Please wait a minute...
浙江大学学报(工学版)  2021, Vol. 55 Issue (9): 1625-1633    DOI: 10.3785/j.issn.1008-973X.2021.09.003
机械工程、能源工程     
基于门控循环单元与误差修正的短期负荷预测
黄炜(),陈田*(),吴入军
上海电机学院 机械学院,上海 201306
Short term load forecasting based on gated recurrent unit and error correction
Wei HUANG(),Tian CHEN*(),Ru-jun WU
School of Mechanical Engineering, Shanghai Dianji University, Shanghai 201306, China
 全文: PDF(1641 KB)   HTML
摘要:

针对负荷预测模型迭代训练过程中存在误差积累的问题,提出结合叠式双向门控循环单元(SBiGRU)、完整自适应噪声集成经验模态分解(CEEMDAN)和误差修正的组合预测模型. 建立SBiGRU模型学习在气温、日期类型影响下负荷序列的时序特征,误差特征体现在SBiGRU模型预测产生的误差序列中;使用CEEMDAN算法将误差序列分解为数个本征模态函数(IMF)分量与趋势分量,对每项分量再次建立SBiGRU模型进行学习与预测,并对各分量的预测值进行序列重构,得到误差的预测结果;对预测结果进行求和以修正误差. 模型评估结果表明,组合模型的预测准确精度为98.86%,与SBiGRU、BiRNN、支持向量回归等方法相比,该模型具有更好的精度.

关键词: 负荷预测门控循环单元误差修正集成经验模态分解    
Abstract:

A combined prediction model combining stacked bidirectional gated recurrent unit (SBiGRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and error correction was proposed, aiming at the problem of error accumulation in iterative training process of load forecasting model. The SBiGRU model was established to learn the time series characteristics of load series under the influence of temperature and date type, and the error characteristics were reflected in the error series generated in the prediction process of SBiGRU model. Then CEEMDAN algorithm was used to decompose the error series into several intrinsic mode function (IMF) components and trend components. For each component, SBiGRU model was established again for learning and forecasting, and the predicted values of all components were reconstructed to obtain the error prediction results. Finally, the prediction results were summed to correct the error. Model evaluation results show that the prediction accuracy of the combined model is 98.86%. Compared with SBiGRU, BiRNN, support vector regression, ect., the combined model has better accuracy.

Key words: load forecasting    gated recurrent unit    error correction    ensemble empirical mode decomposition
收稿日期: 2020-08-28 出版日期: 2021-10-20
CLC:  TP 183  
基金资助: 上海市高峰高原学科资助项目(A1-5701-18-007-03);上海多向模锻工程技术研究中心资助项目(20DZ2253200)
通讯作者: 陈田     E-mail: 1453256773@qq.com;chent@sdju.edu.cn
作者简介: 黄炜(1995—),男,硕士生,从事电力系统检测与控制研究. orcid.org/0000-0001-8575-2989. E-mail: 1453256773@qq.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章  
黄炜
陈田
吴入军

引用本文:

黄炜,陈田,吴入军. 基于门控循环单元与误差修正的短期负荷预测[J]. 浙江大学学报(工学版), 2021, 55(9): 1625-1633.

Wei HUANG,Tian CHEN,Ru-jun WU. Short term load forecasting based on gated recurrent unit and error correction. Journal of ZheJiang University (Engineering Science), 2021, 55(9): 1625-1633.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.09.003        https://www.zjujournals.com/eng/CN/Y2021/V55/I9/1625

