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Ouaomar, Younes
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Energy baseline model enhanced based on artificial neural network in industrial buildings Ouaomar, Younes; Benkachcha, Said; Kaddiri, Mourad
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1493-1502

Abstract

In this article, a new energy-efficient reference model has been established for a plastic injection molding plant. However, the proposed model handled difficulties due to the lack of robust and complete data, such as production mix and cooling degree-days. In addition, the proposed model applies three distinct enhanced modeling methodologies, including regression modeling, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Furthermore, these performance parameters were established to assess the accuracy of each model in this work. Moreover, the numerical results show that among the methodologies used in this work, the ANN demonstrated effective performance despite uncertainties in the measured input variables. The ANN numerical results in this paper highlight the ability to accurately assess baseline consumption in the industrial sector, providing a practical tool for decision-makers to improve energy efficiency.
Co-Authors Benkachcha, Said Kaddiri, Mourad