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.
KEYWORDS: Video, Fuzzy systems, Video compression, Fuzzy logic, Computer simulations, Computer networks, Computing systems, C++, Data modeling, Local area networks
This paper presents a model to treat the problem of process scheduling within a computer network using a fuzzy
inference system. The scheduling system implemented, simulated in the Network Simulator, acts in two particularly
points: first defining the discard priority for the applications according to its characteristics; and further, to redefine their
transfer rate, also considering its particularities; thus providing fitting transfer rates for the applications and, with it,
means for Quality of Service.
One of the main factors for the success of the knowledge discovery process is related to the comprehensibility of the patterns discovered by the data mining techniques used. Among the many data mining techniques found in the literature, we can point the Bayesian networks as one of most prominent when considering the easiness of knowledge interpretation achieved in a domain with uncertainty. However, the static Bayesian networks present two basic disadvantages: the incapacity to correlate the variables, considering its behavior throughout the time; and the difficulty of establishing the optimum combination of states for the variables, which would generate and/or achieve a given requirement. This paper presents an extension for the improvement of Bayesian networks, treating the mentioned problems by incorporating a temporal model, using Markov chains, and for intermediary of the combination of genetic algorithms with the networks obtained from the data.
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.
In the current national scene, many actions point at projects of digital inclusion and citizenship. In this context, providing
access technologies as a requisite for the implementation of these actions is primordial. In this way, many innovative
experiences have been presented in the past few years. This paper presents a study on the Powerline Communication-
PLC technology; as a proposal for a feasible access network for Brazilian Amazon. First, the characteristics of the PLC
technology are studied from an implanted indoor prototype at Federal University of Para. The measures used in this
prototype serve as input for a created model, from which it is intended to study the system more widely, considering
factors such as: scalability, reliability and the physical characteristics.
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