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

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

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
- CSEE Journal of Power and Energy Systems Vol. 10, Issue 3, Pages: 1119-1130(2024)
- 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
  CLC：
- Published：01 May 2024
- 稿件说明：
移动端阅览
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：

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.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Short-term forecasting of individual residential load based on deep learning and K-means clustering

Transmission Power-Oriented Forecasting Towards Stability-Constrained Operation of Renewable Energy Power Plants with Energy Storages

Explainable AI-Based Short-Term Voltage Stability Mechanism Analysis: Explainability Measure and Stability-Oriented Preventive Control

Segmented Forecasting of Electric Load Under Pandemic Period Based on the ESD-ABiLSTMQR Method

Ultra-Short-Term Photovoltaic Power Prediction Based on Improved Temporal Convolutional Network and Feature Modeling

Related Author

Fujia Han

Tianjiao Pu

Maozhen Li

Gareth Taylor

Feilong Fan

Chuanqing Pu

Jun Wang

Nengling Tai

Related Institution

Artificial Intelligence Application Department, China Electric Power Research Institute

Department of Electronic and Computer Engineering, Brunel University London, Uxbridge

Brunel Institute of Power Systems, Brunel University, Uxbridge

College of Smart Energy, Shanghai Jiao Tong University

Shanghai Non-carbon Energy Conversion and Utilization Institute

AI问答

⁰