基于<italic style="font-style: italic">K</italic>NN-RF-VMD-CNN-BiLSTM的日前电价预测算法

余礼苏; 丁垚; 熊永康; 黎子鹏; 张武雄

doi:10.11959/j.issn.2096-3750.2025.00489

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基于KNN-RF-VMD-CNN-BiLSTM的日前电价预测算法

理论与技术 | 更新时间：2026-01-04

- 基于KNN-RF-VMD-CNN-BiLSTM的日前电价预测算法
- Day-ahead electricity price prediction algorithm based on KNN-RF-VMD-CNN-BiLSTM
- 物联网学报 2025年9卷第4期页码：172-183
- 作者机构：
  
  1.南昌大学信息工程学院，江西南昌 330031
  2.先进信号处理与智能通信江西省重点实验室，江西南昌 330031
  3.中国科学院上海微系统与信息技术研究所，上海 200050
- 作者简介：
  
  [ "余礼苏（1990‒ ），男，博士，南昌大学信息工程学院副教授，主要研究方向为可见光通信、机器学习优化算法、区块链、时间序列预测。" ]
  [ "丁垚（2001‒ ），男，南昌大学信息工程学院硕士生，主要研究方向为新能源功率预测、现货交易电价预测、电网异常检测。" ]
  [ "熊永康（1992‒ ），男，博士，南昌大学信息工程学院副教授，主要研究方向为新能源发电并网控制、电力系统规划与控制。" ]
  [ "黎子鹏（1991‒ ），男，博士，南昌大学信息工程学院讲师，主要研究方向为车联网、物联网、人工智能、无线通信。" ]
  [ "张武雄（1985‒ ），男，博士，中国科学院上海微系统与信息技术研究所研究员，主要研究方向为智能传感器研发与应用、无线安全通信。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62161024;52267016);江西省主要学科学术和技术带头人省级人才项目(20232BCJ23085);江西省自然科学基金资助项目(20224BAB212002;20242BAB20051;20252BAC240666;20252BAC240026);江西省科技研发计划(20242BCC32016)
- DOI：10.11959/j.issn.2096-3750.2025.00489
  中图分类号：
- 收稿：2025-05-28，
  
  修回：2025-11-14，
  
  纸质出版：2025-12-10
- 稿件说明：
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余礼苏,丁垚,熊永康等.基于KNN-RF-VMD-CNN-BiLSTM的日前电价预测算法[J].物联网学报,2025,09(04):172-183.

YU Lisu,DING Yao,XIONG Yongkang,et al.Day-ahead electricity price prediction algorithm based on KNN-RF-VMD-CNN-BiLSTM[J].Chinese Journal on Internet of Things,2025,09(04):172-183.
余礼苏,丁垚,熊永康等.基于KNN-RF-VMD-CNN-BiLSTM的日前电价预测算法[J].物联网学报,2025,09(04):172-183. DOI： 10.11959/j.issn.2096-3750.2025.00489.

YU Lisu,DING Yao,XIONG Yongkang,et al.Day-ahead electricity price prediction algorithm based on KNN-RF-VMD-CNN-BiLSTM[J].Chinese Journal on Internet of Things,2025,09(04):172-183. DOI： 10.11959/j.issn.2096-3750.2025.00489.

摘要

当前国内电力市场改革推进，市场主体须掌握电价变化趋势，以灵活调整生产计划与电力采购策略，因此对电价的准确预测需求日益增长。针对电价实际预测中存在的问题，如数据缺失、标错等数据异常导致的模型训练不平滑，首先，设计了

-近邻算法（

-nearest neighbors）-随机森林（RF

random forest）算法捕捉全局特征，精准识别并替换异常数据点；其次，通过变分模态分解（VMD

variational mode decomposition）将电价数据分解为多个子模态；最后，运用卷积神经网络（CNN

convolutional neural network）-双向长短期记忆（BiLSTM

bi-directional long short-term memory）网络组合模型进行预测，并得到最终的日前电价预测结果。经仿真验证，该组合电价预测算法相较于基础模型，在平均绝对误差（MAE

mean absolute error）、均方误差（MSE

mean square error）、均方根误差（RMSE

root mean square error）和平均绝对百分比误差（MAPE

mean absolute percentage error）的指标分别相对提升了15.8%、13.6%、1.54%和32.4%，且单个轮次推断时间在秒级内。该算法有效地兼顾了预测效率与精度。

Abstract

As the domestic electricity market reform advanced in China

market participants are required to understand the electricity price trends to flexibly adjust production plans and procurement strategies. Thus

the demand for accurate electricity price forecasting increases steadily. In actual forecasting

abnormal electricity price data

including missing values and mislabeling

cause non-smooth model training. In response to these problems

the

-nearest neighbors (

NN)- random forest (RF) method was first applied. This method

capable of capturing global features

was used to accurately identify and replace abnormal data points. Then

variational mode decomposition (VMD) was employed to decompose electricity price data into multiple sub-modes. Finally

a convolutional neural networks (CNN)-bi-directional long short-term memory (BiLSTM) combined model was utilized for forecasting

and the results were integrated to obtain the final day-ahead electricity price forecast. The Simulation results show that

compared with the basic model

the combined

NN-RF-VMD-CNN-BiLSTM electricity price forecasting algorithm achieves relative improvements of 15.8%

13.6%

1.54%

and 32.4% in mean absolute error (MAE)

mean square error (MSE)

root mean square error (RMSE)

and mean absolute percentage error (MAPE) indicators

respectively. Moreover

the inference time for a single epoch is within seconds. This algorithm effectively balances the forecasting efficiency and accuracy.

关键词

Keywords

references

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基于变分自编码器的时间序列生成及异常检测模型研究

基于KNN-RF-VMD-CNN-BiLSTM的日前电价预测算法

Day-ahead electricity price prediction algorithm based on KNN-RF-VMD-CNN-BiLSTM

DOI：10.11959/j.issn.2096-3750.2025.00489

摘要

Abstract

关键词

Keywords

references