Electric locomotive is a kind of high power rectifier load, and its load characteristics will have a significant impact to safe, stable and economic operation of power system. This paper mainly analyses load characteristics of harmonic and reactive power for electric locomotive. Simulation model of electric locomotive is set up based on time trigger; it is so better conform to the dynamic chara...

Short term load forecasting (STLF) plays an important role in the economic and reliable operation ofpower systems. Electric load demand has a complex profile with many multivariable and nonlineardependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. Theproposed model is capable of forecasting next 24-hour load profile. The main feature in this networkis ...

5 Abstract—Cloud load balancing is the process of distributing workloads across multiple computing resources in a cloud environment. 6 Load distribution in cloud computing systems is more challenging than in other systems. The purpose of the paper is to address the 7 issue of optimal task dispatching on multiple heterogeneous multiserver systems with dynamic speed and power management. The 8 ma...

Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the y...

To design a class of time-varying economic dispatching algorithms for smart grids based on the Lagrangian dual idea load problems that arise in practical applications grids, and to demonstrate convergence algorithms.

Many computational tasks in image processing are dataseparable in the sense that the correctness of results is irrelevant to the order of computing individual pixels. By taking advantage of this property, we have implemented a distributed image file processing (DIFP) program on an Ethernet network and have illustrated substantial speed-up. A master/slave model for DIFP is proposed, which is bas...

1 Abstract In this paper we present an application of predictive modular neural networks (PREMONN) to short term load forecasting. PREMONNs are a family of probabilistically motivated algorithms which can be used for time series prediction, classification and identification. PREMONNs utilize local predictors of several types (e.g. linear predictors or artificial neural networks) and produce a f...

In distribution networks, failure to smooth the load curve leads to voltage drop and power quality loss. In this regard, electric vehicle batteries can be used to smooth the load curve. However, to persuade vehicle owners to share their vehicle batteries, we must also consider the owners' profits. A challenging problem is that existing methods do not take into account the vehicle owner demands ...