The main purpose of this paper is to achieve a comparative analysis among Autoregressive Integrated Moving Average model, Artificial Neural Networks and Adaptive NeuroFuzzy System techniques for load demand forecasting in distribution substations. The system inputs are three load demand time series, which are composed by data measured at intervals of five minutes each, during seven days, from substations located at Andradina, Ubatuba and Votuporanga. Autoregressive
Integrated Moving Average models with suitable results have been analyzed, whereas several input configurations and different architectures have been investigated for Artificial Neural Networks and Adaptive Neuro-Fuzzy System techniques aiming the forecasting of twelve further steps. The results showed the Artificial Neural Network based technique superiority for such forecasting, followed by Autoregressive Integrated MovingAverage model and Adaptive Neuro-Fuzzy approach. The load demand forecasting can minimize costs of energy generation as well as improve the electric power system safety.Index Terms--Autoregressive integrated moving average processes, feedforward neural networks, fuzzy systems, intelligent systems, load forecasting, time series.
The induction motor is considered one of the most important elements in manufacturing processes. The use of strategies based on intelligent systems capable to classify the presence or absence of failures and also to determine its origin for the diagnosis and faults prediction is widely investigated in three phase induction motors. Thus, the aim of this paper is to present a methodology of bearing failures classification based on artificial neural networks, by using voltage and electric currents values in the time domain. Experimental results collected at real industrial process are presented to validate this proposal.
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