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Energy is crucial to the development of human civilization. Energy infrastructure, such as oil and gas pipelines, power generation systems, and storage facilities, provide core support for the exploitation and utilization of various types of energy. Thus, energy infrastructure is vital to the economic sustainable development of a country. This paper provides the motivations and a brief introduction to the Special Issue entitled "Frontiers in Construction Technology of Advanced Energy Infrastructure", which aims to present advanced technologies and theories for energy infrastructure. The primary challenges in the current construction technology of energy infrastructure are described. Furthermore, the focus of current research in this field addressed in this Special Issue is presented. A comparison of the articles included or considered for inclusion in this Special Issue with other available literature on this topic is performed, which proves the prospects and relevance of this Special Issue. Finally, perspectives on future directions of energy utilization and energy infrastructure construction are provided. [ABSTRACT FROM AUTHOR]

In this study, the performance characteristics of an R404A indirect refrigeration system (IRS) applied to an internal heat exchanger (IHX) is evaluated for supermarkets and hypermarkets. In a direct expansion system, R404A is the primary refrigerant and R744, a brine, is the secondary fluid. While there are abundant studies analyzing the theoretical performance of IRS, experimental research on IRS is lacking, and there are no papers that address the results of changes in the IHX in detail. In this study, the results achieved by modifying various parameters are experimentally evaluated to provide fundamental data for designing the optimal IRS. In the main results, looking at the trend of the increase in IHX efficiency, the change is very minimal when the efficiency is above 50%, so it is ideal to apply an IHX efficiency of about 50% considering economics and COP, etc. Applying the results in this study enables the operation and maintenance of IRSs as an eco-friendly system by achieving energy efficiency through optimizing the system coefficient of performance and securing economic feasibility by minimizing the R404A charging amount of the refrigeration cycle. To serve supermarkets and hypermarkets, R744 as a secondary fluid may help to realize an ecologically friendly, compact IRS system with a high heat transfer coefficient that can operate at low temperatures (−35 to 5 °C). [ABSTRACT FROM AUTHOR]

The construction industry plays a pivotal role in China's achievement of its "dual carbon" goals. This study conducts a decomposition analysis of the carbon emissions from the construction industry (CECI) at both national and provincial levels for the period 2010–2020 and employs the ARIMA model to predict the short-term peak trends at the provincial level. The findings are as follows. (1) Inner Mongolia, Shandong, Sichuan, and Chongqing exhibit an N-shaped trend in CECI, while the northeast region shows an inverted U-shaped trend. (2) Labor productivity and energy intensity are identified as the largest and smallest drivers of national CECI growth, respectively, with the driving force of the study's identified factors fluctuating between 1% and 60%. (3) Energy intensity significantly contributes to the growth of CECI in Tianjin and Zhejiang, while it aids in reducing CECI in western provinces. The "rebound effect" of building energy efficiency is particularly pronounced in provinces with strong resource endowments, such as Ningxia. (4) Between 2021 and 2025, CECI is predicted to decrease in the northern and economically developed provinces, while it is expected to increase in central and western provinces, with Heilongjiang, Shandong, Guangdong, Shanghai, and Shaanxi potentially reaching their peaks within the forecast period. The paper concludes with several recommendations. [ABSTRACT FROM AUTHOR]

The growth of electric vehicles (EVs) and their integration into existing and future buildings bring new considerations for energy efficiency (EE) and balance when combined with renewable energy. However, for buildings with an energy efficiency label, such as Near Zero Energy Building (NZEB) or Positive Energy Building (PEB), the introduction of EVs may result in the declassification of the EE label due to the additional energy required for the charging infrastructure. This underscores the increasing relevance of demand-side management techniques to effectively manage and utilize energy consumption and generation in buildings. This paper evaluates the influence of electric vehicle (EV) charging on NZEB/PEB-labeled buildings of the Brazilian Building Labeling Program (PBE Edifica). Utilizing on-site surveys, computational modeling, and thermos-energetic analysis with software tools such as OpenStudio v. 1.1.0 and EnergyPlus v. 9.4.0, an energy classification was conducted in a building in the city of Belem, State of Para, Brazil. Subsequently, power flow simulations employing probabilistic models and Monte Carlo approaches were executed in the OpenDSS software v. 10.0.0.2 to examine the impact of EV integration, both with and without the implementation of demand-side management techniques. Analyses using the labeling methodology demonstrated that the building has EE level C and NZEB self-sufficiency classification. The assessment of the impact of EV integration on the building's total energy consumption in the base (current) scenario was carried out in two scenarios, with (2) and without (1) supply management. Scenario 01 generated a 69.28% increase in energy consumption, reducing the EE level to D and resulting in the loss of the NZEB class. Scenario 02 resulted in a smaller increase in energy consumption of 40.50%, and guaranteed the return of the NZEB class lost in scenario 1, but it was not enough to return the EE level to class C. The results highlight the need for immediate and comprehensive energy management strategies, as the findings show that the two scenarios present a difference of 41.55% in energy consumption. Nonetheless, these strategies are not enough if other consumption restrictions or energy efficiency measures are not applied to other building systems. [ABSTRACT FROM AUTHOR]

With the explosion in energy consumption demand, the deep penetration of renewable energy into the grid is inevitable and has become trend across the world today. Microgrids with integrated renewable energy are the core components of smart grids and will permeate all areas of human activity. Although this grid has a very flexible working principle, its heavy reliance on renewable energy sources can cause significant disturbances to the electric transmission system. Therefore, the control and monitoring processes for microgrids must be implemented through various mechanisms to ensure the microgrid system operates safely, stably, and effectively. In this paper, the research team will introduce and synthesize the multilevel control scheme of current types of microgrids. We will evaluate the advantages and disadvantages of each type of MG, providing a reference for further research in the field of microgrid control applications, both current and in the near future. [ABSTRACT FROM AUTHOR]