图 1  门控循环单元的网络结构
图 2  双向门控循环单元的网络结构
图 3  组合模型的预测流程
图 4  部分负荷的变化曲线
模型结构 $ {{E}}_{\text{MAP}{E}} $ T/s
单层 0.020 4 13
2+1层 0.017 4 21
3+1层 0.018 6 29
4+1层 0.019 3 38
表 1  不同SBiGRU结构的性能对比
图 5  第1阶段叠式双向门控循环单元的网络参数
图 6  第1阶段叠式双向门控循环单元的预测结果
图 7  第1阶段叠式双向门控循环单元的误差序列
图 8  各IMF分量的负荷变化曲线
图 9  5种预测模型的预测结果对比
图 10  5种模型的预测值与实际值相关性分析
模型 ${ {E} }_{ {{\rm{MAPE}}} }$ ${ {E} }_{ {{\rm{Max}}} }$ ${ {E} }_{ {{\rm{RMSE}}} }$/MW
本文 0.011 4 0.014 9 137.69
SBiGRU- SBiGRU 0.015 5 0.026 2 178.79
SBiGRU 0.017 9 0.030 2 201.55
BiRNN 0.021 0 0.032 1 262.69
SVR 0.031 0 0.051 1 358.83
表 2  5种模型的预测结果误差对比
图 11  5种模型的误差直方图
图 12  5种模型的误差箱线图
1 陈剑强, 杨俊杰, 楼志斌 基于XGBoost算法的新型短期负荷预测模型研究[J]. 电测与仪表, 2019, 56 (21): 23- 29
CHEN Jian-qiang, YANG Jun-jie, LOU Zhi-bin A new short-term load forecasting model based on XGBoost algorithm[J]. Electrical Measurement and Instrumentation, 2019, 56 (21): 23- 29
2 龚钢军, 安晓楠, 陈志敏, 等 基于SAE-ELM的电动汽车充电站负荷预测模型[J]. 现代电力, 2019, 36 (6): 9- 15
GONG Gang-jun, AN Xiao-nan, CHEN Zhi-min, et al Model of load forecasting of electric vehicle charging station based on SAE-ELM[J]. Modern Electric Power, 2019, 36 (6): 9- 15
doi: 10.3969/j.issn.1007-2322.2019.06.002
3 谢敏, 邓佳梁, 吉祥, 等 基于信息熵和变精度粗糙集优化的支持向量机降温负荷预测方法[J]. 电网技术, 2017, 41 (1): 217- 221
XIE Min, DENG Jia-liang, JI Xiang, et al Cooling load forecasting method of based on support vector machine optimized with entropy and variable accuracy roughness set[J]. Power System Technology, 2017, 41 (1): 217- 221
4 赵洪山, 田甜 基于自适应无迹卡尔曼滤波的电力系统动态状态估计[J]. 电网技术, 2014, 38 (1): 188- 192
ZHAO Hong-shan, TIAN Tian Dynamic state estimation for power system based on an adaptive unscented Kalman filter[J]. Power System Technology, 2014, 38 (1): 188- 192
5 LV P, LIU S, YU W, et al EGA-STLF: a hybrid short-term load forecasting model[J]. IEEE Access, 2020, 8: 31742- 31752
doi: 10.1109/ACCESS.2020.2973350
6 陆继翔, 张琪培, 杨志宏, 等 基于CNN-LSTM混合神经网络模型的短期负荷预测方法[J]. 电力系统自动化, 2019, 43 (8): 131- 137
LU Ji-xiang, ZHANG Qi-pei, YANG Zhi-hong, et al Short-term load forecasting method based on CNN-LSTM hybrid neural network model[J]. Automation of Electric Power Systems, 2019, 43 (8): 131- 137
doi: 10.7500/AEPS20181012004
7 CHEN K, WANG Q, HE Z, HU J, et al Short-term load forecasting with deep residual networks[J]. IEEE Transactions on Smart Grid, 2019, 10 (4): 3943- 3952
doi: 10.1109/TSG.2018.2844307
8 HAQ M R, NI Z A new hybrid model for short-term electricity load forecasting[J]. IEEE Access, 2019, 7: 125413- 125423
doi: 10.1109/ACCESS.2019.2937222
9 SHI H, XU M, LI R Deep learning for household load forecasting: a novel pooling deep RNN[J]. IEEE Transactions on Smart Grid, 2018, 9 (5): 5271- 5280
doi: 10.1109/TSG.2017.2686012
10 SEHOVAC L, GROLINGER K Deep Learning for load forecasting: sequence to sequence recurrent neural networks with attention[J]. IEEE Access, 2020, 8: 36411- 36426
doi: 10.1109/ACCESS.2020.2975738
11 TANG X, DAI Y, LIU Q, et al Application of bidirectional recurrent neural network combined with deep belief network in short-term load forecasting[J]. IEEE Access, 2019, 7: 160660- 160670
doi: 10.1109/ACCESS.2019.2950957
12 JIAO R, ZHANG T, JIANG Y, et al Short-term non-residential load forecasting based on multiple sequences LSTM recurrent neural network[J]. IEEE Access, 2018, 6: 59438- 59448
