A novel hybrid data-driven model for multi-input single-output system simulation

作者：Kan, Guangyuan; He, Xiaoyan; Li, Jiren; Ding, Liuqian; Zhang, Dawei^*; Lei, Tianjie; Hong, Yang; Liang, Ke; Zuo, Depeng; Bao, Zhenxin; Zhang, Mengjie

来源：Neural Computing & Applications, 2018, 29(7): 577-593.

DOI：10.1007/s00521-016-2534-y

• MISO system
• Data-driven model
• K-means method
• Ensemble BPANN
• KNN method
• KEK model

Artificial neural network (ANN)-based data-driven model is an effective and robust tool for multi-input single-output (MISO) system simulation task. However, there are several conundrums which deteriorate the performance of the ANN model. These problems include the hard task of topology design, parameter training, and the balance between simulation accuracy and generalization capability. In order to overcome conundrums mentioned above, a novel hybrid data-driven model named KEK was proposed in this paper. The KEK model was developed by coupling the K-means method for input clustering, ensemble back-propagation (BP) ANN for output estimation, and K-nearest neighbor (KNN) method for output error estimation. A novel calibration method was also proposed for the automatic and global calibration of the KEK model. For the purpose of intercomparison of model performance, the ANN model, KNN model, and proposed KEK model were applied for two applications including the Peak benchmark function simulation and the real-world electricity system daily total load forecasting. The testing results indicated that the KEK model outperformed other two models and showed very good simulation accuracy and generalization capability in the MISO system simulation tasks.

• 出版日期2018-4
• 单位水利部交通运输部国家能源局南京水利科学研究院; 清华大学; 北京师范大学; 河海大学; 流域水循环模拟与调控国家重点实验室; 中国水利水电科学研究院; 水沙科学与水利水电工程国家重点实验室