Wind power forecasting is recognized as a means to facilitate large scale wind power integration into power systems. Recently, focus has been given on developing dedicated short-term forecasting approaches for the case of large and sharp wind power variations, so-called ramps. Accurate forecasts of specific ramp characteristics (e.g. timing, probability of occurrence, etc) are important since t...

In this study we develop the hybrid models for forecasting in agricultural production planning. Real data of Thailand’s orchid export and Thailand’s pork product are used to validate candidate models. Autoregressive Integrate Moving Average (ARIMA) is also selected as a benchmarking to compare other developed models. The main concept of building the models is to combine different forecasting te...

The tracking and forecasting of satellite cloud images are very important in term of satellite cloud images used in weather forecast. An approach on automatic tracking of multi-target cloud cluster based on VFC Snake model is proposed on the basis of contour extraction and analysis of cloud cluster, this method can automatically acquire the new location of the target cloud cluster at each momen...

BACKGROUND Intravenous medication administrations have a high incidence of error but there is limited evidence of associated factors or error severity. OBJECTIVE To measure the frequency, type and severity of intravenous administration errors in hospitals and the associations between errors, procedural failures and nurse experience. METHODS Prospective observational study of 107 nurses prep...

Factors estimated from large macroeconomic panels are being used in an increasing number of applications. However, little is known about how the size and composition of the data affect the factor estimates. In this paper, we question whether it is possible to use more series to extract the factors and that yet the resulting factors are less useful for forecasting, and the answer is yes. Such a ...

In this paper the water quality forecasting at the Nanjinguan water quality monitoring station of Yangtze River, China, is presented. The time series data used are weekly water quality data obtained directly from Nanjinguan station measurements over the course of five years. In order to forecast water quality, hybrid models consisting of Autoregressive Integrated Moving Average (ARIMA) models a...

This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of t...