Renewable Energy In Buildings Research Articles

Towards Sustainable Cities: Challenges and Way Forward

A city is a sizable and enduring human settlement distinguished by its intricate network of structures, infrastructure, and social, economic, and cultural institutions. Cities play a vital role in a country's global economic growth and development, fostering agglomeration of economies, serving as centres of innovation and technological advancement, acting as major hubs for finances and employment, and facilitating infrastructure development while providing access to broader markets. Often specializing in specific industries or sectors, cities offer amenities such as healthcare, education, employment, and recreational opportunities that enhance their appeal to residents seeking a high quality of life. However, the majority of unplanned cities grapple with the challenge of balancing economic, social, and environmental perspectives. Key issues include rapid population growth, urban sprawl, inadequate infrastructure, limited resources, conflicting interests among stakeholders-particularly regarding development versus conservation and pressing environmental concerns like pollution, improper waste management, health issues, and climate change. Addressing these challenges requires cities to evolve into 'Living Cities,' where human settlements can flourish indefinitely as part of nature's beauty and diversity. Achieving this balance involves integrating economic, social, and environmental perspectives. This paper presents detailed strategies under various subheadings, including Integrated Urban Planning with a focus on promoting public transport, increasing green spaces, and conserving biodiversity. It also covers Water Conservation, measures to enhance resilience to climate change impacts, such as strategic infrastructure siting and nature-based solutions, and the promotion of renewable energy and green building concepts to conserve finite resources. Efficient waste management is highlighted to ensure a waste-free urban environment. The paper underscores the importance of stakeholder engagement, involving the public, private sector, NGOs, and communities in the decision-making process through participatory planning and citizen engagement. While acknowledging existing supportive policies and strategies in the country, the paper emphasizes the need for their enforcement and proper implementation to achieve the desired target of sustainable cities-living cities where human populations thrive harmoniously as part of nature's beauty and diversity.
 
 Keywords: Living city, Sustainable city, Urban, Settlement, Integrated planning

Comprehensive Design and Transient Analysis of Novel Off‐Grid Zero Energy and Nearly Zero Emission Building with Hydrogen‐Integrated Storage System

The present study investigates the provision of heating, cooling, electricity, and domestic hot water consumption for a 100 m2 off‐grid zero‐energy residential building housing four occupants. Notably, the building is equipped with a hydrogen stove for cooking, and its electricity consumption is sustained throughout the year using renewable energies, independently of the electrical grid, with hydrogen energy storage. Using a Trombe wall for heating, surplus power is converted and stored in a dedicated hydrogen tank. TRNSYS software models analyze the system, demonstrating thermal comfort for 98.3% of the year. With 83 m2 of photovoltaic panels, the house operates off‐grid, slashing CO2 emissions by 8154.7 kg annually by reducing boiler energy consumption through waste heat utilization. The hydrogen tank minimizes the required battery size while the Trombe wall significantly contributes to heating, especially in colder climates. Remarkably, the building's renewable energy usage curtails emissions up to 8.5 times more than a Toyota Prius, showcasing its environmental impact in reducing greenhouse gas emissions.

The Role of Financial Services in the Transition to a Sustainable Economy

The financial services industry plays a critical role in the transition to a sustainable economy. Financial institutions can allocate resources to more environmentally friendly endeavors, manage long-term sustainability risks, and provide financing for sustainable projects.
 One way that financial institutions can support the transition to a sustainable economy is by allocating capital to more environmentally friendly investments. This could include investing in renewable energy, sustainable agriculture, and green buildings. Financial institutions can also help to manage long-term sustainability risks by developing and implementing climate risk management frameworks. This will help to protect investors and financial markets from the potential financial impacts of climate change.
 Finally, financial institutions can provide financing for sustainable projects through a variety of mechanisms, such as green bonds and sustainable loans. This financing can help to accelerate the deployment of clean technologies and other sustainable solutions.

Thermal storage process of phase change materials under high humidity and laminar natural convection condition: Prediction model and sensitivity analysis

The application of phase change materials (PCMs) in renewable energy storage and building temperature regulation is considered an effective method to reduce the use of fossil fuels and carbon emissions. However, issues concerning the utilization of PCMs in high-humidity environments remain to be addressed to fill the gap in real-world applications. This paper establishes a simplified novel two-zone model, comprehensively considering the natural convection on the surface of the PCP in high-humidity environments, as well as the formation, movement, and evaporation of water films on the surface. Subsequently, numerical solutions and experimental validation are conducted, and the influence of high-humidity environments on the heat and moisture transfer is elucidated. The results indicate that a high-humidity environment can significantly suppress the sensible heat transfer with a ratio range of latent heat to sensible heat typically around 1–4. Furthermore, the increase in the height of PCP leads to an increase in the proportion of sensible heat transfer. On the contrary, increasing the thermal conductivity of the PCM can amplify the latent heat transfer. The results can help further study the thermal storage and the dehumidification potential of phase change units in application on a large scale under high humidity conditions.

A data-driven equivalent circuit model’s parameter estimation method applied to Lithium-Titanate battery

Lithium battery systems are becoming essential for a variety of applications, such as maximisation of self-produced renewable energy in buildings, grid balances, and electric vehicles. Accurate and equivalent models are required to study and analyse the behaviour of batteries, both static and dynamic. This paper presents a novel data-driven parameter estimation procedure for high-current battery modelling, which has been applied to experimental measurement data conducted on Lithium Titanate batteries. The proposed estimation methodology has been applied to three different equivalent circuit models characterised by increasing modelling complexity. Genetic algorithm optimisation techniques solve the optimisation problem, estimating the model’s parameters and a look-up table of battery parameters estimated in discretised State of Charge. Enhance the battery operation modelling at high depth of discharge, the model is applied to the battery operated at high currents, covering its entire domain of state of charge. The fully depleted and charged state is given increased focus to maintain good modelling performance. Unlike previous works, the proposed methodology repeats the parameters estimation procedure for more than thirty-five state of charge ranges using non-equal width ranges to increase model density in critical regions. Based on the estimation results for one C-rate, it was found that the parameter estimation error ranges from 0.17% for the Rint model to 0.03% for the Dual-Polarisation model. The validation results confirmed that the proposed approach had a normalised root mean square error lower than 2%. The proposed methodology encompasses the entire process of parameter estimation, starting from raw experimental data. As such, it can be generalised and applied to other battery technologies.

Modeling uncertainty in positive energy districts through a non-probabilistic approach

Positive Energy Districts (PEDs) transcribe an emerging energy transition paradigm for rapid upscaling of building energy efficiency programs to match the urgency of climate mitigation and adaptation. PEDs facilitate pro-active sharing of information and experiences, responsive learning and dissemination, and cooperation across sectors and disciplines. The rapid evolution of technologies for energy efficiency and integration of renewable energy in buildings requires a research-to-design approach. Therefore, it is imperative that a designer makes informed decisions at an early stage of design from an energy-efficiency perspective. However, since PED design involves a significant number of stakeholders, the design parameters could be highly uncertain, which eventually affect the quality of the design solution space. This study uses a bilateral process workflow to quantify risks using a nonprobabilistic technique and a PED roadmap structure. The workflow evaluates different PED scenarios and quantifies the PED potential for the city of Sint-Niklaas in Belgium. The results indicate that risk quantification significantly improves PED decision-making when evaluating collective solutions (district heating) for a district.

Greening smart cities: An investigation of the integration of urban natural resources and smart city technologies for promoting environmental sustainability

The integration of urban natural resources and smart city technologies as a means to promote sustainability is investigated in this study using a descriptive-analytical literature review. The study identifies key elements associated with the incorporation of green spaces and smart technologies within smart cities. The findings show that green spaces can promote environmental sustainability in smart cities when utilized as renewable energy sources, natural filters, and public spaces. However, effectively managing green spaces requires the implementation of smart technologies such as sensors to monitor and analyze data on factors such as air pollution, temperature, and irrigation levels. The study evaluates the challenges of green space data analysis such as cost, data reliability, privacy, and expertise, and reveals that Western and Eastern cities take different approaches to green space data analysis, focusing on environmental/social and urban planning benefits respectively. The importance of technologies and policies aimed at addressing environmental issues in disadvantaged communities, including air pollution, is emphasized. Effective strategies such as the deployment of air quality sensors, green infrastructure, and transit-oriented development can improve air quality and health, though success depends on policy design, resources, and infrastructure. Furthermore, the study explores the potential of advanced technologies and strategies to manage sustainable energy resources in smart cities, including smart grids, renewable energy, and energy-efficient buildings. The study also discusses the potential of smart technologies such as precision irrigation, smart metering, and waste management initiatives to reduce water usage and waste. The study concludes that technology and policy innovation can converge to yield environmental wellbeing through efficiency and reduced harm. By embracing sustainability, communities can lead in creating livable smart cities. The findings offer insights for policymakers, planners, and researchers on managing natural resources and promoting sustainable development in smart cities.

Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health

Artificial intelligence (AI) and deep learning (DL) have shown tremendous potential in driving sustainability across various sectors. This paper reviews recent advancements in AI and DL and explores their applications in achieving sustainable development goals (SDGs), renewable energy, environmental health, and smart building energy management. AI has the potential to contribute to 134 of the 169 targets across all SDGs, but the rapid development of these technologies necessitates comprehensive regulatory oversight to ensure transparency, safety, and ethical standards. In the renewable energy sector, AI and DL have been effectively utilized in optimizing energy management, fault detection, and power grid stability. They have also demonstrated promise in enhancing waste management and predictive analysis in photovoltaic power plants. In the field of environmental health, the integration of AI and DL has facilitated the analysis of complex spatial data, improving exposure modeling and disease prediction. However, challenges such as the explainability and transparency of AI and DL models, the scalability and high dimensionality of data, the integration with next-generation wireless networks, and ethics and privacy concerns need to be addressed. Future research should focus on enhancing the explainability and transparency of AI and DL models, developing scalable algorithms for processing large datasets, exploring the integration of AI with next-generation wireless networks, and addressing ethical and privacy considerations. Additionally, improving the energy efficiency of AI and DL models is crucial to ensure the sustainable use of these technologies. By addressing these challenges and fostering responsible and innovative use, AI and DL can significantly contribute to a more sustainable future.

Climate change: role of banks and financial institutions in greening the banking system

Global warming and climate change are hot topics of environmentalists globally. The World Bank’s ‘Climate Change Action Plan’ for 2021 to 2025 explains the policies and programs to be implemented by banks and financial institutions to combat climate change. The UN and World Bank have taken actions towards achieving minimal carbon emissions that in turn reduce global warming. In India, national-level financial institutions such as RBI, NABARD, SIDBI and Exim Bank took the lead in formulating policies to achieve the goal of greening the banking system. SBI granted financial assistance for windmills and kick-started green financing activity, followed by all other banks. Financial assistance at the concessional interest rate to renewable energy and energy-efficient projects and buildings, electric vehicles, start-ups, trading green bonds and issues of green mutual funds are introduced by various banks. Additionally, within the bank, a sea change in the form green ATMs, digitalization of banking, issue of electronic bank statements and e-mail communication are started. Various other changes, such as green bank building, purchasing electric equipment conforming to green standards, changing LED lighting systems, conducting conferences and meetings electronically, etc., are implemented in the banks. Thus, both the internal changes within the bank and external changes address the issue of climate change. The study reveals that even though banks have taken the green initiatives a decade back, it is not widely popularized among customers and common people for a nationwide speedy adoption of green banking.

Optimization of building demand flexibility using reinforcement learning and rule-based expert systems

The increasing use of renewable energy in buildings requires optimization of building demand flexibility to reduce energy costs and carbon emissions. Nevertheless, the optimization process is generally challenging that needs to consider the on-site intermittent energy supply, dynamic building energy demand, and proper utilization of energy storage systems. Leveraging the growing availability of operational data in buildings, data-driven strategies such as reinforcement learning (RL) have emerged as effective approaches to optimizing building demand flexibility. However, training a reliable RL agent is practically data-demanding and time-consuming, limiting its practical applicability. This study proposes a new strategy that integrates a rule-based expert system (RBES) and RL agents into the decision-making process to jointly reduce building energy costs, minimize the peak-to-average ratio (PAR) of grid power, and maximize PV self-consumption. In this strategy, the RBES determines system operation directly in less complex decision-making scenarios, while, in more intricate decision-making environments, it provides a reference decision for RL to explore optimal solutions further. This integration empowers RL agents to avoid unnecessary exploration and significantly enhance learning efficiency. The proposed strategy was tested using PV generation data and energy consumption data of a low energy office building. The results demonstrated an 85.7% improvement in RL learning efficiency and this strategy can successfully avoid sub-optimal convergence during policy learning. Compared to relying solely on the RBES, the proposed strategy led to 5.4% and 19.2% reductions in the electricity costs and daily PAR of grid power at peak hours, respectively. The strategy also achieved a satisfying PV self-consumption ratio of 62.4%, which is merely 0.4% lower than the optimal value determined by the RBES strategy that prioritized maximizing PV self-consumption. Additionally, compared with a model predictive control method developed for cost reduction, the strategy achieved similar cost savings while significantly reducing the decision time.

