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Energy storage efficiency (η) and large recoverable energy density (Wre) are necessary for antiferroelectric materials in order to develop antiferroelectric-based dielectric capacitors with exceptional energy storage capacity. In the present paper, the effect of doping La3+ on the energy storage capacity of Pb1−xLax(Hf0.45Sn0.55)0.995O3 antiferroelectric ceramics was studied. Adjusting the content of La and changing the phase structure of PLHS from antiferroelectric to relaxor ferroelectric gradually, which narrowed its hysteresis loop, yielded a high energy storage efficiency of 81.9% and the maximum breakdown field strength of 200 kV/cm when x = 2 mol%. In addition, the recoverable energy density and energy storage efficiency both showed excellent temperature stability and frequency stability in the temperature range of 10–110 °C and the frequency range of 10–100 Hz, suggesting that Pb0.98La0.02(Hf0.45Sn0.55)0.995O3 are favorable materials candidates for the preparation of pulsed-power capacitors that can be used in a wide range of conditions. [ABSTRACT FROM AUTHOR]

This paper presents a cooperative highway platooning strategy that integrates Multi-Agent Reinforcement Learning (MARL) with Proximal Policy Optimization (PPO) to effectively manage the complex task of merging. In modern transportation systems, platooning—where multiple vehicles travel closely together under coordinated control—promises significant improvements in traffic flow and fuel efficiency. However, the challenge of merging, which involves dynamically adjusting the formation to incorporate new vehicles, remains challenging. Our approach leverages the strengths of MARL to enable individual vehicles within a platoon to learn optimal behaviors through interactions. PPO ensures stable and efficient learning by optimizing policies balancing exploration and exploitation. Simulation results show that our method achieves merging with safety and operational efficiency. [ABSTRACT FROM AUTHOR]

The volume of flow demand in cyber-physical power systems (CPPSs) fluctuates unevenly across coupled networks and is susceptible to congestion or overload due to consumers' energy demand or extreme disasters. Therefore, considering the elasticity of real networks, communication links with excessive information flow do not immediately disconnect but have a certain degree of redundancy. This paper proposes a dynamic cascading failure iterating model based on the distribution of information flow overload in a communication network and power flow betweenness in the physical power grid. First, a nonlinear load capacity model of a communication network with overload and weighted edges is introduced, fully considering the three link states: normal, failure, and overload. Then, flow betweenness substitutes for branch flows in the physical power network, and power flow on failed lines is redistributed using the load capacity model, simplifying the calculations. Third, under the influence of coupling relations, a comprehensive model based on improved percolation theory is constructed, with attack strategies formulated to more accurately assess the coupled networks. Simulations on the IEEE-39 bus system demonstrate that considering the overload capacity of communication links on a small scale enhances the robustness of coupled networks. Furthermore, deliberate link attacks cause more rapid and extensive damage compared to random attacks. [ABSTRACT FROM AUTHOR]

With the proliferation of intelligent applications, mobile devices are increasingly handling computation-intensive tasks but often struggle with limited computing power and energy resources. Mobile Edge Computing (MEC) offers a solution by enabling these devices to offload computation-intensive tasks to resource-rich edge servers, thus reducing processing latency and energy consumption. However, existing task-offloading strategies often neglect critical security concerns. In this paper, we propose a security-aware task-offloading framework that utilizes Deep Reinforcement Learning (DRL) to solve these challenges. Our framework is designed to minimize the latency of task accomplishment and energy consumption while ensuring data security. We model system utility as a Markov Decision Process (MDP) and design a Proximal Policy Optimization (PPO)-based algorithm to derive optimal offloading strategies. Experimental results demonstrate that the proposed algorithm outperforms traditional methods regarding task execution latency and energy consumption. [ABSTRACT FROM AUTHOR]

Natural ventilation is a critical method for reducing energy consumption for heating, cooling, and ventilating buildings. Recent research has focused on utilizing environmental IoT data from both inside and outside buildings for NVR prediction based on a deep learning model. To design an accurate NVR prediction model while considering individual building environments, various knowledge-sharing methods can be applied, such as transfer learning and ensemble models for cross-building prediction. However, the characteristics of learning data and model parameters should be considered when applying transfer learning and ensemble models to predict NVR with different spatial and temporal domains. In this paper, we propose a way to design an NVR prediction model for a cross-building environment by normalizing the training data, selecting transfer learning layers that are well-suited to the data environment, and augmenting NVR knowledge via ensemble methods. Based on the experimental results, we confirm that the proposed knowledge-sharing deep learning approach, while considering the normalizing of training data, the selecting transfer learning layers, and augmenting the NVR knowledge approach, can improve the accuracy up to 11.8% in the two different offices and seasons. [ABSTRACT FROM AUTHOR]

