基于CEEMD-ITSA-BiLSTM组合模型的短期负荷预测

doi:10.16180/j.cnki.issn1007-7820.2024.04.005

摘要/Abstract

摘要：

准确预测电力系统短期负荷有助于灵活规划系统资源、合理安排机组工作调度以及提高系统运行效率。针对负荷预测精度问题,文中提出了一种基于CEEMD-ITSA-BiLSTM(Complete Ensemble Empirical Mode Decomposition-Improved Tunicate Swarm Algorithm-Bidirectional Long Short-Term Memory)的短期负荷预测模型。对时序性负荷数据进行CEEMD分解,得到若干个平稳的IMF(Intrinsic Mode Function),并对每个IMF进行BiLSTM建模预测。为了提高BiLSTM的精度,采用ITSA算法对BiLSTM的隐含层节点数、学习率和训练次数等超参数进行参数寻优,建立CEEMD-ITSA-BiLSTM负荷预测模型。文中以实际负荷数据进行仿真实验,对比了单一BiLSTM和不同算法优化的BiLSTM模型,结果表明CEEMD-ITSA-BiLSTM模型的RMSE(Root Mean Square Error)、MAE(Mean Absolute Error)和MAPE(Mean Absolute Percentage Error)误差指标相比于BiLSTM模型分别提高了48.54%、51.32%和44.78%,显著低于其他对比模型。

关键词: 短期负荷预测, 预测精度, 完全集成经验模态分解, 本征模函数, 被囊群算法, 参数寻优, 双向长短期记忆神经网络, 误差指标

Abstract:

Accurate short-term load forecasting of power system is helpful to flexible planning of system resources, reasonable scheduling of units, and improvement of system operation efficiency. In view of the accuracy of load forecasting, this study proposes a short-term load forecasting model based on CEEMD-ITSA-BiLSTM (Complete Ensemble Empirical Mode Decomposition-Improved Tunicate Swarm Algorithm-Bidirectional Long Short-Term Memory). CEEMD decomposition is carried out on the time series load data to obtain several stable IMF (Intrinsic Mode Function), and BiLSTM modeling and prediction are carried out for each IMF. To improve the accuracy of BiLSTM, ITSA algorithm is used to optimize the parameters of the super parameters such as the number of hidden layer nodes, learning rate and training times of BiLSTM, and CEEMD-ITSA-BiLSTM load forecasting model is established. The simulation experiment is conducted with the actual load data, and the single BiLSTM model and the BiLSTM model optimized by different algorithms are compared. The results show that the RMSE (Root Mean Square Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) error indexes of CEEMD-ITSA-BiLSTM model are increased by 48.54%, 51.32% and 44.78%, respectively when compared with the BiLSTM model, and are significantly lower than other comparison models.

Key words: short term load forecasting, prediction accuracy, CEEMD, IMF, TSA, parameter optimization, BiLSTM neural network, error index

中图分类号:

TP18

高典, 张菁. 基于CEEMD-ITSA-BiLSTM组合模型的短期负荷预测[J]. 电子科技, 2024, 37(4): 30-37.

GAO Dian, ZHANG Jing. Short-Term Load Forecasting Based on CEEMD-ITSA-BiLSTM Combined Model[J]. Electronic Science and Technology, 2024, 37(4): 30-37.

