Feasibility Study on Parametric Optimization of Daylighting in Building Shading Design

Lee, Kyung Sun; Han, Ki Jun; Lee, Jae Woo

doi:10.3390/su8121220

Cited by 24 publications

(14 citation statements)

References 26 publications

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“…Majority of the design optimization papers in the literature utilized Galapagos tool in dealing with energy [17][18][19], daylight [20,21], both energy and daylight [22,23] and structure [24][25][26].…”

Section: Galapagosmentioning

confidence: 99%

OPTIMUS: Self-Adaptive Differential Evolution with Ensemble of Mutation Strategies for Grasshopper Algorithmic Modeling

et al. 2019

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Most of the architectural design problems are basically real-parameter optimization problems. So, any type of evolutionary and swarm algorithms can be used in this field. However, there is a little attention on using optimization methods within the computer aided design (CAD) programs. In this paper, we present Optimus, which is a new optimization tool for grasshopper algorithmic modeling in Rhinoceros CAD software. Optimus implements self-adaptive differential evolution algorithm with ensemble of mutation strategies (jEDE). We made an experiment using standard test problems in the literature and some of the test problems proposed in IEEE CEC 2005. We reported minimum, maximum, average, standard deviations and number of function evaluations of five replications for each function. Experimental results on the benchmark suite showed that Optimus (jEDE) outperforms other optimization tools, namely Galapagos (genetic algorithm), SilverEye (particle swarm optimization), and Opossum (RbfOpt) by finding better results for 19 out of 20 problems. For only one function, Galapagos presented slightly better result than Optimus. Ultimately, we presented an architectural design problem and compared the tools for testing Optimus in the design domain. We reported minimum, maximum, average and number of function evaluations of one replication for each tool. Galapagos and Silvereye presented infeasible results, whereas Optimus and Opossum found feasible solutions. However, Optimus discovered a much better fitness result than Opossum. As a conclusion, we discuss advantages and limitations of Optimus in comparison to other tools. The target audience of this paper is frequent users of parametric design modelling e.g., architects, engineers, designers. The main contribution of this paper is summarized as follows. Optimus showed that near-optimal solutions of architectural design problems can be improved by testing different types of algorithms with respect to no-free lunch theorem. Moreover, Optimus facilitates implementing different type of algorithms due to its modular system.

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Section: Galapagosmentioning

confidence: 99%

OPTIMUS: Self-Adaptive Differential Evolution with Ensemble of Mutation Strategies for Grasshopper Algorithmic Modeling

et al. 2019

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“…To reduce the energy consumption associated with the installed lighting, light-emitting diode (LED) luminaires are used because they have high efficiency and a long lifetime. Architectural design studies have been performed to reduce the lighting energy consumption by actively using daylight [3][4][5][6][7][8][9][10][11][12][13][14]. In addition, to further reduce the lighting energy consumption, a daylight-responsive dimming system (DRDS) that utilizes indoor incident daylight is typically used.…”

Section: Research Purposementioning

confidence: 99%

“…Equation 7is transformed by Equation (6). To calculate g(L j ), the inverse proportion of the workplane illuminance matrix of Equation 7is transformed into Equation (8). The dimming level that satisfies the required illuminance value of each point can be calculated using the calculated g(L j ).…”

Section: Proposed Dimming Control Algorithm (Advanced Commissioning)mentioning

confidence: 99%

High-Performance Accuracy of Daylight-Responsive Dimming Systems with Illuminance by Distant Luminaires for Energy-Saving Buildings

Kim

Cho

et al. 2019

Energies

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In a conventional daylight-responsive dimming system (DRDS), all the luminaires are turned off during the calibration process except for the luminaire under consideration in order to sense only the workplane illuminance of that luminaire. However, the workplane illuminance of the luminaire is influenced by other luminaires. Therefore, the final workplane illuminance of the actual operated system is higher than the target workplane illuminance, reducing the energy-saving efficiency of the DRDS. Herein, to improve the conventional DRDS, an advanced commissioning prediction method of daylight illuminance, and a dimming control algorithm considering the influences by distant luminaires are proposed. To evaluate the accuracy of the proposed prediction method of daylight illuminance, the daylight illuminance on the workplane and the photo sensor values of six points were measured in a full-scale mockup for 27 consecutive days from 22 June to 18 July 2018. As a result of root-mean-square error (RMSE) analysis of daylight illuminance and the photo sensor values, the RMSE (64.86) of P3 located in the middle of the room was the highest, and the RMSE value (17.60) of P5 located near the window was the lowest. In addition, the power consumption of the luminaires, and the target illuminance accuracy of the proposed DRDS were measured and analyzed for 32 consecutive days from 19 July to 19 August 2018 in a full-scale mockup. The average target illuminance accuracy was 96.9% (SD 2.2%), the average lighting energy-savings ratio was 78.4%, and the daylight illuminance prediction accuracy was 95.5% (SD 3.4%).

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“…To reduce the energy consumption of a lighting installation, LED luminaires with the advantages of high efficiency and a long life are used. In addition, studies were implemented to increase daylight availability and daylight factor through architectural design [3][4][5][6][7][8]. To further reduce the energy consumption of the lighting, a DRDS that utilizes daylight incident to an indoor area is typically used.…”

Section: Study Objectivementioning

confidence: 99%

Advanced Dimming Control Algorithm for Sustainable Buildings by Daylight Responsive Dimming System

Kim

Nam

2018

Sustainability

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This study aims to evaluate the accuracy and energy savings of a daylight responsive dimming system (DRDS) when considering the influence of indirect illuminance. In the case of the existing DRDS, during the calibration process of each luminaire, the other luminaires were turned off to detect the illuminance of both the luminaire and the incoming daylight. However, the work plane illuminance under the luminaires was affected by the indirect illuminance from the other luminaires. The final work plane illuminance would thus be higher than the target illuminance during real system operation. To improve the accuracy and energy savings of the DRDS, an improved dimming control algorithm was applied to the DRDS when considering the influence of indirect illuminance. The real-time power consumption and accuracy of the target illuminance of the DRDS when considering the influence of indirect illuminance in a full-scale mock-up was measured and analyzed (for 23 days, from 22 June to 18 July 2018). As a result, the average accuracy of the target illuminance was 98.9% (SD 0.5%), and the average saving in lighting energy was 77%.

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Feasibility Study on Parametric Optimization of Daylighting in Building Shading Design

Cited by 24 publications

References 26 publications

OPTIMUS: Self-Adaptive Differential Evolution with Ensemble of Mutation Strategies for Grasshopper Algorithmic Modeling

OPTIMUS: Self-Adaptive Differential Evolution with Ensemble of Mutation Strategies for Grasshopper Algorithmic Modeling

High-Performance Accuracy of Daylight-Responsive Dimming Systems with Illuminance by Distant Luminaires for Energy-Saving Buildings

Advanced Dimming Control Algorithm for Sustainable Buildings by Daylight Responsive Dimming System

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