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Grease-proof paper is an energy-demanding paper product to manufacture, especially during refining and dewatering. Increases in energy efficiency in either stage could result in major savings. This article investigates the potential gains with addition of a stepwise progression vacuum suction box to the forming section during production. For both a lighter, 50 g/m², and a heavier paper grade, 100 g/m², with a pulpdrainability of 86 °SR, a stepwise progression vacuum suction box in four steps would result in increased dryness, simultaneously with decreased energy expenditure. The observed effects were higher for the lower basis weight paper (50 g/m²). Both basis weights experienced clogging of the forming fabric due to the high degree of refining. This adversely affected the dewatering rate, decreasing the amount of air pulled through the paper even when increasing the vacuum pressure. When a stepwise progression suction box in four steps was compared to a single vacuum suction box, there was a 14% increase in dryness for lighter paper, over an equal energy consumption, measured as amount of air pulled through the paper. For the 100 g/m² paper, the increase in dryness was 3% compared to the 50 g/m² paper run over a single vacuum suction box. The results show great promise for energy savings when utilizing stepwise progression suction box dewatering for grease-proof paper production. [ABSTRACT FROM AUTHOR]

The rise of the Internet of Things (IoT) has enabled the development of smart cities, intelligent buildings, and advanced industrial ecosystems. When the IoT is matched with machine learning (ML), the advantages of the resulting enhanced environments can span, for example, from energy optimization to security improvement and comfort enhancement. Together, IoT and ML technologies are widely used in smart buildings, in particular, to reduce energy consumption and create Intelligent Energy-Efficient Buildings (IEEBs). In IEEBs, ML models are typically used to analyze and predict various factors such as temperature, humidity, light, occupancy, and human behavior with the aim of optimizing building systems. In the literature, many review papers have been presented so far in the field of IEEBs. Such papers mostly focus on specific subfields of ML or on a limited number of papers. This paper presents a systematic meta-survey, i.e., a review of review articles, that compares the state of the art in the field of IEEBs using the Prisma approach. In more detail, our meta-survey aims to give a broader view, with respect to the already published surveys, of the state-of-the-art in the IEEB field, investigating the use of supervised, unsupervised, semi-supervised, and self-supervised models in a variety of IEEB-based scenarios. Moreover, our paper aims to compare the already published surveys by answering five important research questions about IEEB definitions, architectures, methods/models used, datasets and real implementations utilized, and main challenges/research directions defined. This meta-survey provides insights that are useful both for newcomers to the field and for researchers who want to learn more about the methodologies and technologies used for IEEBs' design and implementation. [ABSTRACT FROM AUTHOR]

A growing research interest currently exists in the use of paper as a building material. This work aims to present the results of a measurement campaign developed on innovative waste paper-based building components. The research was carried out in Southern Italy and used some local by-product aggregates. Three different mixture designs were developed in the laboratory by adding three kinds of biomass to a pulp paper blend: fava bean residues (FB), sawdust powder (SP) and coffee grains (CG) extracted from exhausted chaffs. A physical characterization was carried out measuring the bulk density and bulk porosity. Scanning Electron Microscopy (SEM) analysis of the single aggregates was followed by a microstructure analysis of the final components. Bulk density evaluation showed a range between 200 and 348 kg·m−3. Furthermore, thermal performances were measured; the thermal conductivity of the experimented samples ranged from 0.071 to 0.093 W·m−1·K−1, thus it is possible to classify the tested materials as thermal insulators. Moreover, the acoustic properties were evaluated and tested. The normal incidence sound absorption coefficient was measured by the impedance tube on cylindrical specimens. In general, a different behavior was observed between the upper and lower base of each specimen due to the manufacturing process and the shrinkage caused by the different interactions occurring between the aggregates and the pulp paper waste; for example, the presence of sawdust reduced shrinkage in the final specimens, with consequent smaller physical variations among the two faces. The correlation existing between the manufacturing process and the microstructural properties was also investigated by the estimation of the non-acoustical parameters using the inverse method and taking into account the JCA (Johnson, Champoux and Allard) model as a reference. [ABSTRACT FROM AUTHOR]

The rapid expansion of the Industrial Internet-of-Things (IIoT) has spurred significant research interest due to the growth of security-aware, vehicular, and time-sensitive applications. Unmanned aerial vehicles (UAVs) are widely deployed within wireless communication systems to establish rapid and reliable links between users and devices, attributed to their high flexibility and maneuverability. Leveraging UAVs provides a promising solution to enhance communication system performance and effectiveness while overcoming the unprecedented challenges of stringent spectrum limitations and demanding data traffic. However, due to the dramatic increase in the number of vehicles and devices in the industrial wireless networks and limitations on UAVs' battery storage and computing resources, the adoption of energy-efficient techniques is essential to ensure sustainable system implementation and to prolong the lifetime of the network. This paper provides a comprehensive review of various disruptive methodologies for addressing energy-efficient issues in UAV-assisted industrial wireless networks. We begin by introducing the background of recent research areas from different aspects, including security-enhanced industrial networks, industrial vehicular networks, machine learning for industrial communications, and time-sensitive networks. Our review identifies key challenges from an energy efficiency perspective and evaluates relevant techniques, including resource allocation, UAV trajectory design and wireless power transfer (WPT), across various applications and scenarios. This paper thoroughly discusses the features, strengths, weaknesses, and potential of existing works. Finally, we highlight open research issues and gaps and present promising potential directions for future investigation. [ABSTRACT FROM AUTHOR]

Despite the tightening of energy performance standards for buildings in various countries and the increased use of efficient and renewable energy technologies, it is clear that the sector needs to change more rapidly to meet the Net Zero Emissions (NZE) scenario by 2050. One of the problems that have been analyzed intensively in recent years is that buildings in operation use much more energy than they were designed to. This problem, known as the energy performance gap, is found in many countries and buildings and is often attributed to the poor management of building energy systems. The application of Artificial Intelligence (AI) to Building Energy Management Systems (BEMS) has untapped potential to address this problem and lead to more sustainable buildings. This paper reviews different AI-based models that have been proposed for different applications and different buildings with the intention to reduce energy consumption. It compares the performance of the different AI-based models evaluated in the reviewed papers by presenting the accuracy and error rates of model performance and identifies where the greatest potential for energy savings could be achieved, and to what extent. The review showed that offices have the greatest potential for energy savings (up to 37%) when they employ AI models for HVAC control and optimization. In residential and educational buildings, the lower intelligence of the existing BEMS results in smaller energy savings (up to 23% and 21%, respectively). [ABSTRACT FROM AUTHOR]

