Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.enbuild.2021.110973
Title: Quantitative evaluation of plant evapotranspiration effect for green roof in tropical area: A case study in Singapore
Authors: He, Yang 
Lin, Ervine Shengwei 
Tan, Chun Liang 
Tan, Puay Yok 
Wong, Nyuk Hien 
Keywords: Science & Technology
Technology
Construction & Building Technology
Energy & Fuels
Engineering, Civil
Engineering
Green roof
Evapotranspiration
Field measurement
Prediction model
Passive cooling
ARTIFICIAL NEURAL-NETWORKS
MASS-TRANSFER
BUILDING ENERGY
QUANTIFYING EVAPOTRANSPIRATION
THERMAL PERFORMANCE
VEGETATED ROOF
CLIMATIC DATA
MODEL
IMPACT
IRRIGATION
Issue Date: 13-Apr-2021
Publisher: ELSEVIER SCIENCE SA
Citation: He, Yang, Lin, Ervine Shengwei, Tan, Chun Liang, Tan, Puay Yok, Wong, Nyuk Hien (2021-04-13). Quantitative evaluation of plant evapotranspiration effect for green roof in tropical area: A case study in Singapore. ENERGY AND BUILDINGS 241. ScholarBank@NUS Repository. https://doi.org/10.1016/j.enbuild.2021.110973
Abstract: The efficacy of existing evapotranspiration (ET) models commonly used for hourly thermal performance simulation of green roof remains unclear, especially for the tropical climate. To address the issue, field experiment was conducted to quantify the ET rate of four plant species in Singapore. Results showed that the average daytime rate of ET ranged from 198.4 g m−2 h−1 to 320 g m−2 h−1, while the average nighttime rate of ET ranged from 18.7 g m−2 h−1 to 25.5 g m−2 h−1. The percentage of daytime ET accounting for solar radiation ranged from 51.4% to 62.7%. The hourly ET rate predicted by three types of physical models were compared to the measured ET rate. It was found that the water vapor diffusion model had the best prediction performance, while the energy balance model had the worst prediction performance. Considering the complexities of the water vapor diffusion model, fifteen artificial neural network (ANN) models using multi-layer perceptron regressor were developed and evaluated. It was found that the ANN models had a better average prediction performance compared to water vapor diffusion models. Conclusions drawn from this study could provide a reference for accurate modelling of thermal and hydrological performance of green roof in tropical area.
Source Title: ENERGY AND BUILDINGS
URI: https://scholarbank.nus.edu.sg/handle/10635/228115
ISSN: 03787788
18726178
DOI: 10.1016/j.enbuild.2021.110973
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