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Your search keyword '"de Aquino, Ronaldo R. B."' showing total 37 results
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37 results on '"de Aquino, Ronaldo R. B."'

2. Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models.

Author

Souza, Murilo A., Gouveia, Hugo T. V., Ferreira, Aida A., de Lima Neta, Regina Maria, Nóbrega Neto, Otoni, da Silva Lira, Milde Maria, Torres, Geraldo L., and de Aquino, Ronaldo R. B.

Subjects
ARTIFICIAL intelligence, TEMPORAL databases, ELECTRIC power consumption, ELECTRIC utilities, FRAUD
Abstract

Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Boosting Algorithm to Improve a Voltage Waveform Classifier Based on Artificial Neural Network

Author

Lira, Milde M. S., de Aquino, Ronaldo R. B., Ferreira, Aida A., Carvalho, Manoel A., Jr., Neto, Otoni Nóbrega, Santos, Gabriela S. M., Lira, Carlos Alberto B. O., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, de Sá, Joaquim Marques, editor, Alexandre, Luís A., editor, Duch, Włodzisław, editor, and Mandic, Danilo, editor

Published
2007
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8. Using Genetic Algorithm to Develop a Neural-Network-Based Load Forecasting

Author

de Aquino, Ronaldo R. B., Neto, Otoni Nóbrega, Lira, Milde M. S., Ferreira, Aida A., Santos, Katyusco F., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, de Sá, Joaquim Marques, editor, Alexandre, Luís A., editor, Duch, Włodzisław, editor, and Mandic, Danilo, editor

Published
2007
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9. Combined Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System for Improving a Short-Term Electric Load Forecasting

Author

de Aquino, Ronaldo R. B., Silva, Geane B., Lira, Milde M. S., Ferreira, Aida A., Carvalho, Manoel A., Jr., Neto, Otoni Nóbrega, de Oliveira, Josinaldo. B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, de Sá, Joaquim Marques, editor, Alexandre, Luís A., editor, Duch, Włodzisław, editor, and Mandic, Danilo, editor

Published
2007
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10. Development of a Hybrid Intelligent System for Electrical Load Forecasting

Author

de Aquino, Ronaldo R. B., Ferreira, Aida A., Carvalho, Manoel A., Jr., Lira, Milde M. S., Silva, Geane B., Neto, Otoni Nóbrega, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Sichman, Jaime Simão, editor, Coelho, Helder, editor, and Rezende, Solange Oliveira, editor

Published
2006
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11. Combining Artificial Neural Networks and Heuristic Rules in a Hybrid Intelligent Load Forecast System

Author

de Aquino, Ronaldo R. B., Ferreira, Aida A., Carvalho, Manoel A., Jr, Lira, Milde M. S., Silva, Geane B., Neto, Otoni Nóbrega, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Kollias, Stefanos, editor, Stafylopatis, Andreas, editor, Duch, Włodzisław, editor, and Oja, Erkki, editor

Published
2006
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12. Comparing Neural Network Architecture for Pattern Recognize System on Artificial Noses

Author

Ferreira, Aida A., Ludermir, Teresa B., de Aquino, Ronaldo R. B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Duch, Włodzisław, editor, Kacprzyk, Janusz, editor, Oja, Erkki, editor, and Zadrożny, Sławomir, editor

Published
2005
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24. SAX-quantile based multiresolution approach for finding heatwave events in summer temperature time series.

Author

Herrera, Manuel, Ferreira, Aida A., Coley, David A., and de Aquino, Ronaldo R. B.

Subjects
HEAT waves (Meteorology), TIME series analysis, MULTIRESOLUTION time-domain method, WEATHER forecasting, WATER supply
Abstract

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 andmortality 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. [ABSTRACT FROM AUTHOR]

Published
2016
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36. Combined Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System for Improving a Short-Term Electric Load Forecasting.

Author

Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, de Sá, Joaquim Marques, Alexandre, Luís A., Duch, Włodzisław, Mandic, Danilo, and de Aquino, Ronaldo R. B.

Abstract

The main topic in this work was the development of a hybrid intelligent system for the hourly load forecasting in a time period of 7 days ahead, using a combination of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System. The hourly load forecasting was accomplished in two steps: in the first one, two ANNs are used to forecast the total load of the day, where one of the networks forecasts the working days (Monday through Friday), and the other forecasts the Saturdays, Sundays and public holidays; in the second step, the ANFIS was used to give the hourly consumption rate of the load. The proposed system presented a better performance as against the system currently used by Energy Company of Pernambuco, named PREVER. The simulation results showed an hourly mean absolute percentage error of 2.81% for the year 2005. [ABSTRACT FROM AUTHOR]

Published
2007
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37. Using Genetic Algorithm to Develop a Neural-Network-Based Load Forecasting.

Author

Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, de Sá, Joaquim Marques, Alexandre, Luís A., Duch, Włodzisław, Mandic, Danilo, and de Aquino, Ronaldo R. B.

Abstract

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 proposed Hybrid Intelligent System - HIS was able to find the trade-off between forecast errors and network complexity. 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. 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. [ABSTRACT FROM AUTHOR]

Published
2007
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