Following the required adjustments made by the European Union (EU) to adequately absorb the negative social and economic impacts of the COVID-19 pandemic, the EU is once again confronting a crisis. The extended fiscal instability and environmental imbalance resulting from the energy crisis, primarily caused by rising energy prices owing to geopolitical upheavals (the Russian invasion in Ukraine), have been compounded by rising inflation. The main research objective of this paper is the analysis and evaluation of the effects of the current energy crisis on the Greek economy through the perspective of energy poverty, energy dependence, and climate change. Greece has been negatively impacted by the significant rise in energy costs. In 2022, the percentage of the general population that faced difficulties in paying energy bills exceeded the European average, reaching the level of 34.1%, while almost 19% of the population could not keep their homes sufficiently warm. Additionally, in 2022, Greece was one of the countries most energy-dependent on Russia. Greece achieved most of its targets regarding climate change, with the most representative example being the reduction of GHG emissions by 42% from 2000 to 2022. However, this reduction did not come from the successful green transformation of the Greek economy, but instead was due to the reduction in overall energy consumption that came from the prolonged economic crisis, combined with the restrictions of the COVID-19 pandemic. Nevertheless, the majority of Greek buildings are still not considered to be energy efficient, while the transportation industry continues to rely heavily on oil, coal, and natural gas. [ABSTRACT FROM AUTHOR]

The rapid progress in renewable energy sources and the increasing complexity of energy distribution networks have highlighted the need for efficient and intelligent energy management systems. This paper presents a comparative analysis of two optimisation algorithms, P and M70, used for the optimal control of the operation of microgrids in islanded mode. The main objective is to minimise production costs while ensuring a reliable energy supply. Algorithm P prioritises the use of photovoltaic (PV) and battery storage and operates the diesel generator at minimum capacity to reduce fuel consumption and maximise the use of renewable energy sources. Algorithm M70, on the other hand, uses a heuristic approach to adaptively manage energy resources in real time. In this study, the performance of both algorithms is evaluated through simulation in different operating scenarios. The results show that both algorithms significantly improve the efficiency of the microgrid, with the M70 algorithm showing better adaptability and cost efficiency in dynamic environments. This research contributes to ongoing efforts to develop robust and scalable energy management systems for future smart grids. [ABSTRACT FROM AUTHOR]

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront, and the research interest in microgrids that rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper builds on the existing research framework by combining PPO with machine learning-based load forecasting to produce an optimal solution for an industrial microgrid in Norway under different pricing schemes, including day-ahead pricing and peak pricing. It addresses the peak shaving and price arbitrage challenges by taking the historical data into the algorithm and making the decisions according to the energy consumption pattern, battery characteristics, PV production, and energy price. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared with logic-based optimization. These findings contribute to digitalization and decarbonization of energy technology, and support the fundamental goals and policies of the European Green Deal. [ABSTRACT FROM AUTHOR]

Featured Application: The work in this paper provides some basic theoretical and practical reference value for the energy and exergy analysis, calculation, process optimization, and energy conservation research of the hydrogen-based direct reduction shaft furnace process, which is helpful for promoting its further industrial application. The traditional production mode using coal as the main energy source is not conducive to the sustainable development of the iron and steel industry (ISI). The hydrogen-based direct reduction shaft furnace (HDRSF) process is a feasible technical route for promoting the green development of the ISI. However, there is a lack of comprehensive analysis with respect to the energy utilization and process flow of the HDRSF method. To address these issues, a systemic material–energy–exergy model of HDRSF is established. An improved HDRSF process incorporating waste heat recovery is also proposed, and energy consumption intensity and exergy intensity are used as assessment metrics. This study's findings indicate that the proposed waste heat recovery can considerably lower gas demand and energy consumption intensity, but exergy intensity has little effect. The reducing gas demand drops from 2083 m3 to 1557 m3, the energy consumption intensity drops from 2.75 × 107 kJ to 1.70 × 107 kJ, and the exergy intensity drops from 1.08 × 107 kJ to 1.05 × 107 kJ when the reducing gas temperature is 900 °C, H2:CO = 1:1; meanwhile, the recovery rate of waste heat reaches 40%. This study can serve as a reference for actual HDRSF process production. [ABSTRACT FROM AUTHOR]

With the continuous development of the mining industry and advancements in deep mining technology, mine environment optimization has become key to ensuring safety and improving the efficiency of mining. The high-temperature environment, particularly in deep mines, not only poses a serious threat to miners' health but also significantly reduces operational efficiency. These issues have been determined based on the current application status and development trends of mine cooling technology, including traditional mechanical and non-mechanical cooling technologies, as well as emerging roadway insulation materials and mine cooling clothing applications. By comparing the advantages and disadvantages of each technology, the main challenges related to the use of current mine cooling technologies are pointed out, including the low energy efficiency ratio, high cost, and difficult implementation. Finally, this paper looks forward to the future development directions of mine cooling technologies, emphasizing the importance of intelligent, energy-saving, and environment-improving comprehensive system management and, in turn, promoting the progress and application of mine environment optimization technology and supporting safe and efficient deep mining. [ABSTRACT FROM AUTHOR]

The intermittent and stochastic characteristics of wind power pose a higher demand on the complementarity of hydropower. Studying the optimal coordinated operation of hydro–wind power systems has become an extremely effective way to create safe and efficient systems. This paper aims to study the optimal coordinated operation of a hybrid power system based on a newly established Simulink model. The analysis of the optimal coordinated operation undergoes two simulation steps, including the optimization of the complementary mode and the optimization of capacity allocation. The method of multiple complementary indicators is adopted to enable the optimization analysis. The results from the complementary analysis show that the hydraulic tracing effect obviously mitigates operational risks and reduces power losses under adverse wind speeds. The results from the analysis of capacity allocation also show that the marginal permeation of installed wind capacity will not exceed 250 MW for a 100 MW hydropower plant under random wind speeds. These simulation results are obtained based on the consideration of some real application scenarios, which help power plants to make the optimal operation plan with a high efficiency of wind energy and high hydro flexibility. [ABSTRACT FROM AUTHOR]

