Impact of adjustment strategies on building design process in different climates oriented by multiple performance

Wang, Ran; Lu, Shilei; Feng, Wei

doi:10.1016/j.apenergy.2020.114822

Cited by 32 publications

(17 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, VME is different from the education of other music disciplines, which is a very abstract education and not intuitive except for the external singing performance. Therefore, people deem VME the most challenging musical course in higher institutions [ 1 , 2 ]. Particularly, VME in Chinese higher institutions has gone through four periods: germination period, development period, slow period, and climax period.…”

Section: Introductionmentioning

confidence: 99%

Application of Lightweight Deep Learning Model in Vocal Music Education in Higher Institutions

Zhang

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The aim is to improve the teaching quality of music majors and cultivate their innovative ability. This article takes Vocal Music Education (VME) method as the research object to explore the teaching reform of Music Major courses. Firstly, this article makes an in-depth study on Big Data Analytics (BDA) and Digital Twins (DTs) technology and constructs a DTs platform connecting real teaching space and virtual teaching space. Secondly, the DTs platform is divided into online learning feature analysis and virtual-real teaching space integration functional modules. This article explores the online immersive education process design and technology application of the DTs platform from the two aspects of teaching and technology. Afterward, it designs a student action and expression recognition network based on the Visual Geometry Group (VGG) Net model and Google Net model in teaching data collection and management. Finally, the proposed system is tested. The test results show that the active and passive interaction curves of the traditional VME system have no obvious fluctuation, indicating that the interaction of the traditional VME system is not strong, and the ability of active feedback information is poor. By contrast, the active and passive interaction curves in the proposed VME have large fluctuations, showing that the proposed VME has more frequent interaction, and the teaching information can get real-time and active feedback. Therefore, the proposed VME system can better stimulate students’ learning desire. Meanwhile, the constructed Neural Network (NN) has the highest recognition accuracy of 99.07% on the student action and expression dataset. When tested with the image data taken by the research experiment, the highest accuracy is 89%, with an average of more than 85%. The proposed VME system provides ideas for applying DTs technology in the college of music education.

show abstract

Section: Introductionmentioning

confidence: 99%

Application of Lightweight Deep Learning Model in Vocal Music Education in Higher Institutions

Zhang

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Concerning the first direction of lighting optimizations, it can be classified into three major groups (i.e., a, b, and c). Research efforts in group (a) [29,[99][100][101][102][103] addressed modification strategies of design parameters and the ability of optimization techniques to deal with that issue. In this regard, Kim and Chung [99] presented a daylighting simulation model combined with the architectural design process to obtain high-precision simulations leading to high-performance architectural designs.…”

Section: Lighting Optimization Modelsmentioning

confidence: 99%

“…Furthermore, Acosta et al [102] conducted multiple simulations on room designs using three different shapes of skylights under overcast-sky conditions by the Lightscape simulation program to identify the most appropriate shape of lightscoop skylights. Wang et al [103] adopted a gradient boosted decision trees-based non-dominated sorting genetic algorithm integrated with EnergyPlus in the Python environment to address the influence of window ventilation and shading on multi-objective building design optimization process under different climate conditions. They concluded that with multi-objective optimization strategies, it is possible to achieve 38.1% of energy savings in severely cold weather and 17.6% of improved comfort in both summer and winter conditions.…”

Section: Lighting Optimization Modelsmentioning

confidence: 99%

Performance Optimization Studies on Heating, Cooling and Lighting Energy Systems of Buildings during the Design Stage: A Review

et al. 2021

View full text Add to dashboard Cite

Optimizing the building performance at the early design stage is justified as a promising approach to achieve many sustainable design goals in buildings; in particular, it opens a new era of attractive energy-efficient design for designers and architects to create new building constructions with high-energy efficiency and better overall performance. Accordingly, this study aims to provide a comprehensive review of performance optimization studies on heating, cooling, and lighting energy systems of buildings during the design stages, conducting a systematical review covering various aspects ranging from the building type, optimization inputs, the approach used, and the main conclusion. Furthermore, the benefits and limitations of early optimizations in the energy-efficient design performance of buildings and future research directions are identified and discussed. The review results show that previous research efforts of optimizing energy-efficient design performance in buildings have addressed a wide variety of early stage design optimization issues, including orientation and multi-objective building function-related conflicts, such as cooling and lighting. However, significant research issues related to investigations of design envelope materials, proper energy-efficient design form, and other passive parameters, such as solar photovoltaic systems, are still lacking. Therefore, future research should be directed towards improving existing optimization approach frameworks in the context of appropriate energy-efficient design features; integrating sensitivity and uncertainty analyses in the performance optimization framework of buildings to provide a more balanced assessment of influential design envelope properties and extending optimal design envelope investigations of buildings to include other passive parameters and lifecycle assessment under long-term weather conditions.

show abstract

“…Analyzing the coupling relationship between people and architectural space needs to consider people's life behavior and life scene. Residential behavior affects the interior design of buildings and the energy consumption of structures [ 1 , 2 ]. Parametric design is a new development stage of digital design, which aims to adapt the prototype to the design goal through the collection operation of the prototype.…”

Section: Introductionmentioning

confidence: 99%

Applications of Nondominated Sorting Genetic Algorithm II Combined with WKNN Online Matching Algorithm in Building Indoor Optimization Design

Wang²,

Xiao

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The present work aims to improve the comfort of architectural interior design and reduce indoor energy consumption. The Weight K-Nearest Neighborhood (WKNN) algorithm and Nondominated Sorting Genetic algorithm are proposed to locate and analyze the spatial location of indoor personnel and optimize the indoor energy consumption in combination with residential behavior. Firstly, the indoor human behavior data and energy-saving problems are analyzed based on residential behavior theory and architectural physics. The indoor positioning algorithm is proposed to identify the personnel activities to realize the optimization of indoor energy distribution. Secondly, mean filtering and cluster analysis are adopted to optimize sampling points’ data and fingerprint database to eliminate data noise. Besides, the WKNN algorithm is used for Wireless Fidelity (Wi-Fi) indoor location fingerprint location. Then, aiming at the multiobjective optimization problem of building indoor energy consumption, the Nondominated Sorting Genetic algorithm obtains the optimal solution of the model. Combined with the indoor location information of personnel, the indoor heating and cooling system is monitored and distributed to reduce the energy consumption in the building while ensuring the living comfort of personnel. The test and simulation results demonstrate that the mean filtering algorithm can solve the room’s fluctuation problem of Wi-Fi signals. The cluster analysis algorithm can eliminate the data noise of the fingerprint database and improve the positioning accuracy of the positioning algorithm. Moreover, the location result is independent of the number of nodes; the number of sampling points does not affect the location result. The positioning accuracy of the WKNN algorithm is 2 m, and the positioning error rate within 2 m is about 60%. Compared with other algorithms, the WKNN algorithm has better positioning accuracy and positioning stability. Therefore, the location algorithm designed here can be applied to indoor location optimization. This study provides a reference for optimizing buildings’ indoor positioning and energy consumption.

show abstract

Impact of adjustment strategies on building design process in different climates oriented by multiple performance

Cited by 32 publications

References 65 publications

Application of Lightweight Deep Learning Model in Vocal Music Education in Higher Institutions

Application of Lightweight Deep Learning Model in Vocal Music Education in Higher Institutions

Performance Optimization Studies on Heating, Cooling and Lighting Energy Systems of Buildings during the Design Stage: A Review

Applications of Nondominated Sorting Genetic Algorithm II Combined with WKNN Online Matching Algorithm in Building Indoor Optimization Design

Contact Info

Product

Resources

About