Renewable Energy In Buildings Research Articles

Development Status, Challenges, and Future Prospects of China's Green Bond Market

The high rate has characterized the growth of Chinas green bond market and further emphasizes its involvement in protecting the environment and ensuring sustainable development. Green bonds act as a vital source of investible financial resources for green projects like renewable energy and green buildings, which support China's carbon peaking and carbon neutrality policies. However, the market experiences several difficulties, such as high finance costs, insufficient market capacity, scanty information about the products, and few new products. Such factors restrict the timely provision of funds for green initiatives and prompt concerns over the issue of green-washing. This paper builds on these challenges by exploring the existing prospects of further development of Chinas green bond market as well as the regulatory environment and hurdles to more advanced expansion. It pinpoints certain aspects that could be improved, for example, increasing disclosure requirements for investors, reducing the cost of financing, and creating a variety of financial instruments for longer-term investment. Chinas green bond market has insufficient progress in regulation, innovation, and market diversification which should be addressed for successful continuity. By addressing these issues, China can turn out to be a global leader in green finance while ensuring environmental protection and enhancing economic development.

Research on the design of prefabricated curved structure production capacity residential energy system: a case study of an entry of 2022 China International Solar Decathlon Competition- 'Solar Ark 3.0'

Under the severe situation of global warming, the problem of high energy consumption and carbon emissions in the construction industry need to be addressed urgently, and the method to promote the development of the construction industry in the direction of low-carbon sustainability has become a problem to be solved. Taking ‘Solar Ark 3.0’, the entry of Southeast University United Team in the Third China International Solar Decathlon Competition (SDC2022), as an example. This study proposes the wind and solar complementary capacity design, multi-mode energy storage design, intelligent energy use design and production, storage and use of multi-energy complementary intelligent energy management methods through the exploration of the integrated design and construction of curved structure housing and energy system. These designs of ‘Solar Ark 3.0’ improve the utilization efficiency of renewable energy in buildings and also provide methods for the integrated design of curved structural housing and energy systems.

SUSTAINABLE GROWTH THROUGH GREEN ECONOMY: AN ECONOMETRIC ANALYSIS

Objective: In recent years, the growing interest and awareness of environmental issues have once again brought the issue of population growth to the forefront, exerting significant pressure on production. The increased pressure on production and the resultant rise in production have led to a faster trend in consumption. As a natural consequence of both trends, detrimental effects on ecological balance and the environment have begun to emerge. The concept of "green economy" has emerged as an approach to restore the disrupted ecological balance and operationalize this order. This widely spreading concept has begun to generate significant policies at both national and international levels. Method: This study aims to examine the relationship between renewable energy production, consumption, wastewater, and solid waste data, covering the years 1980-2022, using econometric methods to explore the relationship between green economy and economic growth in the context of Turkey. The objective is to analyze the impact of the recent and rapidly increasing transformation towards "green economy" on the long-term trajectory of economic growth in Turkey. Result and Discussion: Based on the findings of the study, it has been determined that, at the 0.05 level of statistical significance, there is no long-term relationship between the variables and economic growth, as well as no long-term significant relationship between any of the variables. When the statistical significance is accepted at the 0.10 level, a significant long-term relationship is identified between the amount of wastewater and renewable energy, with a 1% increase in wastewater leading to a 0.82% increase in renewable energy. Implications: The findings of this study and the transformation towards a green economy, widely covered in the literature, have been recognized in Turkey and an unnamed green transformation movement has begun. As the results of the analysis of the study indicate, the policies introduced for the transition to a green economy and transformation efforts have a positive and significant impact on economic growth in the long term, but the same cannot be claimed for the short term, but in the future, advances in scientific and technological progress and over-specialization will inevitably have positive consequences on global and domestic sustainable development in the short term. Originality/Value: The distinctive feature of this study from similar studies in the literature is that the relationship between the green economy and sustainable growth was analyzed empirically and with five separate econometric models. Recommendations: Turkey needs serious reforms in education in its transition to a green economy. In addition, there is a need for political determination to use environmentally friendly sectors that are more open to innovation and competition, such as renewable energy and green building sectors.

Smart Environment Implementation Study in Samarinda

Abstract Samarinda, as a buffer for the Indonesian Capital City (IKN), needs to carry out development transformation to suit developments in science and technology and needs. The aim is to overcome environmental problems that cause increased flooding, traffic jams and under resourced neighbourhoods, making Samarinda City an uncomfortable place to live. To overcome this, it is necessary to apply the smart environment concept. This research examines the implementation of a smart environment, and the obstacles faced in its implementation in Samarinda City. Research analysis uses SWOT by determining the IFAS and EFAS for implementing the smart environment. The results of the SWOT analysis are in quadrant I (x, y = 1.71; 1.10), meaning that the implementation of a smart environment in Samarinda City needs to be carried out with a growth strategy (intensifying existing resources). The obstacles to implementing a smart environment in Samarinda City in the environmental protection program are not yet using IoT to monitor pollution, the high volume of waste that has not been handled, the absence of regional regulations regarding the use of renewable energy and energy efficient building design. For this reason, support and cooperation from the government, private sector and society is needed.

