A prediction model for CO2 concentration and multi-objective optimization of CO2 concentration and annual electricity consumption cost in residential buildings using ANN and GA

Baghoolizadeh, Mohammadreza; Rostamzadeh‐Renani, Mohammad; Dehkordi, Seyed Amir Hossein Hashemi; Rostamzadeh‐Renani, Reza; Toghraie, Davood

doi:10.1016/j.jclepro.2022.134753

Cited by 33 publications

(4 citation statements)

References 81 publications

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“…The objective function values for the solutions are the basis for making decisions about how to enhance the solutions. Researchers used evolutionary algorithms or other methods in several studies [ [34] , [35] , [36] , [37] ]. In this study, MOGOA, MALO, MOPSO, MOMFO, MOWOA and NSGA Ⅱ algorithms are used in the direction of coupling with GMDH ANN and optimization.…”

Section: Methodsmentioning

confidence: 99%

“…ANNs are utilized in diverse problems, including regression , classification [ 49 , 50 ], patternization [ 51 ], time series prediction [ 35 ] and clustering [ 52 , 53 ]. For forecasting a mathematical relationship from the simulated data, a regression operation should be conducted.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, GMDH ANN is used to perform regression operations. This ANN type has been employed by researchers in diverse fields of engineering [, 35 , [54] , [55] , [56] , [57] , [58] ]. The production and prediction of meaningful output data is the primary objective of GMDH ANN.…”

Section: Methodsmentioning

confidence: 99%

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Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid

Baghoolizadeh,

Jasim,

Sajadi

et al. 2024

Heliyon

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“…The abovementioned intelligent optimization methods are all based on genetic algorithms that automatically search for a solution. Among them, the NSGA-II [29,32] and NSGA-III [26,33] algorithms are the most widely used. On this basis, Ciardiello et al [27] took aNSGA-II (a peculiar version of NSGA-II) and Mostafazadeh et al [33] used prNSGA-III (a modified version of NSGA-III) as optimization algorithms for research.…”

Section: Studies On Multi-objective Optimization Of Building Performancementioning

confidence: 99%

Multi-Objective Optimization of Envelope Design of Rural Tourism Buildings in Southeastern Coastal Areas of China Based on NSGA-II Algorithm and Entropy-Based TOPSIS Method

et al. 2023

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The rapid development of rural tourism and higher requirements for the indoor environments of rural tourism buildings (RTBs) have led to rapid growth in the energy consumption of RTBs. The aim of this work was to apply a new method to optimize the indoor thermal environments and energy performances of RTBs and promote scientific passive design strategies for RTBs in southeastern coastal areas of China. First, a field survey was carried out to understand the statuses of buildings and the energy consumption of RTBs. Through a building typology analysis, two types of RTBs (renovated from existing buildings and newly built) were chosen as the dominant types in the villages. Second, a comprehensive parametric study was conducted to examine the impact of energy consumption and the indoor thermal environment using a global sensitivity analysis. The passive design parameters with large sensitivity impacts were selected using the Sobol sampling method and by calculating the comprehensive contribution rates of the parameters. Then, the NSGA-II algorithm was used to simultaneously minimize the two objectives and generate the Pareto front solution sets of the two RTB types. Finally, by applying an entropy-based TOPSIS decision-making method, the optimal schemes (the best energy-saving solution, the best comfort solution, and the best compromise solution) for the two RTB types were further obtained from the feasible Pareto-optimal solutions, and the suggested values for the design parameters are presented. This study proposes a new multi-objective optimization approach combining the NSGA-II algorithm and an entropy-based TOPSIS decision-making method, and the findings are valuable, as they can help designers to improve the designs of rural tourism buildings.

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Demand Forecasting Mathematical Models for Residential Electricity Consumption Considering Ambient Temperature

Stanytsina,

Zaporozhets,

Artemchuk

2024

Studies in Systems, Decision and Control

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A prediction model for CO2 concentration and multi-objective optimization of CO2 concentration and annual electricity consumption cost in residential buildings using ANN and GA

Cited by 33 publications

References 81 publications

Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid

Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid

Multi-Objective Optimization of Envelope Design of Rural Tourism Buildings in Southeastern Coastal Areas of China Based on NSGA-II Algorithm and Entropy-Based TOPSIS Method

Demand Forecasting Mathematical Models for Residential Electricity Consumption Considering Ambient Temperature

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