An Integrated Sensitivity Analysis Method for Energy and Comfort Performance of an Office Building along the Chinese Coastline

Chen, Ruijun; Tsay, Yaw-Shyan

doi:10.3390/buildings11080371

Cited by 19 publications

(6 citation statements)

References 57 publications

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“…where cov(x, y) represents the sample covariance of x and y, and var(•) represents sample variance. Mathematically, the Pearson correlation coefficient r xy takes values between −1 and 1 [42,43]. The larger the absolute value of r xy , the stronger it reveals the correlation between the variables.…”

Section: Lasso Regression Modelmentioning

confidence: 99%

Operational Carbon Change in Commercial Buildings under the Carbon Neutral Goal: A LASSO–WOA Approach

Xiang

et al. 2022

Buildings

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The rapid growth of energy consumption in commercial building operations has hindered the pace of carbon emission reduction in the building sector in China. This study used historical data to model the carbon emissions of commercial building operations, the LASSO regression was applied to estimate the model results, and the whale optimization algorithm was used to optimize the nonlinear parameter. The key findings show the following: (1) The major driving forces of carbon emissions from commercial buildings in China were found to be the population size and energy intensity of carbon emissions, and their elastic coefficients were 0.6346 and 0.2487, respectively. (2) The peak emissions of the commercial building sector were 1264.81 MtCO2, and the peak year was estimated to be 2030. Overall, this study analyzed the historical emission reduction levels and prospective peaks of carbon emissions from China’s commercial buildings from a new perspective. The research results are helpful for governments and decision makers to formulate effective emission reduction policies and can also provide references for the low-carbon development of other countries and regions.

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Section: Lasso Regression Modelmentioning

confidence: 99%

Operational Carbon Change in Commercial Buildings under the Carbon Neutral Goal: A LASSO–WOA Approach

Xiang

et al. 2022

Buildings

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“…This module is one of the most useful tools for parameterization that is coupled with the main software, and it is able to adjust the design parameters numerically or categorically. 92,93 Yi Zhang subsequently generated another software called JEPlus + EA to augment the software capabilities, which included the ability to perform optimization (NSGAII), 66,89,94 sensitivity analysis, [95][96][97] and a various of random sampling methodologies. 66,97,98 In present study, design variables are shading angle and shading distance to window edge, window height, and width.…”

Section: Design Of Variables and Objective Functionsmentioning

confidence: 99%

“…92,93 Yi Zhang subsequently generated another software called JEPlus + EA to augment the software capabilities, which included the ability to perform optimization (NSGAII), 66,89,94 sensitivity analysis, [95][96][97] and a various of random sampling methodologies. 66,97,98 In present study, design variables are shading angle and shading distance to window edge, window height, and width. 99 Objective functions are the amount of annual electricity generated by the solar shading and the annual electricity consumption of the building, which is intended to reduce.…”

Section: Design Of Variables and Objective Functionsmentioning

confidence: 99%

Multi‐objective optimization of annual electricity consumption and annual electricity production of a residential building using photovoltaic shadings

Baghoolizadeh

Nadooshan

Dehkordi

et al. 2022

Intl J of Energy Research

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Summary Shading is one of the cases that has effectivity on the household energy consumption. Shadings can be used either in horizontal or vertical form adjacent to the building’s window in order to control the sunlight. In this paper, a multi‐objective optimization is conducted to simultaneously reduce the annual electricity consumption and augment the electricity production with the application of photovoltaic shadings installed over the building window. To initiate the multi‐objective optimization, four design variables based on the positioning and geometry of photovoltaic shading are selected and test samples are simulated through EnergyPlus software. The model is three‐floor building located in Tehran, Iran. Next, the Morris sensitivity analysis (MSA) is implemented to calculate the extent to which each design variable affects the objective functions. Afterwards, the relation between the design variables and objective functions is extracted through the regression modeling using GMDH type‐ANN and the equations extracted are then utilized as inputs for genetic algorithm to conduct a Pareto‐based optimization. The results demonstrate that the maximum sunlight reception is attributed to the PV shading in southern direction of the building and its optimal tilt angle is 19.6 considering an annual period. There are slight changes to the tilt angle while seasonal period is considered for the calculations. The results also show that the use of solar shadings can reduce electricity consumption by approximately 4 to 11, 1 to 4, and 10 to 22% for an annual period, winter and summer season, respectively. At the end, the numerical data for the city of Tehran is compared to the cities of Canberra and Macapa.

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“…The results show that window visible transmittance, roof and wall solar absorptance, and phase change material thickness are the most sensitive parameters affecting energy consumption. Chen [20] and Gagnon [21] used two global sensitivity methods (regression and variance-based methods) and observed that the set-point temperature, the number of air changes, and the thermal parameters of roofs and windows were the most influential factors on the energy use of office buildings.…”

Section: Introductionmentioning

confidence: 99%

Multi-Stage Sensitivity Analysis of the Energy Demand for the Cooling of Grain Warehouses in Cold Regions of China

Zhang,

Ye,

et al. 2024

Agriculture

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The early design parameters exert a considerable influence on the cooling energy demand of a granary building in operation. In order to investigate the impact of various parameters on energy use, a grain warehouse energy model was constructed using the Ladybug + Honeybee tools on the Grasshopper platform. Three global energy sensitivity methods were used to analyze the model, and the sizes of the influential parameters were determined and ranked. The study uncovered that the cooling energy demand of the grain warehouse was primarily influenced by factors such as the cooling set-point temperature, roof solar absorptance, roof and exterior wall insulation thickness, window type, and orientation. On this basis, a local sensitivity analysis was conducted for the highly sensitive parameters to identify their influence trend and optimal design range. The results showed that the cooling energy demand of the grain warehouse increases faster as the cooling set-point temperature decreases, with the highest growth rate occurring at a temperature below 18 °C. Lower solar absorptance of the roof is conducive to reducing the cooling energy demand of the grain warehouse. When the thickness of the roof thermal insulation is less than 120 mm and the thickness of the external wall thermal insulation is less than 60 mm, energy use decreases more quickly with greater insulation thickness. It is advisable to use traditional or new windows with thermal insulation and shuttered windows. Furthermore, the optimal position of the long side of the granary was between 10° west and 10° east of north. This research could provide guidance for the energy-saving design and renovation of granary buildings in cold regions of China.

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An Integrated Sensitivity Analysis Method for Energy and Comfort Performance of an Office Building along the Chinese Coastline

Cited by 19 publications

References 57 publications

Operational Carbon Change in Commercial Buildings under the Carbon Neutral Goal: A LASSO–WOA Approach

Operational Carbon Change in Commercial Buildings under the Carbon Neutral Goal: A LASSO–WOA Approach

Multi‐objective optimization of annual electricity consumption and annual electricity production of a residential building using photovoltaic shadings

Multi-Stage Sensitivity Analysis of the Energy Demand for the Cooling of Grain Warehouses in Cold Regions of China

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