This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a shortterm load forecasting for the different modeled consumption processes. ; Postprint (published version)


    Access

    Download


    Export, share and cite



    An Efficient Weather Forecasting System using Adaptive Neuro-Fuzzy Inference System

    Shereef, I. Kadar / Baboo, Dr. S. Santhosh | BASE | 2017

    Free access

    Fault Identification using Combined Adaptive Neuro-Fuzzy Inference System and Gustafson–Kessel Algorithm

    Abdullah, Amalina / Banmongkol, Channarong / Hoonchareong, Naebboon et al. | BASE | 2018

    Free access

    A NEURO-FUZZY SYSTEM APPROACH FOR FORECASTING SHORT-TERM FREEWAY TRAFFIC FLOWS

    Chen, L. / Wang, F.-Y. / IEEE | British Library Conference Proceedings | 2002