Electricity Price Forecasting for Cloud Computing Using an Enhanced Machine Learning Model Article Swipe
Saleh Albahli
,
Muhammad Shiraz
,
Nasir Ayub
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3035328
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3035328
Cloud computing is rapidly taking over the information technology industry because it makes computing a lot easier without worries of buying the physical hardware needed for computations, rather, these services are hosted by companies with provide the cloud services. These companies contain a lot of computers and servers whose main source of power is electricity, hence, design and maintenance of these companies is dependent on the availability of steady and cheap electrical power supply. Cloud centers are energy-hungry. With recent spikes in electricity prices, one of the main challenges in designing and maintenance of such centers is to minimize electricity consumption of data centers and save energy. Efficient data placement and node scheduling to offload or move storage are some of the main approaches to solve these problems. In this article, we propose an Extreme Gradient Boosting (XGBoost) model to offload or move storage, predict electricity price, and as a result reduce energy consumption costs in data centers. The performance of this method is evaluated on a real-world dataset provided by the Independent Electricity System Operator (IESO) in Ontario, Canada, to offload data storage in data centers and efficiently decrease energy consumption. The data is split into 70% training and 30% testing. We have trained our proposed model on the data and validate our model on the testing data. The results indicate that our model can predict electricity prices with a mean squared error (MSE) of 15.66 and mean absolute error (MAE) of 3.74% respectively, which can result in 25.32% cut in electricity costs. The accuracy of our proposed technique is 91% while the accuracy of benchmark algorithms RF and SVR is 89% and 88%, respectively.
Related Topics
Concepts
Computer science
Cloud computing
Electricity
Server
Mean absolute percentage error
Energy consumption
Boosting (machine learning)
Scheduling (production processes)
Data modeling
Real-time computing
Database
Artificial intelligence
Operating system
Artificial neural network
Operations management
Engineering
Electrical engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2020.3035328
- https://ieeexplore.ieee.org/ielx7/6287639/8948470/09246503.pdf
- OA Status
- gold
- Cited By
- 39
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3095671294
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3095671294Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2020.3035328Digital Object Identifier
- Title
-
Electricity Price Forecasting for Cloud Computing Using an Enhanced Machine Learning ModelWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Saleh Albahli, Muhammad Shiraz, Nasir AyubList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2020.3035328Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/09246503.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/8948470/09246503.pdfDirect OA link when available
- Concepts
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Computer science, Cloud computing, Electricity, Server, Mean absolute percentage error, Energy consumption, Boosting (machine learning), Scheduling (production processes), Data modeling, Real-time computing, Database, Artificial intelligence, Operating system, Artificial neural network, Operations management, Engineering, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
39Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 10, 2024: 7, 2023: 15, 2022: 6, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://ieeexplore.ieee.org/ielx7/6287639/8948470/09246503.pdf |
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| primary_location.raw_type | journal-article |
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| primary_location.is_published | True |
| primary_location.raw_source_name | IEEE Access |
| primary_location.landing_page_url | https://doi.org/10.1109/access.2020.3035328 |
| publication_date | 2020-01-01 |
| publication_year | 2020 |
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