Short-term solar radiation forecasting by using an iterative combination of wavelet artificial neural networks

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 short-term 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 r+1 Wavelet Components (WC); at second one, these r+1 WCs are individually modeled by the k different ANNs, where k>5, and the 5 best forecasts of each WC are combined by means of another ANN, producing the combined forecasts of WC; and, at third one, the combined forecasts WC are simply added, generating the forecasts of the underlying solar radiation data. An iterative algorithm is proposed for iteratively searching for the optimal values for the CWANN parameters, as we will see. In order to evaluate it, ten real solar radiation time series of Brazilian system were modeled here. In all statistical results, the CWANN method has achieved remarkable greater forecasting performances when compared with a traditional ANN (described in Section 2.1)

Organic trace minerals and calcium levels in broilers’ diets to 21 days old

This study was undertaken to evaluate the effects of dietary calcium levels and supplementation with organic trace minerals selenium, copper, iron, zinc and manganese on performance, tissue deposition and litter mineral concentration. A total of 2,496 one-day-old male Cobb 500 broilers were randomly assigned to a 3 × 4 factorial experimental design with three levels of dietary Ca [8, 10 and 12 g kg–1, while maintaining the same Ca:nPP (non-phytate phosphorus) ratio (2:1)] and four levels of micromineral supplementation (0.62, 0.72, 0.82 and 0.92 g kg–1). There was a total of 12 treatments, with eight replicates of 26 birds per pen. Micromineral supplementation (MS) was achieved by adding different levels of the product Bioplex TR Se® and Ca supplementation was achieved by adding increasing levels of limestone and dicalcium phosphate. An interaction between Ca and MS levels was observed (p < 0.05) for the parameters of performance, liver Cu concentration, breast Se and Cu concentrations and litter Se, Mn and Zn concentrations. No interactions were observed (p > 0.05) for Ca, P or ash concentrations in the tibia, which were influenced only by dietary Ca levels (p < 0.05). The Ca level of 10 g kg–1 promoted higher Ca and P concentration in the tibia and lower micromineral excretion in the litter. The combination of MS level of 0.82 g kg–1 with Ca level of 10 g kg–1 led to the best BWG response. The supplementation conditions that led to higher micromineral levels in the liver and breast varied for each mineral

Linear combination of forecasts with numerical adjustment via MINIMAX non-linear programming

This paper proposes a linear combination of forecasts obtained from three forecasting methods (namely, ARIMA, Exponential Smoothing and Artificial Neural Networks) whose adaptive weights are determined via a multi-objective non-linear programming problem, which seeks to minimize, simultaneously, the statistics: MAE, MAPE and MSE. The results achieved by the proposed combination are compared with the traditional approach of linear combinations of forecasts, where the optimum adaptive weights are determined only by minimizing the MSE; with the combination method by arithmetic mean; and with individual methods

Principal components in multivariate control charts applied to data instrumentation of DAMS

Hydroelectric plants are monitored by a high number of instruments that assess various quality characteristics of interest that have an inherent variability. 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 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