Integrated multi-stage sensitivity analysis and multi-objective optimization approach for PCM integrated residential buildings in different climate zones

Saurbayeva, Assemgul; Memon, Shazim Ali; Kim, Jong Ryeol

doi:10.1016/j.energy.2023.127973

Cited by 9 publications

(3 citation statements)

References 43 publications

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“…Diverging from previous studies [13,15], this not only aims to reduce energy consumption but also enhance indoor thermal comfort. Among the factors influencing the number of discomfort hours, Tc has the most significant impact on all four orientations, while Th and WWR exhibit some influence, but not significantly.…”

Section: Discussionmentioning

confidence: 99%

“…Ongoing research focuses on the sensitivity of different building variables. Saurbayeva et al [15] devised a multi-stage sensitivity analysis and multi-objective optimization method for PCM-integrated residential buildings across four different climate zones. By considering energy consumption and economic indicators, their multi-stage sensitivity analysis determined the relative importance of parameters in the early design stage.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity Analysis and Optimization of Energy-Saving Measures for Office Building in Hot Summer and Cold Winter Regions

Xuanyuan,

Zhang,

Yao

et al. 2024

Energies

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To effectively reduce building energy consumption and enhance indoor thermal comfort, this study developed a sensitivity analysis and multi-objective optimization method for an office building in Hangzhou considering the effects of parameter interactions. We utilized Latin hypercube sampling (LHS) to sample the parameters and subsequently simulated the energy consumption and discomfort hours for the building’s four orientations. Following this, Gaussian process regression models were established, and the feature importance ranking method (FIRM) alongside an interaction effects method were employed to quantify the sensitivity of 10 parameters. Lastly, a multi-objective optimization was performed for total energy consumption and discomfort hours, yielding a set of Pareto-optimal solutions. Through the VIKOR method, the optimal solution within the Pareto set was identified, resulting in three sets of optimal solutions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis and Optimization of Energy-Saving Measures for Office Building in Hot Summer and Cold Winter Regions

Xuanyuan,

Zhang,

Yao

et al. 2024

Energies

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show abstract

“…The relative correlation of the input parameters can be readily assessed by comparing the PRCC. In this study, PRCC was calculated for each input and output parameter, and the method is illustrated for a single outcome parameter [28,29]. Initially, the outcome vector was treated as an extra column in the K + 1 columns in addition to the input values.…”

Section: Standardized Regression Coefficient (Src) and Partial Rank R...mentioning

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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A novel framework for developing a machine learning-based forecasting model using multi-stage sensitivity analysis to predict the energy consumption of PCM-integrated building

Nazir,

Memon,

Saurbayeva

2024

Applied Energy

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Integrated multi-stage sensitivity analysis and multi-objective optimization approach for PCM integrated residential buildings in different climate zones

Cited by 9 publications

References 43 publications

Sensitivity Analysis and Optimization of Energy-Saving Measures for Office Building in Hot Summer and Cold Winter Regions

Sensitivity Analysis and Optimization of Energy-Saving Measures for Office Building in Hot Summer and Cold Winter Regions

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

A novel framework for developing a machine learning-based forecasting model using multi-stage sensitivity analysis to predict the energy consumption of PCM-integrated building

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