Dynamic performance and energy efficiency of reflective and insulative composite coating on building exterior wall

Zhang, Guangpeng; Wu, Huijun; Liu, Jia; Yang, Jianming; Huang, Huakun; Ding, Yujie J.; Xie, Lihua

doi:10.1007/s12273-022-0932-x

Cited by 18 publications

(5 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The aforementioned theoretical approach incorporates predictive models for the effective thermal conductivity of aerogel-insulated materials, genetic algorithms for thermal resistance-capacitance parameter identification, and a resistance-capacitance network model for the thermal performance analysis of aerogel-insulated walls. The thermal conductivity models for various aerogel-insulated materials had been validated against experimental measurements in our previous studies; refer to [50] for AP, reference [13] for AFC, and reference [37] for AC.…”

Section: Validation Of the Theoretical Methodsmentioning

confidence: 99%

A Comparative Assessment of Different Aerogel-Insulated Building Walls for Enhanced Thermal Insulation Performance

Yang,

Wu,

Liang

et al. 2023

Gels

View full text Add to dashboard Cite

Aerogel is widely recognized as a superinsulating material with great potential for enhancing the thermal insulation performance of building walls. It can be applied in various forms such as aerogel plasters (AP), aerogel fibrous composites (AFC), and aerogel concrete (AC) in practical engineering applications. This study aims to investigate the most efficient application form for maximizing building insulation performance while minimizing the amount of aerogel used. To predict the thermal insulation performance of aerogel-insulated walls, a resistance–capacitance network model integrating the aerogels’ effective thermal conductivity model was developed and was validated by comparing it with Fluent simulation software results in terms of surface temperature. Using the validated models, the thermophysical parameters, transient thermal properties, and transmission load were predicted and compared among AP, AFC, and AC walls. The results indicate that using AFC can result in approximately 50% cost savings to achieve the same thermal resistance. After adding a 20 mm thickness of aerogel to the reference wall without aerogel, the AFC wall exhibited the highest improvement in thermal insulation performance, reaching 46.0–53.5%, followed by the AP wall, and then the AC wall, aligning with considerations of microstructural perspectives, thermal resistance distributions, and thermal non-uniformity factors. Therefore, giving priority to AFC use could reduce the required amount of silica aerogel and enhance economic efficiency. These results provide valuable insights for theoretical models and the application of aerogel-insulated walls in building engineering insulation.

show abstract

Section: Validation Of the Theoretical Methodsmentioning

confidence: 99%

A Comparative Assessment of Different Aerogel-Insulated Building Walls for Enhanced Thermal Insulation Performance

Yang,

Wu,

Liang

et al. 2023

Gels

View full text Add to dashboard Cite

show abstract

“…The ever-growing facilities unavoidably increase energy consumption and emission of greenhouse gases, which leads to energy conservation a crucial issue in today's sustainable energy development. [1][2][3] Over the past centuries, fossil energy sources, such as coal and oil, have been widely used for power generation and research purposes due to their high energy content. However, the use of these traditional energy sources leads to the emission of greenhouse gases, including carbon dioxide, which contributes to global warming.…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, the number of facilities will thereby have explosive growth in the smart world. The ever‐growing facilities unavoidably increase energy consumption and emission of greenhouse gases, which leads to energy conservation a crucial issue in today's sustainable energy development 1‐3 . Over the past centuries, fossil energy sources, such as coal and oil, have been widely used for power generation and research purposes due to their high energy content.…”

Section: Introductionmentioning

confidence: 99%

SREM: Smart renewable energy management scheme with distributed learning and EV network

Huang,

Xue,

Zhao

et al. 2023

Engineering Reports

View full text Add to dashboard Cite

In this article, aiming to develop the Green Internet of Vehicles (G‐IoV), we propose a smart energy management system that leverages the intelligence edge clients and the distributed electric vehicles (EVs). The system proposed in this article incorporates the benefits of both software, specifically in terms of the user interface, and hardware, specifically in terms of edge clients. In particular, this system integrates intelligence edge clients with an EV CAN bus network as an electronic control unit. By leveraging the intelligent edge clients recommendation system, EVs can make informed decisions on battery charging or discharging actions. As a result, a virtual‐power‐plant (VPP) can treat the EVs network as a vast intelligent energy storage facility, efficiently managing the battery energy of all distributed EVs connected to the platform and fully utilizing the electricity generated from renewable energy sources. We experimentally verify that using federal learning to train models in EV networks versus training models directly in EVs, using federal learning in EV networks yields better experimental results.

show abstract

“…Carbon emissions are an essential driver of climate change. Among the three largest carbon emitters, i.e., transportation, industry, and construction, construction is one of the main areas for carbon emissions [1,2] .Thus, reducing carbon emissions from building sectors will significantly improve the current situation.…”

Section: Introductionmentioning

confidence: 99%

Accurate Building Energy Consumption Prediction with Convolution Recurrent Deep Neural Networks

Dingrong¹,

Huang²,

Guo³

et al. 2023

Preprint

View full text Add to dashboard Cite

Accurate energy consumption prediction is a prerequisite for effectively dispatching distributed power sources. For a building, due to the frequent fluctuations derived from many dynamic factors, the precise energy consumption prediction is still facing challenges. Existing methods usually only use common recurrent neural networks to predict building energy consumption, consider common recurrent neural networks model does not have the ability to extract spatial features and they have a long-term memory problem, so they have limitations to deal with long term task. To overcome these challenges, in this paper, we propose a hybrid model to predict the cooling consumption of a building.Our hybrid model has the merits of convolutional neural network and gated recurrent unit in capturing spatial-temporal features. Experiment results show that our hybrid model has the best performance, compared with other methods. The result will benefits managers to make reasonable scheduling of power and equipments.

show abstract

Dynamic performance and energy efficiency of reflective and insulative composite coating on building exterior wall

Cited by 18 publications

References 46 publications

A Comparative Assessment of Different Aerogel-Insulated Building Walls for Enhanced Thermal Insulation Performance

A Comparative Assessment of Different Aerogel-Insulated Building Walls for Enhanced Thermal Insulation Performance

SREM: Smart renewable energy management scheme with distributed learning and EV network

Accurate Building Energy Consumption Prediction with Convolution Recurrent Deep Neural Networks

Contact Info

Product

Resources

About