Paper Info
Title
Application of AdaSS Ensemble Approach for Prediction of Power Plant Generator Tension
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.
Year
DOI
Venue
2014
10.1007/978-3-319-07995-0_21
INTERNATIONAL JOINT CONFERENCE SOCO'14-CISIS'14-ICEUTE'14
Keywords
Field
DocType
Power output prediction,ensemble of predictors,evolutionary algorithms
Complementarity (molecular biology),Evolutionary algorithm,Computer science,Mean squared error,Artificial intelligence,Ensemble learning,Machine learning,Power station
Conference
Volume
ISSN
Citations 
299
2194-5357
4
PageRank 
References 
Authors
0.39
10
2
Name
Order
Citations
PageRank
Konrad Jackowski113610.46
Jan Platos228658.72