Wave energy has a high energy density and strong predictability, presenting encouraging prospects for development. So far, there are dozens of different wave energy devices (WECs), but the mechanism that ultimately converts wave energy into electrical energy in these devices has always been the focus of research by scholars from various countries. The energy conversion mechanism in wave energy devices is called PTO (power take-off). According to different working principles, PTOs can be classified into the linear motor type, hydraulic type, and mechanical type. Hydraulic PTOs are characterized by their high efficiency, low cost, and simple installation. They are widely used in the energy conversion links of various wave energy devices. However, apart from experimental methods, there is currently almost no concise numerical method to predict and evaluate the power generation performance of hydraulic PTO. Therefore, based on the working principle of hydraulic PTO, this paper proposes a numerical method to simulate the performance of a hydraulic PTO using MATLAB(2018b) Simulink®. Using a point-absorption wave energy device as a carrier, a float hydraulic system power-generation numerical model is built. The method is validated by comparison with previous experimental results. The predicted power generation and conversion efficiency of the point-absorption wave energy device under different regular and irregular wave conditions are compared. Key factors affecting the power generation performance of the device were investigated, providing insight for the subsequent optimal design of the device, which is of great significance to the development and utilization of wave energy resources. [ABSTRACT FROM AUTHOR]

In this paper, a heat recovery ground source heat pump (HR-GSHP) system, in which the primary pipes of the GSHP for air conditioning and the GSHP for hot water are connected to ground heat exchangers (GHEs) and each GSHP is operated simultaneously or within a short period of time, was installed in a dormitory building on a trial basis. Then, the optimal operation method to minimize the energy consumption of the system was investigated. The operating period of the GSHP for HW was changed and simulations were conducted to determine the operating period with the lowest energy consumption, which was 8 months from April to November. Furthermore, the HR-GSHP system was operated for 8 years from 2012 to 2019, and actual measurements were carried out to verify the system performance and the energy saving effect in optimal operation. In actual operation, it was confirmed that the minimum temperature was about 10 °C or higher even when the GSHP for HW was operated year-round. Therefore, the GSHP for HW was operated year-round after the third year of operation. It was confirmed that the operation of the GSHP for HW in summer, especially in August and September when the cooling load is large, can improve the system's efficiency by the effect of recovering cooling exhaust heat. In the eighth year of operation, when the GSHP for HW was operated most during the summer season, the system was able to reduce power consumption for air conditioning and hot water supply by approximately 17%. [ABSTRACT FROM AUTHOR]

Automatic distribution master stations (ADMSs) are software platforms that can be used to monitor energy supply and consumption of power demand-side resources as well as to guarantee grid security. They play a vital role in maintaining the optimal performance and stability of electrical power systems. However, the stability of ADMSs is usually determined by the performance of the information interaction, which includes various challenges and complexities. Therefore, an efficient multiple-indicator filtering-based information interaction evaluation method for the ADMS is proposed in this paper. The proposed method first utilizes the principal component analysis (PCA) to filter the indicators of the ADMS. Then, the data envelopment method and the analytic network process (ANP) are employed to remove the invalid indicators and to determine the indicator weights. In this regard, the secondary screening of the indicators is completed, the effective indicators are obtained, and the accuracy of the evaluation system is improved. Results of the simulation show that the proposed method works well when evaluating information interaction performance, and it offers satisfactory performance evaluation of ADMSs. [ABSTRACT FROM AUTHOR]

The popularity of nuclear power as a high-availability energy source is increasing in countries that currently rely on coal-based energy. The growing use of renewable energy sources emphasizes the need for greater energy supply security and grid stability. However, nuclear reactors remain the most expensive commercially available power-generation technology, which limits investment in this field. This paper explores the feasibility of investing in Coal-to-Nuclear conversion at selected coal-fired power plant sites in Poland. By converting coal-fired infrastructure, it is possible to reduce the financial cost of constructing a nuclear power plant. The study included an analysis of hydrological conditions from 2010 to 2023 at selected locations, which determined the potential for siting high-power nuclear reactors. An analytical model was used to calculate the required water intake for cooling, and the results were compared with actual river flow measurements. The findings suggested that constructing an inland nuclear power plant in Poland is feasible while complying with legal standards regarding maximum cooling water temperature. The assessment of the four sites allowed appropriate recommendations to be made concerning further research into the implementation of Generation III reactors. [ABSTRACT FROM AUTHOR]

