The aim of this paper is to describe the behaviour of packet traffic flowing on realistic networks as load is varied. The use of intermittency in iterated maps to provide various relevant statistical types of binary data will be described. The dynamical modelling of packet traffic using Erramilli intermittency maps is introduced together with the dynamics of Transmission Control Protocols. Regu...

A method has been developed for preparing load models for power flow and stability. The load modeling (LOADMOD) computer software transforms data on load class mix, composition, and characteristics into the from required for commonly–used power flow and transient stability simulation programs. Typical default data have been developed for load composition and characteristics. This paper defines ...

Artificial neural networks have frequently been proposed for electricity load forecasting because of their capabilities for the nonlinear modelling of large multivariate data sets. However, there are still no widely accepted strategies for designing the models and for implementing them, which makes the process of modelling by neural networks largely heuristic, dependent on the experience of the...

The problem of redistributing work load on parallel computers is considered. An optimal redistribution algorithm, which minimises the Euclidean norm of the migrating load, is derived. The problem is further studied by modelling with the unsteady heat conduction equation. Relationship between this algorithm and other dynamic load balancing algorithms is discussed. Convergence of the algorithm fo...

Short term electricity demand forecasts are required by power utilities for efficient operation of the power grid. In a competitive market environment, suppliers and large consumers also require short term forecasts in order to estimate their energy requirements in advance. Electricity demand is influenced (among other things) by the day of the week, the time of year and special periods and/or ...

Artificial neural networks have frequently been proposed for electricity load forecasting because of their capabilities for the nonlinear modelling of large multivariate data sets. Modelling with neural networks is not an easy task though; two of the main challenges are defining the appropriate level of model complexity, and choosing the input variables. This paper evaluates techniques for auto...

The integration of wind farms in power networks has become an important problem. As electricity cannot be preserved because of the highest cost of storage, electricity production must following market demand, necessarily. Short-long term wind forecasting over different time steps is becoming an important process for the management of wind farms. Time series modelling of wind speeds is based on ...