期刊
IET GENERATION TRANSMISSION & DISTRIBUTION
卷 10, 期 6, 页码 1440-1447出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-gtd.2015.1068
关键词
-
资金
- Natural Science Foundation of China [61403274]
- National Natural Science Foundation of China [51337007]
This study presents a non-linear ensemble of partially connected neural networks for short-term load forecasting. Partially connected neural networks are chosen as individual predictors due to their good generalisation capability. A group-based chaos genetic algorithm is developed to generate diverse and effective neural networks. A novel pruning method is employed to develop partially connected neural networks. To further enhance prediction accuracy, an artificial neural network-based non-linear ensemble of partially connected neural network predictors is developed. The proposed non-linear ensemble neural network is evaluated on a PJM market dataset and an ISO New England dataset with promising results of 1.76 and 1.29% error, respectively, demonstrating its capability as a promising predictor.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据