CUED Publications database

Short-term Electricity Price Forecasting with Empirical Mode Decomposition based Ensemble Kernel Machines

Qiu, X and Suganthan, PN and Amaratunga, GAJ (2017) Short-term Electricity Price Forecasting with Empirical Mode Decomposition based Ensemble Kernel Machines. In: UNSPECIFIED pp. 1308-1317..

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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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div B > Electronics, Power & Energy Conversion
Depositing User: Cron Job
Date Deposited: 30 Aug 2017 20:06
Last Modified: 05 Sep 2017 01:50
DOI: