Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141327
Title: Flow-based estimation and comparative study of gas demand profile for residential units in Singapore
Authors: Gupta, Payal
Zan, Thaw Tar Thein
Dauwels, Justin
Ukil, Abhisek
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Gupta, P., Zan, T. T. T., Dauwels, J., & Ukil, A. (2019). Flow-based estimation and comparative study of gas demand profile for residential units in Singapore. IEEE Transactions on Sustainable Energy, 10(3), 1120-1128. doi:10.1109/TSTE.2018.2861821
Journal: IEEE Transactions on Sustainable Energy
Abstract: The residential sector forms a substantial energy consumer; therefore, it is the focus of efforts to reduce energy consumption. To this end, a good understanding of customer load profiling for both the electricity and gas is fundamental to improve the energy utilization efficiency. Unfortunately, the hourly based energy load profiles are not directly available with the energy suppliers due to cost constraints. In this paper, we propose a mathematical model to build gas load profiles using the gas network flow data for the residential sector in Singapore. In addition, we conduct a comparative study between the household gas and electricity load profiles. The gas flow data is generated from a real experimental setup, directly connected to the gas distribution network of Singapore, while the electricity load data is generated from the smart meters installed at the housing units at Nanyang Technological University, Singapore. It is experimentally shown and also validated from EMA statistics that the daily gas consumption is approximately four times lower than the daily electricity consumption. Moreover, the differentiation between the weekdays and weekend for both the electricity and gas usage profiles is also presented. This work can serve as a benchmark study for designing the low-cost prediction models for gas and electricity consumption in Singapore for effective planning of both the gas and electricity networks.
URI: https://hdl.handle.net/10356/141327
ISSN: 1949-3029
DOI: 10.1109/TSTE.2018.2861821
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:ERI@N Journal Articles

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