A Temporal Convolutional Network Based Hybrid Model for Short-Term Electricity Price Forecasting

Haoran Zhang; Weihao Hu; Di Cao; Qi Huang; Zhe Chen; Frede Blaabjerg

doi:10.17775/CSEEJPES.2020.04810

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A Temporal Convolutional Network Based Hybrid Model for Short-Term Electricity Price Forecasting

Regular Papers | 更新时间：2026-10-15

- A Temporal Convolutional Network Based Hybrid Model for Short-Term Electricity Price Forecasting
- A Temporal Convolutional Network Based Hybrid Model for Short-Term Electricity Price Forecasting
- 中国电机工程学会电力与能源系统学报(英文) 2024年10卷第3期页码：1119-1130
- 作者机构：
  
  1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China,Chengdu,China
  2. Department of Energy Technology, Aalborg University, Aalborg,Denmark
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2020.04810
  中图分类号：
- 纸质出版：2024-05-01
- 稿件说明：
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Haoran Zhang, Weihao Hu, Di Cao, 等. A Temporal Convolutional Network Based Hybrid Model for Short-Term Electricity Price Forecasting[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(3):1119-1130.

Haoran Zhang, Weihao Hu, Di Cao, et al. A Temporal Convolutional Network Based Hybrid Model for Short-Term Electricity Price Forecasting[J]. CSEE Journal of Power and Energy Systems, 2024, 10(3): 1119-1130.
Haoran Zhang, Weihao Hu, Di Cao, 等. A Temporal Convolutional Network Based Hybrid Model for Short-Term Electricity Price Forecasting[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(3):1119-1130. DOI： 10.17775/CSEEJPES.2020.04810.

Haoran Zhang, Weihao Hu, Di Cao, et al. A Temporal Convolutional Network Based Hybrid Model for Short-Term Electricity Price Forecasting[J]. CSEE Journal of Power and Energy Systems, 2024, 10(3): 1119-1130. DOI： 10.17775/CSEEJPES.2020.04810.

摘要

Abstract

Electricity prices have complex features

such as high frequency

multiple seasonality

and nonlinearity. These factors will make the prediction of electricity prices difficult. However

accurate electricity price prediction is important for energy producers and consumers to develop bidding strategies. To improve the accuracy of prediction by using each algorithms' advantages

this paper proposes a hybrid model that uses the Empirical Mode Decomposition (EMD)

Autoregressive Integrated Moving Average (ARIMA)

and Temporal Convolutional Network (TCN). EMD is used to decompose the electricity prices into low and high frequency components. Low frequency components are forecasted by the ARIMA model and the high frequency series are predicted by the TCN model. Experimental results using the realistic electricity price data from Pennsylvania-New Jersey-Maryland (PJM) electricity markets show that the proposed method has a higher prediction accuracy than other single methods and hybrid methods.

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