An integrated framework for multi-objective optimization of building performance: Carbon emissions, thermal comfort, and global cost

Chen, Ruijun; Tsay, Yaw-Shyan; Ni, Shiwen

doi:10.1016/j.jclepro.2022.131978

Cited by 33 publications

(13 citation statements)

References 49 publications

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“…As research and the evolution of algorithms progress, scholars are also experimenting with a variety of algorithms like NSGA-III [37,61], SPEA-II [62], HypE [63], MOPSO, and MOEA/E [41,64]. Usman, M., and others achieved optimal passive design for single-family homes in different climates by coupling the NSGA-III genetic algorithm with the building energy simulation tool TRNSYS [65].…”

Section: Multi-objective Optimization Algorithmmentioning

confidence: 99%

“…In the field of architecture, multi-objective optimization can be linked with energy consumption simulation software such as EnergyPlus, Ecotect, and TRNSYS and with software like MATLAB or Python to focus on optimizing energy savings, carbon emissions, and economic performance [36]. Chen and others have developed an optimization framework on the Python platform to simulate the minimum carbon emissions, indoor discomfort hours, and overall costs of buildings [37]. Ascione and colleagues integrated EnergyPlus and MATLAB tools to optimize the energy consumption, thermal comfort, economic, and environmental impacts of office buildings, obtaining Pareto frontier solutions [38].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Optimization for Winter Heating Retrofit in Rural Houses of Cold Regions: A Case Study in the Wusu Area

Xi,

Gao,

Hou

et al. 2024

Applied Sciences

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In regions of China experiencing severe cold, the duration of the winter heating season significantly contributes to elevated heating energy consumption in rural dwellings. This study focuses on typical brick-and-concrete rural homes in the Wusu area. Utilizing the Rhino–Grasshopper parametric modeling platform, it aims to minimize heating-related carbon emissions and the overall costs associated with retrofitting. The approach involves improving the insulation properties of the building envelope to reduce energy requirements. Additionally, the study incorporates solar photovoltaic systems atop rural homes, building upon low-carbon, passive, energy-efficient design principles. By examining the influence of various factors on rural housing energy consumption, the research employs the entropy weight method to identify the most effective design solutions. The goal is to explore strategies for the energy-efficient retrofitting of rural dwellings in areas faced with harsh winter conditions, aligning with the objectives and preferences of Applied Sciences. The simulation results reveal the following: (1). In comparison with the baseline scenario, 42.2% of the optimized solutions within the Pareto frontier satisfy the current standards for 75% energy savings in energy-efficient residential design. (2). The lowest recorded thermal consumption index for the buildings can reach 12.427 W/m2, at which point the rate of energy savings is elevated to 79.5%. (3). Within the solutions identified by the Pareto frontier, 80% exhibit initial investments that are lower than the cost savings over the lifecycle due to reduced energy consumption (dCg < 0), demonstrating the economic feasibility of the proposed retrofitting strategies.

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Section: Multi-objective Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization for Winter Heating Retrofit in Rural Houses of Cold Regions: A Case Study in the Wusu Area

Xi,

Gao,

Hou

et al. 2024

Applied Sciences

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“…Traditional manual design methods struggle to match the precision and speed mandated by these visionary ventures. Furthermore, structural optimization and automation play a pivotal role in crafting environmentally sustainable structures with reduced carbon footprints, addressing the construction industry's growing sustainability concerns [65]. This push drives architects and engineers to explore sustainable materials, lightweight structures, and energy-efficient designs, aligning with The AEC sector's landscape is also witnessing a surge in innovative architectural projects, demanding cutting-edge optimization and automation to address complex geometries and load-bearing prerequisites.…”

Section: Quantitative Analysis Of Current State-of-the-art Conceptsmentioning

confidence: 99%

“…The Automation in Construction journal published the most automating structural design optimization research (33 articles). This journal assumes a pivotal role in driving innovation across AEC practices and fostering the adoption of Construction sustainable structures with reduced carbon footprints, addressing the construction industry's growing sustainability concerns [65]. This push drives architects and engineers to explore sustainable materials, lightweight structures, and energy-efficient designs, aligning with the global thrust towards eco-friendly construction practices.…”

Section: Quantitative Analysis Of Current State-of-the-art Conceptsmentioning

confidence: 99%

Towards BIM-Based Sustainable Structural Design Optimization: A Systematic Review and Industry Perspective

