TY - JOUR
T1 - Forecasting long-term electricity demand for cooling of Singapore's buildings incorporating an innovative air-conditioning technology
AU - Oh, Seung Jin
AU - Ng, Kim Choon
AU - Thu, Kyaw
AU - Chun, Wongee
AU - Chua, Kian Jon Ernest
N1 - Funding Information:
The authors gratefully acknowledged the financial support from National Research Foundation of Singapore (grant no. R-265-000-466-281 ) and Korean National Research Foundation (grant no. 2014R1A2A1A01006421 )
Publisher Copyright:
© 2016 Elsevier B.V. All rights reserved.
PY - 2016/9/1
Y1 - 2016/9/1
N2 - In an effort to accurately plan for investment on energy production and distribution, this paper proposes a long-term electricity consumption forecasting model for buildings' cooling by employing a high energy conservative scenario. The key aspect of the high energy conservative scenario is to adopt an innovative adsorbent-based dehumidifier and an indirect evaporative cooling (AD-IEC) technology as opposed to conventional mechanical vapor compression system. Bottom-up equations were developed to identify the cooling load and electricity consumption of both residential and non-residential buildings for the period 2002-2013. Based on the time-series electricity consumption, a multiple linear regression model is developed to forecast electricity demand for the future period of 2014-2030. It is found that the electricity demands for cooling in the building sectors account for 31 ± 2% of the total electricity consumption in Singapore, This study concluded that the high conservative scenario realizes the best potential of electricity saving of 21,096 GWh until 2030. Using a CO2 emission factor of 4.49 × 10-4 metric tons CO2/kWh, the total carbon footprint saving from all power plants is estimated to be 9491,264 t of CO2. This work evolves a new forecasting methodology to predict buildings' cooling energy consumption involving the use of novel cooling technologies.
AB - In an effort to accurately plan for investment on energy production and distribution, this paper proposes a long-term electricity consumption forecasting model for buildings' cooling by employing a high energy conservative scenario. The key aspect of the high energy conservative scenario is to adopt an innovative adsorbent-based dehumidifier and an indirect evaporative cooling (AD-IEC) technology as opposed to conventional mechanical vapor compression system. Bottom-up equations were developed to identify the cooling load and electricity consumption of both residential and non-residential buildings for the period 2002-2013. Based on the time-series electricity consumption, a multiple linear regression model is developed to forecast electricity demand for the future period of 2014-2030. It is found that the electricity demands for cooling in the building sectors account for 31 ± 2% of the total electricity consumption in Singapore, This study concluded that the high conservative scenario realizes the best potential of electricity saving of 21,096 GWh until 2030. Using a CO2 emission factor of 4.49 × 10-4 metric tons CO2/kWh, the total carbon footprint saving from all power plants is estimated to be 9491,264 t of CO2. This work evolves a new forecasting methodology to predict buildings' cooling energy consumption involving the use of novel cooling technologies.
UR - http://www.scopus.com/inward/record.url?scp=84984567340&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84984567340&partnerID=8YFLogxK
U2 - 10.1016/j.enbuild.2016.05.073
DO - 10.1016/j.enbuild.2016.05.073
M3 - Article
AN - SCOPUS:84984567340
SN - 0378-7788
VL - 127
SP - 183
EP - 193
JO - Energy and Buildings
JF - Energy and Buildings
ER -