BIPV Generation Efficiency Optimal Scheme—— Take Jiangning District of Nanjing City for Example

Building Integrated photovoltaic (BIPV) is more and more applied to the construction field with its advantages of being close to the load center, saving urban land and building materials. Especially under the current goal of 'carbon peak' and 'carbon neutrality', the integration of photovoltaic buildings is one of the important ways to achieve the effective application of renewable energy in buildings. But at present, the lack of relevant application cases of the technology, many owners interested in BIPV have remained on the sidelines. This paper mainly focuses on the economic and environmental benefit evaluation of the roof and curtain wall system of BIPV. By comparing the economic benefits and investment payback period, the owners provide the optimal investment plan, and analyzes the significance of 'two-carbon policy' by calculating the environmental benefits of BIPV. In this paper, Jiangning District of Nanjing city is taken as the pilot object to analyze the benefit of BIPV, and proposed the optimal design strategy of building photovoltaic integration suitable for this area through the benefit comparison of different schemes. In the roof and curtain wall benefit evaluation when we use the photovoltaic garden analysis site for solar radiation estimates, and then get the whole life cycle of photovoltaic system capacity, as the basis of economic benefit evaluation, then through the query related data to save the total coal as the basis of environmental benefit evaluation, combined with the project investment payback period, economic benefits and environmental benefits of comprehensive evaluation of the final plan. Through the above economic benefits and environmental benefits data comparison, photovoltaic roof has more investment value than photovoltaic curtain wall in both economic and environmental aspects. Taking Jiangning District of Nanjing city as an example, this paper illustrates the broad prospect of BIPV, and also provides a relatively clear data support and case reference for the promotion of BIPV nationwide in the future.

A novel triangular pulsating heat pipe with enhanced heat transfer performance for building energy efficiency

Utilizing renewable energy in buildings is promising for alleviating the energy crisis and environmental pollution problems. Pulsating heat pipes (PHPs) are considered as a good alternative to achieve it due to their simple structure and efficient heat transfer performance. However, recently developed PHPs having a non-vertical orientation require a certain number of turns to initiate operation, which greatly limits its application. To solve this starting-up issue in a single-loop PHP, this study proposes a novel triangular pulsating heat pipe (TPHP). A validated volume of fluid (VOF) model is developed to investigate three important factors on the heat transfer performance including the model structure of the TPHP, the heating temperature and the liquid filling ratio. The results show that the TPHP can achieve heating start-up in non-vertical direction and the heat flow resistance can be reduced by up to 38.04% through a rationalized model structure. The filling ratio influences the heat flow resistance by affecting the flow pattern within the TPHP while the optimum liquid filling ratio is about 50%. This study is critical to broaden the application of single-loop PHP in buildings.

A user-friendly and accurate machine learning tool for the evaluation of the worldwide yearly photovoltaic electricity production

While traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm’s applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained.The results demonstrate the algorithm’s vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal.

Rethinking the role of efficiency for the decarbonization of buildings is essential