The self-excited oscillation system, owing to its capability of harvesting environmental energy, exhibits immense potential in diverse fields, such as micromachines, biomedicine, communications, and construction, with its adaptability, efficiency, and sustainability being highly regarded. Despite the current interest in track sliders in self-vibrating systems, LCE fiber-propelled track sliders face significant limitations in two-dime nsional movement, especially self-rotation, necessitating the development of more flexible and mobile designs. In this paper, we design a spatial slider system which ensures the self-rotation of the slider propelled by a light-fueled LCE fiber on a rigid circular track. A nonlinear dynamic model is introduced to analyze the system's dynamic behaviors. The numerical simulations reveal a smooth transition from the static to self-rotating states, supported by ambient illumination. Quantitative analysis shows that increased light intensity, the contraction coefficient, and the elastic coefficient enhance the self-rotating frequency, while more damping decreases it. The track radius exhibits a non-monotonic effect. The initial tangential velocity has no impact. The reliable self-rotating performance under steady light suggests potential applications in periodic motion-demanding fields, especially in the construction industry where energy dissipation and utilization are of utmost urgency. Furthermore, this spatial slider system possesses the ability to rotate and self-vibrate, and it is capable of being adapted to other non-circular curved tracks, thereby highlighting its flexibility and multi-use capabilities. [ABSTRACT FROM AUTHOR]

The utilization of inertial measurement units as wearable sensors is proliferating across various domains, such as health care, sports, and rehabilitation. This expansion has produced a market of devices tailored to accommodate very specific ranges of operational demands. Simultaneously, this growth is creating opportunities for the development of a new class of devices more oriented towards general-purpose use and capable of capturing both high-frequency signals for short-term, event-driven motion analysis and low-frequency signals for extended monitoring. For such a design, which combines flexibility and low cost, a rigorous evaluation of the device in terms of deviation, noise levels, and precision is essential. This evaluation is crucial for identifying potential improvements and refining the design accordingly, yet it is rarely addressed in the literature. This paper presents the development process of such a device. The results of the design process demonstrate acceptable performance in optimizing energy consumption and storage capacity while highlighting the most critical optimizations needed to advance the device towards the goal of a smart, general-purpose unit for human motion monitoring. [ABSTRACT FROM AUTHOR]

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance. [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]

Traditional multi-agent path planning algorithms often lead to path overlap and excessive energy consumption when dealing with cooperative tasks due to the single-agent-single-task configuration. For this reason, the "many-to-one" cooperative planning method has been proposed, which, although improved, still faces challenges in the vast search space for meeting points and unreasonable task handover locations. This paper proposes the Cooperative Dynamic Priority Safe Interval Path Planning with a multi-meeting-point and single-meeting-point solving mode switching (Co- D P S I P P m s ) algorithm to achieve multi-agent path planning with task handovers at multiple or single meeting points. First, the initial priority is set based on the positional relationships among agents within the cooperative group, and the improved Fermat point method is used to locate multiple meeting points quickly. Second, considering that agents must pick up sub-tasks or conduct task handovers midway, a segmented path planning strategy is proposed to ensure that cooperative agents can efficiently and accurately complete task handovers. Finally, an automatic switching strategy between multi-meeting-point and single-meeting-point solving modes is designed to ensure the algorithm's success rate. Tests show that Co- D P S I P P m s outperforms existing algorithms in 1-to-1 and m-to-1 cooperative tasks, demonstrating its efficiency and practicality. [ABSTRACT FROM AUTHOR]

Using unmanned aerial vehicles (UAVs) is an effective way to gather data from Internet of Things (IoT) devices. To reduce data gathering time and redundancy, thereby enabling the timely response of state-of-the-art systems, one can partition a network into clusters and perform aggregation within each cluster. Existing works solved the UAV trajectory planning problem, in which the energy consumption and/or flight time of the UAV is the minimization objective. The aggregation scheduling within each cluster was neglected, and they assumed that data must be ready when the UAV arrives at the cluster heads (CHs). This paper addresses the minimum time aggregation scheduling problem in duty-cycled networks with a single UAV. We propose an adaptive clustering method that takes into account the trajectory and speed of the UAV. The transmission schedule of IoT devices and the UAV departure times are jointly computed so that (1) the UAV flies continuously throughout the shortest path among the CHs to minimize the hovering time and energy consumption, and (2) data are aggregated at each CH right before the UAV arrival, to maximize the data freshness. Intensive simulation shows that the proposed scheme reduces up to 35% of the aggregation delay compared to other benchmarking methods. [ABSTRACT FROM AUTHOR]

The results of measurements of the efficiency of energy harvesting from commonly available general-purpose telecommunications systems, divided into typical bands available under European conditions, have been presented in this paper. Specially designed harvesters were used, dedicated to powering autonomous semi-passive Radio Frequency Identification (RFID) tags. For the assumed resistive loads, the achievable output voltage values of the harvesters were measured across a wide spectrum of electromagnetic field strengths, simulating real conditions. The performance and dynamics of the energy storage process with fixed parameters were studied at an intermediate stage, before the energy conditioning process. The harvesters were treated as typical energy sources with unknown but variable parameters, so their dynamic parameters and instantaneous energy supply were also analyzed. These activities will enable the final development of a power supply system with parameters acceptable for the planned applications and whose efficiency will be maximized under the given conditions. For this purpose, the energy harvesting systems were designed, a suitable laboratory stand was built, and the elaborated circuits were measured to determine the expected parameters of energy harvesting. [ABSTRACT FROM AUTHOR]