Scaling up magnetocaloric heat pump for building decarbonization initiatives

Building decarbonization necessitates renewable heating and cooling solutions such as heat pumps. Magnetocaloric heat pumps (MCHP) offer environmental and efficiency advantages but face challenges when scaling up from existing active magnetic regenerator configurations. This study highlights uneven flow resistance, porosity, and refrigerant magnetocaloric effects as key obstacles to MCHP performance in parallel multi-bed setups. To address these effects, two control strategies for the fluid flow control system were investigated: measurement feedback control and model predictive control. Results show a 36.9 % heating power improvement with measurement feedback control, though with extended control convergence times. Model predictive control achieved approximately seven times faster control convergence compared to the measurement feedback control strategy, despite exhibiting minor overshooting. Utilization factor-based model predictive control increased the heating capacity by 1.6 %–30.9 % and the COP by 1.2 %–10.7 % in scenarios with uneven flow resistance and porosity, offering computational efficiency but assuming even magnetocaloric effects between regenerators. This assumption can be addressed by outlet temperature-based model predictive control, albeit at a higher computational cost using genetic algorithm. The findings emphasize the importance of advanced control methods to scaling up MCHP in renewable energy building systems.

Typical daily scenario extraction method based on key features to promote building renewable energy system optimization efficiency

Given its intrinsic volatility and randomness, the effective design of a building renewable energy system (BRES) necessitates long-period, short-step boundaries. Nevertheless, this poses computational burdens. In this context, time-series aggregation (TSA) emerges as a valuable approach, which involves obtaining a set of typical scenarios to substitute original time series. Numerous studies have focused on mathematical algorithms to minimize the deviation between typical scenarios and original time series. However, the premise of enhancing model-solving efficiency by extracting typical scenarios is to ensure the optimality of planning. Henceforth, identifying and quantifying key features in the original time series that significantly influence the planning, and further extracting need to be addressed. This paper proposes a typical day extraction method based on key features, named the feature-based method. Key features are summarized through an extensive literature review and then quantitatively expressed through different indicator models. Finally, a feature-based mixed integer linear programming (MILP) model is established for extraction. To evaluate the effectiveness of the feature-based method, it is applied to design a BRES and compared with traditional TSA methods such as Averaging Method (AM) and k-means. Results indicate that the feature-based method can effectively retain key features, thereby achieving more efficient and accurate designs.

Predictive Models for Aggregate Available Capacity Prediction in Vehicle-to-Grid Applications

The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicles and the power grid and can provide ancillary services to the grid, such as peak shaving, load balancing, and emergency power supply during power outages, grid faults, or periods of high demand. In this context, reliable prediction of the availability of V2G as an energy source in the grid is fundamental in order to optimize both grid stability and economic returns. This requires both an accurate modeling framework that includes the integration and pre-processing of readily accessible data and a prediction phase over different time horizons for the provision of different time-scale ancillary services. In this research, we propose and compare two data-driven predictive modeling approaches to demonstrate their suitability for dealing with quasi-periodic time series, including those dealing with mobility data, meteorological and calendrical information, and renewable energy generation. These approaches utilize publicly available vehicle tracking data within the floating car data paradigm, information about meteorological conditions, and fuzzy weekend and holiday information to predict the available aggregate capacity with high precision over different time horizons. Two data-driven predictive modeling approaches are then applied to the selected data, and the performance is compared. The first approach is Hankel dynamic mode decomposition with control (HDMDc), a linear state-space representation technique, and the second is long short-term memory (LSTM), a deep learning method based on recurrent nonlinear neural networks. In particular, HDMDc performs well on predictions up to a time horizon of 4 h, demonstrating its effectiveness in capturing global dynamics over an entire year of data, including weekends, holidays, and different meteorological conditions. This capability, along with its state-space representation, enables the extraction of relationships among exogenous inputs and target variables. Consequently, HDMDc is applicable to V2G integration in complex environments such as smart grids, which include various energy suppliers, renewable energy sources, buildings, and mobility data.

