上海交通大学学报

上海交通大学学报 >

2024 , Vol. 58 >Issue 9: 1420 - 1431

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.065

新型电力系统与综合能源

考虑多维影响因素的改进Transformer-PSO短期电价预测方法

  • 孙欣 ,
  • 王思敏 ,
  • 谢敬东 ,
  • 江海林 ,
  • 王森
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  • 上海电力大学 电气工程学院,上海 200090
孙 欣(1980—),博士,副教授,从事电力市场、能源互联网等方面研究; E-mail: sunxin@shiep.edu.cn.

收稿日期: 2023-02-27

  修回日期: 2023-05-04

  录用日期: 2023-05-19

  网络出版日期: 2023-06-12

基金资助

国家自然科学基金(U2066214)

收起

Improved Transformer-PSO Short-Term Electricity Price Prediction Method Considering Multidimensional Influencing Factors

  • SUN Xin ,
  • WANG Simin ,
  • XIE Jingdong ,
  • JIANG Hailin ,
  • WANG Sen
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  • College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Received date: 2023-02-27

  Revised date: 2023-05-04

  Accepted date: 2023-05-19

  Online published: 2023-06-12

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摘要

随着多元化电力市场的建设,电价影响因素日益增加,市场环境变化也更加剧烈.为提高市场短期电价的预测精度,提出一种考虑多种电价影响因素的改进Transformer-粒子群优化(PSO)算法短期电价预测方法.首先,在考虑历史电价、负荷的基础上进一步分析电价形成的相关因素,利用自相关函数分析电价的多周期特性并在此基础上调整输入序列,克服了仅采用历史数据以及经验调整输入序列导致预测精度受限的问题.其次,结合长短期记忆(LSTM)、自注意力机制与多层注意力机制并采用多输入结构建立改进Transformer模型,进一步提升LSTM模型捕获不同时间步信息间的长短期依赖关系的能力,克服LSTM的信息利用瓶颈,适应包括历史电价及多种电价成因的复杂多序列输入.此外,还利用PSO智能算法搜索模型不同学习阶段的最佳学习率克服手动调整学习率的局限性.最后,采用PJM市场电价进行算例分析,结果表明所提短期电价预测模型能应用于电价影响因素多、变化剧烈的市场环境,并有效提升短期电价预测精度.

关键词： 短期电价预测; 多维影响因素; 自相关分析; 改进Transformer模型; 粒子群优化

本文引用格式

孙欣 , 王思敏 , 谢敬东 , 江海林 , 王森 . 考虑多维影响因素的改进Transformer-PSO短期电价预测方法[J]. 上海交通大学学报, 2024 , 58(9) : 1420 -1431 . DOI: 10.16183/j.cnki.jsjtu.2023.065

Abstract

With the construction of a diversified electricity market, the factors affecting electricity prices are gradually increasing, and the market environment has undergone more drastic changes. In order to improve the accuracy of short-term electricity price prediction, an improved Transformer-particle swarm optimization (PSO) short-term electricity price prediction method considering multiple factors affecting electricity prices is proposed. First, based on the consideration of historical electricity prices and historical loads, the relevant factors of electricity price formation are further analyzed. The autocorrelation function is used to analyze the multi-cycle characteristics of electricity price and adjust input sequence, which overcomes the problem of limited prediction accuracy caused by using historical data only and adjusting the input sequence by experience. Then, by combining long short-term memory (LSTM), self-attention mechanism, multi-layer attention mechanism, and adopting a multi-input structure, an improved Transformer model is established to further enhance the ability of the LSTM model to capture long short-term dependencies between different time step information, to overcome the information utilization bottleneck of LSTM, and to adapt to complex multiple sequence inputs including historical electricity prices and various electricity price causes. In addition, the PSO intelligent algorithm is utilized to search for the optimal learning rate of the model at different learning stages, overcoming the limitations of manually adjusting the learning rate. Finally, the PJM market electricity price is used for example analysis. The results show that the proposed short-term electricity price prediction model can be applied to the market environment where electricity prices are affected by various factors and drastic changes, and effectively improve the accuracy of short-term electricity price prediction.

Key words： short-term electricity price prediction; multidimensional influencing factors; autocorrelation analysis; improved Transformer model; particle swarm optimization (PSO)

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