Minimizing delivered energy and life cycle cost using Graphical script: An office building retrofitting case

Rabani, Mehrdad; Madessa, Habtamu Bayera; Mohseni, Omid; Nord, Nataša

doi:10.1016/j.apenergy.2020.114929

Cited by 40 publications

(21 citation statements)

References 37 publications

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“…As highlighted in (Rabani et al 2020), there are numerous established approaches to couple building energy models with optimization algorithms. Among these, we employed the genetic algorithm, as implemented in Matlab's ga function.…”

Section: Optimization Settingsmentioning

confidence: 99%

Deriving sequences of operation for air handling units through building performance optimization

Gunay

Newsham

Ashouri

et al. 2020

Journal of Building Performance Simulation

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Operational parameters of air handling units (AHUs) play an important role in the energy and comfort performance of commercial buildings. Current guidelines to determine these parameters are based on heuristics and are not informed by formal optimization. This paper presents a building performance optimization method to derive sequence of operations for multi-zone AHUs. To this end, 27 variants of a generic EnergyPlus office building model are built representing three levels of occupancy, envelope, and HVAC capacity scenarios. The supply temperature, morning start time, and economizer settings of the model are optimized for the 27 scenarios by using a genetic algorithm. The results highlight that optimal supply temperature setpoints transition from ∼ 12°C to a range of ∼ 16°C to ∼ 20°C over an outdoor temperature range of ∼ 20°C to ∼ 0°C. Energy savings are estimated as ∼ 20% relative to a reference case with a constant supply temperature and preheating/precooling period.

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Section: Optimization Settingsmentioning

confidence: 99%

Deriving sequences of operation for air handling units through building performance optimization

Gunay

Newsham

Ashouri

et al. 2020

Journal of Building Performance Simulation

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“…GS interface is an available option in IDA-ICE (dotted orange objects in Figure 1) considered as an intermediate step to manipulate the outputs from IDA-ICE regarding the thermal and visual comfort constraints. Details of GS interface functions can be found in the work done by Rabani et al [6].…”

Section: Bes-opt Processmentioning

confidence: 99%

“…Regarding objective functions, various parameters dealing with energy, visual, and acoustic performance of buildings are selected. For example, Djuric et al [5], Rabani et al [6], Karaguzel et al [7], Chantrelle et al [8] and Ferrara et al [9] conducted a single objective optimization and considered the retrofitting costs or the building energy use (the two latter studies) as the objective. Several studies such as those of Mangnier and Haghighat [10], Harkouss et al [11], Asadi et al [12], Niemelä et al [13], Wu et al [14], Hamdy et al [15], and Palonen et al [16] conducted multi objective optimization to improve building performance.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding optimization tools, several algorithms, software systems, and platforms have been commonly integrated with building performance tools. For example, GenOpt [5][6][7]29], MOO [31,36], GAMS [37], jEPlus [25][26][27], Rhinoceros [38], MATLAB [15,39,40], non-dominated sorting genetic algorithm II (NSGA II) [13,28,35], and CPLEX algorithm [14,41] are among the widespread optimization tools and platforms. A recent study has shown that the integration of artificial neural networks such as Multilayer Feedforward Neural Networks (MFNN) with metaheuristic algorithms such as NSGA II and Multi-Objective Particle Swarm Optimization (MOPSO) can minimize the computation time [42].…”

Section: Introductionmentioning

confidence: 99%

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Building Retrofitting through Coupling of Building Energy Simulation-Optimization Tool with CFD and Daylight Programs

2021

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Simultaneous satisfaction of both thermal and visual comfort in buildings may be a challenging task. Therefore, this paper suggests a comprehensive framework for the building energy optimization process integrating computational fluid dynamics (CFD) daylight simulations. A building energy simulation tool, IDA Indoor Climate and Energy (IDA-ICE), was coupled with three open-source tools including GenOpt, OpenFOAM, and Radiance. In the optimization phase, several design variables i.e., building envelope properties, fenestration parameters, and Heating, Ventilation and Air-Conditioning (HVAC) system set points, were selected to minimize the total building energy use and simultaneously improve thermal and visual comfort. Two different scenarios were investigated for retrofitting of a generic office building located in Oslo, Norway. In the first scenario a constant air volume (CAV) ventilation system with a local radiator in each zone was used, while an all-air system equipped with a demand control ventilation (DCV) was applied in the second scenario. Findings showed that, compared to the reference design, significant reduction of total building energy use, around 77% and 79% in the first and second scenarios, was achieved respectively, and thermal and visual comfort conditions were also improved considerably. However, the overall thermal and visual comfort satisfactions were higher when all-air system was applied.

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“…Adding thermal insulation, solar shielding, reducing thermal load, updating old inefficient appliances, and adding green energy technologies are only a few of the energysaving steps that can be applied by energy retrofitting programs [16][17][18]. Improving the energy efficiency of buildings, especially those that are already in use, is critical for combating climate change [19].…”

Section: Introductionmentioning

confidence: 99%

Towards Sustainable Energy Retrofitting, a Simulation for Potential Energy Use Reduction in Residential Buildings in Palestine

et al. 2021

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Since buildings are one of the major contributors to global warming, efforts should be intensified to make them more energy-efficient, particularly existing buildings. This research intends to analyze the energy savings from a suggested retrofitting program using energy simulation for typical existing residential buildings. For the assessment of the energy retrofitting program using computer simulation, the most commonly utilized residential building types were selected. The energy consumption of those selected residential buildings was assessed, and a baseline for evaluating energy retrofitting was established. Three levels of retrofitting programs were implemented. These levels were ordered by cost, with the first level being the least costly and the third level is the most expensive. The simulation models were created for two different types of buildings in three different climatic zones in Palestine. The findings suggest that water heating, space heating, space cooling, and electric lighting are the highest energy consumers in ordinary houses. Level one measures resulted in a 19–24 percent decrease in energy consumption due to reduced heating and cooling loads. The use of a combination of levels one and two resulted in a decrease of energy consumption for heating, cooling, and lighting by 50–57%. The use of the three levels resulted in a decrease of 71–80% in total energy usage for heating, cooling, lighting, water heating, and air conditioning.

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Minimizing delivered energy and life cycle cost using Graphical script: An office building retrofitting case

Cited by 40 publications

References 37 publications

Deriving sequences of operation for air handling units through building performance optimization

Deriving sequences of operation for air handling units through building performance optimization

Building Retrofitting through Coupling of Building Energy Simulation-Optimization Tool with CFD and Daylight Programs

Towards Sustainable Energy Retrofitting, a Simulation for Potential Energy Use Reduction in Residential Buildings in Palestine

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