An improved Transformer day-ahead electricity price forecasting model based on Patch mechanism and channel-independent structure

CHEN Zihong; HUANG Ningxin; LAI Zhihang; LAI Xiaowen; CHEN Xiaoting; CHEN Shuonan; GAO Feng

doi:10.11930/j.issn.1004-9649.202506072

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An improved Transformer day-ahead electricity price forecasting model based on Patch mechanism and channel-independent structure

更新时间：2026-08-04

- An improved Transformer day-ahead electricity price forecasting model based on Patch mechanism and channel-independent structure
- Vol. 59, Issue 5, Pages: 1-8(2026)
- 作者机构：
  
  1. 广东粤电电力销售有限公司,广东,广州,510630
  2. 北京清能互联科技有限公司,北京,100084
  3. 北京工业大学,北京,100124
- 作者简介：
- 基金信息：
- DOI：10.11930/j.issn.1004-9649.202506072
  CLC：
- Online First：15 May 2026，
  
  Published：2026
- 稿件说明：
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陈梓宏, 黄宁馨, 赖智航, 赖晓文, 陈潇婷, 陈硕楠, 高锋. 一种基于Patch机制与通道独立结构的改进Transformer日前电价预测方法[J]. 中国电力, 2026, 59(5): 1-8.

CHEN Zihong, HUANG Ningxin, LAI Zhihang, et al. An improved Transformer day-ahead electricity price forecasting model based on Patch mechanism and channel-independent structure[J]. 2026, 59(5): 1-8.
陈梓宏, 黄宁馨, 赖智航, 赖晓文, 陈潇婷, 陈硕楠, 高锋. 一种基于Patch机制与通道独立结构的改进Transformer日前电价预测方法[J]. 中国电力, 2026, 59(5): 1-8. DOI： 10.11930/j.issn.1004-9649.202506072.

CHEN Zihong, HUANG Ningxin, LAI Zhihang, et al. An improved Transformer day-ahead electricity price forecasting model based on Patch mechanism and channel-independent structure[J]. 2026, 59(5): 1-8. DOI： 10.11930/j.issn.1004-9649.202506072.

摘要

针对电力现货市场日前电价预测中普遍存在的时序特征提取不足、特殊日类型场景适应性差的问题，提出一种基于Transformer架构的改进预测模型。引入Patch机制增强局部时序特征提取，结合通道独立结构增加多变量特征学习效率，通过多头注意力机制捕获全局电价波动规律。基于广东省电力现货市场历史数据进行方法验证，与基准Transformer模型相比，周末场景的平均绝对误差从32.95降低至 23.88，节假日场景的平均绝对误差从78.33降低至70.33。对量价偏移现象的适应性显著优于基准模型，在竞价空间大于6万MW时能准确捕捉价格下限上升趋势，所提方法在不同场景（特别是特殊场景）预测精度显著提升，对量价偏移现象适应性好。

Abstract

To address the common problems of insufficient temporal feature extraction and poor adaptability to special day scenarios in day-ahead electricity spot market price forecasting

this paper proposes an improved forecasting model based on the Transformer architecture. The Patch mechanism is introduced to enhance local temporal feature extraction

and the channel-independent structure is combined to improve the learning efficiency of multivariate features. In addition

the multi-head attention mechanism is adopted to capture the global price fluctuation patterns. The proposed method is verified based on the historical data of the Guangdong electricity spot market. Compared with the baseline Transformer model

the mean absolute error (MAE) of the proposed model is decreased from 32.95 to 23.88 in weekend scenarios

and from 78.33 to 70.33 in holiday scenarios. The model exhibits significantly better adaptability to the phenomenon of quantity-price deviation than the baseline model

and can accurately capture the upward trend of price floors when the bidding space exceeds 60000 MW. The proposed model achieves a significant improvement in prediction accuracy under different scenarios (especially special scenarios) and has good adaptability to quantity-price deviations.

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Keywords

references

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