Short-term electricity price forecasting with empirical mode decomposition based ensemble Kernel machines

• Main Authors: Qiu, Xueheng, Suganthan, Ponnuthurai Nagaratnam, Amaratunga, Gehan A. J.
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2018
• Subjects: Kernel Ridge Regression; DRNTU::Engineering::Electrical and electronic engineering; Electricity Price Forecasting
• Online Access: https://hdl.handle.net/10356/89299; http://hdl.handle.net/10220/46162
• Institution: Nanyang Technological University

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.