A new methodology for cost-optimal analysis by means of the multi-objective optimization of building energy performance

Ascione, Fabrizio; Bianco, Nicola; Stasio, Claudio De; Mauro, Gerardo Maria; Vanoli, Giuseppe Peter

doi:10.1016/j.enbuild.2014.11.058

Cited by 157 publications

(97 citation statements)

References 22 publications

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“…The GA generates inputs to the building simulation software, while the later return simulation results on energy consumption, cost and return the values back to GA for optimization. Selection of the inputs are based on the findings from open literature (Wang et al, 2006;Dubrow and Krarti, 2010;Hamdy et al, 2011;Zhang et al, 2011;Gong et al, 2012;Jin and Jeong, 2014;Ascione et al, 2015;Liu et al, 2015;Yu et al, 2015). The design parameters selected in this paper are related to building orientation, WWR, window shading, heating temperature set point, cooling temperature set point, external wall structure (insulation), roof structure (insulation), and glazing type.…”

Section: Methodology Optimization Frameworkmentioning

confidence: 99%

“…The population size is very sensitive to the computation performance and it is recommended to select smaller size of population if premature convergence can be avoided. Based on the results from Alajmi and Wright (2014) and Ascione et al (2015) and through fine-tuning, on the population size, crossover rate and mutation rate are set to be 200, 0.95, and 0.02, respectively. The first set of object functions are to find the tradeoff between annual building energy consumption and initial construction cost for building design in the five selected cities.…”

Section: Analysis On the Optimization Processmentioning

confidence: 99%

“…Hamdy et al (2011) proposed a modified MOGA method to minimize the investment cost and carbon dioxide equivalent (CO 2 -eq) emissions for a two-story residential building and its HVAC system, which predicted 26% in investment cost and 32% in CO 2 -eq emission as compared to the initial design. Ascione et al (2015) coupled MATLAB with EnergyPlus to find the optimum building retrofit cost over the life cycle using MOGA approach to reduce energy demand and improve indoor thermal comfort, considering changes in the solar radiation absorption coefficient of the roof, insulation layer thickness of the roof and external wall, air temperature setpoint, window type, boiler type and chiller type. Znouda et al (2007) combined GA with a simplified building thermal evaluation tool (CHEOPS) to investigate the impact of building shape, structures of roof and external walls, and shading device on building energy consumption and construction cost.…”

Section: Introductionmentioning

confidence: 99%

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Application of Multi-Objective Genetic Algorithm Based Simulation for Cost-Effective Building Energy Efficiency Design and Thermal Comfort Improvement

Lin

Wang

2018

Front. Energy Res.

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Building design following the energy efficiency standards may not achieve the optimal performance in terms of investment cost, energy consumption and thermal comfort. In this paper, an improved multi-objective genetic algorithm (NSGA-II) is combined with building simulation to assist building design optimization for five selected cities located in the hot summer and cold winter region in China. The trade-offs between the annual energy consumption (AEC) and initial construction cost, as well as between life cycle cost (LCC) and number of thermal discomfort hours, were explored. Sensitivity analysis of various design parameters on building energy consumption is performed. The optimizations predicted AEC reduction of 29.08% on average, as compared to a reference building designed following the standard, and 38.6% with 3.18% more cost on the initial investment. New values for a number of building design parameters are recommended for the revision of relevant building energy efficiency standard.

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Section: Methodology Optimization Frameworkmentioning

confidence: 99%

Section: Analysis On the Optimization Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Multi-Objective Genetic Algorithm Based Simulation for Cost-Effective Building Energy Efficiency Design and Thermal Comfort Improvement

Lin

Wang

2018

Front. Energy Res.

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“…The design of a building energy retrofit is a challenging assignment that requires an integrated team approach because conflicting objectives generally persist [10], i.e., the minimization of energy consumption and the maximization of economic benefits. This is the reason why a multi-objective optimization approach is commonly recommended in literature [11][12][13]. Marrone et al investigated [14] proper cost-effective strategies applied to educational buildings for retrofitting.…”

Section: Introductionmentioning

confidence: 99%

Energy Retrofitting Strategies and Economic Assessments: The Case Study of a Residential Complex Using Utility Bills

et al. 2018

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Promotion of retrofit actions on existing buildings is a goal in Italy, since most of them were built before the 80 s when little attention was paid to energy saving. This paper presents an integrated passive design approach to reduce the heating demand and limit the costs of a representative existing residential complex located in Bologna, in the northern part of Italy. To this purpose, we explored different scenarios upon actions taken on the building structure: (1) High efficiency windows; (2) additional insulation on the external walls; or (3) the simultaneous application of high efficiency windows and improved thermal envelope, on both external walls and roofing. The numerical optimization has been performed dynamically using TRNSYS simulation tool, to evaluate energy consumptions in different structural conditions. Then, the developed model has been calibrated by the real consumption data deduced from energy bills (years 2009-2015). Finally, the energy results obtained in the above mentioned different scenarios have been evaluated under an economic assessment of cost investment: It has been highlighted that the payback time (PBT) results to be strongly influenced by the national policies of fiscal incentives. According to the present model, the most profitable condition is obtained when additional insulation on the external walls is applied: The total amount of energy saving resulted to be equal to 930.4 MWh, with an optimal PBT of roughly six years, when tax refund was contemplated.

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“…Some researches apply multi-objective [2,5,6,25,26,[36][37][38][39][40] or multi-criterion [41,42] optimization models. In the study conducted by Ascione et al [39], a procedure that combines EnergyPlus simulation and a genetic algorithm was implemented to determine the best solutions for the HVAC system control in a residential building.…”

Section: Introductionmentioning

confidence: 99%

Multi-Variable Optimization of Building Thermal Design Using Genetic Algorithms

Ferdyn-Grygierek

Grygierek

2017

Energies

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Abstract:The building sector is one of the largest energy consumers in the world, comprising about 40% of the total energy consumption in numerous countries. Early design decisions have a significant impact on the energy performance of buildings. The paper presents the multi-variable optimization of the selected design parameters in a single-family building in temperate climate conditions. The influence of four types of windows, their size, building orientation, insulation of external wall, roof and ground floor and infiltration on the life cycle costs (LCC) is analyzed. Optimal selection of the design parameters is carried out using genetic algorithms by coupling the building performance simulation program EnergyPlus with optimization environment. The simulations were conducted for seven optimization cases. The analysis is performed for two variants of a building with heating and cooling systems and with a heating system only. Depending on the analyzed case, the life cycle costs decreased from 7% to 34% LCC value of the reference building. In the case of temperate climate, the building optimization (in terms of heat demand only) substantially reduces the heating costs, yet the summer thermal comfort conditions deteriorate significantly.

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A new methodology for cost-optimal analysis by means of the multi-objective optimization of building energy performance

Cited by 157 publications

References 22 publications

Application of Multi-Objective Genetic Algorithm Based Simulation for Cost-Effective Building Energy Efficiency Design and Thermal Comfort Improvement

Application of Multi-Objective Genetic Algorithm Based Simulation for Cost-Effective Building Energy Efficiency Design and Thermal Comfort Improvement

Energy Retrofitting Strategies and Economic Assessments: The Case Study of a Residential Complex Using Utility Bills

Multi-Variable Optimization of Building Thermal Design Using Genetic Algorithms

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