Abstract:Integrating renewable and intermittent energy sources into the electricity sector challenges traditional energy systems based on predictability and constant supply. Studies oncomplementarity between climate-related resources from different regions and countries are proving to be an efficient means to overcome the variability of single-source use. Although Rio de Janeiro State (Brazil) has set goals of increasing its use of clean and low carbon energy, there is no study to support the expansion process. Given that, this work aims to assess the complementarity potential of small hydropower plants, wind farms, and photovoltaic panels in the state. Power output estimates have been based on wind speeds, solar radiation and river flow data and without generation technologies assumptions. The Pearson correlation coefficient and linear programming have been used to comprehend and optimize the renewable mix. Daily complementarity has been observed among the energy sources considered, especially between hydro and solar resources. The optimization process showed an improvement of 61% in the total power standard deviation, from the worst-100% hydro power-to the best case-62% of photovoltaic, 21% of wind, and 17% of hydro power. The results highlight the benefits of appropriately joining the three sources and suggest investing in photovoltaic generation.
In the last few years, the automotive industry has adopted the Six Sigma program aiming the performance increasing in the competition market. This tool is used as basis of quality control and strategic planning for production, providing significant improvements in direct and indirect costs of production. This paper provides a successful result obtained in an automotive industry in which the quality had been improved through the strategy proposed by Six Sigma. This company, after applying the Six Sigma methodology, had successfully reduces the amount of defects in the automotive seats in the assembly line. After the implementation, the company achieved higher quality process and its costs decreased, reducing to less than 50 % the number of seats with defects. The study contributes to the management practices, by identifying new applications with Six Sigma and further analysis of manufacturing with financial performance as well as to discuss the implications of these findings for practice and for future research.
This article presents an alternative way of evaluating the efficiency of the electric distribution companies in Brazil. This assessment is currently performed and designed by the National Electric Energy Agency (ANEEL), a Brazilian regulatory agency, to regulate energy prices. This involves calculating the X-factor, which represents the efficiency evolution in the price-cap regulation model. The proposed model aims to use a network Data Envelopment Analysis (DEA) model with the network dimension as an intermediate variable and to use Kohonen Self-Organizing Maps (SOM) to correct the difficulties presented by environmental variables. In order to find which environmental variables influence the efficiency, factor analysis was used to reduce the dimensionality of the model. The analysis still uses multiple regression with the previous efficiency as the dependent variable and the four factors extracted from factor analysis as independent variables. The SOM generated four clusters based on the environment and the efficiency for each distributor in each group. This allows for a better evaluation of the correction in the X-factor, since it can be conducted inside each cluster with a maintained margin for comparison. It is expected that the use of this model will reduce the margin of questioning by distributors about the evaluation.
The clean development mechanism (CDM) was created with two main goals: help developed countries achieve the greenhouse gas emissions reduction targets set out in the Kyoto Protocol and provide sustainable development to developing countries who host the projects. Sustainable project management shares the same goals as the CDM project, which could be a valuable asset in the development of new projects. Much has been discussed about the effectiveness of the program in achieving those goals and even more in the post-2012 period when the CDM has experienced fewer new projects and lower certified emissions reduction (CER) prices compared to previous years. Based on Brazilian CDM project data, this works aims to analyze the economic aspect of sustainable management by observing the influence of the obtained CERs on the internal rates of return (IRRs) of a small hydro plant (SHP) according to the current CDM scenario, marked by CER prices reaching their lowest historical values. For this study, a spreadsheet was developed to simulate a project's cash flows and thus determine its IRRs. The results show that using the current CER prices, carbon credits do not significantly affect SHP projects' IRRs, with increases of less than 0.2% compared to a scenario with no credits. CDM projects may benefit more from optimizing the investment costs or increasing energy production. These results highlight a need to emphasize the sustainable benefits of the CDM program rather than only focusing on the economic return perspective.
Customers are each day more demanding with costs reduction, sustainability, quality improvement and shorter lead times. Since logistics operation is involved in every step of the chain, it becomes an important asset for companies to win market share. Seeking to attend the organizations and customers’ needs, Lean Six Sigma methodology could bring benefits to logistics services. Aiming to study those benefits, this article presents a case study about the interaction between the LSS and the loading process in a paper mill located in Brazil. The site had already adopted the DMAIC method in the manufacturing areas, but not in the service ones, which made it easier the implementation, guaranteeing the support and involvement of management. In the end of the study, it was possible to notice several benefits in implementing the LSS, as reduction of 32% of cycle time and 43% of performance improvement.
With the expansion of connectivity and information exchange, monitoring Internet traffic becomes a priority in network management to identify anomalies and resource use. This paper presents a study of data traffic forecasting on a computer network, by using known approaching methods for Time Series analysis. The objective of this work is to monitoring the connection of users to network -based applications, including resource availability and network stability of a Brazilian educational institute. To estimate the traffic at a given time, the adjustments made with Exponential Smoothing, AR and ARIMA models were compa red in order to detect possible future abnormal behavior of network usage. The results indicate that the chosen models, mainly the ARIMA, can be used to predict both input and output traffic of a network, also allowing the generation of alerts in real time. It is possible to predict how Internet traffic will be in the next few moments in order to detect possible anomaly on the network in a short period of time when they differ considerably from the forecast made for that specific period. Efficient network monitoring favors the quality of applications and services available to users, helping the network manager to make decisions for maintenance and constant improvement.
The study of forecasting of energy in Brazil is important for future planning, as the country has experienced crises of energy supply. And a model developed in java is an affordable and efficient tool to be used both in Brazil and in other countries. Time series analysis is highly important in many different application areas, for it allows description and modeling of a variable of interest’s behavior, thus enabling the forecasting of its future values, which serves as support for decision making. When the data used in regression analysis comprises time series, the dependency between the observations grants a dynamic quality to the regression model. In this situation, it is common to come across a problem known as residual autocorrelation, which invalidates the assumptions made about the term of error in the classical linear regression models. This paper presents a program created in Java by implementing the method of Cochrane-Orcutt for the correction of residual autocorrelation. And the application is made in the Brazilian energy final consumption forecasting.
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