Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

A fast electric load forecasting using neural networks

The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

Electrical load forecasting formulation by a fast neural network

The objective of this work is to develop a methodology for electric load forecasting based on a neural network. Here, backpropagation algorithm is used with an adaptive process that based on fuzzy logic and using a decaying exponential function to avoid instability in the convergence process. This methodology results in fast training, when compared to the conventional formulation of backpropagation algorithm. The results are presented using data from a Brazilian Electric Company, and shows a very good performance for the proposal objective.

Wetland projects developed in Brazil

The first studies with constructed wetlands undertaken in Brazil were the result of observations made from the Amazon flood plains. The first attempt to use this capacity to change the quality of the water, in the sense of purification performed in Brazil using constructed wetland systems, was made by Salati et al. After that, new technologies were developed in a focused attempt to increase the efficiency of the system and reduce investments. Over these 18 years, persuading the Brazilian scientific community as well as the environmental control agencies to give due attention to this kind of research has required endless efforts. Only in recent years have major institutions responsible for sewage treatment and potable water supply been concerned with this type of technology for solving real problems. These institutions are as follows: SABESP (Basic Sanitation Company of Sao Paulo State), SANEPAR (Sanitation Company of Parana State) and CESP (Electric Company of Sao Paulo State). One of the private institutions that has systematically worked in the design and projects of constructed wetlands is the Institute of Applied Ecology. This institution has enhanced and developed a water depuration system based on the purifying capacity of the soil. The wetlands with filtering soils are systems formed by overlapping layers of crushed stone, gravel and soil planted with rice. This technology has been used in sewage treatment and also in water supply systems.

Previsão de cargas elétricas através de um modelo híbrido de regressão com redes neurais

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