This research evaluates the feasibility of using eucalyptus oil blended with conventional diesel fuel in diesel engines. Eucalyptus globulus is one of the main tree species cultivated for paper pulp in western European countries such as Portugal, and eucalyptus oil is one of the byproducts that so far has not been sufficiently evaluated as a biofuel. This study assesses the impact of using this additive on engine performance parameters and emissions as a means to contribute to reducing fossil fuel consumption and pollutant and greenhouse gas (GHG) emissions. The analysis revealed that the addition of eucalyptus oil had a positive effect on torque, a critical performance parameter, with biofuel blends showing consistent torque increases at lower engine speeds. However, torque tended to decrease towards the higher range of engine speed for eucalyptus oil–diesel blends. Several blends showed lower brake specific fuel consumption compared to regular diesel at high engine loads and low engine speeds. Brake thermal efficiency did not vary substantially at lower engine speeds and loads but decreased at higher speeds and loads. Pollutant emissions, particularly unburned hydrocarbons and nitrogen oxides, were influenced by fuel composition, with biofuel blends showing both increases and decreases compared to diesel. It is noteworthy that eucalyptus oil blends exhibited up to a 60% reduction in smoke opacity under specific operating conditions at low speed and high load for 10% incorporation (10EU90D), suggesting that in addition to the already positive effects of cutting down fossil CO2 emissions in proportion to the substitution of fossil diesel with nearly carbon-neutral eucalyptus oil, more environmental benefits may be expected from the incorporation of this product. Although the present economic viability of using eucalyptus oil as a biofuel is still not guaranteed, the present study seems to reinforce its technical viability. Future prospects for the improvement of oil yield through biotechnology, the economic interest of this product for several countries, and the updating and upscaling industrial processes may allow the viability of this biofuel to remain a possibility in the future [ABSTRACT FROM AUTHOR]

Socioeconomic growth and population increase are driving a constant global demand for energy. Renewable energy is emerging as a leading solution to minimise the use of fossil fuels. However, renewable resources are characterised by significant intermittency and unpredictability, which impact their energy production and integration into the power grid. Forecasting models are increasingly being developed to address these challenges and have become crucial as renewable energy sources are integrated in energy systems. In this paper, a comparative analysis of forecasting methods for renewable energy production is developed, focusing on photovoltaic and wind power. A review of state-of-the-art techniques is conducted to synthesise and categorise different forecasting models, taking into account climatic variables, optimisation algorithms, pre-processing techniques, and various forecasting horizons. By integrating diverse techniques such as optimisation algorithms and pre-processing methods and carefully selecting the forecast horizon, it is possible to highlight the accuracy and stability of forecasts. Overall, the ongoing development and refinement of forecasting methods are crucial to achieve a sustainable and reliable energy future. [ABSTRACT FROM AUTHOR]

Aiming at the current power control problems of grid-side electrochemical energy storage power station in multiple scenarios, this paper proposes an optimal power model prediction control (MPC) strategy for electrochemical energy storage power station. This method is based on the power conversion system (PCS) grid-connected voltage and current to establish a power prediction model for energy storage power stations, achieving a one-step prediction of the power of the power station. The power prediction error is used as a power regulation feedback quantity to correct the reference power input. Considering the state of charge ( S O C ) constraint of the battery, partition the S O C into different states. Using S O C as the power regulation feedback, the power of the battery compartment can be adjusted according to the range of the battery S O C to prevent S O C from exceeding the limit value, simultaneously calculating the power loss of the energy storage power station to improve the energy efficiency. The objective function is to minimize the power deviation and power loss of the power station. By solving the objective function, the optimal switching voltage vector of the converter output is achieved to achieve optimal power control of the energy storage power station. The simulation results in various application scenarios of the energy storage power station show that the proposed control strategy enables the power of the storage station to quickly and accurately track the demand of grid scheduling, achieving the optimal power control of the electrochemical energy storage power station. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to forecast electrical demand for the Chilean Electrical Power System considering a national, regional, district and bus spatial disaggregation. The methodology developed was based on different kinds of econometric models and end-use models to represent the massification of low carbon emission technologies such as electromobility, electric heating, electric water heating, and distributed generation. In addition, the methodology developed allows for the projection of the electric demand considering different kinds of clients as regulated and non-regulated clients, and different economic sectors. The model was applied to forecast the long-term electricity demand in Chile for the period 2022–2042 for 207 districts and 474 buses. The results include projections under the base case and low carbon scenarios, highlighting the significant influence of new technologies on future demand. [ABSTRACT FROM AUTHOR]

