Accurate electricity price forecasting plays an important role in the profits of electricity market participants and the healthy development of electricity market. However, the electricity price time series hold the characteristics of volatility and randomness, which make it quite hard to forecast electricity price accurately. In this paper, a novel hybrid model for electricity price forecastin...

This paper describes three software applications for distribution network load forecasting in a Smart Grid environment: (i) short-term feeder load forecasting, (ii) shortterm substation transformer load forecasting and transformer rating, and (iii) next-year load pocket forecasting. The shortterm feeder load forecasting allows a utility to reduce the possibility of feeder overloading. The subst...

It has been deemed as an effective tool of forecasting performance improvement to combine different component forecasting models. However, current nonlinear combining models are not able to meet the requirement of high forecasting accuracy in practice. To tackle this challenge, this paper constructs a hybrid, named genetic programming and least squared estimation based nonlinear combining metho...

The CMIP3 multi-model ensemble has been widely utilised for climate research and prediction, but the properties and behavior of the ensemble are not yet fully understood. Here we present some investigations into various aspects of the ensemble’s behaviour, in particular focussing on the performance of the multi-model mean. We present an explanation of this phenomenon in the context of the stati...

We apply the method of determinants to study the distribution of the largest singular values of large real rectangular random matrices with independent Cauchy entries. We show that statistical properties of the largest singular values are different from the Tracy-Widom law. Among other corollaries of our method we show an interesting connection between the mathematical expectations of the deter...

Tree-based ensemble models are heavy memorywise. An undesired state of affairs considering nowadays datasets, memory-constrained environment and fitting/prediction times. In this paper, we propose the Globally Induced Forest (GIF) to remedy this problem. GIF is a fast prepruning approach to build lightweight ensembles by iteratively deepening the current forest. It mixes local and global optimi...

We investigate the eigenvalues statistics of ensembles of normal randommatrices when their order N tends to infinite. In the model the eigenvalues have uniform density within a region determined by a simple analytic polynomial curve. We study the conformal deformations of normal random ensembles to Hermitian random ensembles and give sufficient conditions for the latter to be a Wigner ensemble.

We describe a method to improve detection of disease outbreaks in pre-diagnostic time series data. The method uses multiple forecasters and learns the linear combination to minimize the expected squared error of the next day's forecast. This combination adaptively changes over time. This adaptive ensemble combination is used to generate a disease alert score for each day, using a separate multi...

abstract one of the major factors on the amount of water resources is river flow which is so dependent to the hydrologic and meteorologic phenomena. simulation and forecasting of river flow makes the decision maker capable to effectively manage the water resources projects. so, simulation and forecasting models such as artificial neural networks (anns) are commonly used for simulation and predi...

RÉSUMÉ. Cet article présente un système pour transformer, de façon automatique, des programmes écrits en C--, un langage impératif simple, en un ensemble d’équations du premier ordre. Cet ensemble d’équations utilisé pour représenter un programme C-a une signification mathématique précise et les techniques standards de mécanisation du raisonnement équationnel peuvent être déployées pour vérifie...