Ieee papers on short term load forecasting

In this paper several hints to be considered in the design of a commercial broadband wireless network are presented.

Ieee papers on short term load forecasting

What is claimed is: A computer-implemented system, comprising: The computer-implemented system of claim 1, wherein the number of servers needed is based upon two ratios, one ratio between total login rate of the cluster and a maximum login rate per server and the other ratio between total number of connections of the cluster and a maximum number of connections per server.

The computer-implemented system of claim 2, wherein each ratio is multiplied by an appropriate multiplicative factor to include the additional margin. The computer-implemented system of claim 3, wherein the number of servers needed is a next highest integer value greater than or equal to a larger product between the two ratios and the appropriate multiplicative factors.

The computer-implemented system of claim 1, wherein the provisioning component is configured to schedule one or more servers to be turned on from a set of inactive servers.

The computer-implemented of claim 1, wherein the provisioning component is configured to schedule one or more servers to be shut down from a set of active servers. The computer-implemented system of claim 6, further comprising a starvation component configured to drain servers scheduled to be turned off of active connections prior to shut down.

The computer-implemented system of claim 7, wherein the starvation component is configured to drain the servers until a predetermined period of time expires. The computer-implemented system of claim 8, wherein the period of time is two hours. The computer-implemented system of claim 1, the forecast component further comprising a load skewing component configured to attempt to route new login requests to the active servers while the active servers are able to handle the new login requests, and Ieee papers on short term load forecasting utilize tail servers in reserve to handle a surge in new login requests until new servers can be turned on.

The computer-implemented system of claim 10, the load skewing component further configured to, based on an upper bound on a number of connections per server and a target number of connections per server, distribute new login requests to active servers with loads less than the target number and closest to the target number.

The computer-implemented system of claim 1, the evaluation component configured to compare predicted values provided by the model component and observed values produced by the monitor component, and ascertain standard deviations of relative errors between the predicted values and the observed values to generate the forecast factors portion of the multiplicative factors.

The computer-implemented system of claim 1, the dynamic load analysis component configured to ascertain a load dispatching mechanism selected by the load dispatching component to distribute new login requests collected by the cluster. The computer-implemented system of claim 1, the dynamic load analysis component configured to calculate the dynamic factors portion of the multiplicative factors based at least in part on one or more parameters employed by a selected load dispatching mechanism.

A computer-implemented method, comprising: The computer-implemented method of claim 15, further comprising: A computer-readable storage medium storing instructions, the instructions if executed by a computing device causing the computing device to perform operations comprising: Cell phones, smart phones, personal digital assistants, computers, laptops, and other smart devices are pervasive in today's world.

Moreover, the Internet has become a global establishment that is omnipresent in the lives of many people. In addition, the Internet can be employed as a means to retrieve information retained all over the globe.

For example, such services can include, but not limited to, search engines, web mail, online chat e. Many Internet services have become integrated into everyday lives of people.

Scope of the T-ASE

To accommodate increased integration, such services are expected to scale well, to provide high performance e. To achieve these goals, Internet services are typically deployed in clusters that include a large number of servers hosted in dedicated data centers.

A data center can house a variety of heterogeneous components such as, but not limited to, computers, storage, networking equipment. In addition, the data center includes infrastructure that distributes power to the components and provides cooling to the components.

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Viewed from outside, a data center can be seen as a black box that responds to a stream of user requests via the Internet while consuming power from the electrical grid and producing waste heat. With drastic increases in demand for Internet services, data centers consume more and more resources.

Ieee papers on short term load forecasting

The amount of resources consumed is directly related to number of hosted servers in data center as well workload of the servers. As data centers scale up to meet demand for hosted services, electricity usage skyrockets.

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In the United States, it is estimated that billions of kilowatt-hours are consumed by data centers; an amount sufficient to power millions of homes. SUMMARY The following discloses a simplified summary of the specification in order to provide a basic understanding of some aspects of the specification.

This summary is not an extensive overview of the specification. It is intended to neither identify key or critical elements of the specification nor delineate the scope of the specification.

Its sole purpose is to disclose some concepts of the specification in a simplified form as a prelude to the more detailed description that is disclosed later.Christiaanse, W.R. () Short Term Load Forecasting Using Genera Exponential Smoothing.

Ieee papers on short term load forecasting

IEEE Transactions on Power Apparatus and Systems, PAS, The aim of this paper was to analyse the effect of particulate matter PM , a recent air quality guideline value for the protection of health, on hospital admissions in Madrid, tranceformingnlp.com dependent variable was used as a measure against the daily number of emergency hospital admissions from – This paper proposes four different models for an Artificial Neural Network (ANN) based on short term load forecasting.

Historical load data from Bogota from to is used for testing, showing the good performance of the different methods. Essay from insider interview judge money scholarship strategy winner winning ieee research papers on computer networks essay on co education in india voorbeeld thesis sociale wetenschappen ma thesis in english linguistics.

Term Papers: Development of Web Based Gravity Model for Forecasting the Commuters. This paper is concerned primarily with the way in which transport affects pop.

With the April issue of the IEEE STC-SC Newsletter it is time to announce some important changes in our community: our new Chair, Prof.

Christopher Stewart, is currently in the process of reorganizing and renovating the whole management team.

ANN-based short-term load forecasting in Bogotá