An Ensemble Data Mining and FLANN Combining Short-term Load Forecasting System for Abnormal Days

Li, Ming; Gao, Junli

doi:10.4304/jsw.6.6.961-968

Cited by 3 publications

(1 citation statement)

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“…In general, combining the outputs of several predicting models could improve the performance of the single one, which is based on a suitable decomposition of the prediction error [26][27][28][29][30][31]. Expected ensemble members must be accurate and diverse, which poses the problem of generating a set of predictors with reasonably individual performances and independently distributed predictions for the test points [26].…”

Section: G Ensemble Methodsmentioning

confidence: 99%

Ensemble of Flexible Neural Tree and Ordinary Differential Equations for Small-time Scale Network Traffic Prediction

Yang¹,

Jiang²,

Chen³

et al. 2013

JCP

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Abstract-Accurate models play important roles in capturing the salient characteristics of the network traffic, analyzing and simulating for the network dynamic, and improving the predictive ability for system dynamics. In this study, the ensemble of the flexible neural tree (FNT) and system models expressed by the ordinary differential equations (ODEs) is proposed to further improve the accuracy of time series forecasting. Firstly, the additive tree model is introduced to represent more precisely ODEs for the network dynamics. Secondly, the structures and parameters of FNT and the additive tree model are optimized based on the Genetic Programming (GP) and the Particle Swarm Optimization algorithm (PSO). Finally, the expected level of performance is verified by using the proposed method, which provides a reliable forecast model for small-time scale network traffic. Experimental results reveal that the proposed method is able to estimate the small-time scale network traffic measurement data with decent accuracy.Index Terms-hybrid evolutionary method, small-time scale network traffic, the additive tree models, ordinary differential equations, ensemble learning

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