The paper presents a novel decision support system designed to improve the efficiency and effectiveness of decision-making for electric arc furnace (EAF) operators. The system integrates two primary tools: the EAF Simulator, which is based on advanced mechanistic models, and the EAF Optimiser, which uses data-driven models trained on historical data. These tools enable the simulation and optimisation of furnace settings in real time and provide operators with important insights. A key objective was to develop a user-friendly interface with the Siemens Insights Hub Cloud Service and Node-RED that enables interactive management and support. The interface allows operators to analyse and compare past and simulated batches by adjusting the input data and parameters, resulting in improved optimisation and reduced costs. In addition, the system focuses on the collection and pre-processing of input data for the simulator and optimiser and uses Message Queuing Telemetry Transport (MQTT)communication between the user interfaces and models to ensure seamless data exchange. The EAF Simulator uses a comprehensive mathematical model to simulate the complex dynamics of heat and mass transfer, while the EAF Optimiser uses a fuzzy logic-based approach to predict optimal energy consumption. The integration with Siemens Edge Streaming Analytics ensures robust data collection and real-time responsiveness. The dual-interface design improves user accessibility and operational flexibility. This system has significant potential to reduce energy consumption by up to 10% and melting times by up to 15%, improving the efficiency and sustainability of the entire process. [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]

The energy management system of a fuel cell electric robot should be highly responsive to provide the required power for various tactical operations, navigation of different routes, and acceleration. This paper presents a new multi-level online energy management strategy for a fuel cell electric robot based on the proposed functions of equivalent hydrogen fuel value evaluation, classification of the battery state of charge via the squared combined efficiency function, identification of the robot maneuver condition based on the proposed operation state of robot function, improvement of the overall energy efficiency based on the proposed function of the battery overcharge control, and separation of the functional points of the fuel cell based on the operational mode control strategy. The simulation study of the proposed online multi-level energy management strategy was carried out with MATLAB R2018b software to verify its superiority by comparing with other strategies. The results indicate a reduction in hydrogen consumption, reduction in fuel cell power fluctuations, prevention of battery overcharging, and incrementation in the total energy efficiency of energy storage systems compared to other energy management strategies. [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]

One of the key advantages of terahertz (THz) communication is its potential for energy efficiency, making it an attractive option for green communication systems. Coherent THz transmission technology has recently been explored in the literature. However, there exist few error performance results for such a wireless link employing coherent THz technology. In this paper, we explore a comprehensive terrestrial channel model designed for wireless line-of-sight communication using THz frequencies. The performance of coherent THz links is analyzed, and it is found to be notably affected by two significant factors, atmospheric turbulence and pointing errors. These could occur between the terahertz transmitter and receiver in terrestrial links. The exact and asymptotic solutions are derived for bit error rate and interrupt probability for binary phase-shift keying coherent THz systems, respectively, over log-normal and Gamma–Gamma turbulent channels. The asymptotic outage probability analysis is also performed. It is shown that the presented results offer a precise estimation of coherent THz transmission performance and its link budget. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect is one of the largest climate-related issues concerning our cities due to the localized temperature increase in highly urbanized areas. This paper aims to investigate the impact of UHI mitigation techniques in promoting climate resilience, by reducing urban air temperatures and cooling energy consumption in buildings. To this end, four mitigation solutions regarding the building envelope—green roofs, green walls, cool roofs, and cool walls—were investigated for the city of Bari in Southern Italy and compared with the current baseline scenario. Hence, five scenarios were simulated—using the ENVI-met microclimate software—during three representative summer days, and the resulting microclimate changes were assessed. Based on these analyses, new climate files—one for each scenario—were generated and used as input to run energy simulations in EnergyPlus to estimate the building cooling consumption. Coupling the microclimate and the consumption outcomes, the mitigation strategies were evaluated from both an urban and building point of view. The study shows that urban characteristics, mainly geometry and materials, are crucial for the UHI phenomenon. All the applied technologies seem to be effective. However, green walls proved to be more efficient in reducing outdoor temperatures (1 °C reduction in daily temperatures), while cool walls performed better in reducing cooling energy consumption, with an overall saving of 6% compared to the current scenario. [ABSTRACT FROM AUTHOR]

Smart agriculture stands as a promising domain for IoT-enabled technologies, with the potential to elevate crop quality, quantity, and operational efficiency. However, implementing a smart agriculture system encounters challenges such as the high latency and bandwidth consumption linked to cloud computing, Internet disconnections in rural locales, and the imperative of cost efficiency for farmers. Addressing these hurdles, this paper advocates a fog-based smart agriculture infrastructure integrating edge computing and LoRa communication. We tackle farmers' prime concern of animal intrusion by presenting a solution leveraging low-cost PIR sensors, cameras, and computer vision to detect intrusions and predict animal locations using an innovative algorithm. Our system detects intrusions pre-emptively, identifies intruders, forecasts their movements, and promptly alerts farmers. Additionally, we compare our proposed strategy with other approaches and measure their power consumptions, demonstrating significant energy savings afforded by our strategy. Experimental results highlight the effectiveness, energy efficiency, and cost-effectiveness of our system compared to state-of-the-art systems. [ABSTRACT FROM AUTHOR]

In resource-intensive Internet of Things applications, Lightweight Stream Ciphers (LWSCs) play a vital role in influencing both the security and performance of the system. Numerous LWSCs have been proposed, each offering certain properties and trade-offs that carefully balance security and performance requirements. This paper presents a comprehensive evaluation of prominent LWSCs, with a focus on their performance and resource consumption, providing insights into efficiency, efficacy, and suitability in the real-world application of resource-intensive live video feed encryption on an ARM processor. The study involves the development of a benchmarking tool designed to evaluate key metrics, including encryption frame rate, throughput, processing cycles, memory footprint, ROM utilization, and energy consumption. In addition, we apply the E−Rank metric, which combines key performance and resource metrics to derive a unified comparative measure for overall software performance. [ABSTRACT FROM AUTHOR]

