Short-term power grid load forecasting based on variable weight combination hybrid model

被引:0
作者
Lin, Tingting [1 ]
Fan, Sen [2 ]
Zhang, Xinying [1 ]
机构
[1] Zhengzhou Univ Econ & Business, Smart Mfg Coll, 2 Shuanghu Ave, Zhengzhou 451191, Henan, Peoples R China
[2] SIPPR Engn Grp Co Ltd, 126 Zhongyuan West Rd, Zhengzhou 473500, Henan, Peoples R China
关键词
power network load forecasting; variable weight combination model(sic)LSSVM; WOA-LSTM; XGBoost; deep learning;
D O I
10.1093/ijlct/ctae028
中图分类号
O414.1 [热力学];
学科分类号
摘要
The power grid load exhibits non-linearity and volatility, posing challenges to power grid dispatching. To enhance the precision of power grid load forecasting, a variable weight combination forecasting model is suggested to address the issue of inadequate forecasting efficacy of individual algorithms. Considering the impact of various environmental factors on power grid load, a load influence feature dataset is formulated. Initially, support vector machines, genetic algorithm-optimized back propagation (BP) neural networks and radial basis neural networks are employed to forecast individual loads. Subsequently, a variance-covariance weight dynamic distribution method is utilized to merge the prediction results of the three individual algorithms, thereby establishing a short-term power grid load prediction model with variable weight combination. Taking a regional power grid as an example, the simulation results show that the prediction accuracy of the variable weight combination model is higher than that of the single algorithm. Taking the evaluation index MAPE as an example, compared with the three single algorithms, the prediction accuracy is improved by 42.31%, 48.56% and 65.33%. The practice proves that the proposed variable weight combination forecasting model greatly improves the accuracy of power network load forecasting.
引用
收藏
页码:683 / 689
页数:7
相关论文
共 50 条
[41]   An Ensemble Model based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting [J].
Shen, Yamin ;
Ma, Yuxuan ;
Deng, Simin ;
Huang, Chiou-Jye ;
Kuo, Ping-Huan .
SUSTAINABILITY, 2021, 13 (04) :1-21
[42]   A Novel Temporal Feature Selection Based LSTM Model for Electrical Short-Term Load Forecasting [J].
Ijaz, Khalid ;
Hussain, Zawar ;
Ahmad, Jameel ;
Ali, Syed Farooq ;
Adnan, Muhammad ;
Khosa, Ikramullah .
IEEE ACCESS, 2022, 10 :82596-82613
[43]   Short-Term Residential Load Forecasting Based on Resident Behaviour Learning [J].
Kong, Weicong ;
Dong, Zhao Yang ;
Hill, David J. ;
Luo, Fengji ;
Xu, Yan .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2018, 33 (01) :1087-1088
[44]   Short-term household load forecasting based on EMD-SLSTM [J].
Liu J. ;
Li J. ;
Yang L. ;
Yan Y. ;
Liu Y. ;
Zhang Y. .
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2019, 47 (06) :40-47
[45]   Short-term load forecasting based on conditional generating adversarial networks [J].
Chang, Douxing .
JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING, 2025, 25 (03) :2185-2195
[46]   Short-term load forecasting of industrial customers based on SVMD and XGBoost [J].
Wang, Yuanyuan ;
Sun, Shanfeng ;
Chen, Xiaoqiao ;
Zeng, Xiangjun ;
Kong, Yang ;
Chen, Jun ;
Guo, Yongsheng ;
Wang, Tingyuan .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2021, 129 (129)
[47]   A hybrid model based on bidirectional long short-term memory neural network and Catboost for short-term electricity spot price forecasting [J].
Zhang, Fan ;
Fleyeh, Hasan ;
Bales, Chris .
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2022, 73 (02) :301-325
[48]   A Reliable Short-Term Power Load Forecasting Method Based on VMD-IWOA-LSTM Algorithm [J].
Zhuang, Zhiyuan ;
Zheng, Xidong ;
Chen, Zixing ;
Jin, Tao .
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2022, 17 (08) :1121-1132
[49]   Hybrid Deep Learning Applied on Saudi Smart Grids for Short-Term Load Forecasting [J].
Alrasheedi, Abdullah ;
Almalaq, Abdulaziz .
MATHEMATICS, 2022, 10 (15)
[50]   Short-Term Load Forecasting Using Encoder-Decoder WaveNet: Application to the French Grid [J].
Dorado Rueda, Fernando ;
Duran Suarez, Jaime ;
del Real Torres, Alejandro .
ENERGIES, 2021, 14 (09)