Antoine Levesque is a postdoctoral researcher at the Potsdam Institute for Climate Impact research (PIK), where he started working in 2014. His research focuses on the decarbonization of buildings, efficiency policies, and energy demand projections. Prior to the PIK, he worked at the International Institute for Applied Systems Analysis (IIASA) in Austria. He earned a PhD in economics at the Technische Universität Berlin (2021). Sebastian Osorio is a postdoc researcher at the PIK in the “Climate and Energy Policy” group. His work concentrates on applying energy systems models to evaluate low-carbon policies, with a clear focus on the EU power sector, the Emission Trading System, and their interaction with the decarbonization of buildings. Sebastian holds a PhD in information systems from the University of Lausanne in Switzerland. Sebastian Herkel is head of the “Energy Efficient Buildings” department at Fraunhofer Institute for Solar Energy Systems in Freiburg, Germany. He graduated from University of Karlsruhe with the degree of diplom-ingenieur in mechanical engineering. Sebastian Herkel is a researcher working in applied research in building energy performance and renewable energy systems. His focus is on integral energy concepts for buildings and neighborhoods, scientific analysis of building performance, and technologies for integration of renewable energy in buildings. Michael Pahle is head of the working group “Climate and Energy Policy” at the PIK. He holds a PhD in economics from TU Berlin. His research focuses on carbon pricing and more recently on climate policies for the buildings sector. He is co-principle investigator for Germany’s largest household panel on energy efficiency. Based on his research, Michael advises the German government and EU institutions. He is a member of the Global Climate Policy Partnership, a US-based global network of research institutions that helps major economies and businesses achieve ambitious climate goals. Antoine Levesque is a postdoctoral researcher at the Potsdam Institute for Climate Impact research (PIK), where he started working in 2014. His research focuses on the decarbonization of buildings, efficiency policies, and energy demand projections. Prior to the PIK, he worked at the International Institute for Applied Systems Analysis (IIASA) in Austria. He earned a PhD in economics at the Technische Universität Berlin (2021). Sebastian Osorio is a postdoc researcher at the PIK in the “Climate and Energy Policy” group. His work concentrates on applying energy systems models to evaluate low-carbon policies, with a clear focus on the EU power sector, the Emission Trading System, and their interaction with the decarbonization of buildings. Sebastian holds a PhD in information systems from the University of Lausanne in Switzerland. Sebastian Herkel is head of the “Energy Efficient Buildings” department at Fraunhofer Institute for Solar Energy Systems in Freiburg, Germany. He graduated from University of Karlsruhe with the degree of diplom-ingenieur in mechanical engineering. Sebastian Herkel is a researcher working in applied research in building energy performance and renewable energy systems. His focus is on integral energy concepts for buildings and neighborhoods, scientific analysis of building performance, and technologies for integration of renewable energy in buildings. Michael Pahle is head of the working group “Climate and Energy Policy” at the PIK. He holds a PhD in economics from TU Berlin. His research focuses on carbon pricing and more recently on climate policies for the buildings sector. He is co-principle investigator for Germany’s largest household panel on energy efficiency. Based on his research, Michael advises the German government and EU institutions. He is a member of the Global Climate Policy Partnership, a US-based global network of research institutions that helps major economies and businesses achieve ambitious climate goals.

Assessing motivations and barriers to renewable energy development: Insights from a survey of municipal decision-makers in Alberta, Canada

Municipal renewable energy development is a component of community energy that remains under-examined. Particularly, within jurisdictions like Alberta, Canada, where fossil fuels remain dominant, transition to decentralized and renewable energy systems can be challenging. To gain a deeper understanding of the motivations and the barriers to building renewable energy projects within municipalities, we utilize a province-wide survey of municipal decision-makers (n = 101). Results are examined through a framework that combines the theory of planned behaviour with transition theory to identify specific motivations and challenges associated with renewable energy development in this jurisdiction. Consistent with other research, results highlight the perception of economic value that encourages municipal renewable development. Key constraints on the development of renewable projects are associated with difficulties in accessing grants, limited support from the provincial government, and a concern about aesthetics and negative impacts on natural landscapes. Results are discussed in the context of published literature on this topic, with insights on the value of this blended theoretical framework for unpacking and understanding constraints and opportunities for renewable energy development at the municipal level.

GIS for the Potential Application of Renewable Energy in Buildings towards Net Zero: A Perspective

Environmental, economic, and social activities involve inherent spatial dimensions. The geospatial information system (GIS), a platform containing principles, methods, and tools to link, create, visualize, analyze, and model artificial activities and environment, provides the possibility to develop sustainability in the building sector. With globally political collaborations across governments, the demands to manage and visualize sustainable data (e.g., building energy and environment with geospatial reference) and implement more rigorous building modelling are increasing. A systematic mapping at multiple scales will help urban engineers, architectural engineers, policymakers, and energy planners identify emission hotspots, locate spatial resources, restructure district energy mix, and achieve net zero energy targets. To achieve net zero energy goals, it is crucial to minimize energy consumption, improve energy efficiency, and most importantly, apply renewable energy in buildings. However, these processes imply many aspects and challenges, regarding e.g., data availability, scalability, integrability, and a lack of clear and applicable frameworks. In this conceptional perspective paper, we aim to explore the potential of applying and installing renewable energy in net zero energy buildings using the GIS. More specifically, the described virtual framework will effectively support policy- and decision-makers in optimizing the energy structure, reducing building emissions, and applying renewable energy technologies. We also present challenges, limitations, and future directions for real practice.