In this paper, partial energy processing is applied to the current sharing technique for multiphase LLC resonant converters. The proposed circuit consists of an LLC resonant converter and a flyback converter, where the flyback converter is only used for partial energy processing. The input voltage of the LLC resonant converter is fine-tuned by the flyback converter to solve the problem of a voltage gain difference between the two phases of the LLC resonant converter caused by the error of the resonant tank components, which prevents the output current from being nonequalized. Since the compensation power is much smaller than the output power, and only one phase will be during circuit operation, the impact on the overall efficiency is minimal. Due to the low dependence between the LLC resonant converter and the flyback converter, they are operated at different switching frequencies. In addition, due to the low dependence between each phase, the circuit can be expanded using odd and even phases. [ABSTRACT FROM AUTHOR]

This paper presents the results of research on conductive layers dedicated to flexible photovoltaic cells based on semiconductors integrated with a textile substrate. The presented work is part of a broader project aimed at producing flexible solar cells based on the CdTe semiconductor component and manufactured directly on textiles. The research focuses on the selection of textile substrates and contact materials, as well as the methods of their application. This study compares three types of fabrics (basalt, glass, and silicone fibers) and three metals (copper, molybdenum, and silver), evaluating their mechanical and electrical properties. During the experiments, flexible metallic layers with a thickness ranging from 160 to 415 nm were obtained. Preliminary experiments indicated that metallic layers deposited directly on textiles do not provide adequate conductivity, reaching the levels of several hundred Ω/sq and necessitating the introduction of intermediate layers, such as screen-printed graphite. The results show that molybdenum layers on basalt fabrics exhibit the lowest increase in resistance after dynamic bending tests. The obtained relative resistance changes in Mo layers varied from 50% to as low as 5% after a complete set of 200 bending cycles. This article also discusses current challenges and future research directions in the field of textile-integrated photovoltaics, emphasizing the importance of further technological development to improve the energy efficiency and durability of such solutions. [ABSTRACT FROM AUTHOR]

P2A (Power to ammonia) is one of the important ways of large-scale consumption of renewable energy, and one of the important technological routes for the chemical industry to realize low-carbon and clean development. The new off-grid energy power to hydrogen ammonia system lacks the support of large power grids due to the complex mathematical model of the system, more variables, and cumbersome constraints, which leads to model solving difficulties, and the production simulation results obtained suffer from the problems of low economic efficiency and high new energy power abandonment rate. To address the shortcomings of the algorithm, which converges slowly and easily falls into the local optimum when solving the model, this paper applies the Sparrow Search Algorithm (SSA) to the problem of economic optimization of new energy hydrogen synthesis and ammonia system scheduling. Firstly, based on the characteristics of wind and light, the operating characteristics of an electrolyzer, and the characteristics of an electrochemical energy storage device, and taking the economic optimization of the electric hydrogen synthesis ammonia system as the objective function, the economic optimization scheduling model of an off-grid new energy electric hydrogen synthesis ammonia system is established for 24 h production simulation. Secondly, the model is solved based on the sparrow search algorithm, and the speed of solving and the economic benefits of the system are analyzed in comparison with the conventional algorithm. Finally, the proposed off-grid wind-powered hydrogen synthesis ammonia system based on the sparrow search algorithm is verified to achieve the optimal operation of the 24 h production simulation through an actual example in the Daan area of Baicheng City, Jilin Province, which shows that the optimized system has better economic efficiency and the new energy is completely consumed, thus verifying the reasonableness and validity of the algorithm proposed in this article. [ABSTRACT FROM AUTHOR]

A large proportion of building energy consumption in tropical countries like Thailand primarily comes from air conditioning systems used to maintain the comfort level of building occupants. This paper aims to evaluate the performance of an alternative cooling system based on the evaporative principle in terms of thermal characteristics and energy consumption. A simulation model using computational fluid dynamics (CFD) software ANSYS version 16.0 and an actual experimental setup at the laboratory level were built to verify the results of the proposed cooling system. Additionally, factors that influence performance, such as components of the building envelope and the building's orientation, are considered. This research aims to demonstrate the impact of building envelope material and building characteristics on the energy usage in air conditioning systems and to propose an energy-efficient cooling system. The results demonstrate that the proposed cooling system can reduce the temperature inside the building. However, the characteristics of the building also affect the energy performance. Thus, the proposed cooling system, in combination with an efficient envelope material, can achieve energy savings of around 35–43%. Therefore, a combination of the proposed cooling system and optimal building design can ensure comfort for building occupants while saving energy. [ABSTRACT FROM AUTHOR]

The operation of the utility grid is being reshaped by the continuous addition of distributed energy resources and advanced metering infrastructure, which challenge existing grid control strategies. Some utilities deploy advanced distribution management systems (ADMS) to assist with the consolidation of various applications and to augment situational awareness in response to the new power delivery dynamics. An ADMS is an integrated software platform that provides utilities with a way to enhance their reliability, control, and optimization with advanced applications, such as volt/VAR optimization (VVO). A VVO application could serve as a vehicle to deliver cost savings by providing the utility with a method to reduce rates by controlling the voltage and decreasing the energy usage in their service territory. Some utilities are reluctant to integrate an ADMS, because it is a significant investment that requires approval from the public regulatory commission and/or their customers. This paper evaluates the impact on VVO performance when using a lower-quality network model supplemented with additional measurements, which could provide an implementation for cost savings. The results show that a better model quality would provide the highest energy savings; however, some level of telemetry is necessary in all scenarios to prevent voltage exceedances. [ABSTRACT FROM AUTHOR]

