Open AccessGroup‐based chaos genetic algorithm and non‐linear ensemble of neural networks for short‐term load forecasting
Author(s) -
Chen LiGuo,
Chiang HsiaoDong,
Dong Na,
Liu RongPeng
Publication year - 2016
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2015.1068
Subject(s) - artificial neural network , pruning , computer science , genetic algorithm , term (time) , chaos (operating system) , time delay neural network , artificial intelligence , algorithm , machine learning , physics , computer security , quantum mechanics , agronomy , biology
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.