The pulp and paper industry (PPI) has several opportunities to contribute to meeting prevailing climate targets. It can cut its own CO2 emissions, which currently account for 2% of global industrial fossil CO2 emissions, and it has an opportunity to produce renewable energy, fuels, and materials for other sectors. The purpose of this study is to improve understanding of the decarbonization prospects of the PPI. The study provides insights on the magnitude of needed annual renewal rates for several possible net-zero target years of industrial fossil CO2 emissions in the PPI and discusses decarbonization opportunities, namely, energy and material efficiency improvement, fuel switching, electrification, renewable energy production, carbon capture, and new products. The effects of climate policies on the decarbonization opportunities are critically evaluated to provide an overview of the current and future business environment of the European PPI. The focus is on Europe, but other regions are analyzed briefly to widen the view. The analysis shows that there are no major technical barriers to the fossil-free operation of the PPI, but the sector renovates slowly, and many new opportunities are not implemented on a large scale due to immature technology, poor economic feasibility, or unclear political environment. [ABSTRACT FROM AUTHOR]

Based on panel data for 30 provinces in China from 2000 to 2020, this paper uses entropy method to construct the integrated development indicators of energy Internet, and explores the impact of the development of the energy Internet on reducing carbon emissions in China and its internal transmission mechanism. The research results show that the development of energy Internet can significantly reduce regional carbon dioxide emissions, and increasing the use of renewable energy is an important mechanism for the comprehensive development of energy Internet to curb carbon emissions. Heterogeneity analysis shows that the emission reduction effect brought by the development of energy Internet is more remarkable in the regions with lower carbon emissions and western China. Furthermore, this paper finds that in the setting of the development of energy Internet, the development of renewable energy can promote natural gas consumption and advance energy conversion efficiency. The government's emphasis on regional technology development has an external strengthening effect on carbon emission reduction. The research in this paper expands and diversifies carbon reduction pathways and provides reference for more developing countries to utilize the development of energy internet to achieve carbon reduction. [ABSTRACT FROM AUTHOR]

The Logarithmic Mean Divisia Index (LMDI) method is widely applied in research on carbon emissions, urban energy consumption, and the building sector, and is useful for theoretical research and evaluation. The approach is especially beneficial for combating climate change and encouraging energy transitions. During the method's development, there are opportunities to develop advanced formulas to improve the accuracy of studies, as indicated by past research, that have yet to be fully explored through experimentation. This study reviews previous research on the LMDI method in the context of building carbon emissions, offering a comprehensive overview of its application. It summarizes the technical foundations, applications, and evaluations of the LMDI method and analyzes the major research trends and common calculation methods used in the past 25 years in the LMDI-related field. Moreover, it reviews the use of the LMDI in the building sector, urban energy, and carbon emissions and discusses other methods, such as the Generalized Divisia Index Method (GDIM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM) techniques. This study explores and compares the advantages and disadvantages of these methods and their use in the building sector to the LMDI. Finally, this paper concludes by highlighting future possibilities of the LMDI, suggesting how the LMDI can be integrated with other models for more comprehensive analysis. However, in current research, there is still a lack of an extensive study of the driving factors in low-carbon city development. The previous related studies often focused on single factors or specific domains without an interdisciplinary understanding of the interactions between factors. Moreover, traditional decomposition methods, such as the LMDI, face challenges in handling large-scale data and highly depend on data quality. Together with the estimation of kernel density and spatial correlation analysis, the enhanced LMDI method overcomes these drawbacks by offering a more comprehensive review of the drivers of energy usage and carbon emissions. Integrating machine learning and big data technologies can enhance data-processing capabilities and analytical accuracy, offering scientific policy recommendations and practical tools for low-carbon city development. Through particular case studies, this paper indicates the effectiveness of these approaches and proposes measures that include optimizing building design, enhancing energy efficiency, and refining energy-management procedures. These efforts aim to promote smart cities and achieve sustainable development goals. [ABSTRACT FROM AUTHOR]

Background: Milkrun-based material supply plays an important role in the automotive industry, as it is a material supply concept where high efficiency can be achieved. When implementing milkrun-based material supply, the milkrun supply of the production plant often has to be integrated with an existing warehouse material handling system, which frequently leads to a less efficient solution. Methods: In this paper, the author investigates the impact of a hub-based milkrun supply, where the collection processes in the component's warehouse and the distribution processes in the assembly plant are connected to a hub, which is responsible for the sequencing of component demands. After a systematic literature review, the paper introduces a novel mathematical model, which makes it possible to describe the conventional milkrun-based solutions, the hub-based milkrun solutions, and to compare them in terms of the length of transportation routes, transportation time, total service time, and virtual emission points of view. Results: The scenario analysis demonstrates that the hub-based solution can lead to an efficiency improvement of about 13% in total service time, 23% savings in transportation time, and 45% savings in transportation time in the component's warehouse. Conclusions: The article's findings suggest that implementing a hub-based milkrun system in automotive material supply can significantly enhance efficiency. The described approach could lead to more streamlined operations in production plants by optimizing the integration of milkrun systems. [ABSTRACT FROM AUTHOR]

In financial computation, Field Programmable Gate Arrays (FPGAs) have emerged as a transformative technology, particularly in the domain of option pricing. This study presents the impact of Field Programmable Gate Arrays (FPGAs) on computational methods in finance, with an emphasis on option pricing. Our review examined 99 selected studies from an initial pool of 131, revealing how FPGAs substantially enhance both the speed and energy efficiency of various financial models, particularly Black–Scholes and Monte Carlo simulations. Notably, the performance gains—ranging from 270- to 5400-times faster than conventional CPU implementations—are highly dependent on the specific option pricing model employed. These findings illustrate FPGAs' capability to efficiently process complex financial computations while consuming less energy. Despite these benefits, this paper highlights persistent challenges in FPGA design optimization and programming complexity. This study not only emphasises the potential of FPGAs to further innovate financial computing but also outlines the critical areas for future research to overcome existing barriers and fully leverage FPGA technology in future financial applications. [ABSTRACT FROM AUTHOR]

With the increasing global focus on energy efficiency and environmental sustainability, intelligent building materials such as thermochromic glazing have emerged as a hot topic of research. The intent of this paper is to explore the utilization of gel-type thermochromic glazing within the realm of architectural energy conservation calculations. It conducts an exhaustive examination of the material's attributes, its capacity for energy savings, and the obstacles encountered in real-world applications. Through simulation studies and case analyses, this paper assesses the energy efficiency of gel-type thermochromic glazing across various climates and suggests strategies for optimization. The study revealed that the incorporation of gel-based thermochromic glazing leads to a marked reduction in energy usage within buildings, an improvement in indoor comfort levels, and significant environmental advantages. [ABSTRACT FROM AUTHOR]

