期刊
INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE (ICCS 2017)
卷 108, 期 -, 页码 1308-1317出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2017.05.055
关键词
Electricity Price Forecasting; Kernel Ridge Regression; Support Vector Regression; Empirical Mode Decomposition; Ensemble Learning
类别
资金
- National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme
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. (C) 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Science
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