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.
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Jackowski, K., Platos, J. (2014). Application of AdaSS Ensemble Approach for Prediction of Power Plant Generator Tension. In: de la Puerta, J., et al. International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. Advances in Intelligent Systems and Computing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-07995-0_21
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DOI: https://doi.org/10.1007/978-3-319-07995-0_21
Publisher Name: Springer, Cham
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