The demand for low-carbon and energy-efficient building designs is urgent, especially considering that building energy consumption constitutes a significant part of global energy usage. Unlike small to medium-sized buildings such as residential and office spaces, large public buildings, like sports facilities, have unique usage patterns and architectural forms, offering more significant potential for energy-saving strategies. This review focuses on sports buildings, selecting 62 high-quality papers published in building science over the past 30 years that investigate low-carbon and energy-efficient research. Summarizing and synthesizing these papers reveals that current studies predominantly concentrate on four main areas: indoor air quality, ventilation, thermal environment, and energy consumption. Notably, many studies emphasize improving indoor thermal comfort and reducing energy consumption in sports buildings through measurements and evaluations of indoor thermal environments, temperature distributions, heat transfer phenomena, and energy consumption analyses. Key outcomes indicate that green technology innovations, such as energy substitution technologies, significantly enhance energy efficiency and reduce CO2 emissions. However, present research emphasizes singular energy-saving approaches, suggesting future directions could integrate comprehensive green technologies, life-cycle assessments, and applications of intelligent technologies and the Internet of Things (IoT). These enhancements aim to provide more effective and sustainable solutions for implementing green, low-carbon energy practices in sports buildings. The review emphasizes that in order to accomplish sustainable urban growth and achieve global carbon neutrality targets, a comprehensive approach involving technical innovation, legislative assistance, and extensive preparation is crucial. [ABSTRACT FROM AUTHOR]

This paper employs the instrumental variable method and the intermediary model to explore the influence of monocentric and polycentric spatial structures of innovation agglomeration on energy efficiency. This paper uses data from 2010 to 2021 in China, and also panel data regression models were used. The findings indicate that the monocentric spatial structure of provincial system innovation agglomeration is primarily concentrated in northeastern and western regions of China, whereas the polycentric spatial structure is mainly distributed in eastern coastal areas. A monocentric spatial structure inhibits the enhancement of energy efficiency, but there is an environmental paradox of excessive resource agglomeration. In contrast, a polycentric spatial structure and its strengthening tendency facilitate the improvement of energy efficiency. A monocentric spatial structure has an inhibitory impact on the improvement of energy efficiency in the three sectors of agriculture, industry, and construction, while a polycentric spatial structure aids it. A monocentric spatial structure further hinders the improvement of energy efficiency by impeding the upgrading of the industrial structure, while the upgrading of the industrial structure driven by the polycentric spatial structure plays a role in enhancing energy efficiency. The policy recommendations of this paper are intended to help coordinate the pace of innovation development between cities, adjust the spatial structure of innovation agglomeration between regions, and promote the upgrading of the comprehensive industrial structure, thereby improving overall energy efficiency. [ABSTRACT FROM AUTHOR]

Data centers consume significant amounts of energy, contributing indirectly to environmental pollution through greenhouse gas emissions during electricity generation. According to the Natural Resources Defense Council, information and communication technologies and networks account for roughly 10% of global energy consumption. Reducing power consumption in Data Center Networks (DCNs) is crucial, especially given that many data center components operate at full capacity even under low traffic conditions, resulting in high costs for both service providers and consumers. Current solutions often prioritize power optimization without considering Quality of Service (QoS). Services such as video streaming and Voice over IP (VoIP) are particularly sensitive to loss or delay and require QoS to be maintained below certain thresholds. This paper introduces a novel framework called QoS-Aware Power-Optimized Path Selection (Q-PoPS) for software-defined DCNs. The objective of Q-PoPS is to minimize DCN power consumption while ensuring that an acceptable QoS is provided, meeting the requirements of DCN services. This paper describes the implementation of a prototype for the Q-PoPS framework that leverages the POX Software-Defined Networking (SDN) controller. The performance of the prototype is evaluated using the Mininet emulator. Our findings demonstrate the performance of the proposed Q-PoPS algorithm in three scenarios. Best-case: Enhancing real-time traffic protocol quality without increasing power consumption. midrange-case: Replacing bottleneck links while preserving real-time traffic quality. Worst-case: Identifying new paths that may increase power consumption but maintain real-time traffic quality. This paper underscores the need for a holistic approach to DCN management, optimizing both power consumption and QoS for critical real-time applications. We present the Q-PoPS framework as evidence that such an approach is achievable. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive bibliometric review and visualization of smart and sustainable energy consumption, delving into the challenges and opportunities of developing renewable and non-renewable energy sources. The study examines research trends and emerging themes about integrating smart solutions and sustainable energy resource consumption. The analytical methods used involve thoroughly analyzing empirical data, case studies, and review papers to map the research landscape. The results highlight dominant research topics, influential authors, and publication timelines in this field. The review identifies the key challenges in harnessing renewable and non-renewable energy sources, including the need for reliable energy sources, energy storage systems, and smart grid technologies. The paper concludes with insights into the most effective practices for promoting smart and energy-efficient methods while emphasizing the complexity of sustainable energy solutions. [ABSTRACT FROM AUTHOR]

The provision of sustainable housing should factor energy efficiency, the use of sustainable building methods and materials and address the current associated barriers. This consideration triggered an ongoing study in UK which has so far conducted a bibliometric analysis of 275 papers using the VOSviewer software. More papers have been published in the last 5 years compared to the previous 15, and the UK is less engaged in successful collaborations in comparison to other economically developed nations. The lesser use of innovation in the UK has been identified. Also, the uses of sustainable methods and materials in residential housing are not fully exploited for achieving energy efficiency. A future research direction emerging is the potential to study the combined impact of using innovative materials and methods to achieve greater energy efficiency in UK housing provision. Through technological innovations and the need to move from fossil fuels (for example, through the use of heat pumps) a new approach can move the UK closer to achieving net-zero in housing provision and operations. [ABSTRACT FROM AUTHOR]

Reducing energy consumption is vital to save natural resources and contribute to the sustainable development in any sector of society. In the building sector, there are many well-known energy efficiency strategies currently being applied. However, considering the advances in technology and in comfort studies, it is possible to see that the current building sector scenario demands new energy efficiency strategies. Such strategies need to be capable of identifying and assuring comfortable environments according to users' perceptions. Machine learning techniques can be a useful alternative to identify users' preferences and control lighting and heating, ventilation and air-conditioning systems in buildings. This paper shows a systematic literature review on the use of machine learning algorithms on preference identification and environmental adequacy according to users' demands. Its contribution is to explore beyond the performance and configurations of the algorithms, addressing users' preference aspects as well. The strategies found in the literature provided promising results. The most used approach was supervised learning because data can be treated as categories. In general, the control systems have shown good performance, and so have the algorithms. Users were mostly satisfied with environmental conditions. Situations of dissatisfaction were associated with the occupant's willingness to use the system more than with the control system's performance. Furthermore, it is also possible to ally user-centred control and energy savings but this relies on occupants' characteristics and the control strategies used. We underline the importance of identifying whether the users are willing to deal with an automatic control system before making any decision, even if the operation of the system is based on their preferred environmental conditions. [ABSTRACT FROM AUTHOR]

State-of-the-art manufacturing of tissue paper by Through Air Drying provides excellent product performance, although at a high production cost and energy use. In this work, a laboratory scale vacuum suction box was used to mimic the initial dewatering and the Through Air Drying molding, together with a pilot-scale trial. The purpose was to investigate both how the laboratory scale corresponds to pilot scale testing and investigate how fabric design, basis weight, beating, and fibers affect dewatering and sheet caliper. This study reevaluates dewatering mechanisms during molding, challenging the previous hypothesis of pure air displacement dewatering. Results show a parallel mechanism of compression dewatering and air displacement. The influence of rush transfer is examined, impacting the sheets' visual appearance, thickness, and solids content. Correlations between molding box solids content and headbox freeness emphasize significance of fibers and beating levels. Pilot results confirm the link between former solids and molding box solids. Pilot trials validate the laboratory results, facilitating comprehensive simulation of full-scale manufacturing. This research reveals dewatering mechanisms, highlights operational parameters, and enables effective Through Air Drying process design and refinement. [ABSTRACT FROM AUTHOR]

Energy-inefficient buildings are a major driver of climate change. The aging, energy inefficient housing occupied by low-income households in the US and other high-income countries is a driver of notable environmental and health disparities as well. Public policies for alleviating the financial and health burdens of energy inefficient housing have existed for decades, but fallen short of reaching most households in need. This paper proposes a promising new approach to filling gaps left by public programs -- one that relies on mobilizing the tools of private governance, carbon offsetting and the voluntary carbon market (VCM) to finance energy upgrades for low-income households. We develop a new and readily applicable methodology for calculating energy and carbon savings from efficiency upgrades based on readily accessible publicly available data. Tailored to the needs of estimating energy and carbon savings from efficiency upgrades to low-income units, rentals in particular, this methodology can be fruitfully deployed in monetizing the carbon savings from efficiency driven reductions in household energy use. Specifically, we propose packaging the emission reductions generated through energy savings as carbon offsets, then selling these offsets on the voluntary carbon market to generate financing for energy upgrades to low-income homes not served by public energy efficiency programs. Given the multiple economic and health co-benefits from low-income energy upgrades, we expect that carbon offsets generated through such upgrades will be attractive to many corporate and institutional offset buyers, particularly those who seek to fulfill climate commitments while also advancing economic and human development in their host communities. [ABSTRACT FROM AUTHOR]

The pursuit of climate neutrality requires global systemic actions involving the use of solutions aimed at reducing emissions. Changes must be introduced in all sectors affecting climate change, namely power engineering and district heating, construction, transport, and industry, as well as agriculture and forestry. Analyzing the structure of final energy consumption in the EU by sector, it can be stated that households account for 27% of the total energy consumption. Comprehensive actions are needed to increase the energy efficiency of buildings. The aim of this paper was to indicate aspects of improving energy efficiency in buildings and their equipment, taking into account the striving for climate neutrality. Analyzed possibilities and conditions of using various solutions of energy-efficient systems aimed at increasing energy resilience and security and preventing environmental degradation. Particular attention was paid to construction and material solutions, as well as installation solutions, which increased the accumulation and energy efficiency of the building. These activities are closely related to the conditions and dynamics of the heat exchange process in the applied solutions and are also related to the factors influencing thermal comfort and energy consumption in buildings. Due to the growing popularity of modern information technologies and artificial intelligence in energy management in recent years, this article reviews the latest research in this area. One of the directions of future research indicated by scientists is autonomous building control in real time, adapting to the momentary needs of users. The analysis of the possibilities of using modern energy efficiency solutions in buildings conducted in this work may be useful for optimizing heat and energy management models and models of society's consumption as an element of energy transformation towards climate neutrality and counteracting the deepening of energy poverty. [ABSTRACT FROM AUTHOR]

The Yangtze River's substantial variation in water depth and current speeds means that inland ships face diverse operational conditions within a single voyage. This paper discusses the adoption of controllable-pitch propellers, which adjust their pitch to adapt to varying navigational environments, thereby optimizing energy efficiency. We developed an optimization framework to determine the ideal pitch angle and rotation speed (RPM) under different sailing conditions. The energy performance model for inland ships was enhanced to account for the open-water efficiency of CPPs across various pitch angles and RPMs, considering the impacts of current and shallow water, among other factors. The optimization approach was refined by incorporating an improved genetic algorithm with an annealing algorithm to enhance the initial population, applying the K-means clustering algorithm for population segmentation, and using multi-parent crossover from diverse clusters. The efficacy of the optimization method for CPP operations was validated by analyzing three operational scenarios of a Yangtze sailing ship. Additionally, key components of the ship performance model were calibrated through experimental tests, demonstrating an anticipated fuel consumption reduction of approximately 5% compared to conventional fixed-pitch propellers. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the possibility of using two different methods together to detect more accurately and to evaluate thermal defects in the envelope of agricultural buildings. The structure of heated agricultural buildings is significantly influenced by the characteristics of the indoor environment (aggressive gaseous and liquid substances, high humidity, higher heating temperatures, etc.). In this study, research has been conducted that synergistically combines two methods for the evaluation of thermal defects, where the structure is evaluated using infrared thermography and at the same time described as a numerical model for a more detailed identification of envelope defects and thermal bridges. The study confirmed that in agricultural building structures, thermal defects are most commonly found in the areas of window lintels, ceiling and bracing structures, window and door joints, and foundation and floor joints, while the proposed combination of the two methods for assessing thermal defects in structures demonstrated superior performance in terms of accuracy and detail of results obtained compared to the standard partial use of only one of these methods. [ABSTRACT FROM AUTHOR]

Global warming is a challenge to the sustainable development of all humanity. Innovation is the main driver of the world's development and a powerful response to economic development and environmental protection. In this paper, a bidirectional fixed effect model and spatial Durbin model are used to explore the impact mechanism, heterogeneity and spatial impact of the low-carbon effect of technological innovation based on panel data of 279 Chinese cities from 2010 to 2019. The findings indicate that (1) technological innovation significantly negatively impacts carbon intensity, while the impact of high-quality technological innovation is greater. After the robustness test with the instrumental variable method and DID method, the conclusion is still valid. (2) The low-carbon effect is stronger in cities where governments pay more attention to green development and in central China. (3) Technological innovation improves energy efficiency (direct mechanism) and optimizes energy and industrial structures (indirect mechanism), and the digital economy strengthens this effect. (4) Furthermore, innovation can negatively impact the carbon intensity of adjacent cities. This paper provides new evidence and perspectives on the impact of technological innovation on carbon emissions more precisely at the city level. Additionally, this study provides an essential reference for countries to cope with global climate change and formulate differentiated policies among regions. [ABSTRACT FROM AUTHOR]

