基于长短期记忆网络的电力市场价格预测研究

邵云姝; 张琳; 周乃康; 陈晓利; 王菲

doi:10.19805/j.cnki.jcspe.2025.250019

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基于长短期记忆网络的电力市场价格预测研究

数字化与智能化 | 更新时间：2025-10-16

- 基于长短期记忆网络的电力市场价格预测研究
- Research on Price Prediction of Electricity Market Based on Long-Short Term Memory Network
- 动力工程学报 2025年45卷第5期页码：733-737
- 作者机构：
  
  1. 大韩民国高丽大学,首尔,02841
  2. 国家电投集团电站运营技术(北京)有限公司,北京,102200
  3. 国家电投集团东北电力有限公司,辽宁,沈阳,110181
- 作者简介：
  
  [ "邵云姝(1996—),女,黑龙江讷河人,博士研究生,研究方向为电力市场交易、碳交易等,E-mail:cristalshao@0521korea.ac.kr" ]
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.250019
  中图分类号： F426.61;TP183
- 网络出版：2025-05-19，
  
  纸质出版：2025
- 稿件说明：
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邵云姝,张琳,周乃康,陈晓利,王菲. 基于长短期记忆网络的电力市场价格预测研究动力工程学报, 2025, 45(5): 733-737 https://doi.

org/10.19805/j.cnki.jcspe.2025.250019
邵云姝,张琳,周乃康,陈晓利,王菲. 基于长短期记忆网络的电力市场价格预测研究动力工程学报, 2025, 45(5): 733-737 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.250019.

org/10.19805/j.cnki.jcspe.2025.250019 DOI：

摘要

考虑电力市场多因素耦合对电力价格预测的影响

建立了基于长短期记忆(LSTM)神经网络的电力市场价格预测模型。设置历史新能源出力、外送电量、电力负荷等影响电力供需关系的边界因素作为LSTM学习参数

并进行数据预处理;优化LSTM神经网络的层数、迭代次数、学习率等模型参数

生成电价预测模型

得到交易日的电价预测曲线。通过算例仿真验证方法的正确性

构建某现货省份电力交易的预测场景

引入电价预测准确率评估指标。结果表明:该方法为日前出清电价预测研究提供参考

可为电力市场交易主体提供有效的竞价策略

并获取可观的电力营销收入。

Abstract

Considering the impact of multi-factor coupling in the power market on power price forecasting

a power market price forecasting model based on long-short term memory (LSTM) neural network was established. The historical new energy output

power transmission

power load and other factors affecting the power supply and demand relationship were set as the boundary factors of LSTM learning parameters

and data preprocessing was carried out. The model parameters of LSTM neural network

including the number of layers

iteration times and learning rate

were optimized to generate a training model for electricity price forecasting

which was then used to forecast the electricity price curves for trading days. The correctness of the proposed method was verified through example simulation

a prediction scenario for electricity trading in a spot market province was constructed

and the evaluation indicators of power price forecasting accuracy were introduced. The results indicate that this method provides a reference for research on day-ahead clearing price prediction and offers market participants in the electricity market effective bidding strategies to achieve substantial marketing revenue.

关键词

Keywords

references

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