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Item

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Electricity Price Prediction for Geographically Distributed Data Centers in Multi-Region Electricity Markets

http://hdl.handle.net/20.500.12678/0000007324
8f2b5c3b-f5af-4c5c-86a1-f43088da89a3
e202cd21-e608-49a8-b1c7-aa0f0ebf547d
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MohMohThan(CS).pdf MohMohThan(CS).pdf (2.7 MB)
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Publication type
Conference paper
Upload type
Publication
Title
Title Electricity Price Prediction for Geographically Distributed Data Centers in Multi-Region Electricity Markets
Language en
Publication date 2018-09-13
Authors
Moh Moh Than, Thandar Thein
Description
Geographically distributed data centers (GDCs)
serving as infrastructures for cloud services, are growing in
both number and scale. They usually consume enormous
amount of electric power, which lead to high operational costs
and this has been recognized as a main challenge in cloud
computing. Energy cost can be reduced by directing the
requests to the favor of data center with lower electricity price
by incorporating spatially and temporally price diversity,
especially in the multi-region electricity markets. If the
electricity prices of data centers are predicted in advance, the
cloud provider can reduce energy cost. An efficient electricity
price prediction is needed for minimizing electricity bill of
GDCs. This paper proposes electricity price prediction for
GDCs in multi-region electricity markets. Experiment is
conducted on real-life electricity price data sets with machine
learning algorithms. By comparatively assessing the prediction
accuracy of the models, the most accurate one is selected.
Experiment results show that the prediction model can provide
promising accuracy.
Keywords
electricity price prediction, geographically distributed data centers, machine learning, multi-region electricity markets
Identifier 10.1109/CCOMS.2018.8463272
Conference papers
Prediction
27-30 April 2018
2018 3rd International Conference on Computer and Communication Systems (ICCCS)
Nagoya, Japan
Data Theory and Engineering
http://www.icccs.org
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