This paper aims to scrutinize the effect of Green technology innovation, digitalization, renewable energy use, environmental taxes, GDP, energy prices, and population on energy efficiency in a panel comprising 22 member nations of the European Union. Using the generalized least squares and the panel-corrected standard error, we found (1) the positive effect of digitalization, green patterns, and renewable sources on energy efficiency. (2) The environmental tax has a limited and insignificant effect. (3) On the contrary, the population, GDP, and energy prices negatively affect energy efficiency. Based on the findings, relevant economic and environmental policies have been proposed for energy, technology stakeholders, and policy decision-makers, including substantial investment in digital infrastructure to facilitate the adoption of smart grids, Internet of Things devices, and advanced data analytics for energy management [ABSTRACT FROM AUTHOR]

Sustainable technologies to replace current fossil solutions are essential to meet future CO2 emission reduction targets. Therefore, this paper compares key performance indicators of a hydrogen- and a methane-flame-fired crucible furnace with computational fluid dynamics simulations at identical firing powers, aiming to fully decarbonize the process. Validated numerical models from the literature were used to compare temperatures, radiation fields, radiation parameters and heat transfer characteristics. As a result, we observed higher combustion temperatures and a 19.0% higher fuel utilization rate in the hydrogen case, indicating more efficient operating modes, which could be related to the increased radiant heat flux and temperature ranges above 1750 K. Furthermore, higher scattering of the heat flux distribution on the crucible surface could be determined indicating more uneven melt bath temperatures. Further research could focus on quantifying the total fuel consumption required for the heating up of the furnace, for which a transient numerical model could be developed. [ABSTRACT FROM AUTHOR]

Deploying Cellular Internet of Things (CIoT) devices in urban and remote areas faces significant energy efficiency challenges. This is especially true for Narrowband IoT (NB-IoT) devices, which are expected to function on a single charge for up to 10 years while transmitting small amounts of data daily. The 3rd Generation Partnership Project (3GPP) has introduced energy-saving mechanisms in Releases 13 to 16, including Early Data Transmission (EDT) and Preconfigured Uplink Resources (PURs). These mechanisms extend battery life and reduce latency by enabling data transmission without an active Radio Resource Control (RRC) connection or Random Access Procedure (RAP). This paper examines these mechanisms using the LENA-NB simulator in the ns-3 environment, which is a comprehensive framework for studying various aspects of NB-IoT. The LENA-NB has been extended with PURs, and our analysis shows that PURs significantly enhance battery life and latency efficiency, particularly in high-density environments. Compared to the default RAP method, PURs reduce energy consumption by more than 2.5 times and increases battery life by 1.6 times. Additionally, PURs achieve latency reductions of 2.5–3.5 times. The improvements with PURs are most notable for packets up to 125 bytes. Our findings highlight PURs' potential to enable more efficient and effective CIoT deployments across various scenarios. This study represents a detailed analysis of latency and energy consumption in a simulated environment, advancing the understanding of PURs' benefits. [ABSTRACT FROM AUTHOR]

In the face of the rapid rise in global waste production and the pressing need to shift towards sustainable energy options, advanced Waste-to-Energy (WtE) technologies have emerged as a highly promising solution. These innovative technologies effectively utilize waste as a valuable resource, presenting a viable pathway for sustainable energy recovery and making a substantial contribution to the principles of the circular economy paradigm. This review provides a comprehensive overview of advanced WtE technologies, including thermal, biological, and chemical methods, such as gasification, pyrolysis, plasma arc gasification, anaerobic digestion, fermentation, transesterification, and hydrothermal carbonization. The efficiency of these technologies is evaluated based on their energy recovery potential, environmental impact, and economic feasibility. Case studies on successful implementations of advanced WtE technologies are analyzed to highlight their practicality and effectiveness. Finally, the paper addresses technical, regulatory, and policy challenges in this field and provides future perspectives. The objective is to underscore the role of advanced WtE technologies in achieving a sustainable and resource-efficient circular economy. [ABSTRACT FROM AUTHOR]

The energy challenges of overcoming climate change and economic and global political crises require not only the increased integration of renewable energies but also an optimisation of energy generation and use and, as a result, a reduction in energy consumption in various sectors. Thermal energy consumption in buildings in particular accounts for a significant proportion of final energy consumption. With respect to commercial buildings, a central problem in optimising the system settings is the lack of or only limited information about the actual room temperatures as well as the comfort requirements and temperature perception of the users in the rooms on the one hand and the operational management and settings specified by the facility management, for example, the heating curves of the heating circuits, on the other. The aim here is to create a bidirectional flow of data and information so that the compromise between the necessary room temperatures and the comfort of the users and the most energy-efficient operational management possible can be achieved. In this context, the paper presents a developed and tested web platform that makes it possible to optimise the operation of the system technology, particularly the heating system in the building, from an energy point of view and to involve the building user (e.g., office employees) and to pass on information to the facility management, thus pursuing a holistic approach. In the associated Living Lab project (called ComfortLab), it was possible to obtain over 6500 votes on temperature perception and combine this with building operation and the parameters relevant to facility management. This made it possible to bridge the gap between user requirements and room temperatures on the one hand and energy consumption and the inlet temperature of the heating system and supply circuits on the other. The use of the platform makes it possible to optimise the setpoint specification, specifically the inlet temperature of individual heating circuits, considering both regular building operation at times of presence and the setting of weekend and night setback times. The results show a diversified picture regarding temperature perception and possible room temperature reductions of several degrees Celsius and energy savings in the double-digit percentage range. [ABSTRACT FROM AUTHOR]

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified. [ABSTRACT FROM AUTHOR]

This review paper explores the significance of machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) techniques in improving traffic management based on cloud and mobile edge computing (MEC) architectures. The key findings and contributions of this review highlight the potential of these techniques for transforming traffic management systems through data-driven decision-making, adaptive control, and optimization. The challenges identified in this field include data availability and quality, scalability and computational requirements, privacy and security concerns, and ethical considerations. In conclusion, ML, DL, RL, and DRL techniques, in conjunction with cloud and MEC architectures, have significant implications for improving traffic management. Their ability to process and analyse large-scale and real-time traffic data enables improved traffic flow, reduced congestion, enhanced energy efficiency, and enhanced overall transportation system performance. [ABSTRACT FROM AUTHOR]

