Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids. This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model. OPEN ACCESSEnergies 2013, 6 4490

No abstract

The present work reports a comparative study of ductile failure models applied to an Al-2011 aluminum alloy single-pass wire drawing process using different reductions. The material damage experienced in the wire after passing through the die is evaluated using the well-known Rice and Tracey, Cockcroft and Latham, Brozzo and Modified Chaouadi models. Due to the fact that nonrealistic damage predictions are found for the highest studied wire reduction, an alternative uncoupled failure criterion combining the effect of deformation and triaxility is proposed. The ability of these five models in predicting the formation of chevrons in the process is the main focus of this research. First, the model parameters are characterized by means of numerical simulations of the tensile test. Then, the predictions of the numerical analyses of the drawing process are compared with available experimental results where physical evidence of chevrons was found. Relevant variables are analyzed to determine their incidence in the formation of central bursts. Finally, the performance of this new model is assessed for the full reduction scenarios.

No abstract

No abstract