干旱区生态环境知识资源中心(Arid): Predicting freshwater production and energy consumption in a seawater greenhouse based on ensemble frameworks using optimized multi-layer perceptron

Arid

DOI	10.1016/j.egyr.2021.09.079
	Predicting freshwater production and energy consumption in a seawater greenhouse based on ensemble frameworks using optimized multi-layer perceptron
	Ehteram, Mohammad; Ahmed, Ali Najah; Kumar, Pavitra; Sherif, Mohsen; El-Shafie, Ahmed
通讯作者	Sherif, M (corresponding author), United Arab Emirates Univ, Natl Water & Energy Ctr, POB 15551, Al Ain, U Arab Emirates.
来源期刊	ENERGY REPORTS
ISSN	2352-4847
出版年	2021
卷号	7 页码:6308-6326
英文摘要	Water shortage in arid and semi-arid land is one of the most important challenges of decision-makers. The seawater greenhouse (SWG) is a useful solution for water supply in the agriculture sector. The optimal design of a SWG with lower consumption of energy and higher freshwater production is a real challenge for the decision-makers. This study used two ensemble models and multiple multi-layer perceptron (MLP) models based on non-climate data to predict freshwater production energy consumption in the SWG. The Copula Bayesian average model (CBMA) was used to develop the BMA model using different copula functions and distributions. In the first level, multiple MLP models using the dimension of SWG as inputs predicted freshwater and energy consumption in a SWG. In the next level, The CBMA and BMA were used to predict freshwater production and energy consumption. The uncertainty analysis of outputs, use of new models and non-climate data are the novelties of the current study. The results indicated that the CBMA decreased the mean absolute error (MAE) value of the BMA, MLP-SEOA, MLP-SCA, MLP-BA, MLP-PSO, and MLP models by 2.7%, 19%, 31%, 40%, 41%, and 42%, respectively for predicting freshwater production. The root mean square error (RMSE) of the CBMA was 40%, 49%, 56%, 57%, 62%, and 64% lower than those of the BMA, MLP-SEOA, MLP-SCA, MLP-BA, MLP-PSO, and MLP models, respectively for predicting energy consumption. The uncertainty analysis indicated that the CBMA and BMA provided the lowest uncertainty among other models. The current study results indicated that the use of ensemble models improved the accuracy of individual models for predicting energy consumption and freshwater production. The findings of the study indicated that the ensemble models using the dimension of SWGs as inputs successfully predicted energy consumption and freshwater production in a SWG. (C) 2021 The Authors. Published by Elsevier Ltd.
英文关键词	Freshwater production Energy consumption Optimization algorithms Copula Bayesian average model
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000706198300010
WOS关键词	DESALINATION ; ALGORITHM ; SYSTEMS ; MODEL
WOS类目	Energy & Fuels
WOS研究方向	Energy & Fuels
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/363084
作者单位	[Ehteram, Mohammad] Semnan Univ, Dept Water Engn & Hydraul Structures, Fac Civil Engn, Semnan, Iran; [Ahmed, Ali Najah] Univ Tenaga Nasl UNITEN, Inst Energy Infrastruct IEI, Selangor 43000, Malaysia; [Kumar, Pavitra; El-Shafie, Ahmed] Univ Malaya UM, Fac Engn, Dept Civil Engn, Kuala Lumpur 50603, Malaysia; [Sherif, Mohsen; El-Shafie, Ahmed] United Arab Emirates Univ, Natl Water & Energy Ctr, POB 15551, Al Ain, U Arab Emirates
推荐引用方式 GB/T 7714	Ehteram, Mohammad,Ahmed, Ali Najah,Kumar, Pavitra,et al. Predicting freshwater production and energy consumption in a seawater greenhouse based on ensemble frameworks using optimized multi-layer perceptron[J],2021,7:6308-6326.
APA	Ehteram, Mohammad,Ahmed, Ali Najah,Kumar, Pavitra,Sherif, Mohsen,&El-Shafie, Ahmed.(2021).Predicting freshwater production and energy consumption in a seawater greenhouse based on ensemble frameworks using optimized multi-layer perceptron.ENERGY REPORTS,7,6308-6326.
MLA	Ehteram, Mohammad,et al."Predicting freshwater production and energy consumption in a seawater greenhouse based on ensemble frameworks using optimized multi-layer perceptron".ENERGY REPORTS 7(2021):6308-6326.