Afzal,

Li,

Ayyub

et al. 2023

Sustainability

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Structural design optimization (SDO) plays a pivotal role in enhancing various aspects of construction projects, including design quality, cost efficiency, safety, and structural reliability. Recent endeavors in academia and industry have sought to harness the potential of building information modeling (BIM) and optimization algorithms to optimize SDO and improve design outcomes. This review paper aims to synthesize these efforts, shedding light on how SDO contributes to project coordination. Furthermore, the integration of sustainability considerations and the application of innovative technologies and optimization algorithms in SDO necessitate more interactive early stage collaboration among project stakeholders. This study offers a comprehensive exploration of contemporary research in integrated SDO employing BIM and optimization algorithms. It commences with an exploratory investigation, employing both qualitative and quantitative analysis techniques following the PRISMA systematic review methodology. Subsequently, an open-ended opinion survey was conducted among construction industry professionals in Europe. This survey yields valuable insights into the coordination challenges and potential solutions arising from technological shifts and interoperability concerns associated with the widespread implementation of SDO. These preliminary steps of systematic review and industry survey furnish a robust foundation of knowledge, enabling the proposal of an intelligent framework for automating early stage sustainable structural design optimization (ESSDO) within the construction sector. The ESSDO framework addresses the challenges of fragmented collaboration between architects and structural engineers. This proposed framework seamlessly integrates with the BIM platform, i.e., Autodesk Revit for architects. It extracts crucial architectural data and transfers it to the structural design and analysis platform, i.e., Autodesk Robot Structural Analysis (RSA), for structural engineers via the visual programming tool Dynamo. Once the optimization occurs, optimal outcomes are visualized within BIM environments. This visualization elevates interactive collaborations between architects and engineers, facilitating automation throughout the workflow and smoother information exchange.

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“…In these cases, the equivalent cost of pollutants is accounted for in the main economic goal (Setlhaolo et al 2017) or their amount in tons constitutes an alternative or additional objective function of the optimization problem (Fiorini and Aiello 2018;Brahman et al 2015;Tabar et al 2017;Imran et al 2020). The emissions tied to the energy consumption is commonly assessed using the average CO 2 -EI of the generation mix, either defined as a constant (Chen et al 2022;Setlhaolo et al 2017;Imran et al 2020;Tabar et al 2017;Brahman et al 2015) or as a hourly value (Fiorini andAiello 2018, 2019b). However, the generation output of the power plants composing the generation mix does not adjust evenly to a load variation owning to multiple technical and economic factors.…”

Section: Related Workmentioning

confidence: 99%

Automatic optimal multi-energy management of smart homes

Fiorini

Aiello

2022

Energy Inform

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Residential and commercial buildings are responsible for approximately 35% of carbon emissions in industrialized countries. Making buildings more efficient and sustainable is, therefore, a fundamental step toward a low-carbon energy society. A key to achieving sustainability is by leveraging on energy storage systems and smart technologies to switch between energy carriers in order to optimize environmental impact. However, the research on energy management in buildings has mostly focused on its economic aspect, overlooking the environmental dimension. Additionally, the concept of energy system flexibility has been mostly proposed as the ability to shift demand over time or, at most, to curtail it, aiming at reducing the system’s operating costs. We propose a multi-energy multi-objective scheduling model to optimally manage the supply, demand, and interchange of multiple energy carriers, based on dynamic price and carbon emission signals. Our holistic and integrated approach is applied to a group of 200 smart homes with varying thermal and electric loads, and equipped with different types of smart technologies. The effectiveness of the approach in reducing the home carbon footprint, while remunerating the users, is evaluated using historical and statistical data of three European countries.

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An integrated framework for multi-objective optimization of building performance: Carbon emissions, thermal comfort, and global cost

Cited by 33 publications

References 49 publications

Multi-Objective Optimization for Winter Heating Retrofit in Rural Houses of Cold Regions: A Case Study in the Wusu Area

Multi-Objective Optimization for Winter Heating Retrofit in Rural Houses of Cold Regions: A Case Study in the Wusu Area

Towards BIM-Based Sustainable Structural Design Optimization: A Systematic Review and Industry Perspective

Automatic optimal multi-energy management of smart homes

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