Load forecasting, the importance of the probability “tails” in the definition of the input vector

The load forecast is part of the global management of the electrical networks, namely at the transport and distribution levels. This type of methodologies allows to the system operator, to establish and take some important decisions concerning to the mix production and network management, with the minimum of discretionarity. The load forecast in particularly the peak load forecast, represents an important economic improvement in the global electrical systems. Also in certain circumstances, allow reducing the contribution of the non-renewable units, in the daily mixing production. The regressive methodologies specially the artificial neural networks, are normally used in this type of approaches, with satisfactory results. In this paper is proposed a careful analysis in order to define the best-input vector in order to feed the regressive methodology. It was establish careful analyses of the load consumption series. It makes use of a procedural sequence for the pre-processing phase that allows capturing certain predominant relations among certain different sets of available data, providing a more solid basis to decisions regarding the composition of the input vector to ANN. The methodological approach is discussed and a real life case study is used for illustrating the defined steps, the ANN and the quality level of the results

An Adaptive PID Speed Controller for an 8/6 Switched Reluctance Machine

This paper presents a classical controller with parameters adaptation capability, in an automatic way. This controller is based on a PID where a parameters adaptation algorithm is used and applied to the switched reluctance motor (SRM) speed control. This PID design do not require any kind of adjustment or calibration from the operator. The parameters adaptation algorithm implemented is based on one fuzzy system with a Takagi-Sugeno inference mechanism with some simplifications. These simplifications had the goal to select the parameters adaptation algorithm contributing for a fast controller response. The developed adaptive PID algorithm was modelled and simulated

Designing the input vector to ANN-based models for short-term load forecast in electricity distribution systems

The present trend to electricity market restructuring increases the need for reliable short-term load forecast (STLF) algorithms, in order to assist electric utilities in activities such as planning, operating and controlling electric energy systems. Methodologies such as artificial neural networks (ANN) have been widely used in the next hour load forecast horizon with satisfactory results. However, this type of approach has had some shortcomings. Usually, the input vector (IV) is defined in a arbitrary way, mainly based on experience, on engineering judgment criteria and on concern about the ANN dimension, always taking into consideration the apparent correlations within the available endogenous and exogenous data. In this paper, a proposal is made of an approach to define the IV composition, with the main focus on reducing the influence of trial-and-error and common sense judgments, which usually are not based on sufficient evidence of comparative advantages over previous alternatives. The proposal includes the assessment of the strictly necessary instances of the endogenous variable, both from the point of view of the contiguous values prior to the forecast to be made, and of the past values representing the trend of consumption at homologous time intervals of the past. It also assesses the influence of exogenous variables, again limiting their presence at the IV to the indispensable minimum. A comparison is made with two alternative IV structures previously proposed in the literature, also applied to the distribution sector. The paper is supported by a real case study at the distribution sector.http://www.sciencedirect.com/science/article/B6V2T-4MCW9X5-1/1/00590212b5295357d45465c710d645a

Avaliação de genótipos de girassol no Planalto Médio do Rio Grande do Sul na safra 2010/2011.

bitstream/item/62564/1/2011boletimdepesquisaonline80.pd