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Structural configuration of a PCM and aerogel integrated window glazing system The structural configuration of a PCM integrated aerogel glazing system includes silica aerogel, PCM, and glass layers. The exterior aerogel layer can provide super-insulation with a relatively low thermal conductivity at 0.018 W/(m K). The interior PCM is charged by the daytime solar radiation, and then the stored thermal energy is discharged to maintain the indoor thermal comfort during the nighttime. An aerogel-PCM glass window was designed for heating applications (Li et al., 2020b). Figure 2 is reprinted from, Applied Thermal Engineering. Li et al. (2020b). Thermal performance evaluation of glass window combining silica aerogels and phase-change materials for the cold climate of China. Copyright with permission from Elsevier
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Aerogel materials with super-insulating, visual-penetrable and sound-proof properties, are promising in buildings, whereas coupling effect of various parameters in complex porous aerogels proposes challenges for thermal/visual performance prediction. Traditional physics-based models face challenges, like modelling complexity, heavy computational lo...
Contexts in source publication
Context 1
... holistic The synergistic functions between aerogels and other materials have been exploited, through the novel structural designs. Figure 2 demonstrates the structural configuration of a PCM integrated aerogel glazing system. The underlying mechanism is that the exterior aerogel layer can provide super-insulation with a relatively low thermal conductivity at 0.018 W/(m K). ...
Context 2
... to the doubleglazing, aerogel windows show a lower daylight transmission coefficient, owing to the opacity of aerogel particles ( Garnier et al., 2015). Figure 6 shows the visual performance of granular and monolithic aerogel glazing systems (Zinzi et al., 2019). The light transmission of the monolithic silica aerogel glazing is The investigated variables and systems include granular and monolithic aerogel glazing systems in different orientations. ...
Citations
... Rapid developments in ML technology have provided enormous opportunities for building demand-side control and intelligent energy management strategies (Zhou, 2022a. It can intelligently control indoor environmental parameters, such as temperature, humidity, and air quality, by using sensor data and user feedback to improve indoor comfort and health while reducing unnecessary energy consumption Tien et al., 2022;Zhou, 2021). ML applied to building-integrated PV systems can be divided into supervised learning, unsupervised learning, and reinforcement learning. ...
Buildings are the most significant contributors to energy consumption and greenhouse gas emissions worldwide. Building integrated photovoltaics (BIPV) represents an effective measure toward reducing the primary energy consumption and carbon emissions of building operations. Using efficient and advanced demand-side controllers to accurately forecast demand for heating, cooling, and lighting loads and the electricity that can be generated by BIPV is essential to improve building energy management and energy flexibility in buildings. Because of the diversity and complexity of weather conditions and the unpredictability of residential electricity consumption, the accurate matching of building energy demands and the power production capacity of BIPV has become more uncontrollable and challenging. With the rapid development of artificial intelligence (AI) techniques, a number of researchers have used various machine learning methods to predict the feasibility of BIPV power, lighting consumption, cooling and heating load demands and electricity consumption, as well as to demonstrate the effectiveness of machine learning in energy consumption control and load demand prediction. This paper reviews the current development and application status of BIPV, intelligent algorithms applicable to BIPV, and intelligent algorithms for demand-side control of building energy. Then, this paper analyzes the challenges, existing issues, and rationalized research recommendations for the current applications of intelligent learning methods for demand-side controllers for BIPV-integrated buildings. In addition, this paper statistically analyzes representative case studies of intelligent learning methods for demand-side controllers for BIPV-integrated buildings and identifies best practices for their applications. This paper can contribute to theoretical recommendations for developing efficient and advanced controller models and accurate algorithmic models for short-term building energy prediction, and consequently further promoting energy-flexible buildings.
... Fig. 3. A PCM and aerogel integrated window glazing system's structural configuration (according to [27]). ...
Buildings protect people from external climatic influences. The envelope structures of the building mitigate the impact of changes in the external environment. However, in recent decades we have been facing climate change, which is affected by many factors and the construction industry is one of them. As a result of long-term climate change, we can expect a more frequent series of warm days. In many cases, existing constructions cannot adapt to sudden climate changes. In summer, buildings overheat due to excessive solar radiation. It causes internal discomfort and temperature stress in the indoor environment. Summer overheating of buildings is influenced by many factors, but the potential source of excessive heat is unwanted solar gains through transparent constructions. This paper presents an overview of the elimination options of solar gains through transparent surfaces of envelope structures. Furthermore, it also deals with assessing the efficiency of the shading systems concerning thermal comfort and sufficient daylight in the workspace.
... The study provides an in-depth analysis of the aerogel material, its production, and construction applications. The results show the potential of this material as a promising candidate for the energy efficiency of buildings [29]. Fig. 3. Structural configuration of a PCM and aerogel-integrated window glazing system (according to [29]). ...