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参考文献 26

[1]	廖旎焕, 胡智宏, 马莹莹, 等. 电力系统短期负荷预测方法综述[J]. 电力系统保护与控制, 2011, 39(1):147-152.
	Liao Nihuan, Hu Zhihong, Ma Yingying, et al. Review of the short-term load forecasting methods of electric power system[J]. Power System Protection and Control, 2011, 39(1):147-152.
[2]	李东东, 覃子珊, 林顺富, 等. 基于混沌时间序列法的微网短期负荷预测[J]. 电力系统及其自动化学报, 2015, 27(5):14-18.
	Li Dongdong, Qin Zishan, Lin Shunfu, et al. Short-term load forecasting for microgrid based on method of chaotic time series[J]. Proceedings of the CSU-EPSA, 2015, 27(5):14-18.
[3]	李晨熙. 基于ARIMA模型的短期电力负荷预测[J]. 吉林电力, 2015, 43(6):22-24.
	Li Chenxi. A short-term load forecasting model based on ARIMA[J]. Jilin Electric Power, 2015, 43(6):22-24.
[4]	孙珍. 基于协方差稳健模糊线性回归的系统负荷预测数学模型[J]. 现代电子技术, 2019, 42(15):94-96,100.
	Sun Zhen. System load forecasting mathematical model based on covariance robust fuzzy linear regression[J]. Modern Electronics Technique, 2019, 42(15):94-96,100.
[5]	陈培垠, 方彦军. 基于卡尔曼滤波预测节假日逐点增长率的电力系统短期负荷预测[J]. 武汉大学学报(工学版), 2020, 53(2):139-144.
	Chen Peiyin, Fang Yanjun. Short term load forecasting of power system for holiday point by point growth rate based on Kalman filtering[J]. Engineering Journal of Wuhan University, 2020, 53(2):139-144.
[6]	王瑞, 陈诗雯, 逯静. 基于模糊聚类的BOA-SVR分时段精细化短期负荷预测[J]. 武汉大学学报(工学版), 2021, 54(12):1140-1149.
	Wang Rui, Chen Shiwen,Lu Jing.Time division refined shortterm load forecasting based on BOASVR and fuzzy clustering[J]. Engineering Journal of Wuhan University, 2021, 54(12):1140-1149.
[7]	张刚, 刘福潮, 王维洲, 等. 电网短期负荷预测的BP-ANN方法及应用[J]. 电力建设, 2014, 35(3):54-58. doi: 10.3969/j.issn.1000－7229.2014.03.010
	Zhang Gang, Liu Fuchao, Wang Weizhou, et al. BP-ANNmethod for power grid short-term load forecasting and its application[J]. Electric Power Construction, 2014, 35(3):54-58.
[8]	谢丽蓉, 王斌, 包洪印, 等. 基于EEMD-WOA-LSSVM的超短期风电功率预测[J]. 太阳能学报, 2021, 42(7):290-296.
	Xie Lirong, Wang Bin, Bao Hongyin, et al. Super-short-term wind power forecasting based on EEMD-WOA-LSSVM[J]. Acta Energiae Solaris Sinica, 2021, 42(7):290-296.
[9]	张淑清, 段晓宁, 张立国, 等. Tsne降维可视化分析及飞蛾火焰优化ELM算法在电力负荷预测中应用[J]. 中国电机工程学报, 2021, 41(9):3120-3130.
	Zhang Shuqing, Duan Xiaoning, Zhang Liguo, et al. Tsne dimension reduction visualization analysis and moth flame optimized ELM algorithm applied in power load forecasting[J]. Proceedings of the CSEE, 2021, 41(9):3120-3130.
[10]	龚飘怡, 罗云峰, 方哲梅, 等. 基于Attention-BiLSTM-LSTM神经网络的短期电力负荷预测方法[J]. 计算机应用, 2021, 41(Z1):81-86.
	Gong Piaoyi, Luo Yunfeng, Fang Zhemei, et al. Short-term power load forecasting method based on Attention-BiLSTM-LSTM neural network[J]. Journal of Computer Applications, 2021, 41(Z1):81-86.
[11]	李玉志, 刘晓亮, 邢方方, 等. 基于Bi-LSTM和特征关联性分析的日尖峰负荷预测[J]. 电网技术, 2021, 45(7):2719-2730.
	Li Yuzhi, Liu Xiaoliang, Xing Fangfang, et al. Daily peak load prediction based on correlation analysis and Bi-directional long short-term memory network[J]. Power System Technology, 2021, 45(7):2719-2730.
[12]	朱凌建, 荀子涵, 王裕鑫, 等. 基于CNN-BiLSTM的短期电力负荷预测[J]. 电网技术, 2021, 45(11):4532-4539.
	Zhu Lingjian, Xun Zihan, Wang Yuxin, et al. Short-term power load forecasting based on CNN-BiLSTM[J]. Power System Technology, 2021, 45(11):4532-4539.
[13]	孙辉, 杨帆, 高正男, 等. 考虑特征重要性值波动的MI-BI-LSTM短期负荷预测[J]. 电力系统自动化, 2022, 46(8):95-103.