Introduction: To achieve substantial energy efficiency improvements in the privately owned building stock, it is important to communicate with potential renovators at the right point in time and provide them with targeted information to strengthen their renovation ambitions. The European Union recommends using one-stop-shops (OSSs), which provide information and support throughout the whole process, from planning to acquisition of funding, implementation, and evaluation as a measure to remove unnecessary barriers. Methods: For this paper, we invited visitors of two Norwegian websites with OSS characteristics to answer an online survey about their renovation plans and energy efficiency ambitions. The participants visited the websites out of their own interest; no recruitment for the websites was conducted as part of the study (N = 437). They also rated a range of psychological drivers, facilitators, and barriers to including energy upgrades in a renovation project. Their answers were then compared to existing data from representative samples of Norwegian households regarding home renovation in 2014, 2018, and 2023, as well as data from a sample of people who were engaged in renovation projects in 2014, which was collected by the research team with a similar online survey. Furthermore, 78 visitors completed a brief follow-up online survey one year later to report the implemented measures. Results: We found that visitors of the websites are involved in more comprehensive renovation projects and have substantially higher ambitions for the upgrade of energy efficiency compared to the representative samples. They also perceive stronger personal and social norms, as well as have a different profile of facilitators and barriers. Discussion: The findings suggest to policymakers that OSSs should be marketed especially to people motivated to upgrade energy efficiency but lack information and are unable to implement their plans alone. Also, the construction industry might refer interested people to such low-threshold online solutions to assist informed and more ambitious decisions. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the outlook of energy generation by means of transformation within the context of sustainable energy development. An analysis is conducted to assess the stability of energy systems so to implement cutting-edge energy production models at the national level, with a focus on a contemporary approach to energy modeling. Considering the energy transition and the existing constraints within the energy industry, the model assesses the feasibility of the practical advancement of renewable energy sources. The bottom-up energy model was used to determine how the components of energy development sustainability can be applied until the year 2050. To perform comparison testing with the reference state scenario, the LEAP energy model was used. This instrument was selected because of its ability to provide flexible and advanced options for selecting suitable parameters for energy transformation prediction. A progressive reduction in environmental pollution can be achieved by the deployment of current methods of energy generation by transformation until the year 2050 in Serbia, as indicated by the findings. The research highlights the significance of utilizing green energy sources for the continuing development of energy and the gradual reduction in environmental pollution through value co-creation. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal dynamics, and event-driven computation. The direct training algorithms based on the surrogate gradient method provide sufficient flexibility to design novel SNN architectures and explore the spatial-temporal dynamics of SNNs. According to previous studies, the performance of models is highly dependent on their sizes. Recently, direct training deep SNNs have achieved great progress on both neuromorphic datasets and large-scale static datasets. Notably, transformer-based SNNs show comparable performance with their ANN counterparts. In this paper, we provide a new perspective to summarize the theories and methods for training deep SNNs with high performance in a systematic and comprehensive way, including theory fundamentals, spiking neuron models, advanced SNN models and residual architectures, software frameworks and neuromorphic hardware, applications, and future trends. [ABSTRACT FROM AUTHOR]

Wireless local area networks (WLANs) have recently evolved into technologies featuring extremely high throughput and ultra-high reliability. As WLANs are predominantly utilized in Internet of Things (IoT) and Wi-Fi-enabled sensor applications powered by coin cell batteries, these high-efficiency, high-performance technologies often cause significant battery depletion. The introduction of the trigger frame-based uplink transmission method, designed to enhance network throughput, lacks adequate security measures, enabling attackers to manipulate trigger frames. Devices receiving such frames must respond immediately; however, if a device receives a fake trigger frame, it fails to enter sleep mode, continuously sending response signals and thereby increasing power consumption. This problem is specifically acute in next-generation devices that support multi-link operation (MLO), capable of simultaneous transmission and reception across multiple links, rendering them more susceptible to battery draining attacks than conventional single-link devices. To address this, this paper introduces a Secure Triggering Frame-Based Dynamic Power Saving Mechanism (STF-DPSM) specifically designed for multi-link environments. Experimental results indicate that even in a multi-link environment with only two links, the STF-DPSM improves energy efficiency by an average of approximately 55.69% over conventional methods and reduces delay times by an average of approximately 44.7% compared with methods that consistently utilize encryption/decryption and integrity checks. [ABSTRACT FROM AUTHOR]

Public transport is an important part of sustainable economic development, sustainable cities, and communities. Reducing energy consumption in public transport can be achieved through better organisation of the transport system, changes in infrastructure, the use of new energy-efficient means of transport, and other ways to achieve intelligent mobility. The operation of a city bus involves frequent stops. These stops are due to the need to exchange passengers at bus stops and traffic conditions. Each stop and the subsequent acceleration process require additional energy consumption. In this paper, an analysis of bus operation within the Rzeszów ITS on a selected route is carried out to determine the energy consumption in these special modes. First, the number and duration of stops were determined based on data recorded during the bus operation using the tracker. Then, taking into account the idle fuel consumption and the energy consumption required to reach a set speed, the total energy consumption associated with the stops was determined. The results obtained on the selected route indicate a significant share of energy associated with stops at bus stops and outside bus stops in total fuel consumption. These shares are about 26.2% and about 42.5%, respectively. The opportunity to improve the energy efficiency of the city bus on the route due to the reduction of stops at bus stops by introducing on-demand stops as one of the elements of ITS has been evaluated. The number of stops related to traffic conditions can be reduced by further improving traffic management and measures to modify urban infrastructure. [ABSTRACT FROM AUTHOR]

Urban water distribution systems (WDSs) exhibit significant energy potential that is currently dissipated in the form of excess pressure, either at brake-pressure tanks (BPTs) or pressure reduction valves (PRVs). Recent research focuses on the implementation of energy harvesting methods within WDSs in order to improve the energy efficiency of such systems. This paper provides a systematic review of the technologies developed for energy exploitation in WDSs, covering both their technical and economic aspects, while considering their reliability in providing water pressure regulation. Drawn from the existing literature and state of the art, a systematic analysis was carried out that specifies and categorizes the most essential parameters that impact the implementation of energy recovery turbines into WDSs. Different turbine types, design parameters, and performance properties, such as generation efficiency and pressure regulation precision, were considered. Finally, practical challenges and consequences emerging from the joint optimization of water and power systems are addressed. [ABSTRACT FROM AUTHOR]

The integration of wireless power transfer (WPT) with massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks can provide operational capabilities to energy-constrained Internet of Things (IoT) devices in cyber-physical systems such as smart autonomous vehicles. However, during downlink WPT, co-channel interference (CCI) can limit the energy efficiency (EE) gains in such systems. This paper proposes a user equipment (UE)–base station (BS) connection model to assign each UE to a single BS for WPT to mitigate CCI. An energy-efficient resource allocation scheme is developed that integrates the UE–BS connection approach with joint optimization of power control, time allocation, antenna selection, and subcarrier assignment. The proposed scheme improves EE by 24.72% and 33.76% under perfect and imperfect CSI conditions, respectively, compared to a benchmark scheme without UE–BS connections. The scheme requires fewer BS antennas to maximize EE and the distributed algorithm exhibits fast convergence. Furthermore, UE–BS connections' impact on EE provided significant gains. Dedicated links improve EE by 24.72% (perfect CSI) and 33.76% (imperfect CSI) over standard connections. Imperfect CSI reduces EE, with the proposed scheme outperforming by 6.97% to 12.75% across error rates. More antennas enhance EE, with improvements of up to 123.12% (conventional MIMO) and 38.14% (massive MIMO) over standard setups. Larger convergence parameters improve convergence, achieving EE gains of 7.09% to 11.31% over the baseline with different convergence rates. The findings validate the effectiveness of the proposed techniques in improving WPT efficiency and EE in wireless-powered MIMO–NOMA networks. [ABSTRACT FROM AUTHOR]