... The results show the potential of this material as a promising candidate for the energy efficiency of buildings [29]. Fig. 3. Structural configuration of a PCM and aerogel-integrated window glazing system (according to [29]). ...
Under the term demanding climatic conditions in connection with the topic, we can primarily imagine the excessive overheating of buildings in summer and on the other hand the exposure of the building to low temperatures in winter. Considering the extreme climatic conditions, the design of the building requires an individual approach. First of all, it is necessary to pay increased attention to the envelope structures such as roof and perimeter walls. The building can also be affected by other extreme conditions such as solar radiation and associated excessive lighting, i.e. glare in the work environment. This topic is increasingly coming to the fore due to climate change. Summer seasons are getting warmer and this problem is getting even worse. Even in locations where this issue has never been dealt with before. The article summarizes the conducted research related to the issue of overheating and excessive lighting of buildings. It also informs about how researchers in Slovakia and different locations around the world deal with this problem.
... The AI-based models are considered as the data-driven-based modeling approach. Unlike process-based models, AI-based models use data to obtain the relationships between variables of the system without including any form of physical processes within the system (Zhou, 2021(Zhou, , 2022a(Zhou, , 2022b. In addition, the AI-based models have a relatively higher computational efficiency and higher prediction accuracy that reduced the uncertainties presented in the processed-based models (Mentch & Hooker, 2016;Tiwari & Adamowski, 2015;Wani et al., 2017). ...
Energy, water, and the environment have remarkably complex interrelationships, with these linkages being both direct and indirect. The innovative development of artificial intelligence (AI) technologies brings significant opportunities for investigating renewable energy (RE), water, and the environment and presents a multitude of challenges. Using AI to promote sustainable production and consumption of RE and water and protection of the environment has become a hot research area that enables AI to empower sustainable human development and promote its own sustainable development. However, there are few summaries and analyses of studies on the application of AI to RE, water, and environment (REWE) nexus. The objective of this chapter is to discuss the application of AI to the REWE nexus. First, this chapter presents and analyzes the AI techniques that are applicable to RE, water, and the environment fields, respectively, and categorizes and integrates them. Also, the chapter summarizes AI application in the REWE nexus. Furthermore, the chapter analyzes the application feasibility of AI for establishing city-level REWE nexus studies and identifies challenges and barriers to their implementation. Finally, the chapter presents the future perspectives for the application of AI to urban-level REWE nexus.
... Recently, there has been increasing interests in advanced energy management strategies using ML methods, such as supervised learning (SL), reinforcement learning (RL), unsupervised learning (UNSL), and semi-supervised learning [22,23]. ML applications mainly include operations, optimization, control, scheduling, and management [24][25][26]. Zhou [27] comprehensively reviewed the applications of AI in carbon neutral and community energy management from the view of energy supply and storage, regional demand, and energy management. ...
Advanced controls have attracted increasing interests due to the high requirement on smart and energy-efficient (SEE) buildings and decarbonization in the building industry with optimal tradeoff strategies between energy consumption and thermal comfort of built environment. However, a state-of-the-art review is lacking on advanced controls for SEE buildings, especially considering advanced building energy systems, machine learning based advanced controls, and advanced occupant-centric controls (OCC). This study presents a comprehensive review on the latest advancement of advanced controls for SEE buildings, which covers recent research on data collection through smart metering and sensors, big data and building automation, energy digitization, and building energy simulation. Machine learning based advanced controls are comprehensively reviewed, including supervised, unsupervised and reinforcement learning, together with their roles and underlying mechanisms. In addition, advanced controls for energy security, reliability, robustness, flexibility, and resilience are further reviewed for energy-efficient and low-carbon buildings, with respect to fault detection and diagnosis, fire alarming and building energy safety, and climate change adaptation. Moreover, this study explores the advanced OCC systems and their applications in SEE buildings. Last but not the least, this study emphasizes the challenges and future prospects of the trade-off between complexity and predictive/control performance, AI-based controllers and climate change adaptation, OCC in thermal comfort and energy saving for the SEE buildings. This study offers valuable insights into the latest research progress concerning the underlying mechanisms, algorithms and applications of advanced controls for SEE buildings, paving the path for sustainable and low-carbon transition in building sectors.
... In this process, the features of the processed data allow the artificial intelligence to learn automatically. Artificial intelligence is a general concept and has approaches such as machine learning (ML) (Zhou, 2021a), artificial neural networks (ANN) (Zhou, 2021b), deep learning (DL) (LeCun, Bengio, & Hinton, 2015), natural language processing (NLP) (Chowdhary, 2020) and cognitive computing (Modha, et al., 2011). The most important issue to be considered in AI studies is to formulate the problem. ...