	Sun Hui, Yang Fan, Gao Zhengnan, et al. Short-term load forecasting based on mutual information and Bi-directional long short-term memory network considering fluctuation in importance values of features[J]. Automation of Electric Power Systems, 2022, 46(8):95-103.
[14]	Shi H, Miao K, Ren X. Short-term load forecasting based on CNN-BiLSTM with Bayesian optimization and attention mechanism[J]. Concurrency Computation Practice and Experience, 2021, 10(2):66-76.
[15]	Guo Y, Li Y, Qiao X, et al. BiLSTM multitask learning-based combined load forecasting considering the loads coupling relationship for multienergy system[J]. IEEE Transactions on Smart Grid, 2022, 13(5):3481-3492. doi: 10.1109/TSG.2022.3173964
[16]	Zheng H, Yuan J, Chen L. Short-term load forecasting using EMD-LSTM neural networks with a XGBoost algorithm for feature importance evaluation[J]. Energies, 2017, 10(8):1168-1188. doi: 10.3390/en10081168
[17]	Huang S, Zheng J, Pan H, et al. Order-statistic filtering Fourier decomposition and its application to rolling bearing fault diagnosis[J]. Journal of Vibration and Control, 2022, 28(13-14):1605-1620. doi: 10.1177/1077546321997598
[18]	Ding Y F, Chen Z J, Zhang H W, et al. A short-term wind power prediction model based on CEEMD and WOA-KELM[J]. Renewable Energy, 2022, 189(4):188-198. doi: 10.1016/j.renene.2022.02.108
[19]	Liu X, Zhang Y, Zhang Q. Comparison of EEMD-ARI-MA,EEMD-BP and EEMD-SVM algorithms for predicting the hourly urban water consumption[J]. Journal of Hydroinformatics, 2022, 24(3):535-558. doi: 10.2166/hydro.2022.146
[20]	Kaur S, Awasthi L K, Sangal A L, et al. Tunicate swarm algorithm:A new bio-inspired based metaheuristic paradigm for global optimization[J]. Engineering Applications of Artificial Intelligence, 2020, 90(10):3541-3570.
[21]	刘辉, 凌宁青, 罗志强, 等. 基于TCN-LSTM和气象相似日集的电网短期负荷预测方法[J]. 智慧电力, 2022, 50(8):30-37.
	Liu Hui, Ling Ningqing, Luo Zhiqiang, et al. Power grid short-term load forecasting method based on TCN-LSTM and meteorological similar day sets[J]. Smart Power, 2022, 50(8):30-37.
[22]	程换新, 黄震. 基于改进PSO优化RNN的短期电力负荷预测模型[J]. 电子测量技术, 2019, 42(20):94-98.
	Cheng Huanxin, Huang Zhen. Short-term electric load forecasting model based on improved PSO optimized RNN[J]. Electronic Measurement Technology, 2019, 42(20):94-98.
[23]	Dhiman G, Kumar V. Seagull optimization algorithm:Theory and its applications for large-scale industrial engineering problems[J]. Knowledge-Based Systems, 2019, 165(1):169-196. doi: 10.1016/j.knosys.2018.11.024
[24]	Mirjaliu S, Mirjaliu S M, Lewis A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69(3):46-61. doi: 10.1016/j.advengsoft.2013.12.007
[25]	冯先丁, 魏镜弢, 吴张永, 等. 基于PCA-PSO-SVM的球磨机负荷预测研究[J]. 电子科技, 2022, 35(1):29-34.
	Feng Xianding, Wei Jingtao, Wu Zhangyong, et al. Research on load forcast of ball mill based on PCA-PSO-SVM[J]. Electronic Science and Technology, 2022, 35(1):29-34.
[26]	张崇崇, 黄亚宇. GA-BP神经网络对片烟结构的预测研究[J]. 电子科技, 2022, 35(6):35-42.
	Zhang Chongchong, Huang Yayu. A GA-BP neural net-work for predicting the structure of leaf tobacco[J]. Electronic Science and Technology, 2022, 35(6):35-42.

算法模型	RMSE/MW	MAE/MW	MAPE/%
BiLSTM	326.43	270.15	3.35
TSA-BiLSTM	253.19	195.30	2.83
CEEMD-BiILSTM	271.50	230.19	3.06
ITSA-BiLSTM	210.99	174.82	2.26
CEEMD-GA-BiLSTM	198.69	163.64	2.31
CEEMD-PSO-BiLSTM	193.94	149.89	2.07
CEEMD-SOA-BiLSTM	201.68	162.30	2.28
CEEMD-GWO-BiLSTM	189.32	149.70	2.07
CEEMD-TSA-BiLSTM	179.33	144.59	1.93
CEEMD-ITSA-BiLSTM	167.98	131.52	1.85