Enhances of nearly zero-energy public buildings in regions with hot summers and cold winter

Nearly Zero-energy residential buildings have attracted widespread attention due to their superior energy-saving performance. In order to achieve a balance between energy production and demand in residential buildings, the application of renewable energy is essential. This paper optimizes typical zero-energy public buildings in hot summer and cold winter locales using the concepts of passive priority, active optimization, and 100% renewable energy supply guarantee. The importance of passive energy-saving techniques like heating, shading, and heat recovery; in accordance with local resource conditions; analysis of building renewable energy utilization systems like solar energy and air energy; and recommendation of the most technologically efficient building energy system. According to the simulation results, if the criteria for zero-energy buildings are met, the annual energy consumption index per unit area is 37.6 kWh/m2, and the 8.9 kW building photovoltaic system can generate 36.4 kWh/m2 of power annually.

Evaluating and Prioritizing the Green Infrastructure Finance Risks for Sustainable Development in China

China has become a global leader in green infrastructure finance, investing heavily in renewable energy, sustainable transportation, and green buildings. However, there are multiple risks and challenges that impede the development of green infrastructure finance. Thus, this study analyzes and prioritizes the risks associated with green infrastructure finance in China and proposes policy plans to mitigate these risks. A Fuzzy analytical hierarchy process (AHP) is used to identify the main risks associated with green infrastructure finance. The main risks are further decomposed into sub-risks. After, the Fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is used to prioritize the key policy plans to mitigate risks and sub-risks. The results of Fuzzy AHP show that policy and regulations are the most significant risk associated with green infrastructure finance in China, followed by financial risks, and technical risks. The results of Fuzzy VIKOR reveal that increasing the availability of financing options is the most crucial policy plan to mitigate the risks and sub-risks for green infrastructure finance. The developed standardized technical guidelines and procedures and a legal and regulatory framework are ranked second and third are the most effective and feasible policy plans.

Performance evaluation of a pipe-embedded phase change material (PE-PCM) roof integrated with solar collector

Utilizing renewable energy in buildings is important for the development of zero-energy buildings to achieve the goals of carbon peak and carbon neutrality. A novel roof with a pipe-embedded phase change material (PE-PCM) integrated with a solar collector is proposed to fully utilize solar energy and improve building efficiency. The PE-PCM layer is attached to the inner side of the roof for effective utilization of solar energy and enables all-day indoor heating via thermal storage. In this study, a simplified model of a PE-PCM structure was developed based on its physical configuration, which included a variable thermal capacitance and thermal resistance model (VarRC model). The simplified PE-PCM roof model integrated with the VarRC model is validated using a numerical model. Additionally, a platform for a room with the PE-PCM roof and solar collector is developed to investigate the system performance. The results showed that the heating demand can be met, except under extreme weather conditions. When the indoor temperature is controlled above 17 °C by auxiliary heating, the total energy required for heating the room with the PE-PCM roof can be reduced by 54% compared to that of a conventional roof room.

Thermal and Flammability Properties of Flame Retardant Phase Change Material for Buildings Applications

Phase Change Materials (PCMs) have attracted growing attention recently in the renewable energy utilization and building energy efficiency due to their high capacity for storing thermal energy but their applications have been restricted by leakage, poor thermal conductivity, and high flammability. In this work, the flame retardant phase change material has been successfully prepared through integrating Resorcinol bis (diphenyl phosphate) (RDP) as a flame retardant into lauric acid (LA) as a phase change material. The DSC (differential scanning calorimeter) test is used to measure the thermal properties of LA_RDP. The thermogravimetric analysis (TGA) test demonstrates that the thermal stability of composite LA_RDP is improved. Cone calorimeter is applied to study the flammability properties of the flame retardant phase change material. As a result, the RDP-based flame-retardant PCMs developed could be used for safe and efficient solar energy storage applications.