A high number of instruments that assess various quality characteristics of interest that have an inherent variability monitors hydroelectric plants. The readings of these instruments generate time series of data on many occasions have correlation. Each project of a dam plant has characteristics that make it unique. Faced with the need to establish statistical control limits for the instrumentation data, this article makes an approach to multivariate statistical analysis and proposes a model that uses principal components control charts and statistical T 2 and to explain variability and establish a method of monitoring to control future observations. An application for section E of the Itaipu hydroelectric plant is performed to validate the model. The results show that the method used is appropriate and can help identify the type of outliers, reducing false alarms and reveal instruments that have higher contribution to the variability.

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Este artigo propõe a combinação linear das previsões obtidas por três métodos de previsão (a saber, ARIMA, Amortecimento Exponencial e Redes Neurais Artificiais) cujos pesos adaptativos determinados por meio de um problema de programação não linear multiobjetivo, em que se busca minimizar, simultaneamente, as estatísticas: MAE, MAPE e MSE. Os resultados alcançados pela combinação proposta são comparados com a abordagem tradicional de combinação linear de previsões, onde os pesos adaptativos ótimos são determinados somente pela minimização do MSE; com o método de combinação por média aritmética; e com os métodos individuais.

The information provided by accurate forecasts of solar energy time series are considered essential for performing an appropriate prediction of the electrical power that will be available in an electric system, as pointed out in Zhou et al. (2011). However, since the underlying data are highly non-stationary, it follows that to produce their accurate predictions is a very difficult assignment. In order to accomplish it, this paper proposes an iterative Combination of Wavelet Artificial Neural Networks (CWANN) which is aimed to produce shortterm solar radiation time series forecasting. Basically, the CWANN method can be split into three stages: at first one, a decomposition of level p, defined in terms of a wavelet basis, of a given solar radiation time series is performed, generating Wavelet Components (WC); at second one, these WCs are individually modeled by the k different ANNs, where , and the 5 best forecasts of each WC are combined by means of another ANN,

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