Application of AdaSS Ensemble Approach for Prediction of Power Plant Generator Tension

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 299)

Abstract

The paper presents the application of ensemble approach in the prediction of tension in a power plant generator. The proposed Adaptive Splitting and Selection (AdaSS) ensemble algorithm performs fusion of several elementary predictors and is based on the assumption that the fusion should take into account the competence of the elementary predictors. To take full advantage of complementarity of the predictors, the algorithm evaluates their local specialization, and creates a set of locally specialized predictors. System parameters are adjusted using evolutionary algorithms in the course of the learning process, which aims to minimize the mean squared error of prediction. Evaluation of the system is carried on an empirical data set and is compared to other classical ensemble methods. The results show that the proposed approach effectively returns a more consistent and accurate prediction of tension, thereby outperforming classical ensemble approaches.

Keywords

Power output prediction ensemble of predictors evolutionary algorithms 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.IT4InnovationsVSB-Technical University of OstravaOstravaCzech Republic
  2. 2.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

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