电工电气杂志社

基于相似日和CNN-LSTM的短期负荷预测

来源：电工电气发布时间：2022-08-29 14:29浏览次数：432

基于相似日和CNN-LSTM的短期负荷预测

童占北¹，钟建伟¹，李祯维²，吴建军²，李家俊²

(1 湖北民族大学智能科学与工程学院，湖北恩施 445000；

2 国网湖北省电力有限公司恩施供电公司，湖北恩施 445000)

摘要：为充分发掘历史信息，解决气象数据不足影响预测精度的问题，采用灰色关联分析 (GRA) 选取天气相似日和 CNN-LSTM 混合神经网络的方法来预测电力负荷。利用 GRA 计算每日各气象因素与日总负荷的灰色关联度，再计算各日与典型日的相同气象因素之间的欧氏距离，将各气象因素的欧氏距离分别乘以对应因素的关联度，并将同一天的结果累加，得到一个综合得分。选取待预测日之前分数最低的 5 天作为相似日，将相似日各时刻的负荷数据输入 CNN-LSTM 网络中，预测出待预测日的负荷，通过与其他模型对比，验证了该方法的有效性。

关键词： 灰色关联分析；相似日；CNN-LSTM 混合神经网络；短期负荷预测

中图分类号：TM715 文献标识码：A 文章编号：1007-3175(2022)08-0017-06

Short-Term Load Forecasting Based on Grey Relational

Analysis and CNN-LSTM

TONG Zhan-bei¹, ZHONG Jian-wei¹, LI Zhen-wei², WU Jian-jun², LI Jia-jun²

（1 College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China;

2 Enshi Power Supply Company, State Grid Hubei Electric Power Co., Ltd, Enshi 445000, China）

Abstract: This paper used grey relational analysis(GRA) to select days with similar weather conditions and explore more historical information.In addition, it employed the CNN-LSTM hybrid neural network method to predict power load and solve the problem of insufficient meteorological data affecting prediction accuracy. This research used GRA to calculate the grey relational grade between daily meteorological factors and overall load. In addition, it computed the Euclidean distance of the same meteorological factors between each day and the typical day. The Euclidean distance of each meteorological factor was multiplied by the relevancy of corresponding factors. The accumulation of the calculation results in the same day could obtain an overall score. This study took five days with the lowest score before predicted days as the similar days. It inputted load data into the CNN-LSTM network to forecast the load of prediction days. Compared with other models, the effectiveness of this method is verified.

Key words: grey relational analysis; similar day; CNN-LSTM hybrid neural network; short-term load forecasting

参考文献

[1] 梁智，孙国强，李虎成. 基于 VMD 与 PSO 优化深度信念网络的短期负荷预测[J] . 电网技术，2018，42(2)：598-606.

[2] KIN J, CHO S, KO K, et al.Short-Term Electric Load Prediction Using Multiple Linear Regression Method[C]//2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids，2018.

[3] LI Hong, ZHAO Yang, ZHANG Zizi, et al.Shortterm load forecasting based on the grid method and the time series fuzzy load forecasting method[C]//International Conference on Renewable Power Generation，2015.

[4] 陈磊，张青云，向晓，等. 改进灰色预测模型在电力负荷预测中的应用[J] . 河北电力技术，2021，40(6)：27-30.

[5] SHARMA S, MAJUMDAR A, ELVIRA V, et al.Blind Kalman Filtering for Short-Term Load Forecasting[J].IEEE Transactions on Power Systems，2020，35(6)：4916-4919.

[6] 谭风雷，张军，马宏忠. 基于趋势变化分段的电力负荷组合预测方法[J] . 华北电力大学学报，2020，47(2)：17-24.

[7] YANG Wangwang, SHI Jing, LI Shujian, et al.A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior[J].Applied Energy，2022，307(C) ：S0306261921014665.

[8] JALALI S M J, AHMADIAN S, KHOSRAVI A, et al.A Novel Evolutionary-Based Deep Convolutional Neural Network Model for Intelligent Load Forecasting[J].IEEE Transactions on Industrial Informatics，2021，PP(99) ：1.

[9] ZHU Fuyun, WU Guoqing.Load Forecasting of the Power System: An Investigation Based on the Method of Random Forest Regression[J].Energy Engineering，2021，118(6) ：1703-1712.

[10] LI Gen, LI Yunhua, ROOZITALAB Farzad.Midterm Load Forecasting: A Multistep Approach Based on Phase Space Reconstruction and Support Vector Machine[J].IEEE Systems Journal，2020，14(4) ：4967-4977.

[11] 邓春红，王蒙. 基于相似日和灰色理论的短期电力负荷预测研究[J] . 重庆工商大学学报( 自然科学版)，2017，34(3)：93-97.

[12] 王瑞，孙忆枫，逯静. 基于相似日和 RBF 神经网络的短期电力负荷预测[J] . 制造业自动化，2021，43(4)：24-29.

[13] JANKOVI Z, SELAKOV A, BEKUT D, et al.Day similarity metric model for short-term load forecasting supported by PSO and artificial neural network[J].Electrical Engineering，2021，103(1) ：2973-2988.

[14] DING Jianmin, YUE Yunli, CHEN Jianhua, et al.Analysis of Factors Affecting Power Load Characteristics Based on Grey Relational Analysis Model [C]//2019 International Conference on Sensing , Diagnostics ,Prognostics, and Control(SDPC)，2020.

[15] 刘亚珲，赵倩. 基于聚类经验模态分解的 CNN-LSTM 超短期电力负荷预测[J] . 电网技术，2021，45(11)：4444-4451.

[16] RAFI S H, MASOOD Nahid-Al, DEEBA S R, et al.A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network[J].IEEE Access，2021，9：32436-32448.

[17] 刘可真，阮俊枭，赵现平，等. 基于麻雀搜索优化的 Attention-GRU 短期负荷预测方法[J]. 电力系统及其自动化学报，2022，34(4) ：99-106.

返回索引列表

SUBSCRIPTION MANAGEMENT

发行征订

基于相似日和CNN-LSTM的短期负荷预测