Modelling forecast uncertainty is a difficult task in any forecasting problem. In weather forecasting a possible solution is the use of forecast ensembles, which are obtained from multiple runs of numerical weather prediction ...

Few areas in economics are as controversial as economic forecasting. While the field has sparked great hopes for the prediction of economic trends and events throughout the 20th and 21st centuries, economic forecasts have ...

Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate ...

Abstract— Solar photovoltaic energy has shown a significant growth in the last decade. In the face of this growth, there are challenges to consider for the high penetration rates of solar photovoltaic, since this type of ...

Multiple linear regression models were developed for 1–3-day lead forecasts of maximum and minimum temperature for two locations in the city of Lima, on the central coast of Peru (12°S), and contrasted with the operational ...

This work reviews the latest works on load forecasting, classifying them according to presented methods and models, as statistical, intelligent systems, neural networks and fuzzy logic. As there are many different models ...

The importance of forecasting extreme wet and dry conditions from weeks to months in advance relies on the need to prevent considerable socioeconomic losses, mainly in regions of large populations and where agriculture is ...

Agricultural production responses to climate variability require salient information to support decisions. We coupled a new hybrid stochastic weather generator (combining parametric and nonparametric components) with a ...