Virtual power plants (VPPs) aggregate a large number of distributed energy resources (DERs) through IoT technology to provide flexibility to the grid. It is an effective means to promote the utilization of renewable energy, and enable carbon neutrality for future power systems. This paper addresses the evaluation issue of DERs' low-carbon benefits, proposes a flexibility assessment model for self-organized VPP to quantify the low-carbon value of DERs' response behavior in different time periods. Firstly, we introduce the definition of zero-carbon index based on the curve simultaneous rate of renewable energy and load demand. Then, we establish a multi-level self-organized aggregation method for virtual power plants, define the basic rules of DER, and characterize its self-organized aggregation as a Markov game process. Moreover, we use QMIX to achieve a bottom-up, hierarchical construction of VPP from simple to complex. Experimental results show that when users track the zero-carbon curve, they can achieve zero carbon emissions without reducing the overall load, significantly enhancing the grid's regulation capabilities and the consumption of renewable energy. Additionally, self-organized algorithms can optimize the combinations of DERs to improve the coordination efficiency of VPPs in complex environments. [ABSTRACT FROM AUTHOR]

Developing high-efficiency and low-carbon propulsion systems is a pressing concern within the aviation field. This paper studies a hybrid power system that combines a solid oxide fuel cell and a gas turbine (SOFC-GT) with propane as fuel, which is easy to store and has a high energy density. The analysis focuses on key parameters such as compressor pressure ratio, fuel utilization rate, and fuel distribution. And a balance between system efficiency and the power-to-weight ratio has been achieved through multi-objective optimization. The conclusions indicate that system efficiency and system weight in the hybrid power system are optimized in opposite directions. Within the design parameters, the hybrid power system's efficiency achieves 0.621, the specific fuel consumption is 115.2 g/kWh, and the power-to-weight ratio is 0.569 kW/kg. Further discussion on the application of this hybrid system in long-endurance unmanned aerial vehicles shows an efficiency of 0.651 during the cruise phase, indicating a promising application prospect of a propane-fueled SOFC-GT hybrid system in the aviation field. [ABSTRACT FROM AUTHOR]

Demand-side response (DR) and energy storage system (ESS) are both important means of providing operational flexibility to the power system. Thus, DR has a certain substitution role for ESS, but unlike DR, ESS planning has a coupling relationship between years, which makes it difficult to guarantee the reasonableness of the ESS planning results by considering only a single year. To achieve the optimal construction timing of ESS, this paper develops a consecutive year-by-year framework integrating DR and ESS to analyse and quantify the substitution effect of DR on energy storage while realizing year-by-year ESS planning. Our methods are as follows: (1) A consecutive year-by-year DR model and an ESS model are proposed; (2) These two models are combined together to achieve the purpose of considering DR in the ESS planning stage. Here, system reserve, renewable energy consumption, and preservation of power supply are given consideration to optimise the reliability and economy of the system; (3) The method is validated using a provincial real-world power grid in the eastern part of China. The optimal results of five consecutive years of planning show that DR substitutes 19.7% of the ESS capacity. [ABSTRACT FROM AUTHOR]

Understanding the regional disparities, dynamic evolution, and convergence–divergence characteristics of global agricultural energy efficiency is crucial for enhancing agricultural energy efficiency, ensuring food security, and responding to global green development trends. This paper utilizes 2002–2021 panel data from 144 countries globally, employing the epsilon-based measure–global Malmquist–Luenberger (EBM-GML) model to estimate agricultural energy efficiency, considering unexpected output. The Dagum Gini coefficient, kernel density estimation, spatial Markov matrix, and spatial convergence model are employed to explain the spatial patterns and evolving trends of global and regional agricultural energy efficiency at three levels: regional disparities, dynamic evolution, and convergence. The results indicate significant spatial heterogeneity in global agricultural energy efficiency, with Europe exhibiting the highest efficiency, followed by Asia and the Americas, while Oceania and Africa demonstrate the lowest efficiency. Agricultural energy efficiency globally and in each region continues to improve, with increasing regional disparities, and difficulties in grade transitions in agricultural energy efficiency across regions. Each region exhibits β-convergence characteristics, but the convergence rates vary, and various factors influence growth rates of agricultural energy efficiency differently across regions. Therefore, countries should tailor their strategies based on local conditions, considering their own resource endowments and developmental stages, and strengthen international exchanges and cooperation. [ABSTRACT FROM AUTHOR]

At present, most double-slope greenhouse shade systems utilize insulation quilts on a portion of the roof to enhance the light and thermal environment for winter production. However, this method often leads to challenges such as uneven light distribution and significant heat dissipation. To improve the uniformity of light distribution and optimize the light and thermal environment inside a shade room, this study employed Ecotect software 2011 for simulating and analyzing the light and thermal conditions of a double-slope greenhouse shade system. The study aimed to investigate the optimal spacing and width of roof windows. The results demonstrated that a double-slope greenhouse with roof windows of 300 mm width spaced at 3000 mm intervals achieved optimal performance. This configuration yielded a light distribution uniformity of 74.4% and an average temperature of 8.1 °C. Such conditions are conducive to creating an environment suitable for cultivating edible mushrooms, thereby enhancing the quality and consistency of mushroom production. This paper provides an effective method for designing the roof structure of double-slope greenhouses to enhance light capture, thermal regulation, and energy efficiency. [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]

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]

