Roberto C. L. Oliveira

Energy price forecasting in the North Brazilian market using NN - ARIMA model and explanatory variables

This paper proposes a new hybrid approach for short-term energy price prediction. This approach combines ARIMA and NN models in a cascaded structure and uses explanatory variables. A two step procedure is applied. In the first step, the explanatory variables are predicted. In the second one, the energy prices are forecasted by using the explanatory variables prediction. The prediction time horizon is 12 weeks-ahead and is applied to the North Brazilian submarket, which adopts a cost-based model with unique characteristics of price behavior. The proposed strategy is compared with traditional techniques like ARIMA and NN and the results show satisfactory accuracy and good ability to predict spikes. Thus, the model can be an attractive tool to mitigate risks in purchasing power.

A fuzzy intelligent controller for genetic algorithms' parameters

Genetic Algorithms have been successful in several practical appliances and obtained great prominence among the techniques of Evolutionary Computation. A large portion of methods and parameters adopted in its use are heuristic or random choices, which remain unchanged throughout the execution once they are set. This can lead to a certain lack of exploration of the search surface and also compromise the variability of the population. For that matter, this work introduces the implementation of an intelligent agent based on fuzzy logic, enabling dynamic monitoring and regulation of six GA parameters. The results obtained surpass the GA traditional implementation in many aspects, and open a wide new space for research and study.

Simulations on The Bath Chemistry Variables Using Neural Networks

In this work we present a single layer neural network based model for bath chemistry variables in the aluminum smelting process. This model is designed to be simulated with real data as if it worked online in parallel with the process. The model is built using a very fast machine learning algorithm, the Extreme Learning Machines, which provides excellent results in regression problems in a very short time. Also we applied statistical analysis for data collection, preprocessing and filtering and for validation we performed several simulations to attest the neural model’s capability to respond to new data. A comparison of this model against linear and traditional nonlinear structures is performed to show how single layer neural networks can be applied on the bath chemistry modeling.

Optimization of Geodesic Self-Organizing Map

The Geodesic Self-Organizing Map (GeoSOM) is a variation of traditional SOM, which uses an icosahedron-based tessellation as spherical lattice to eliminate the border effect to minimize the distortion in the reduction of high-dimensional spaces. Border effect is a problem intrinsic of low-dimensional neural grid, where neurons in the border have a less possibility to have its synaptic weights updated. The almost perfect regularity of a tessellated icosahedron projection onto a sphere solves this problem, reducing in two thirds of the distortion of 2D SOM. However, two problems appear resulting from complex shape of this Platonic polyhedron. First, the growth curve of lattice sizing follows a strong upward tendency that means a loss of control over the lattice sizing. Second, an overall visualization of topographic map is only possible with a geodesic projection of prototype vector positions from the surface of the sphere to a 2D plane that results in a elliptical map, flat on top and bottom, that avoids an orthogonal alignment of the data in the left and right sides, causing some distortion in the presentation of results and avoid an intuitive visualization of the map. This work proposes a geodesic self-organizing map, called 4HSOM, which uses a tessellated tetrahedron as lattice to eliminate the border effect, maximizing the control over the lattice sizing, with an easier overall visualization of topographic map without any geodetic projection, resulting from the minimalist geometric structure of the tetrahedron, although the tessellated tetrahedron has the greatest irregularity among the Platonic polyhedra. The work presents a comparative analysis between the results achieved by 4HSOM and GeoSOM.

Clustering Aluminum Smelting Potlines Using Fuzzy C-Means and K-Means Algorithms

Aluminum smelting potlines usually have a big number of cells, producing aluminum in a continuous and complex process. Analytical monitoring is essential to increase the industries’ competitive advantage, however, during their operation, some cells share similar behaviors, therefore forming clusters of cells. These clusters rely on data patterns that are usually implicit or invisible to operation, but can be found by means data analysis. In this work we present two clustering techniques (Fuzzy C-Means and K-Means) to find and cluster the cells that present similar behaviors. The benefits of clustering are mainly in the simplification of potline analysis, since a large number of cells can be summarized in one single cluster, which can provide richer but compacted information for control and modelling.

Short-Term Energy Price Prediction Multi-Step-Ahead in the Brazilian Market Using Data Mining

© 2012 Filho et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Short-Term Energy Price Prediction Multi-Step-Ahead in the Brazilian Market Using Data Mining

Fuzzy Control Applied to Aluminum Smelting

Aluminum is a modern and new metal, since it has been produced for industry no earlier than 1886, when Hall and Héroult concurrently found out a method to produce free Aluminum through electrolysis (Beck, 2008). In 1900, the Aluminum production worldwide had reached a thousand tons. Nevertheless, at the beginning of the 21st century, global production reached 32 million tons encompassed by 24 million of primary Aluminum and 8 million of recycled material. This fact puts Aluminum at the second place in the list of the most used metals on earth. The world without Aluminum became inacceptable: the businessmen, the tourists, the delivery offices fly over the world in airplanes made of Aluminum, as well as many enterprises and industries are strongly dependent of this metal. Figure 1 shows in a widely perspective where Aluminum is most used.