电力市场价格预测的综述与展望

郭瑛; 曹蕃; 宋寅; 马康; 王伟; 蒋东

doi:10.16513/j.2096-2185.DE.24090727

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分布式能源 ›› 2025, Vol. 10 ›› Issue (4) : 1-12. DOI: 10.16513/j.2096-2185.DE.24090727

电力市场价格预测的综述与展望

作者信息 +

Review and Prospect of Electricity Market Price Prediction

Author information +

文章历史 +

摘要

在电力市场交易中，电价预测逐渐成为能源企业决策机制的一个必不可少的环节，是市场参与者制定竞价策略的重要依据。准确的电价预测有助于电力市场各交易主体降低竞价风险，实现利益最大化，因此，研究电价预测具有重要意义。然而，由于气象条件、负荷需求、线路阻塞及政策导向等多重因素的影响，电价呈现出复杂的不确定性和显著的波动性。为了解决这一问题，电价预测的方法日益多样化。但由于高质量电力交易数据匮乏以及预测算法存在缺陷等问题，精准的电价预测面临诸多挑战。该文梳理了国内外关于电价预测的相关研究成果。首先，对电价形成机理及其影响因素进行了分析，并归纳了相关理论研究方法；其次，详细介绍了最新的电价预测方法，将其分为时间序列预测模型、传统机器学习模型、深度学习模型和混合模型这4个主要方面，并对各类方法进行了深入探讨与分析；最后，从影响因素、数据预处理、方法选择及评价指标等角度，对未来电价预测的发展趋势进行了展望。

Abstract

In the context of electricity market transactions， price forecasting has increasingly become an indispensable component of decision-making mechanisms for energy enterprises and serves as a crucial basis for market participants to formulate bidding strategies. Accurate electricity price forecasts assist various trading entities in the power market in reducing bidding risks and maximizing their interests. Therefore， researching electricity price forecasting holds significant importance. However， due to multiple influencing factors such as meteorological conditions， load demand， line congestion， and policy changes， electricity prices exhibit complex uncertainties and notable volatility. To address this issue， methods for predicting electricity prices have diversified over time. Nevertheless， challenges remain in achieving precise forecasts due to the scarcity of high-quality trading data and inherent flaws in prediction algorithms. This paper reviews relevant research findings on electricity price forecasting both domestically and internationally. Firstly， it analyzes the mechanisms behind price formation along with its influencing factors while summarizing related theoretical research methodologies. Secondly， it provides a detailed overview of recent advancements in electricity price forecasting methods by categorizing them into four main areas： time series prediction models， traditional machine learning models， deep learning models， and hybrid models； each method is discussed thoroughly with critical analysis. Finally， from perspectives including influencing factors， data preprocessing techniques， method selection criteria as well as evaluation metrics， this study anticipates future trends in electricity price forecasting.

导出引用

郭瑛, 曹蕃, 宋寅, 等. 电力市场价格预测的综述与展望[J]. 分布式能源. 2025, 10(4): 1-12 https://doi.org/10.16513/j.2096-2185.DE.24090727

GUO Ying, CAO Fan, SONG Yin, et al. Review and Prospect of Electricity Market Price Prediction[J]. Distributed Energy Resources. 2025, 10(4): 1-12 https://doi.org/10.16513/j.2096-2185.DE.24090727

中图分类号： TK-9

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摘要

时间序列一般是指对某种事物发展变化过程进行观测并按照一定频率采集得出的一组随机变量。时间序列预测的任务就是从众多数据中挖掘出其蕴含的核心规律并且依据已知的因素对未来的数据做出准确的估计。由于大量物联网数据采集设备的接入、多维数据的爆炸增长和对预测精度的要求愈发苛刻，经典的参数模型以及传统机器学习算法难以满足预测任务的高效率和高精度需求。近年来，以卷积神经网络、循环神经网络和Transformer模型为代表的深度学习算法在时间序列预测任务中取得了丰硕的成果。为进一步促进时间序列预测技术的发展，综述了时间序列数据的常见特性、数据集和模型的评价指标，并以时间和算法架构为研究主线，实验对比分析了各预测算法的特点、优势和局限；着重介绍对比了多个基于Transformer模型的时间序列预测方法；最后结合深度学习应用于时间序列预测任务存在的问题与挑战，对未来该方向的研究趋势进行了展望。

LIANG

Hongtao

, LIU

Shuo

, DU

Junwei

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https://doi.org/10.11896／j.issn.1002-137X.2019.01.004

摘要

时间序列是按照时间排序的一组随机变量,它通常是在相等间隔的时间段内依照给定的采样率对某种潜在过程进行观测的结果。时间序列数据本质上反映的是某个或者某些随机变量随时间不断变化的趋势,而时间序列预测方法的核心就是从数据中挖掘出这种规律,并利用其对将来的数据做出估计。针对时间序列预测方法,着重介绍了传统的时间序列预测方法、基于机器学习的时间序列预测方法和基于参数模型的在线时间序列预测方法,并对未来的研究方向进行了进一步的展望。

YANG

Haimin

, PAN

Zhisong

, BAI

Wei

. Review of time series prediction methods[J]. Computer Science, 2019, 46(1): 21-28.

https://doi.org/10.11896／j.issn.1002-137X.2019.01.004

本文引用 [1] 摘要

Time series is a set of random variables ordered in timestamp.It is often the observation of an underlying process,in which values are collected from uniformly spaced time instants,according to a given sampling rate.Time series data essentially reflects the trend that one or some random variables change with time.The core of time series prediction is mining the rule from data and making use of it to estimate future data.This paper emphatically introduced a summary of time series prediction method,namely the traditional time series prediction method,machine learning based time series prediction method and online time series prediction method based on parameter model,andfurther prospected the future research direction.

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

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.

SUN

Xin

, WANG

Simin

, XIE

Jingdong

, et al. Improved transformer-PSO short-term electricity price prediction method considering multidimensional influencing factors[J]. Journal of Shanghai Jiao Tong University, 2024, 58(9): 1420-1431.

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https://kns.cnki.net/KCMS/detail/detail.aspx?filename=ZGDC20240621006&dbname=CJFD&dbcode=CJFQ

ZHANG

Pengfei

, HU

Zhanshuo

, et al. Long step spatial-temporal prediction of spot electricity price using dual channel ST-former framework based on seasonal trend decomposition[J/OL]. Proceedings of the CSEE, 1-23 [2024-07-19]. https://kns.cnki.net/KCMS/detail/detail.aspx?filename=ZGDC20240621006&dbname=CJFD&dbcode=CJFQ.

https://kns.cnki.net/KCMS/detail/detail.aspx?filename=ZGDC20240621006&dbname=CJFD&dbcode=CJFQ

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