Distributed Denial of Service (DDoS) attacks disrupt service availability, leading to significant financial setbacks for individuals and businesses. This paper introduces Eye-Net, a deep learning-based system optimized for DDoS attack detection that combines feature selection, balancing methods, Multilayer Perceptron (MLP), and quantization-aware training (QAT) techniques. An Analysis of Variance (ANOVA) algorithm is initially applied to the dataset to identify the most distinctive features. Subsequently, the Synthetic Minority Oversampling Technique (SMOTE) balances the dataset by augmenting samples for under-represented classes. Two distinct MLP models are developed: one for the binary classification of flow packets as regular or DDoS traffic and another for identifying six specific DDoS attack types. We store MLP model weights at 8-bit precision by incorporating the quantization-aware training technique. This adjustment slashes memory use by a factor of four and reduces computational cost similarly, making Eye-Net suitable for Internet of Things (IoT) devices. Both models are rigorously trained and assessed using the CICDDoS2019 dataset. Test results reveal that Eye-Net excels, surpassing contemporary DDoS detection techniques in accuracy, recall, precision, and F1 Score. The multiclass model achieves an impressive accuracy of 96.47% with an error rate of 8.78%, while the binary model showcases an outstanding 99.99% accuracy, maintaining a negligible error rate of 0.02%. [ABSTRACT FROM AUTHOR]

The purpose of this research is to compare clustering methods and pick up the optimal clustered approach for rural building energy consumption data. Research undertaken so far has mainly focused on solving specific issues when employing the clustered method. This paper concerns Yushan island resident's time-series electricity usage data as a database for analysis. Fourteen algorithms in five categories were used for cluster analysis of the basic data sets. The result shows that Km_Euclidean and Km_shape present better clustering effects and fitting performance on continuous data than other algorithms, with a high accuracy rate of 67.05% and 65.09%. Km_DTW is applicable to intermittent curves instead of continuous data with a low precision rate of 35.29% for line curves. The final conclusion indicates that the K-means algorithm with Euclidean distance calculation and the k-shape algorithm are the two best clustering algorithms for building time-series energy curves. The deep learning algorithm can not cluster time-series-building electricity usage data under default parameters in high precision. [ABSTRACT FROM AUTHOR]

Clothing adjustment by building occupants is a highly effective and prevalent thermal adaptation behavior aimed at achieving thermal comfort. This paper aims to quantify the impact of climate-responsive indoor clothing adaptation on heating/cooling energy consumption. A climate-responsive indoor temperature control strategy based on rural residents' indoor clothing adaptation was proposed and integrated into building energy simulations. Indoor clothing insulations were obtained using a predictive model from the author's prior research. These values were used to calculate indoor setpoint temperatures in terms of the PMV model, which were then input into the building energy simulations. The simulations were conducted using "Ladybug Tools" in Grasshopper. Four simulation scenarios were proposed for winter and summer, respectively, to compare heating/cooling energy use with different indoor clothing strategies (constant and dynamic) and thermal comfort requirements (neutral and 80% acceptable). The results indicated that indoor clothing adaptation significantly reduced indoor setpoint temperatures by 5.0–6.7 °C in winter. In contrast, the impacts on summer indoor setpoint temperatures were not significant. The impacts of indoor clothing adaptation on energy use were evident in both seasons and more pronounced in winter. With a neutral thermal comfort requirement (PMV = 0), total heating and cooling energy use decreased by 35.6% and 20.2%, respectively. The influence was further enhanced with lower indoor thermal comfort requirements. With an 80% acceptable thermal comfort requirement ( P M V = ± 0.85 ), total heating and cooling energy use decreased by 63.1% and 34.4%, respectively. The climate-responsive indoor temperature control strategy based on indoor clothing adaptation and its impact on heating/cooling energy consumption suggested a viable approach for improving building energy efficiency in China's rural area and similar cost-sensitive and fuel-poverty contexts. [ABSTRACT FROM AUTHOR]

As the global deployment of Fifth Generation (5G) is being well consolidated, the exploration of Sixth Generation (6G) wireless networks has intensified, focusing on novel Key Performance Indicators (KPIs) and Key Value Indicators (KVIs) that extend beyond traditional metrics like throughput and latency. As 5G begins transitioning to vertical-oriented applications, 6G aims go beyond, providing a ubiquitous communication experience by integrating diverse Radio Access Networks (RANs) and fixed-access networks to form a hyper-converged edge. This unified platform will enable seamless network federation, thus realizing the so-called network of networks vision. Emphasizing energy efficiency, the present paper discusses the importance of reducing telecommunications' environmental impact, aligning with global sustainability goals. Central to this vision is the proposal of a novel user plane network protocol architecture, called 6G Recursive User Plane Architecture (6G-RUPA), designed to be scalable, flexible, and energy-efficient. Briefly, 6G-RUPA offers superior flexibility in network adaptation, federation, scalability, and mobility management, aiming to enhance overall network performance and sustainability. This study provides a comprehensive analysis of 6G's potential, from its conceptual framework to the high-level design of 6G-RUPA, addressing current challenges and proposing actionable solutions for next-generation mobile networks. [ABSTRACT FROM AUTHOR]

This paper uses the exogenous shock of China's formal implementation of the environmental information disclosure system in 2008 to construct a quasi-natural experiment. Based on the panel data of prefecture-level cities from 2003 to 2019, the propensity score matching and difference in difference (PSM-DID) approach was used to systematically evaluate the impact of environmental information disclosure on energy efficiency. Overcoming the difficulties in measuring environmental information disclosure and the endogenous problem, this paper investigates the energy-saving effect of environmental information disclosure and illustrates its mechanism for the first time. The results show that environmental information disclosure significantly improved energy efficiency, and public participation played an important role in energy conservation, a conclusion that remained true after a series of robustness tests. The test of the impact mechanism shows that environmental information disclosure can achieve the goal of improving energy efficiency by promoting industrial structure upgrading and technological innovation. This paper enriches the discussion on the relationship between environmental information disclosure and energy efficiency, and provides useful policy inspiration for improving the level of energy efficiency and achieving sustainable economic development. [ABSTRACT FROM AUTHOR]

