Fátima Cibele Soares

Modelo para estimativa da produtividade para a cultura da soja

This study aimed estimate and validate a simulation model for the soybean crop in the region of Santiago, RS, Brazil which will, to the agricultural sector, support in decision making. For this, it was necessary to develop experimental procedure for generating the data needed to calibrate and test the simulation model, the extraction of water from the soil, and the response in crop production. As experimental results, obtained variations in the growth and development of soybean by applying different strategies for irrigation, resulting in different productivities. The increase in irrigation provided an increment in production components. Tests of simulated data showed a variation of less than 8% when comparing the data measured in field experiments with simulated data. The production model proposed was successfully able to simulate the accumulation of total dry matter and grain production for soybeans.

Modeling Typical Power Demand Curves Using Climatic Data

This paper presents a methodology for modeling typical power demand curves and shows the influence of climatic data as a deterministic factor for this characterization by using Artificial Neural Networks. The methodology presented was applied on real data, what made possible to obtain typical curves with a high degree of accuracy in forecasting demand curves. By means of the investigation carried out it can be seen the closest relationship between climatic data and electric power demand. Furthermore, a comparison between the proposed methodology and the kVAs method is presented.