NDLI: Group-based chaos genetic algorithm and non-linear ensemble of neural networks for short-term load forecasting

Content Provider	IET Digital Library
Author	Chen, Li Guo Chiang, Hsiao Dong Dong, Na Liu, Rong Peng
Abstract	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.
Starting Page	1440
Ending Page	1447
Page Count	8
ISSN	17518687
Volume Number	10
e-ISSN	17518695
Issue Number	Issue 6, Apr (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/10/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2015.1068
Journal	IET Generation, Transmission & Distribution
Publisher Date	2016-04-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Artificial Neural Network-based Nonlinear Ensemble Genetic Algorithm Group-based Chaos Genetic Algorithm Load Forecasting Neural Computing Technique Neural Nets Nonlinear Ensemble Optimisation Technique Partially Connected Neural Network Predictors Power Engineering Computing Power System Planning And Layout Pruning Method Short-term Load Forecasting
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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