Comparative Analysis of Reinforcement Learning Approaches for Multi-Objective Optimization in Residential Hybrid Energy Systems

The rapid expansion of renewable energy in buildings has been expedited by technological advancements and government policies. However, including highly permeable intermittent renewables and energy storage presents significant challenges for traditional home energy management systems (HEMSs). Deep reinforcement learning (DRL) is regarded as the most efficient approach for tackling these problems because of its robust nonlinear fitting capacity and capability to operate without a predefined model. This paper presents a DRL control method intended to lower energy expenses and elevate renewable energy usage by optimizing the actions of the battery and heat pump in HEMS. We propose four DRL algorithms and thoroughly assess their performance. In pursuit of this objective, we also devise a new reward function for multi-objective optimization and an interactive environment grounded in expert experience. The results demonstrate that the TD3 algorithm excels in cost savings and PV self-consumption. Compared to the baseline model, the TD3 model achieved a 13.79% reduction in operating costs and a 5.07% increase in PV self-consumption. Additionally, we explored the impact of the feed-in tariff (FiT) on TD3’s performance, revealing its resilience even when the FiT decreases. This comparison provides insights into algorithm selection for specific applications, promoting the development of DRL-driven energy management solutions.

International English Teaching Planning and Evaluation of Renewable Energy Application Projects from Perspective of Sustainability

The application of renewable energy has positively contributed to the Eco-city by alleviating energy tension, improving energy structure, enhancing energy utilization efficiency, and building a resource-saving and environment-friendly society. At the same time, several demonstration projects with advanced and applicable technology, stable and reliable operation, and high promotion value have been completed, and a relatively complete system of regulations, policies, technical standards, application modes, and capacity building for the application of renewable energy buildings has been initially formed. This study analyzes the current global demand for renewable energy and education status quo and proposes a set of teaching programs combined with practical applications. In response to the needs, a comprehensive framework integrating theoretical teaching, hands-on practice, rigorous program evaluation, and dynamic feedback loops is designed to cultivate students' global perspectives and emphasize the importance of cross-cultural dialogue and international cooperation. The implementation results showed a significant improvement in the students' understanding of renewable energy, with a 40% improvement at the end of the class. As a result of the IEP implementation, 85% of the students targeting the Renewable Energy Applications Program have achieved the expected teaching objectives in terms of energy and environmental policy comprehension, renewable energy technology mastery, and intercultural communication skills, which is a 30% improvement from the pre-implementation period. In addition, there has been a significant increase in international exchanges between students, contributing to creating a more holistic approach to sustainable development.

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

Renewable Energy Integration in Buildings A Case Study in Portugal

This paper aims at the presentation of a proposal for the integration of solar thermal technology in buildings, particularised for the Portuguese context through a case study, a new sport public building in Coimbra, which consists of two swimming pools, gym, jacuzzi and sanitary facilities. This proposal has been developed in the framework of a Master dissertation currently under development, which the main objective is the selection of the most appropriate technological solution for the integration of renewable energy in buildings. In this paper we propose the selection of the most appropriate technological solution for the integration of three thermal solar systems in the new building. Two systems are used for heating the swimming pools and the third one is dedicated to domestic hot water. The selection of the best solution will take into account the constructive aspects of the building, the building's energy needs and the local availability of renewable resources and market technologies. For each solar thermal system, the general scheme of the proposed technical solution is presented. Investment costs and economic viability of the selected solutions are also presented.

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.

Is it Possible to Develop Renewable Energy as a Learning Media at a University in The Coastal Area of Indonesia?

Climate change is a problem that, if left unchecked, will threaten the lives of living things on Earth. The use of renewable energy can be a solution to this problem. This can be started by making it a learning medium at university. This research is a descriptive-analytical study conducted at Raja Ali Haji Maritime University. The study aims to analyze the possibility of building renewable energy as a learning medium at the university by considering aspects of regional conditions, including exposure to sunlight, wind power, and sea wave power. With its area located in the coastal area, the development of renewable energy as a learning medium is possible at Universitas Maritim Raja Ali Haji. The habituation of students to using renewable energy can be started by universities that facilitate the development of renewable energy as a learning media that can be used by students.

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.

The Green Economy Transformation of GCC Countries and China-GCC Cooperation in Green Development

Abstract In 2015, all GCC countries acceded to the Paris Climate Agreement and submitted their Intended Nationally Determined Contributions since 2020. The Russian-Ukrainian conflict in 2022 led to a surge in world oil prices. Meanwhile, the increase in oil revenues created the new impetus for the green economy transformation and pushed it to an accelerated period. The green economy transition aimed at replacing fossil energy with hydrogen energy and making it a new driving force for export trade growth, on the other hand, it will incubate a number of private enterprises in the field of green science and technology, and realize economic diversification and private economy-led growth, and eventually led to economic structural adjustment which can increase the growth rate. GCC governments are committed to building renewable energy infrastructures, developing carbon capture and sequestration technologies, and emphasizing green and low-carbon building environmental governance. However, shortcomings in finance, technology and human resources restricted the transformation. China and the GCC countries need to cooperate to alleviate the difficulties and realize common green development. Currently, bilateral cooperation has already achieved fruitful results in areas of renewable energy infrastructure construction, green transportation and green capacity-building, In the future, green financial cooperation still needs to be strengthened.

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.