The traditional centralized optimization method encounters challenges in representing the interaction among multi-agents and cannot consider the interests of each agent. In traditional low-carbon scheduling, the fixed carbon quota trading price can easily cause arbitrage behavior of the trading subject, and the carbon reduction effect is poor. This paper proposes a two-layer dynamic community integrated energy system (CIES) low-carbon collaborative optimization operation method. Firstly, a multi-agent stage feedback carbon trading model is proposed, which calculates carbon trading costs in stages and introduces feedback factors to reduce carbon emissions indirectly. Secondly, a two-layer CIES low-carbon optimal scheduling model is constructed. The upper energy seller (ES) sets energy prices. The lower layer is the combined cooling, heating, and power (CCHP) system and load aggregator (LA), which is responsible for energy output and consumption. The energy supply and consumption are determined according to the ES energy price strategy, which reversely affects the energy quotation. Then, the non-dominated sorting genetic algorithm embedded with quadratic programming is utilized to solve the established scheduling model, which reduces the difficulty and improves the solving efficiency. Finally, the simulation results under the actual CIES example show that compared with the traditional centralized scheduling method, the total carbon emission of the proposed method is reduced by 16.34%, which can improve the income of each subject and make the energy supply lower carbon economy. [ABSTRACT FROM AUTHOR]

In this paper, we introduce WindFormer, an innovative transformer-based model engineered for short-term wind speed forecasting, leveraging multivariate time series data. Unlike traditional approaches, WindFormer excels in processing diverse meteorological features—temperature, humidity, and power—to intricately map their spatiotemporal interdependencies with wind speeds. Utilizing a novel unsupervised pre-training strategy, WindFormer initially learns from vast volumes of unlabeled data to capture generalized feature representations. This foundation enhances the subsequent fine-tuning phase on labeled wind speed data, in which our model demonstrates exceptional predictive accuracy. Empirical evaluations across various public datasets illustrate that WindFormer markedly surpasses both conventional statistical models and contemporary deep learning techniques. The model not only achieves superior accuracy in forecasting wind speeds but also reveals a significant enhancement in handling complex spatiotemporal data dynamics. These advancements facilitate more effective wind farm management and power grid scheduling, making a substantial impact on operational efficiencies and renewable energy utilization. Our findings confirm the robustness of WindFormer in a real-world setting, underscoring its potential as a pivotal tool in meteorological and energy sectors. The integration of unsupervised pre-training with multi-task fine-tuning establishes a new benchmark for short-term wind speed prediction. [ABSTRACT FROM AUTHOR]

Currently, the construction and operation of buildings are responsible for 36% of global final energy usage and nearly 40% of energy-related carbon dioxide (CO2) emissions. From the perspective of sustainable development, and taking into account economy and thermal comfort, it is crucial to consider the influence of multi-objective realization on design parameters. In this paper, high-rise residential buildings in the cities of Xi'an and Yulin, which have differences in solar radiation, in the western solar enrichment area of China are taken as the research objects. The four objectives of building energy consumption, thermal comfort, life-cycle cost, and life-cycle carbon emissions are weighed using the SPEA-2 algorithm by adjusting eleven design variables, thereby obtaining the Pareto non-dominated solutions. The TOPSIS method is applied to obtain the suitable parameter combinations under different scenarios. The results show that the differences in climate and solar radiation influence the solution distribution, the range of objective function values, and the values of the design variables in Pareto non-dominated solutions. The obtained optimal scheme for the Xi'an area has an energy-saving rate of 61.7%, a TDHP improvement rate of 20.3%, an LCC of 254.8 CNY/m2, and an LCCO2 of 72.3 kgCO2/m2. The corresponding values in the Yulin area are 69.7%, 19.4%, 230.2 CNY/m2, and 0 kgCO2/m2. This reflects the potential of solar energy utilization to reduce buildings' energy consumption and carbon emissions. The methodology and findings can provide references for high-rise residential building design in Northwestern China. [ABSTRACT FROM AUTHOR]

The paper presents conclusions from research conducted at the Warsaw University of Technology in 2019–2023 regarding the combustion of fuel–water emulsions in a miniature gas turbine. The presented conclusions were made taking the current state of knowledge available in the literature into account. Particular emphasis was placed on explaining the discrepancies in the results of the experimental studies available in the literature. The main aspects of the combustion of the fuel–water emulsions that were analyzed were their impact on the emissions of NOx and CO, as well as the impact of the surfactant included in the fuel mixture on the combustion process, emissions and the formation of deposits on the walls of the combustion chamber. The impact of the emulsion fuel on fuel consumption was also discussed. In order to explain the changes occurring in the combustion chamber as a result of adding water to the fuel, numerical methods and methods of fluid mechanics were used. Studies have shown a positive impact of the use of fuel–water emulsions on CO and NOx emissions and fuel consumption. It was also demonstrated that fuel additives used for emulsification can create deposits on the walls of the hot engine section. [ABSTRACT FROM AUTHOR]

A large amount of cargo is transported between European and Southeast Asian countries. Ships sometimes take different routes when sailing between ports due to the best commercial speed; navigational, economical, and hydrometeorological conditions; and political and military situations. Several routes are available for sailing between Europe and Southeast Asia: sailing the Suez Canal, sailing around the African continent, sailing the Panama Canal, as well as sailing the Northern Sea route. This article analyzes the possible sailing routes between Southeast Asia and Europe and presents a developed methodology for the evaluation of sailing routes. This sea route evaluation methodology is based on a comparative mathematical model that evaluates the main factors of cargo transportation by sea: transportation cost and time, possible maximum ship parameters, transportation energy (fuel) demand, and other possible factors, such as the probability of various restrictions. This paper presents a case study of cargo transportation between Rotterdam (The Netherlands) and Shanghai (China) using different possible sea routes. Assessments of various possible routes are presented; the main topics of discussion and conclusions are formulated. [ABSTRACT FROM AUTHOR]

