基于EEMD-SE和PSO-KELM的短期负荷区间预测方法

doi:10.11930/j.issn.1004-9649.202001103

摘要/Abstract

摘要： 准确的短期负荷预测在电力系统中发挥着至关重要的作用。近年来，大量短期负荷预测研究表明，与点预测相比，负荷的区间预测可以更有效地保证电力系统的安全运行。因此，提出一种基于EEMD-SE和PSO-KELM的短期负荷区间预测方法。首先，使用集合经验模态分解（EEMD）将原始负荷序列分解为一系列的子序列；然后，通过样本熵（SE）对各序列进行计算，量化序列的复杂程度，将SE值较小的序列进行重构；最后，通过粒子群（PSO）优化核极限学习机（KELM）的输出层权重，建立预测模型，并对各序列进行区间构造。采用南方某市不同季节的实际负荷数据对所提模型进行实验验证，仿真结果表明，与其他预测方法相比，所提方法在区间可靠性以及宽度上具有更好的效果。

关键词: 负荷预测, 集合经验模态分解, 核极限学习机, 区间预测, 负荷特性, 参数优化

Abstract: Accurate load forecasting plays an important role in power system. In recent years, a large number of load forecasting studies show that compared with point forecasting, interval forecasting of load can effectively ensure the safe operation of power system. This paper presents a short-term load interval forecasting method based on EEMD-SE and PSO-KELM. Firstly, the ensemble empirical mode decomposition (EEMD) is used to decompose the original load series into a series of subseries. Then, the sample entropy (SE) is used to calculate and quantify the complexity of the series, and the series with small SE values are reconstructed. Finally, the particle swarm optimization (PSO) is used to optimize the weight of output layer of kernel extreme learning machine (KELM), and a prediction model is established to reconstruct the interval of each subseries. The proposed model was tested with the actual load data of a city in South China in different seasons under different nominal confidence, and the simulation results show that compared with other prediction methods, the proposed method has better performance in interval reliability and width.

Key words: load forecasting, ensemble empirical mode decomposition, kernel extreme learning machine, interval forecasting, load characteristics, parameter optimization

张林, 刘继春. 基于EEMD-SE和PSO-KELM的短期负荷区间预测方法[J]. 中国电力, 2021, 54(3): 132-140.

ZHANG Lin, LIU Jichun. A Short-Term Load Interval Forecasting Method Based on EEMD-SE and PSO-KELM[J]. Electric Power, 2021, 54(3): 132-140.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202001103

