A.A. Ferreira

Investigating the use of Reservoir Computing for forecasting the hourly wind speed in short -term

This paper presents the results of the models created for forecasting the hourly wind speed in 24-step-forward using Reservoir Computing (RC). RC is a new paradigm that offers an intuitive methodology for using the temporal processing power of recurrent neural networks (RNN) without the inconvenience of training them. Originally, introduced independently as Liquid State Machine [5] or Echo State Network [6], whose basic concept is randomly construct a RNN and leave the weights unchanged. In this work we used Echo State Network (ESN) to create the models and Multi-Layer Networks (MLP) to compare the results. The results showed that the ESN performed significantly better than MLP networks, even though it presents a significantly simpler, and faster, training algorithm.

Analysis of mammogram using self-organizing neural networks based on spatial isomorphism

The correct segmentation and measurement of mammography images is of fundamental importance for the development of automatic or computer-aided cancer detection systems. In this paper we propose a method to segment mammogram image using a self-organizing neural network based on spatial isomorphism. The method used is a modified version of the algorithm proposed by Venkatesh and Rishikesh [1] to extract object boundaries in an image. This model explores the principle of spatial isomorphism and self-organization in order to create flexible contours that characterize shapes in images. We modified the original algorithm to overcame problems of local minimum, poor performance for image object with large concavity and imprecise results when simple or far from object border contour are chosen. A comparison of both algorithm and original segmentation used by the MIAS database [9] is presented.

Combining Multiple Artificial Neural Networks Using Random Committee to Decide upon Electrical Disturbance Classification

An ANN-based automatic classifier for power system disturbance waveforms was developed. Actual voltage waveforms were applied in the training process. Signals were processed in two steps: i) decomposition through wavelet transformation up to the 5th decomposition level; ii) the resultant wavelet coefficients are processed via PCA, reducing the input space of the classifier to a much lower dimension. The classification was carried out using a combination of six MLPs with different architectures: five representing the first to fifth-level details, and one representing the fifth-level approximation. The RPROP algorithm was applied for training the networks. Network combination was formed using random committee which builds an ensemble of randomized base classifiers. Experimental results with real data indicate that the random committee is clearly an effective way to improve disturbance classification accuracy when compared with the simple average and the individual models.

A Hybrid Intelligent System for Short and Mid-term Forecasting for the CELPE Distribution Utility

This paper presents the development of a hybrid intelligent system, joining an artificial neural network (ANN) based technique and heuristic rules to adjust the short and mid-term electric load forecasting in the 3, 7, 15, 30, and 45 days ahead. The study was based on load demand data of Energy Company of Pernambuco (CELPE), whose data contain the hourly load consumption in the period from January-2000 until December-2004. The proposed system forecasts a holiday as one Saturday or Sunday based on the specialists information that analyzes the load behaviors of each holiday. The hybrid intelligent system presented an improvement in the load forecasts in relation to the results achieved by the ANN alone. The program was implemented in MATLAB 7.0 R14.

Improving Disturbance Classification by Combining Multiple Artificial Neural Networks

An ANN-based automatic classifier for power system disturbance waveforms was developed. Actual voltage waveforms were applied in the training process. Signals are processed in two steps: i) decomposition through wavelet transformation up to the 5th decomposition level; ii) the resultant wavelet coefficients are processed via PCA, reducing the input space of the classifier to a much lower dimension. The classification is carried out using a combination of 3 MLPs with different architectures. The RPROP algorithm is applied for training the networks. Network combination was formed and the final decision of the classifier corresponds to the combination output with the highest value. The results showed to be quite promising for five disturbance types tested so far: sags, swells, harmonics, oscillatory transients and interruptions, as well as in the particular case of no disturbance.

