Application of the bin weather data for building energy analysis in the tropics

Yau, YH; Wong, CM; Ong, HC; Chin, WM

Application of the bin weather data for building energy analysis in the tropics

Yau, YH Wong, CM Ong, HC Chin, WM

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Energy Efficiency, 2020, 13, (5), pp. 935-953
Issue Date:: 2020-06-01

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Field	Value	Language
dc.contributor.author	Yau, YH
dc.contributor.author	Wong, CM
dc.contributor.author	Ong, HC
dc.contributor.author	Chin, WM
dc.date.accessioned	2020-11-05T02:49:25Z
dc.date.available	2020-11-05T02:49:25Z
dc.date.issued	2020-06-01
dc.identifier.citation	Energy Efficiency, 2020, 13, (5), pp. 935-953
dc.identifier.issn	1570-646X
dc.identifier.issn	1570-6478
dc.identifier.uri	http://hdl.handle.net/10453/143758
dc.description.abstract	© 2020, Springer Nature B.V. The present study presents the bin weather data for four cities in the tropical region and its significance in the building energy analysis. The bin weather data for four cities in Malaysia, which are Bayan Lepas, Kuala Terengganu, Senai and Kuching, were generated. The bin weather data are in the range from 18 to 38 °C with 2 °C intervals and tabulated based on six 4-h shifts. Since there is a lack of updated bin weather data in Malaysia, the present paper provides valuable data for engineers and designers to conduct an energy analysis using the bin method in this region. This data may also serve as an important reference for all tropical regions with similar weather characteristics. Energy analysis is an important tool for estimating the energy usage of a building in order to minimize energy wastage and maximize energy efficiency. The significance of applying bin weather data in energy analysis is also investigated using a case simulation. When the mean outdoor temperature of occupied period is used in simulation, the results deviate less than 1.6% from the results with the bin weather data, but the deviation rises up to 16.68% with the use of the annual mean outdoor dry bulb temperature.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Energy Efficiency
dc.relation.isbasedon	10.1007/s12053-020-09862-8
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering, 0906 Electrical and Electronic Engineering, 1205 Urban and Regional Planning
dc.title	Application of the bin weather data for building energy analysis in the tropics
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	0905 Civil Engineering
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1205 Urban and Regional Planning
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-11-05T02:49:21Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	5

Abstract:

© 2020, Springer Nature B.V. The present study presents the bin weather data for four cities in the tropical region and its significance in the building energy analysis. The bin weather data for four cities in Malaysia, which are Bayan Lepas, Kuala Terengganu, Senai and Kuching, were generated. The bin weather data are in the range from 18 to 38 °C with 2 °C intervals and tabulated based on six 4-h shifts. Since there is a lack of updated bin weather data in Malaysia, the present paper provides valuable data for engineers and designers to conduct an energy analysis using the bin method in this region. This data may also serve as an important reference for all tropical regions with similar weather characteristics. Energy analysis is an important tool for estimating the energy usage of a building in order to minimize energy wastage and maximize energy efficiency. The significance of applying bin weather data in energy analysis is also investigated using a case simulation. When the mean outdoor temperature of occupied period is used in simulation, the results deviate less than 1.6% from the results with the bin weather data, but the deviation rises up to 16.68% with the use of the annual mean outdoor dry bulb temperature.

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http://hdl.handle.net/10453/143758