https://www.electricpower.com.cn/CN/Y2021/V54/I3/132

参考文献

[1] 王凌谊, 王志敏, 钱纹, 等. 适应供给侧结构性改革的中长期负荷预测拓展索洛模型研究[J]. 电力系统保护与控制, 2019, 47(18): 49-59
WANG Lingyi, WANG Zhimin, QIAN Wen, et al. Extended Solow regression model for mid/long-term load forecasting adapted to supply-side structural reform[J]. Power System Protection and Control, 2019, 47(18): 49-59
[2] 王德文, 孙志伟. 电力用户侧大数据分析与并行负荷预测[J]. 中国电机工程学报, 2015, 35(3): 527-537
WANG Dewen, SUN Zhiwei. Big data analysis and parallel load forecasting of electric power user side[J]. Proceedings of the CSEE, 2015, 35(3): 527-537
[3] 郭艳飞, 程林, 李洪涛, 等. 基于支持向量机和互联网信息修正的空间负荷预测方法[J]. 中国电力, 2019, 52(4): 80-88
GUO Yanfei, CHENG Lin, LI Hongtao, et al. Spatial load forecasting method based on support vector machine and internet information correction[J]. Electric Power, 2019, 52(4): 80-88
[4] 李知艺, 丁剑鹰, 吴迪, 等. 电力负荷区间预测的集成极限学习机方法[J]. 华北电力大学学报(自然科学版), 2014, 41(2): 78-88
LI Zhiyi, DING Jianying, WU Di, et al. An ensemble model of the extreme learning machine for load interval prediction[J]. Journal of North China Electric Power University (Natural Science Edition), 2014, 41(2): 78-88
[5] 张宇献, 郝双, 钱小毅. 基于误差分解和Bootstrap方法的风电功率区间预测[J]. 电网技术, 2019, 43(6): 1941-1947
ZHANG Yuxian, HAO Shuang, QIAN Xiaoyi. Interval prediction of wind power based on error decomposition and bootstrap method revoke[J]. Power System Technology, 2019, 43(6): 1941-1947
[6] GUAN C, LUH P B, MICHEL L D, et al. Hybrid Kalman filters for very short-term load forecasting and prediction interval estimation[J]. IEEE Transactions on Power Systems, 2013, 28(4): 3806-3817.
[7] QUAN H, SRINIVASAN D, KHOSRAVI A. Uncertainty handling using neural network-based prediction intervals for electrical load forecasting[J]. Energy, 2014, 73: 916-925.
[8] KHOSRAVI A, NAHAVANDI S, CREIGHTON D. Construction of optimal prediction intervals for load forecasting problems[J]. IEEE Transactions on Power Systems, 2010, 25(3): 1496-1503.
[9] 何耀耀, 许启发, 杨善林, 等. 基于RBF神经网络分位数回归的电力负荷概率密度预测方法[J]. 中国电机工程学报, 2013, 33(1): 93-98
HE Yaoyao, XU Qifa, YANG Shanlin, et al. A power load probability density forecasting method based on RBF neural network quantile regression[J]. Proceedings of the CSEE, 2013, 33(1): 93-98
[10] 黄彦璐, 张震, 张喆, 等. 基于非侵入式负荷辨识和关联规则挖掘的用户柔性负荷区间预测[J]. 南方电网技术, 2019, 13(4): 60-66
HUANG Yanlu, ZHANG Zhen, ZHANG Zhe, et al. User-side flexible load interval prediction based on non-intrusive load identification and association rule mining[J]. Southern Power System Technology, 2019, 13(4): 60-66
[11] CHARYTONIUK W, NIEBRZYDOWSKI J. Confidence interval construction for load forecast[J]. Electric Power Systems Research, 1998, 48(2): 97-103.
[12] KHOSRAVI A, NAHAVANDI S, CREIGHTON D, et al. Lower upper bound estimation method for construction of neural network-based prediction intervals[J]. IEEE Transactions on Neural Networks, 2011, 22(3): 337-346.
[13] 姜鹏飞, 蔡之华. 基于遗传算法和梯度下降的RBF神经网络组合训练方法[J]. 计算机应用, 2007(2): 366-368, 372
JIANG Pengfei, CAI Zhihua. Combined algorithms for training RBF neural networks based on genetic algorithms and gradient descent[J]. Journal of Computer Applications, 2007(2): 366-368, 372
[14] 石红伟, 杨明红. 基于ELM的跨越前馈神经网络及其应用研究[J]. 现代电子技术, 2013, 36(15): 108-111
SHI Hongwei, YANG Minghong. Span feedforward neural network based on ELM and its application[J]. Modern Electronics Technique, 2013, 36(15): 108-111
[15] WANG J Z, DU P, LU H Y, et al. An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting[J]. Applied Soft Computing, 2018, 72: 321-337.
[16] WANG J Z, YANG W D, DU P, et al. Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system[J]. Energy, 2018, 148: 59-78.
[17] ZHANG J L, WEI Y M, LI D Z, et al. Short term electricity load forecasting using a hybrid model[J]. Energy, 2018, 158: 774-781.
[18] MALLAT S G. A theory for multi-resolution signal decomposition: the wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693.
[19] LI B W, ZHANG J, HE Y, et al. Short-term load-forecasting method based on wavelet decomposition with second-order gray neural network model combined with ADF test[J]. IEEE Access, 2017, 5: 16324-16331.
[20] LIU H, CHEN C, TIAN H Q, et al. A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks[J]. Renewable Energy, 2012, 48: 545-556.
[21] HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903-995.
[22] 谢宏, 陈志业, 牛东晓, 等. 基于小波分解与气象因素影响的电力系统日负荷预测模型研究[J]. 中国电机工程学报, 2001(5): 5-10
XIE Hong, CHEN Zhiye, NIU Dongxiao, et al. The research of daily load forecasting model based on wavelet decomposing and climatic influence[J]. Proceedings of the CSEE, 2001(5): 5-10
[23] 汤庆峰, 刘念, 张建华, 等. 基于EMD-KELM-EKF与参数优选的用户侧微电网短期负荷预测方法[J]. 电网技术, 2014, 38(10): 2691-2699
TANG Qingfeng, LIU Nian, ZHANG Jianhua, et al. A short-term load forecasting method for micro-grid based on EMD-KELM-EKF and parameter optimization[J]. Power System Technology, 2014, 38(10): 2691-2699
[24] HU X Y, PENG S L, HWANG W L. EMD revisited: a new understanding of the envelope and resolving the mode-mixing problem in AM-FM signals[J]. IEEE Transactions on Signal Processing, 2012, 60(3): 1075-1086.
[25] TANG B P, DONG S J, SONG T. Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation[J]. Signal Processing, 2012, 92(1): 248-258.
[26] WANG T, ZHANG M C, YU Q H, et al. Comparing the applications of EMD and EEMD on time-frequency analysis of seismic signal[J]. Journal of Applied Geophysics, 2012, 83: 29-34.
[27] WU Z H, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
[28] RICHMAN J S, MOORMAN J R. Physiological time-series analysis using approximate entropy and sample entropy[J]. American Journal of Physiology Heart and Circulatory Physiology, 2000, 278(6): H2039-H2049.
[29] HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1/2/3): 489-501.
[30] HUANG G B. An insight into extreme learning machines: random neurons, random features and kernels[J]. Cognitive Computation, 2014, 6(3): 376-390.
[31] DEL VALLE Y, VENAYAGAMOORTHY G K, MOHAGHEGHI S, et al. Particle swarm optimization: basic concepts, variants and applications in power systems[J]. IEEE Transactions on Evolutionary Computation, 2008, 12(2): 171-195.