The authors propose an evidence-based virtual AI application geared to using a successful implementation of AI for language learners and language teaching professionals and a virtual speaking tool that will serve as a foundation for further research and experimentation. The study promotes interdisciplinary collaboration among corpus linguists, language educators, and computer engineers for fostering the skills and the competences and the exchange of ideas and a shared vision for the future of AI-assisted language learning. The main objective of this action plan is to propose an application in which language teaching professionals and language learners will engage in English-speaking practice through a multidimensional (MD) environment incorporating cubic images and bilateral sound perception (hearing and speaking). The AI application will assign tasks to multidimensional characters using realistic sound effects to create a highly immersive and interactive environment for language teaching professionals.
... Recently, there has been increasing interests in advanced energy management strategies using 78 ML methods, such as supervised learning (SL), reinforcement learning (RL), unsupervised learning 79 (UNSL), and semi-supervised learning [19, 20]. ML applications mainly include operations, 80 optimization, control, scheduling, and management [21][22][23]. Zhou [24] comprehensively reviewed 81 the applications of AI in carbon neutral and community energy management from the view of energy 82 supply and storage, regional demand, and energy management. ...
... This OCC framework 752 focuses on understanding stochastic occupants' behavior to effectively optimize the said objectives.In this study, a stochastic hot water usage model, powered by offline training, is introduced to mimic 754 the hot water use behavior of tenants. Based on a case study of a residential home in Switzerland, 755 this study demonstrated that this RL-based occupant-centric control framework successfully met 756 both occupants' hot water demand and maintain comfort, while achieving a remarkable23.8% 757 reduction in energy consumption, by a deeper understanding of occupants' behavior and its 758 integration into the control framework. More studies on RL for OCC could be found in Ref. [147, 759 6.1. ...
Advanced controls have attracted increasing interests due to the high requirement on 24 smart and energy-efficient (SEE) buildings and decarbonization in the building industry with 25 optimal tradeoff strategies between energy consumption and thermal comfort of built environment. 26 However, a state-of-the-art review is lacking on advanced controls for SEE buildings, especially 27 considering advanced building energy systems, machine learning based advanced controls, 28 advanced occupant-centric control (OCC). This paper presents a comprehensive review on the latest 29 advancement of advanced controls for SEE buildings, which covers recent research on data 30 collection through smart metering and sensors, big data and building automation, energy digitization, 31 and building energy simulation. Machine learning algorithms based advanced controls are 32 comprehensively reviewed, including supervised, unsupervised and reinforcement learning, 33 together with their roles and underlying mechanisms. Advanced controls for energy security, 34 reliability, robustness, flexibility, and resilience are reviewed for energy-efficient and low-carbon 35 buildings, with respect to fault detection and diagnosis, fire alarming and building energy safety, 36 and climate change adaptation. Moreover, this study explores the advanced OCC systems and their 37 applications in SEE buildings. Last but not the least, this paper emphasizes the challenges and future 38 prospects of the trade-off between complexity and predictive/control performance, AI-based 39 controllers and climate change adaptation, OCC in thermal comfort and energy saving for the SEE 40 buildings. This review provides valuable insights into the latest research progress on underlying 41 Z Liu, X Zhang, Y Sun*, Y Zhou*. Advanced controls on energy reliability, flexibility, resilience, and occupant-centric control for smart and energy-efficient buildings-A state-of-the-art review. Energy and Buildings 2023, 113436. DOI: https://doi.org/10.1016/j.enbuild.2023.113436 2 mechanisms, algorithms and applications of advanced controls for SEE buildings, paving the path 42 for sustainable and low-carbon transition in building sectors. 43 44
... The third stage is the explosive period, where the average annual publication volume reaches 118.8 articles in the period from 2018-2022. With the increasing requirements of low carbon and environmental protection in the construction industry and the in-depth research on aerogel applications, aerogels in buildings have now also become a research hotspot in the field, and many scholars have started to conduct comprehensive research on the technology, process, evaluation, and influencing factors of aerogels in construction [26][27][28][29][30]. By the third stage, the research of aerogels in the field of construction developed like never before and had a certain system and structure. ...
... Moreover, the amount of literature in this field was steadily increasing, and scholars began deepening and expanding the applications of aerogels in different building fields during this period. During 2018-2023, with the advent of the nano-age, various new materials have been continuously applied in the construction industry, and research on aerogels in construction has emerged with phase change material, carbon aerogel, vacuum insulation panel, graphene oxide, energy efficiency, cellulose nanofibril, composite material, cellulose, and other research topics in the field of construction [29,30,[57][58][59][60][61]. ...