doi: 10.1109/ACCESS.2018.2873712
13 KONG W, DONG Z Y, JIA Y, et al Short-term residential load forecasting based on LSTM recurrent neural network[J]. IEEE Transactions on Smart Grid, 2019, 10 (1): 841- 851
doi: 10.1109/TSG.2017.2753802
14 ZHENG C, WANG S, LIU Y, et al A novel equivalent model of active distribution networks based on LSTM[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30 (9): 2611- 2624
doi: 10.1109/TNNLS.2018.2885219
15 XIUYUN G, YING W, YANG G, et al. Short-term load forecasting model of GRU network based on deep learning framework [C]// 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2). Beijing: IEEE, 2018: 1-4.
16 DONG M, GRUMBACH L A hybrid distribution feeder long-term load forecasting method based on sequence prediction[J]. IEEE Transactions on Smart Grid, 2020, 11 (1): 470- 482
doi: 10.1109/TSG.2019.2924183
17 SEHOVAC L, NESEN C, GROLINGER K. Forecasting building energy consumption with deep learning: a sequence to sequence approach [C]// 2019 IEEE International Congress on Internet of Things (ICIOT). Milan: IEEE, 2019: 108-116.
18 AFRASIABI M, MOHAMMADI M, RASTEGAR M, et al Probabilistic deep neural network price forecasting based on residential load and wind speed predictions[J]. IET Renewable Power Generation, 2019, 13 (11): 1840- 1848
doi: 10.1049/iet-rpg.2018.6257
19 TANG X, DAI Y, WANG T, et al Short-term power load forecasting based on multi-layer bidirectional recurrent neural network[J]. IET Generation, Transmission and Distribution, 2019, 13 (17): 3847- 3854
doi: 10.1049/iet-gtd.2018.6687
20 PAN D, XU B, MA J, et al. Short-term load forecasting based on EEMD-approximate entropy and ELM [C]// 2019 IEEE Sustainable Power and Energy Conference. Beijing: IEEE, 2019: 1772-1775.
21 YE J, YANG L. Short-term load forecasting using ensemble empirical mode decomposition and harmony search optimized support vector regression [C]// 2019 14th IEEE Conference on Industrial Electronics and Applications. Xi'an: IEEE, 2019: 851-855.
22 ZHU X, QI H, HUANG X, et al. A hybrid method for short-term load forecasting in power system [C]// Proceedings of the 10th World Congress on Intelligent Control and Automation. Beijing: [s.n], 2012: 696-699.
23 LI P, LIU H, LI M, et al. Power load forecasting based on VMD multifrequency combinations [C]// 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). Chongqing: [s.n], 2019: 1214-1218.
[1] 章超波,刘永政,李宏波,赵阳,张丽珠,王子豪. 基于加权残差聚类的建筑负荷预测区间估计[J]. 浙江大学学报(工学版), 2022, 56(5): 930-937.
[2] 童林,官铮,王立威,杨文韬,姚洋. 基于时序分解与误差修正的新能源爬坡事件预测[J]. 浙江大学学报(工学版), 2022, 56(2): 338-346.
[3] 刘兴,余建波. 注意力卷积GRU自编码器及其在工业过程监控的应用[J]. 浙江大学学报(工学版), 2021, 55(9): 1643-1651.
[4] 王晨霖,杨洁,居文军,顾复,陈芨熙,纪杨建. 基于智能家电的短期电力负荷预测与削峰填谷优化[J]. 浙江大学学报(工学版), 2020, 54(7): 1418-1424.
[5] 王文浩,张筱,万永菁. 改进深度信念网络在语音转换中的应用[J]. 浙江大学学报(工学版), 2019, 53(12): 2372-2380.
[6] 王敬昌,陈岭,余珊珊,蒋晨书,吴勇. 基于门控循环单元的多因素感知短期游客人数预测模型[J]. 浙江大学学报(工学版), 2019, 53(12): 2357-2364.
[7] 张凌, 田传浩. 城市住房市场差异与房价连锁反应——以35个大中城市为例[J]. J4, 2010, 44(1): 197-202.
[8] 孔祥玉 房大中 侯佑华. 电力市场条件下安全经济AGC模型的应用[J]. J4, 2008, 42(9): 1597-1600.
[9] 王志勇 曹一家. 层次匹配范例推理在短期负荷预测中的应用[J]. J4, 2007, 41(9): 1598-1603.