SAX-quantile based multiresolution approach for finding heatwave events in summer temperature time series

Time series pattern discovery is of great importance in a large variety of environmental and engineering applications, from supporting predictive models to helping to understand hidden underlying processes. This work develops a multiresolution time series method for extracting patterns in weather records, particular temperature data. The topic is important, as, given a warming climate, morbidity and mortality are expected to rise as heatwave frequency and intensity increase. By analysing summer temperature quantiles at different levels of coarseness, it was found that compounding models can contain a complete description of severe weather events. This new multiresolution quantile approach is developed as an extension of the symbolic aggregate approximation of the temperature time series in which quantiles are computed at every stretch of the piecewise partition. The process is iterated at different scales of the partition, and it was found to be a very useful approach for finding patterns related to both heatwave periods and intensities. The method is successfully tested using real weather records from Brazil (Recife) and the UK (London), and it was found that in both locations heatwave intensity and frequency are increasing at a substantial rate. In addition, it was found that the rate of increase in intensity of the heatwaves is far outstripping the rate of increase in mean summer temperature: by a factor of 2 in Recife and a factor of 6 in London. The approach will be of use to those looking at the impact of future climates on civil engineering, water resources, energy use, agriculture and health care, or those looking for sustained extreme events in any time series.

A comparative study of neural network to artificial noses

Artificial neural networks have been used to classify odor patterns and are showing promising results. In this paper we present four different models of neural networks to implement pattern recognition system in artificial noses. The models investigated are the multi-layer perceptrons, two different implementations of the radial basis function networks and the probabilistic neural network. All the models were tested with and without temporal processing. A complex data base with nine different classes was used in this paper

Development of an Artificial Neural Network by Genetic Algorithm to Mid-Term Load Forecasting

This paper gives an alternative strategy to solve a problem found daily in the distribution utilities of electric energy in regard to hourly load forecasting. The load forecasting produces the essence to increase and strengthen in the basic grid, moreover study into program and planning of the system operation. The load forecasting quality contributes substantially to indicating more accurate consuming market, and making electrical system planning and operating more efficient. This work uses artificial neural networks, whose architecture were developed using genetic algorithm to realize the hourly load forecasting based on the monthly total load consumption registered by the Energy Company of Pernambuco (CELPE). The forecast models developed comprise the period of 45 and 49 days ahead. Comparisons between the four models were achieved by using historical data from 2005.

Creating extreme weather time series through a quantile regression ensemble

Abstract Heat waves give rise to order of magnitude higher mortality rates than other weather-related natural disasters. Unfortunately both the severity and amplitude of heat waves are predicted to increase worldwide as a consequence of climate change. Hence, meteorological services have a growing need to identify such periods in order to setxa0alerts, whilst researchers and industry need representative future heat waves to study risk. This paper introduces a new location-specific mortality risk focused definition of heat waves and a new mathematical framework for the creation of time series that represents them. It focuses on identifying periods when temperatures are high during the day and night, as this coincidence is strongly linked to mortality. The approach is tested using observed data from Brazil and the UK. Comparisons with previous methods demonstrate that this new approach represents a major advance that can be adopted worldwide by governments, researchers and industry.

Previsão de Carga Horária em Médio Prazo Utilizando Redes Neurais com Foco na Previsão dos Feriados

Uma das dificuldades encontradas freqüentemente no problema de previsão de carga horária é a estimação adequada da carga dos dias feriados. Isto ocorre devido ao comportamento anômalo destes dias e a quantidade insuficiente de padrões disponíveis para treinamento. Este artigo é uma continuação das recentes pesquisas publicadas em [1,[2] nas quais é propostos um sistema que utiliza Redes Neurais Artificiais (RNAs) para realizar previsão de carga horária em médio prazo (30 dias), e tem por foco a redução do erro de previsão dos dias feriados. O estudo foi baseado no modelo de consumo de energia elétrica da Companhia Energética de Pernambuco (CELPE). As RNA utilizadas são do tipo Multilayer Perceptrons (MLP), treinada com o algoritmo RPROP. O trabalho está dividido em três etapas: a) criação da base de dados; b) busca da melhor arquitetura para criação do sistema; c) realização da previsão e análise dos resultados. Os resultados obtidos indicam a potencialidade do sistema de previsão de carga horária em médio prazo desenvolvido, destacando-se a redução do erro dos dias feriados.