Sensitivity analysis and optimization of PCM integrated buildings in a tropical savanna climate

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

doi:10.1016/j.jobe.2022.105603

Cited by 7 publications

(3 citation statements)

References 52 publications

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“…Finally, Zhussupbekov et al, 2023 delves into machine learning techniques for forecasting energy demand in PCM-integrated residential buildings in the Mediterranean climate region. The study not only proposes a model for predicting energy consumption but also establishes the most influential design parameters, with SVM and ANN methods emerging as more reliable for such predictions [76]. Therefore, these studies collectively showcase the diverse approaches and innovations in leveraging advanced technologies, alternative materials, and predictive models to enhance energy efficiency in buildings.…”

Section: Simulation Outcomesmentioning

confidence: 99%

Phase Change Materials (PCM) As A Passive System in The Opaque Building Envelope: A Simulation-Based Analysis

Aghoei,

Astanbous,

Khaksar

et al. 2023

Preprint

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The energy crisis is currently a major topic in scientific and managerial discussions in the building and construction industry. While employing low-cost strategies for reducing energy usage is beneficial for principals and decision-makers, the primary purpose of this study is to recommend the use of phase change materials (PCM) to improve cooling and heating load management in buildings. In order to accomplish this, the study applies design-builder simulations to a developing country case study. The results indicate that integrating PCM in buildings can reduce energy usage by around 6 percent. Although this value may be lower than other techniques, it is nevertheless important for reaching green building goals in both developed and developing nations. The case study, which takes place in a four-season region, demonstrates the significant potential of the PCM system for both cooling and heating energy management. In addition, the reciprocal performance improvement of PCMs is a remarkable characteristic, especially when contrasted to some conventional systems that focus on a single energy management objective in buildings. Lastly, it should be underlined that the suggested platform is consistent with Sustainable Development Goal 7 (Affordable and Clean Energy Targets).

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Section: Simulation Outcomesmentioning

confidence: 99%

Phase Change Materials (PCM) As A Passive System in The Opaque Building Envelope: A Simulation-Based Analysis

Aghoei,

Astanbous,

Khaksar

et al. 2023

Preprint

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“…Taking the sunroom attached to a Tibetan Buddhist building in the Gannan region as an example, Zhang [18] found that the thickness of roof insulation and the roof had the most significant effect on thermal comfort and energy use through GSA and LSA. Saurbayeva [19] conducted the multi-stage sensitivity analysis to investigate the factors affecting the energy consumption of phase change material-integrated residential buildings in the savannah climate zone. 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.…”

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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“…In this field, multi-objective optimization (MOO) has emerged as a vital tool. MOO facilitates the simultaneous balancing of various performance criteria, responding adeptly to the dynamic environmental conditions [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Sustainable Architecture for Future Climates: Optimizing a Library Building through Multi-Objective Design

Miao,

Chen,

Chen

et al. 2024

Buildings

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In the context of the escalating challenge of climate change, optimizing buildings’ energy performance has become a critical research area, yet studies specifically targeting library buildings are scarce. This study addresses this gap by investigating the impact of multi-objective optimization on energy efficiency and occupant comfort in educational library buildings under future climate scenarios. Utilizing the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), this research optimizes a range of building parameters, including the cooling and heating setpoints, air change rates, shading device depths, window visible transmittance, and window gas types. The optimization aims to balance energy consumption and comfort, using simulations based on future weather data for the years 2020, 2050, and 2080. The results indicate that the optimized solutions can significantly reduce the heating energy by up to 95.34% and the cooling energy by up to 63.74% compared to the baseline models, while maintaining or improving the occupant comfort levels. This study highlights the necessity for dynamic, responsive architectural designs that can adapt to changing environmental conditions, ensuring both sustainability and occupant well-being. Furthermore, integrating these building-level optimizations into a City Information Model (CIM) framework can enhance urban planning and development, contributing to more resilient and energy-efficient cities. These findings underscore the importance of sustainable design practices in the context of climate change and the critical role of advanced optimization techniques in achieving energy-efficient, comfortable educational spaces.

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Sensitivity analysis and optimization of PCM integrated buildings in a tropical savanna climate

Cited by 7 publications

References 52 publications

Phase Change Materials (PCM) As A Passive System in The Opaque Building Envelope: A Simulation-Based Analysis

Phase Change Materials (PCM) As A Passive System in The Opaque Building Envelope: A Simulation-Based Analysis

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

Sustainable Architecture for Future Climates: Optimizing a Library Building through Multi-Objective Design

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