Forecasting Multivariate Time Series Meteorological Data for Solar Thermal Cogeneration Systems
The high usage of fossil fuel to produce energy for the increasing demand of energy has been the primary culprit behind global warming. Alternative energy supply is thus necessary in order to prevent the situation from worsening. Recently, renewable energies such as solar energy has emerged as poten...
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| Format: | Conference or Workshop Item |
| Language: | English |
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Higher Education Forum
2017
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| Online Access: | http://umpir.ump.edu.my/id/eprint/19837/ http://umpir.ump.edu.my/id/eprint/19837/1/SICASE-0002.pdf |
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eprints |
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ump-198372018-07-18T08:25:01Z http://umpir.ump.edu.my/id/eprint/19837/ Forecasting Multivariate Time Series Meteorological Data for Solar Thermal Cogeneration Systems Tan, Lit Ken Ong, Sie Meng Nor Azwadi, Che Sidik Asako, Yutaka Lee, Kee Quen Gan, Yee Siang Goh, Chien Yong Tey, Wah Yen Ngien, S. K. Chuan, Zun Liang TA Engineering (General). Civil engineering (General) The high usage of fossil fuel to produce energy for the increasing demand of energy has been the primary culprit behind global warming. Alternative energy supply is thus necessary in order to prevent the situation from worsening. Recently, renewable energies such as solar energy has emerged as potential alternative energy resources due to its abundance all over the globe Solar energy can be harnessed using available system such as solar thermal cogeneration systems. However, fluctuations of solar radiation is one of the main challenge faced by the implementation of solar thermal cogeneration system due to its high variability. In order to have solar thermal cogeneration systems function smoothly and continuously, knowledge on solar radiation’s intensity several minutes in advance are required. While there exist various solar radiation forecast models, most of the proposed model are time consuming. In this research, a new methodology to forecast solar radiation via several meteorological data that incorporates dimension reduction technique is proposed. Based on the proposed methodology, two prediction models, Artificial Neural Network and statistical are established. Higher Education Forum 2017-12-07 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/19837/1/SICASE-0002.pdf Tan, Lit Ken and Ong, Sie Meng and Nor Azwadi, Che Sidik and Asako, Yutaka and Lee, Kee Quen and Gan, Yee Siang and Goh, Chien Yong and Tey, Wah Yen and Ngien, S. K. and Chuan, Zun Liang (2017) Forecasting Multivariate Time Series Meteorological Data for Solar Thermal Cogeneration Systems. In: Seoul International Conference on Applied Science and Engineering, 5-7 December 2017 , Seoul, Korea. pp. 69-83. (SICASE-0002). ISBN 978-986-89536-5-9 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Malaysia Pahang |
| building |
UMP Institutional Repository |
| collection |
Online Access |
| language |
English |
| topic |
TA Engineering (General). Civil engineering (General) |
| spellingShingle |
TA Engineering (General). Civil engineering (General) Tan, Lit Ken Ong, Sie Meng Nor Azwadi, Che Sidik Asako, Yutaka Lee, Kee Quen Gan, Yee Siang Goh, Chien Yong Tey, Wah Yen Ngien, S. K. Chuan, Zun Liang Forecasting Multivariate Time Series Meteorological Data for Solar Thermal Cogeneration Systems |
| description |
The high usage of fossil fuel to produce energy for the increasing demand of energy has been the primary culprit behind global warming. Alternative energy supply is thus necessary in order to prevent the situation from worsening. Recently, renewable energies such as solar energy has emerged as potential alternative energy resources due to its abundance all over the globe Solar energy can be harnessed using available system such as solar thermal cogeneration systems. However, fluctuations of solar radiation is one of the main challenge faced by the implementation of solar thermal cogeneration system due to its high variability. In order to have solar thermal cogeneration systems function smoothly and continuously, knowledge on solar radiation’s intensity several minutes in advance are required. While there exist various solar radiation forecast models, most of the proposed model are time consuming. In this research, a new methodology to forecast solar radiation via several meteorological data that incorporates dimension reduction technique is proposed. Based on the proposed methodology, two prediction models, Artificial Neural Network and statistical are established. |
| format |
Conference or Workshop Item |
| author |
Tan, Lit Ken Ong, Sie Meng Nor Azwadi, Che Sidik Asako, Yutaka Lee, Kee Quen Gan, Yee Siang Goh, Chien Yong Tey, Wah Yen Ngien, S. K. Chuan, Zun Liang |
| author_facet |
Tan, Lit Ken Ong, Sie Meng Nor Azwadi, Che Sidik Asako, Yutaka Lee, Kee Quen Gan, Yee Siang Goh, Chien Yong Tey, Wah Yen Ngien, S. K. Chuan, Zun Liang |
| author_sort |
Tan, Lit Ken |
| title |
Forecasting Multivariate Time Series Meteorological Data for Solar Thermal Cogeneration Systems |
| title_short |
Forecasting Multivariate Time Series Meteorological Data for Solar Thermal Cogeneration Systems |
| title_full |
Forecasting Multivariate Time Series Meteorological Data for Solar Thermal Cogeneration Systems |
| title_fullStr |
Forecasting Multivariate Time Series Meteorological Data for Solar Thermal Cogeneration Systems |
| title_full_unstemmed |
Forecasting Multivariate Time Series Meteorological Data for Solar Thermal Cogeneration Systems |
| title_sort |
forecasting multivariate time series meteorological data for solar thermal cogeneration systems |
| publisher |
Higher Education Forum |
| publishDate |
2017 |
| url |
http://umpir.ump.edu.my/id/eprint/19837/ http://umpir.ump.edu.my/id/eprint/19837/1/SICASE-0002.pdf |
| first_indexed |
2023-09-18T22:28:26Z |
| last_indexed |
2023-09-18T22:28:26Z |
| _version_ |
1777416123488141312 |