), Kernel Ridge Regression (KRR) and Support Vector Regression (SVR) is presented in this paper. For this purpose, the electricity price signal was first decomposed into several intrinsic mode functions (IMFs) by EMD, followed by a KRR which was used to model each extracted IMF and predict the tendencies. Finally, the prediction results of all IMFs were combined by an SVR to obtain an aggregated output for electricity price. The electricity price datasets from Australian Energy Market Operator (AEMO) are used to test the effectiveness of the proposed EMD-KRR-SVR approach. Simulation results demonstrated attractiveness of the proposed method based on both accuracy and efficiency.||URI:||https://hdl.handle.net/10356/89299
|ISSN:||1877-0509||DOI:||10.1016/j.procs.2017.05.055||Rights:||© 2017 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
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