Featured Application: The main application of this paper is to review and improve a model that enables the use of water heaters by small electricity customers in electricity markets and services in the short and medium term. For this goal, it is necessary to review the service of loads to avoid customer discomfort and the way control mechanisms work, usually thermostats. To guarantee a minimum level of accuracy in the flexibility of the response, the understanding of heat and electric processes in the load is a cornerstone for the participation of these appliances. This must be done by comprehensive validation of the model in different conditions, including water draws, temperature, and load control. To exemplify the potential of the model, an application in a prosumer scenario is presented. Residential segments are of the greatest interest from the point of view of Demand-Side Resources and Decarbonization. Main end-uses such as water heaters, heating, and cooling have interesting opportunities: first, they can store energy, and this is relevant for the integration of renewables. Second, they are candidates for efficiency and electrification, increasing their demand share and the flexibility of demand. This paper aims to formulate an elemental Physical-Based Heat Pump Water Heater model that will enable the use of these energy-efficient appliances through aggregation in complex products, considering the advantages for demand and supply sides. Simulation results show that the individual performance is quite accurate and that the proposed model is flexible enough to be used to take more profit from energy markets or to easily respond to fast-occurring events. The model can be easily aggregated and used to obtain baselines, an important point for Demand Response evaluation. Results also demonstrate that demand–supply coordination and balance can be improved using these models to reduce or mitigate the risks and volatility of renewables without inducing a noticeable loss of service. Consequently, the contribution of this responsive load can be modelled through this methodology, making the engagement of more customer segments in Demand Response policies more credible and deploying new segments, such as prosumers. [ABSTRACT FROM AUTHOR]

The research addresses the pressing need to understand wind conditions for optimal wind energy utilization, a crucial aspect of achieving global energy sustainability. While previous studies have focused on assessing wind energy potential and terrain roughness, gaps persist in understanding how wind and spatial conditions affect blade geometry. Therefore, this study aims to investigate the impact of local wind conditions on wind turbine blade geometry, integrating nonuniform wind inflow due to wind shear into the theoretical model. Through the paper, it explores blade design for varying inflow conditions and analyses the relationship between terrain type and blade designs. A novel approach to wind turbine blade design, utilizing a theoretical model that combines the local momentum theorem with insights from propeller vortex theory was adopted. Through the analysis, the authors demonstrate that variations in terrain significantly influence blade geometry, underscoring the necessity for tailored turbine designs based on local conditions. The findings reveal discrepancies in blade geometry across different turbine capacities and terrain types, offering novel insights into wind turbine performance optimization. By tailoring blade geometry to specific locations enhances resource utilization, offering insights for energy policymakers. The research provides validated methods for optimizing turbine geometric parameters, opening avenues for increased local investment in wind energy and promoting distributed energy development. Illustrated with wind plants examples, the paper concludes by outlining future research directions in this field. [ABSTRACT FROM AUTHOR]

Modern building automation systems implement plenty of advanced control and monitoring functions that consider various parameters like users' activity, lighting, temperature changes, etc. Moreover, novel solutions based on the Internet of Things and cloud services are also being developed for smart buildings to ensure comfort of use, user safety, energy efficiency improvements, and integration with smart grids and smart city platforms. Such a wide spectrum of technologies and functions requires a novel approach in building automation systems design to provide effective implementation and flexibility during operation. At the same time, in the building design and operation industries, tools based on building information modeling and digital twins are being developed. This paper discusses the development directions and application areas of these solutions, identifying new trends and possibilities of their use in smart homes and buildings. In particular, the focus is on procedures for selecting automation functions, effective integration, and interoperability of building management systems with the Internet of Things, considering the organization of prediction mechanisms and dynamic functional changes in buildings and smart networks. Chosen solutions and functions should consider the requirements set out in the EN ISO 52120 standard and the guidelines defined for the Smart Readiness Indicator. [ABSTRACT FROM AUTHOR]

Energy efficiency is crucial for achieving a balance between economic growth and sustainable development. As carbon-neutral targets continue to gain momentum, green investments are becoming increasingly important. Therefore, it is critical to investigate the relationship between green investment and energy efficiency. This paper aims to fill a gap in the macroeconomic literature by using a generalized method of moments (GMM) technique based on panel data from 30 provinces in China from 2006 to 2019. The econometric results indicate that green investment is positively associated with energy efficiency. Additionally, green investment indirectly promotes energy efficiency by fostering technology innovation and research and development (R&D) intensity. The results also suggest that more advanced digital development is linked to improvements in energy efficiency. Furthermore, regions characterized by higher levels of digital economy exhibit a more pronounced contribution of green investment to energy efficiency. Based on these findings, this paper provides valuable insights for Chinese policymakers on green investment and low-carbon development. [ABSTRACT FROM AUTHOR]

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The urgent need to achieve net-zero carbon emissions by 2050 has led to a growing focus on innovative approaches to producing, storing, and consuming energy. Integrated energy systems (IES) have emerged as a promising solution, capitalising on synergies between energy networks and enhancing efficiency. Such a holistic approach enables the integration of renewable energy sources and flexibility provision from one energy network to another, reducing emissions while facilitating strategies for operational optimisation of energy systems. However, emphasis has been mostly made on steady-state methodologies, with a dynamic verification of the optimal solutions not given sufficient attention. To contribute towards bridging this research gap, a methodology to verify the outcomes of an optimisation algorithm is presented in this paper. The methodology has been applied to assess the operation of a civic building in the UK dedicated to health services. This has been done making use of real energy demand data. Optimisation is aimed at improving power dispatch of the energy system by minimising operational costs and carbon emissions. To quantify potential discrepancies in power flows and operational costs obtained from the optimisation, a dynamic model of the IES that better captures real-world system operation is employed. By incorporating slow transients of thermal systems, control loops, and non-linearity of components in the dynamic model, often overlooked in traditional optimisation modules, the methodology provides a more accurate assessment of energy consumption and operational costs. The effectiveness of the methodology is assessed through model-in-the- loop co-simulations between MATLAB/Simulink and Apros alongside a series of scenarios. Results indicate significant discrepancies in power flows and operational costs between the optimisation and the dynamic model. These findings illustrate potential limitations of conventional operational optimisation modules in addressing real-world complexities, emphasising the significance of dynamic verification methods for informed energy management and decision-planning. [ABSTRACT FROM AUTHOR]

A prerequisite for decreasing the intensification of energy in buildings is to evaluate and understand the influencing factors of building energy performance (BEP). These factors include building envelope features and outdoor climactic conditions, among others. Based on the importance of the influencing factors in the development of the building energy prediction model, various researchers are continuously employing different types of factors based on their popularity in academic literature, without a proper investigation of the most relevant factors, which, in some cases, potentially leads to poor model performance. However, this can be due to the absence of an adequate comprehensive analysis or review of all factors influencing BEP ubiquitously. Therefore, this paper conducts a holistic and comprehensive review of studies that have explored the various factors influencing energy use in residential and commercial buildings. In total, 74 research articles were systematically selected from the Scopus, ScienceDirect, and Institute of Electrical Electronics Engineers (IEEE) databases. Subsequently, by means of a systematic and bibliometric analysis, this paper comprehensively analyzed several important factors influencing BEP. The results reveals the important factors (such as windows and roofs) and engendered or shed light on the application of some energy-efficient strategies such as the utilization of a green roof and photovoltaic (PV) window, among others. [ABSTRACT FROM AUTHOR]