|
|
|
| Short-term load forecasting based on WT-IPSO-BPNN |
| KANG Yi1,2, SHI Liujun1, GUO Gang3 |
1. School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045;
2. Zhengzhou Skong Electric Technology Co., Ltd, Zhengzhou 450001;
3. State Grid Hebei Electric Power Co., Ltd, Handan Power Supply Company, Handan, Hebei 056000 |
|
|
|
|
Abstract In order to improve the accuracy of short-term load forecasting, a combined forecasting model based on wavelet decomposition (WT), improved particle swarm optimization (IPSO) and BP neural network is proposed. Firstly, we use wavelet decomposition to preprocess the load data, and decompose the historical data into cd1, cd2, cd3 and ca3; then we use neural network to model and predict the decomposed wavelet sequence; finally, we use wavelet to reconstruct the final forecast of the load sequence. In view of the accuracy of BP neural network samples and to increase the convergence speed and stability of the neural network, the improved particle swarm optimization method is used to optimize the network, forming a “decomposition prediction reconstruction” model. Compared with the wavelet decomposition BP neural network method, it has stronger training and learning ability, faster convergence speed, high prediction accuracy and strong adaptability.
|
|
Received: 11 April 2020
|
|
|
|
[1] 康重庆, 夏清, 刘梅. 电力系统负荷预测[M]. 北京:中国电力出版社, 2017.
[2] 蒋敏, 顾东健, 孔军, 等. 基于在线序列极限支持向量回归的短期负荷预测模型[J]. 电网技术, 2018, 42(7): 2240-2247.
[3] 王瑞, 王强强, 逯静. 基于随机神经网络的短期负荷预测[J]. 制造业自动化, 2019, 41(7): 44-48.
[4] 马天男, 王超, 彭丽霖, 等. 计及需求响应和深度结构多任务学习的电力系统短期负荷预测[J]. 电测与仪表, 2019, 56(16): 50-60.
[5] 李龙, 魏靖, 黎灿兵, 等. 基于人工神经网络的负荷模型预测[J]. 电工技术学报, 2015, 30(8): 225-230.
[6] 王玉龙, 崔玉, 李鹏, 等. 基于小波分析改进的神经网络模型电力系统负荷预测[J]. 电网与清洁能源, 2015, 31(2): 16-20, 27.
[7] 王增平, 赵兵, 纪维佳, 等. 基于GRU-NN模型的短期负荷预测方法[J]. 电力系统自动化, 2019, 43(5): 53-62.
[8] 魏宏阳, 苏舟, 姚李孝, 等. 基于人体舒适度日特征向量的PSO-NN短期负荷预测[J]. 电网与清洁能源, 2016, 32(8): 135-140.
[9] 李若晨, 朱帆, 朱永利, 等. 结合受限玻尔兹曼机的递归神经网络电力系统短期负荷预测[J]. 电力系统保护与控制, 2018, 46(17): 83-88.
[10] 苏学能, 刘天琪, 曹鸿谦, 等. 基于Hadoop架构的多重分布式BP神经网络的短期负荷预测方法[J]. 中国电机工程学报, 2017, 37(17): 4966-4973, 5216.
[11] 宋学伟, 刘天羽, 江秀臣, 等. 基于改进鱼群算法与最小二乘支持向量机的短期负荷预测[J]. 电气技术, 2019, 20(11): 20-26.
[12] 丁坚勇, 朱炳翔, 田世明, 等. 改进F-score特征选择的MPSO-BP神经网络短期负荷预测[J]. 电测与仪表, 2018, 55(15): 36-41.
[13] 吴云, 雷建文, 鲍丽山, 等. 基于改进灰色关联分析与蝙蝠优化神经网络的短期负荷预测[J]. 电力系统自动化, 2018, 42(20): 67-74.
[14] 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.
[15] 张平, 潘学萍, 薛文超. 基于小波分解模糊灰色聚类和BP神经网络的短期负荷预测[J]. 电力自动化设备, 2012, 32(11): 121-125, 141.
[16] 姜雲腾, 李萍. 基于改进粒子群神经网络短期负荷预测[J]. 电气技术, 2018, 19(2): 87-91.
[17] 王瑞, 周晨曦, 逯静. 改进粒子群优化BP神经网络短期负荷预测研究[J]. 制造业自动化, 2019, 41(6): 39-42, 77. |
|
|
|
2021, Vol. 22 
