Environment data processing for a data centre (1)
Data centres play an essential role in today's global digital economy. Data centre construction has been surging worldwide due to digitalisation and digital solutions needs such as cloud technology. Data centres in Singapore consumed 7 per cent of the country’s total electricity in 2020. Hen...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/162935 |
| _version_ | 1826113807681847296 |
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| author | Seah, Yong Zhi |
| author2 | Tan Rui |
| author_facet | Tan Rui Seah, Yong Zhi |
| author_sort | Seah, Yong Zhi |
| collection | NTU |
| description | Data centres play an essential role in today's global digital economy. Data centre construction
has been surging worldwide due to digitalisation and digital solutions needs such as cloud
technology. Data centres in Singapore consumed 7 per cent of the country’s total electricity in
2020. Hence, in Singapore, energy-efficient cooling methodologies such as air free cooling
data centres are studied to determine their feasibility.
An initial experimental result of an air-free cooled data centre testbed proves that air-free
cooling may be feasible in Singapore, and machine learning models may be used to predict
and improve the efficiency of the air-free cooling PID controller. Hence, researchers often
seek methodologies that enhance these models. This project aims to study the dataset
obtained from the testbed to determine if the accuracy of the machine learning model, namely
the Neural Network, Decision Tree, Random Forest and Support Vector Machine model, may
be improved by providing them with an additional feature generated using known physics
law. The fan energy and airflow may be described using physics law, namely, fan law.
The experiment implemented a polynomial regression model that references the fan law to
predict the sum of fan energy, representing the additional feature input used by the other
machine model. The experiment results show slight improvement for the Neural Network and
Decision Tree Model. Hence, future work may focus on optimising the machine learning
modal or the polynomial regression model to improve the accuracy further. |
| first_indexed | 2024-10-01T03:29:17Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/162935 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:29:17Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1629352022-11-14T05:31:28Z Environment data processing for a data centre (1) Seah, Yong Zhi Tan Rui School of Computer Science and Engineering tanrui@ntu.edu.sg Engineering::Computer science and engineering Data centres play an essential role in today's global digital economy. Data centre construction has been surging worldwide due to digitalisation and digital solutions needs such as cloud technology. Data centres in Singapore consumed 7 per cent of the country’s total electricity in 2020. Hence, in Singapore, energy-efficient cooling methodologies such as air free cooling data centres are studied to determine their feasibility. An initial experimental result of an air-free cooled data centre testbed proves that air-free cooling may be feasible in Singapore, and machine learning models may be used to predict and improve the efficiency of the air-free cooling PID controller. Hence, researchers often seek methodologies that enhance these models. This project aims to study the dataset obtained from the testbed to determine if the accuracy of the machine learning model, namely the Neural Network, Decision Tree, Random Forest and Support Vector Machine model, may be improved by providing them with an additional feature generated using known physics law. The fan energy and airflow may be described using physics law, namely, fan law. The experiment implemented a polynomial regression model that references the fan law to predict the sum of fan energy, representing the additional feature input used by the other machine model. The experiment results show slight improvement for the Neural Network and Decision Tree Model. Hence, future work may focus on optimising the machine learning modal or the polynomial regression model to improve the accuracy further. Bachelor of Engineering (Computer Science) 2022-11-14T05:31:28Z 2022-11-14T05:31:28Z 2022 Final Year Project (FYP) Seah, Y. Z. (2022). Environment data processing for a data centre (1). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162935 https://hdl.handle.net/10356/162935 en SCSE21-0578 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Seah, Yong Zhi Environment data processing for a data centre (1) |
| title | Environment data processing for a data centre (1) |
| title_full | Environment data processing for a data centre (1) |
| title_fullStr | Environment data processing for a data centre (1) |
| title_full_unstemmed | Environment data processing for a data centre (1) |
| title_short | Environment data processing for a data centre (1) |
| title_sort | environment data processing for a data centre 1 |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/162935 |
| work_keys_str_mv | AT seahyongzhi environmentdataprocessingforadatacentre1 |