Liviane Rego

Strategies for improving the modeling and interpretability of Bayesian networks

One of the main factors for the knowledge discovery success is related to the comprehensibility of the patterns discovered by applying data mining techniques. Amongst which we can point out the Bayesian networks as one of the most prominent when considering the easiness of knowledge interpretation achieved. Bayesian networks, however, present limitations and disadvantages regarding their use and applicability. This paper presents an extension for the improvement of Bayesian networks, treating aspects such as performance, as well as interpretability and use of their results; incorporating genetic algorithms in the model, multivariate regression for structure learning and temporal aspects using Markov chains.

Decision support in power systems based on load forecasting models and influence analysis of climatic and socio-economic factors

This paper presents a decision support system for power load forecast and the learning of influence patterns of the socio-economic and climatic factors on the power consumption based on mathematical and computational intelligenge methods, with the purpose of defining the future power consumption of a given region, as well as to provide a mean for the analysis of correlations between the power consumption and these factors. Here we use a linear modelo of regression for the forecasting, also presenting a comparative analysis with neural networks, to prove its efectiveness; and also Bayesian networks for the learning of causal relationships from the data.

Multimedia transmission on Amazon region using wireless broadband networks

This work presents results for multimedia transmission using wireless broadband networks installed in some remote cities in Amazon region. In 15 cities at State of Pará, Brazil, the local government is installing wireless networks to provide wideband internet access to public agents, such as schools, hospitals, police stations and office buildings in a great project named NavegaPará. Each city has a base station working on 5.7 GHz and serving around 30 subscribers, which use an individual broadband transceiver (IBT) to access the network. The aggregate throughput (downstream + upstream) for each client is 7 Mbps, 14 Mbps or 30 Mbps depending on the installed equipment and the distance between the base station and the client unit. It is presented a typical scenario for multimedia transmission using the available infrastructure. In this case, an IBT is connected directly to a computer, which is connected to a video camera transmitting video traffic. The available infrastructure is useful for broadcasting the public television signal (Funtelpa) in uncovered areas of State of Pará. Metrics such as throughput and latency are collected and analyzed. Obtained results show that multimedia transmission using NavegaPará infrastructure has satisfactory results.

Comparative studies of Statistical and Neural Networks Models for Short and Long Term Load Forecasting: a Case Study in the Brazilian Amazon Power Suppliers

One of the most desired aspects for power suppliers is the acquisition/sell of energy in a future time. This paper presents a study for power supply forecasting of the residential class, based on time series methods and neural networks, considering short and long term forecast, both of great importance for power suppliers in order to define the future power consumption of a given region.

Comparative Analyses of Computational Intelligence Models for Load Forecasting: A Case Study in the Brazilian Amazon Power Suppliers

One of the most desired aspects for power suppliers is the acquisition/sale of energy for a future demand. However, power consumption forecast is characterized not only by the variable of the power system itself, but also related to socio-economic and climatic factors. Hence, it is imperative for the power suppliers to design and correlate these parameters. This paper presents a study of power load forecast for power suppliers, comparing application of techniques of wavelets, time series analysis methods and neural networks, considering long term forecasts; thus defining the future power consumption of a given region. The results obtained proved to be much more effective when compared to those projected by the power suppliers based on specialist information, thus contributing to the decision making for acquisition/sale of energy at a future demand.

Performance evaluation of load forecasting models for energy purchase of power suppliers in the Amazonian region

One of the most desired aspects for power suppliers is the acquisition/sell of energy in a future time. This paper presents a study of load forecasting for power suppliers, presenting a comparative application of the techniques of wavelets, time series methods and neural networks, considering short and long term forecast; both of great importance for power suppliers in order to define the future power consumption of a given region.