Short-Term Power Load Forecasting Based on Feature Selection and Optimized Extreme Learning Machine

被引：3
|
作者：
Shang L. [1 ]
Li H. [1 ]
Hou Y. [1 ]
Huang C. [1 ]
Zhang J. [1 ]
机构：
[1] School of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an
来源：
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2022年 / 56卷 / 04期
关键词：
Extreme learning machine; Gram-Schmidt orthogonalization; Gray wolf optimization algorithm; Particle swarm optimization algorithm; Pearson correlation analysis; Short-term power load forecasting; Tent chaotic mapping;
D O I：
10.7652/xjtuxb202204018
中图分类号：
学科分类号：
摘要：
For the difficulty in determining the feature quantity and the low output stability of the extreme learning machine (ELM) due to randomly generated initial weights and thresholds during load forecasting, it is proposed in this paper that the Gram-Schmidt orthogonalization and Pearson correlation analysis (GSO-PCA) and improved gray wolf optimization (IGWO) algorithm be used to optimize the short-term power load forecasting model of ELM (IGWO-ELM). GSO and PCA are used for optimal selection of two distinct features respectively, and the mean absolute percentage error (MAPE) is based for determination of the optimal feature set. MAPE in GSO-PCA is 1.3%, 0.55% and 0.83% lower respectively compared with that in traditional experience, maximum information coefficient and random forest for feature selection. This verifies its superiority. The Tent chaotic mapping and particle swarm optimization (PSO) algorithm are integrated into the gray wolf optimization algorithm to obtain IGWO algorithm which is tested with two typical test functions and proved with better optimization capability. The IGWO algorithm is used to dynamically optimize initial weights and threshold of the ELM and establish IGWO-ELM short-term power load forecasting model. This established model is then compared with a traditional model in terms of four evaluation indexes, i.e., goodness of fit test coefficient, mean absolute error, root mean square error and MAPE and in line with case study. The simulation results show that these four indexes are 0.997 8, 54.90 kW, 72.02 kW and 1.52% and are siqnificantly better other models. This verifies the effectiveness and superiority of the proposed model. © 2022, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.
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页码：165 / 175
页数：10
相关论文
共 29 条
  • [11] KAZEMI S M R, SEIED HOSEINI M M, ABBASIAN-NAGHNEH S, Et al., An evolutionary-based adaptive neuro-fuzzy inference system for intelligent short-term load forecasting, International Transactions in Operational Research, 21, 2, pp. 311-326, (2014)
  • [12] LI Wenwu, SHI Qiang, LI Dan, Et al., Multi-stage optimization forecast of short-term power load based on VMD and PSO-SVR
  • [13] LI Gen, LI Yunhua, ROOZITALAB F., Midterm load forecasting: a multistep approach based on phase space reconstruction and support vector machine, IEEE Systems Journal, 14, 4, pp. 4967-4977, (2020)
  • [14] GAO Lin, GAO Feng, GUAN Xiaohong, Et al., Neural networks ensemble model based on Boosting algorithm for short-term load forecasting, Journal of Xi'an Jiaotong University, 38, 10, pp. 1026-1030, (2004)
  • [15] GE Quanbo, JIANG Haoyu, HE Meiguang, Et al., Power load forecast based on fuzzy BP neural networks with dynamical estimation of weights, International Journal of Fuzzy Systems, 22, 3, pp. 956-969, (2020)
  • [16] CHENG Song, YAN Jianwei, ZHAO Dengfu, Et al., Short-term load forecasting method based on ensemble improved extreme learning machine, Journal of Xi'an Jiaotong University, 43, 2, pp. 106-110, (2009)
  • [17] DONG Hao, LI Mingxing, ZHANG Shuqing, Et al., Short-term power load forecasting based on kernel principal component analysis and extreme learning machine, Journal of Electronic Measurement and Instrumentation, 32, 1, pp. 188-193, (2018)
  • [18] ZHANG Zhenzhong, GUO Fuao, LIU Daming, Et al., Multi-model integrated short-term load prediction based on maximum mutual information coefficient and wavelet decomposition, Computer Applications and Software, 38, 5, pp. 82-87, (2021)
  • [19] KONG Lingchun, SUN Qiongqiong, YANG Zhaofeng, Load forecasting based on bat algorithm optimizing extreme learning machine, Journal of Liaoning Technical University (Natural Science), 35, 1, pp. 89-92, (2016)
  • [20] LU Fangcheng, LIU Yi, QI Yanxun, Et al., Short-term load forecasting based on optimized learning machine using improved genetic algorithm, Journal of North China Electric Power University (Natural Science Edition), 45, 6, pp. 1-7, (2018)
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