Electricity prices have been exceptionally volatile in recent years. In 2022, we witnessed a sharp increase in electricity prices in many countries. Several factors contributed to this, including reduced electricity production by hydropower plants due to climatic conditions and geopolitical situations around the world, amongst other factors. The research presented in this paper aims to analyze electricity price data and determine the parameters CPS (Capacity Payment Scheme) and CRS (Capacity Remuneration Scheme). These parameters are calculated from hourly data on a monthly basis from 2018 to the present. Determining these parameters provides a clearer understanding of the efficiency of utilizing photovoltaic power plants. However, the results indicate that identical CRS values can signify different situations in the electricity market. Given the current support for projects utilizing photovoltaic energy, the research findings may offer insights into the future direction of photovoltaic energy utilization. If CRS and CPS show positive trends (e.g., increasing values), this may suggest that current political measures (subsidies, supports, etc.) are effective. Policymakers could then strengthen or expand these measures. The research outcomes could shape the geopolitical situation in individual states. The research results show how differences in electricity prices in 2022 compared to pre-2022 affected CSP and CRS parameters. The findings also indicate that electricity prices vary across different countries, which naturally impacts the calculation of CPS and CRS. In 2024, a significant decline in CRS was observed across all analyzed countries, which may indicate issues with integrating solar energy into the market, market saturation, or changes in market dynamics. [ABSTRACT FROM AUTHOR]

Accurate energy consumption prediction is crucial for addressing energy scheduling problems. Traditional machine learning models often struggle with small-scale datasets and nonlinear data patterns. To address these challenges, this paper proposes a hybrid grey model based on stacked LSTM layers. This approach leverages neural network structures to enhance feature learning and harnesses the strengths of grey models in handling small-scale data. The model is trained using the Adam algorithm with parameter optimization facilitated by the grid search algorithm. We use the latest annual data on coal, electricity, and gasoline consumption in Henan Province as the application background. The model's performance is evaluated against nine machine learning models and fifteen grey models based on four performance metrics. Our results show that the proposed model achieves the smallest prediction errors across all four metrics (RMSE, MAE, MAPE, TIC, U1, U2) compared with other 15 grey system models and 9 machine learning models during the testing phase, indicating higher prediction accuracy and stronger generalization performance. Additionally, the study investigates the impact of different LSTM layers on the model's prediction performance, concluding that while increasing the number of layers initially improves prediction performance, too many layers lead to overfitting. [ABSTRACT FROM AUTHOR]

Mannitol is the most abundant polyol in nature and has wide commercial applications due to its properties in the food, chemical, medical, and pharmaceutical industries. Mannitol was traditionally produced from kelp, but this process was eliminated due to high water and energy consumption and gradually replaced by methods with less energy consumption and environmentally friendly practices. Studies show that brown seaweed contains large amounts of mannitol. Therefore, this paper intends to describe the structure of this sugar alcohol present in brown algae, its commercial production, and its biosynthetic pathway in algae, as well as its medical and nutritional applications. [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]

In order to establish a proper geometry of an Unmanned Underwater Vehicle (UUV) for stable motion control and energy usage reduction, this paper analyzes the hydrodynamic performance of a complex shape underwater vehicle and develops a systematic Computational Fluid Dynamics (CFD) simulation method to solve the hydrodynamic parameters of the system. Based on the simulation method and their results, the streamlines and pressure distributions of the water flow around the underwater vehicle are analyzed, and the geometric model design is improved based on the drag characteristics. Also, a comparison scheme is designed to evaluate the vehicle model before and after the geometry improvement. Simulation result shows that the design schemes brings 18% drag reductions in surge direction and 32% in heave direction. Moreover, by establishing the physical and mathematical models of the UUV on a physical simulation platform, a complete model of the underwater vehicle is constructed, laying the foundation for further simulations and experiments. [ABSTRACT FROM AUTHOR]

Efficiency has always been one of the most critical indicators for evaluating wireless power transfer (WPT) systems. To achieve fast maximum energy efficiency tracking (MEET), this paper provides an innovative control method utilizing the hill climbing-golden section search (HC-GSS) method of an LCC-S compensated WPT system. The receiver side includes a buck-boost converter that regulates the output current or voltage to meet output requirements. In the meantime, the buck-boost converter on the transmitter side is managed by the HC-GSS approach for MEET by minimizing the input power under the premise of output stability. Compared with the conventional P&O method, the HC-GSS method can eliminate the trade-off between the oscillation and convergence rate because it is designed for different system stages. In this WPT system, there is no need for direct communication between the transmitter and receiver. Therefore, the system is potentially cheaper to implement and does not suffer from annoying communication delays, which are prevalent in underwater environments for unmanned underwater vehicles' (UUV) WPT systems. Both the simulation and experiment results show that this method can improve the efficiency of the WPT system without communication. The proposed method remains valid with coupler displacement as it does not include the mutual inductance of the system. [ABSTRACT FROM AUTHOR]

Wave energy conversion is a promising field of renewable energy, but it still faces several technological and economic challenges. One of these challenges is to improve the energy efficiency and adaptability of Wave Energy Converters to varying wave conditions. A technological approach to solve this efficiency challenge is the negative spring mechanisms illustrated in recent studies. This paper proposes and analyzes a novel negative spring technological concept that dynamically modifies the mass and inertia of a Wave Energy Converter by transferring seawater between its compartments. The added value of the presented technology relies on interoperability, ease of manufacturing and operating, and increased energy efficiency for heterogeneous sea states. The concept is presented in two analyzed alternatives: a passive one, which requires no electrical consumption and is purely based on the relative motion of the bodies, and an active one, which uses a controlled pump system to force the water transfer. The system is evaluated numerically using widely accepted simulation tools, such as WECSIM, and validated by physical testing in a wave flume using decay and regular test scenarios. Key findings include a relevant discussion about system limitations and a demonstrated increase in the extracted energy efficiency up to 12.7% while limiting the maximum power extraction for a singular wave frequency to 3.41%, indicating an increased adaptability to different wave frequencies because of the amplified range of near-resonance operation of the WEC up to 0.21 rad/s. [ABSTRACT FROM AUTHOR]