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The purpose of this work is to employ the Adaptive Neuro Fuzzy Inference System for performing very short-term load forecasting in power distribution substations, which can enable the development of more efficient automatic load control of electrical power load systems. The system inputs are two load demand time series, composed of data measured in five(More)
A type of recurrent neural network has been proposed by H. Jaeger. This model, called Echo State Network (ESN), possesses a highly interconnected and recurrent topology of nonlinear processing elements, which constitutes a "reservoir of rich dynamics" and contains information about the history of input or/and output patterns. The interesting property of ESN(More)
This paper presents a method for the identification of consumer load profiles in a totally automatic way. The main objective was to develop an interface between utilities and consumers in order to obtain data from smart meters and identify the load profile. To this end, client/server software interfaces capable of transmitting and receiving data through(More)
This paper presents a methodology aimed at extracting features to obtain information that will highlight disturbances related to the field of power quality. Due to the concept of smart grids, it is clear that the classification of the disturbances should be undertaken using smart meters, so that a large amount of data corresponding to the voltage and(More)
The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way,(More)