Enhanced Machine Learning Model for Electricity Price Forecasting for Cloud Computing

Authors

  • K. Niprutha  Department Of Computer Science, Besant theosophical College, Madanapalli, Andhra Pradesh, India
  • D. Venkata Siva Reddy  Assistant Professor, Head of Department of Computer Science, Besant Theosophical College, Madanapalli, Andhra Pradesh, India

Keywords:

Cloud computing, Move stocksExtreme Gradient Boosting (XGBoost).

Abstract

In the IT industry cloud computing is rapidly gaining traction because it eliminates the need for physical computing hardware, which are instead hosted by companies providing cloud services. These firms have a large number of computers and servers whose main power source is electricity. The design and maintenance of these companies therefore depends on the availability of a consistent and cost-effective electricity supply.Energy-hungry are cloud centres. One of the most recent electricity price increases means that maintenance of these centres is to minimise the electricity usage of datacenters and to save energy, as well as to create and efficiently store data placement and to schedule node to download or move storage. A way to solve these issues. In this project, we propose to load and move stocks, predict electricity costs and reduce energy costs in data centres, an Extreme Gradient Boosting (XGBoost) model.Data are divided into 70% training and 30% testing.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2021-06-30

Issue

Volume 9, Issue 2, May-June-2021

Section

Research Articles

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How to Cite

[1]
K. Niprutha, D. Venkata Siva Reddy "Enhanced Machine Learning Model for Electricity Price Forecasting for Cloud Computing" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 2, pp.99-106, May-June-2021.