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“Various multistage ensembles for prediction of heating energy consumption”

Authors: Radisa Jovanovic and Aleksandra Sretenovic,
Affiliation: University of Belgrade
Reference: 2015, Vol 36, No 2, pp. 119-132.

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Keywords: heating consumption prediction, multistage neural network ensemble, adaptive neuro-fuzzy inference

Abstract: Feedforward neural network models are created for prediction of daily heating energy consumption of a NTNU university campus Gloshaugen using actual measured data for training and testing. Improvement of prediction accuracy is proposed by using neural network ensemble. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best network of each cluster is chosen as member of an ensemble. Two conventional averaging methods for obtaining ensemble output are applied; simple and weighted. In order to achieve better prediction results, multistage ensemble is investigated. As second level, adaptive neuro-fuzzy inference system with various clustering and membership functions are used to aggregate the selected ensemble members. Feedforward neural network in second stage is also analyzed. It is shown that using ensemble of neural networks can predict heating energy consumption with better accuracy than the best trained single neural network, while the best results are achieved with multistage ensemble.

PDF PDF (6125 Kb)        DOI: 10.4173/mic.2015.2.4

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  title={{Various multistage ensembles for prediction of heating energy consumption}},
  author={Jovanovic, Radisa and Sretenovic, Aleksandra},
  journal={Modeling, Identification and Control},
  publisher={Norwegian Society of Automatic Control}


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