No abstract

No abstract

No abstract

Estimating future streamflows is a key step in producing electricity for countries with hydroelectric plants. Accurate predictions are particularly important due to environmental and economic impact they lead. In order to analyze the forecasting capability of models regarding monthly seasonal streamflow series, we realized an extensive investigation considering: six versions of unorganized machines—extreme learning machines (ELM) with and without regularization coefficient (RC), and echo state network (ESN) using the reservoirs from Jaeger’s and Ozturk et al., with and without RC. Additionally, we addressed the ELM as the combiner of a neural-based ensemble, an investigation not yet accomplished in such context. A comparative analysis was performed utilizing two linear approaches (autoregressive model (AR) and autoregressive and moving average model (ARMA)), four artificial neural networks (multilayer perceptron, radial basis function, Elman network, and Jordan network), and four ensembles. The tests were conducted at five hydroelectric plants, using horizons of 1, 3, 6, and 12 steps ahead. The results indicated that the unorganized machines and the ELM ensembles performed better than the linear models in all simulations. Moreover, the errors showed that the unorganized machines and the ELM-based ensembles reached the best general performances.

No abstract

No abstract

No abstract

There is an increasing interest in the analysis of power distribution systems, including demands to improve the distribution networks reliability. Regulatory agencies define reliability indices to quantify and evaluate the electric quality. In order to improve system reliability and provide a good quality service, this work proposes to install a minimum amount of switch devices at appropriate locations in the distribution network.A methodology is presented to effectively evaluate, even for large real networks the impact on reliability following contigencies.Firstly, are propose a constructive heuristic that allocates sectionalizers and tie switches, automatic and non-automatic, in a radial distribution system. This procedure aims to minimize the unsupplied energy caused in the network by determining a proper number, location and type of switches. A genetic algorithm is designed to further improve the switches location suggested by the constructive heuristic.The good performance of the proposed approach is confirmed by some case studies with large real energy distribution network.