With the deepening of aerogel research and the popularization of its application, the demands for energy saving in the construction field has brought aerogels into the limelight. To explore state-of-the-art research and development trends related to aerogels applied in construction, CiteSpace was used to conduct a quantitative analysis based on the Web of Science core database. Results show that: (1) in the past 10 years, the number of papers on aerogels in the field of constructions has increased significantly; (2) the top producing countries in the aerogel field are mainly China and the United States, and the top two research institutions are all Chinese institutions (Univ Sci & Technol China and Chinese Acad Sci); (3) the main publishing journals are ENERGY AND BUILDINGS, CONSTRUCTION AND BUILDING MATERIALS, and CHEMICAL ENGINEERING JOURNAL; (4) the hot keywords are thermal insulation, silica aerogel, thermal conductivity, phase change material, mechanical property, graphene aerogel, self-assembly, energy saving, etc.; (5) aerogel is mostly used in building insulation, mainly in the form of aerogel glass, aerogel mortar, aerogel felt, and aerogel coating. In summary, in addition to systematically strengthening theoretical research, it is necessary to optimize the technical process and reduce costs in order to effectively promote aerogels in construction energy conservation and carbon reduction. Through this study, the current situation, hot spots, and development trend of aerogel application in construction can be revealed systematically. Overall, this study helps advance research on aerogels applied in buildings and help in tackling energy efficiency challenges.
... Indeed, this phenomenon has been observed in response to the implementation of S-L PCM glazing systems. Instead of optimizing the melting temperature of PCM, improving the insulation performance of S-L PCM glazing systems is also proven as a promising way to promote the phase transition process of PCM [24]. In this regard, adopting silica aerogel panes on the exterior of the PCM glazing systems as insulation has been considered as an effective solution [25]. ...
Responding to the appeal of realizing carbon neutrality in response to the field of buildings, minimizing the energy demand and improving the energy management of the buildings by the development of energy saving technologies related to transparent components are considered as promising means. In this paper, an innovative glazing window integrated with solid-solid phase change material and silica aerogel is proposed, and a parametric study is conducted with a focus on evaluating the implementation potential of our innovative window in the severe cold region of China. Firstly, to address the issue that the transparent media such as phase change material and silica aerogel cannot be directly incorporated into glazing elements of EnergyPlus software, an equivalent model of the innovative window compatible with the modeling capabilities of EnergyPlus is developed. Then, the contributions of the thermal and optical properties of phase change material to energy savings are quantitively distinguished via sensitivity analysis. Additionally, the energy performance of the building containing the innovative window under different thermal and optical properties of phase change material is numerically investigated aiming to provide guidance to the design strategies of phase change material parameters adopted in the innovative window. Finally, in light of counterbalancing the need for energy saving and daylighting performance indoors, the optimal silica aerogel thickness employed in the innovative window is studied. The results show that the phase change material properties of melting temperature, latent heat, absorption coefficient, and refractive index are of remarkable relevance to the energy performance of the buildings in conditions of 10 % property variations. In comparison to the 4 mm single glazing window installed, the maximum energy saving of the building containing the innovative window can be realized by 18.22 % within the realistic range of phase change material properties. Moreover, for the sake of providing the maximum possibility of energy saving under the premise of meeting the daylighting design standards in China, the thickness of silica aerogel is recommended to be selected as 10 mm in the innovative window.
... The use of conventional energy systems (e.g., coal, oil and natural gas) has caused a dramatic rise in CO 2 emissions and resulted in global warming [1][2][3]. The global energy and environmental issues necessitate the deployment of large-scale renewable energy. ...
The vigorous expansion of renewable energy as a substitute for fossil energy is the predominant route of action to achieve worldwide carbon neutrality. However, cleaner energy supplies in multi-energy building districts are still at the preliminary stages for energy paradigm transitions. In particular, technologies and methodologies for large-scale renewable energy integrations are still not sufficiently sophisticated, in terms of intelligent control management. Artificial intelligent (AI) techniques powered renewable energy systems can learn from bio-inspired lessons and provide power systems with intelligence. However, there are few in-depth dissections and deliberations on the roles of AI techniques for large-scale integrations of renewable energy and decarbonisation in multi-energy systems. This study summarizes the commonly used AI-related approaches and discusses their functional advantages when being applied in various renewable energy sectors, as well as their functional contribution to optimizing the operational control modalities of renewable energy and improving the overall operational effectiveness. This study also presents practical applications of various AI techniques in large-scale renewable energy integration systems, and analyzes their effectiveness through theoretical explanations and diverse case studies. In addition, this study introduces limitations and challenges associated with the large-scale renewable energy integrations for carbon neutrality transition using relevant AI techniques, and proposes further promising research perspectives and recommendations. This comprehensive review ignites advanced AI techniques for large-scale renewable integrations and provides valuable informational instructions and guidelines to different stakeholders (e.g., engineers, designers and scientists) for carbon neutrality transition.