With the development of the economy and society, the importance of a secure and stable electricity supply continues to increase. However, the power grid is facing the test of excess installed capacity, the waste of renewable energy, and a low comprehensive utilization rate. This problem stems from the inconsistent peak–valley differences between power production and consumption, and the lack of clear electricity price signals, which disrupts the safe and stable operation of the power market. This paper combines the interactive transactions among clean energy power generation companies, users, and energy storage, explores how the system optimization model can be reflected in the power market through regulatory measures, and formulates the optimal output scheme of the system under the constraints of clean energy power generation forecast data, user base load forecast data, demand-side resource regulation ability, and energy storage system regulation ability to achieve the goals of comprehensive clean energy power consumption and minimum cost for users. A comprehensive analysis of the proposed model was conducted using actual data from a certain province in China, the results show that the consumption of clean energy will increase by 3% to full consumption and the total cost of users will be 32% lower than that of time-of-use (TOU) power prices, which proves the potential of the proposed joint optimization model in absorbing clean energy and the effectiveness of the market mechanism. [ABSTRACT FROM AUTHOR]

In the context of increasing energy demands and the integration of renewable energy sources, this review focuses on recent advancements in energy storage control strategies from 2016 to the present, evaluating both experimental and simulation studies at component, system, building, and district scales. Out of 426 papers screened, 147 were assessed for eligibility, with 56 included in the final review. As a first outcome, this work proposes a novel classification and taxonomy update for advanced storage control systems, aiming to bridge the gap between theoretical research and practical implementation. Furthermore, the study emphasizes experimental case studies, moving beyond numerical analyses to provide practical insights. It investigates how the literature on energy storage is enhancing building flexibility and resilience, highlighting the application of advanced algorithms and artificial intelligence methods and their impact on energy and financial savings. By exploring the correlation between control algorithms and the resulting benefits, this review provides a comprehensive analysis of the current state and future perspectives of energy storage control in smart grids and buildings. [ABSTRACT FROM AUTHOR]

The transportation sector is responsible for about 16% of worldwide greenhouse gas emissions. Despite efforts for a sensible reduction by means of new technologies' development, the average age of a vehicle fleet is 12.3 years in the European Union. In light of this, actions aiming at improving the efficiency of circulating vehicles can prove effective in the short to mid-term. Introducing performance packages in standard fuels could allow a reduction in the CO2 emissions of whole vehicle fleets without any modification to powertrain. Such a kind of additive is generally used in premium fuels; deposit control additives can reduce or control the deposits at intake valves and at nozzle holes with benefits for the fuel efficiency and exhaust emissions. Further improvements in combustion phasing can be achieved with cetane/octane improver. This paper aims to assess the influence of two performance packages on the exhaust emissions and fuel consumption of five vehicles set to be as representative as possible of circulating Italian passenger cars and light commercial fleet vehicles (LCVs). Based on the literature datasets, three Euro 4 vehicles were selected with a mileage representative of each single vehicle class: two passenger cars (one spark ignition and one diesel) and an LCV. Further, two diesel Euro 6 vehicles, a passenger car and an LCV, were tested to investigate the effect of fuel additives on the combustion of vehicles compliant with current homologation regulation. Exhaust emissions and fuel consumption were experimentally estimated on a chassis dynamometer over a worldwide harmonized light vehicles test cycle (WLTC) in a climate-controlled laboratory. Each vehicle was preliminarily tested when running with base fuel, then a 3000 km clean-up stage was performed using the additive package. Finally, WLTC tests were repeated. Results demonstrated the efficiency of the performance packages with a reduction between 1.2% (diesel Euro 6 passenger car) and 8.1% (diesel Euro 4 passenger car) in fuel consumption. Similar trends were found for CO2 emissions. Further, a sensible reduction in THCs, CO and PM was found for each vehicle class. [ABSTRACT FROM AUTHOR]

Bluetooth Low Energy Mesh (BLE Mesh) enables Bluetooth flexibility and coverage by introducing Low-Power Nodes (LPNs) and enhanced networking protocol. It is also a commonly used communication method in sensor networks. In BLE Mesh, LPNs are periodically woken to exchange messages in a stop-and-wait way, where the tradeoff between energy and efficiency is a hard problem. Related works have reduced the energy consumption of LPNs mainly in the direction of changing the bearer layer, improving time synchronization and broadcast channel utilization. These algorithms improve communication efficiency; however, they cause energy loss, especially for the LPNs. In this paper, we propose a constrained flooding algorithm based on time series prediction and lightweight GBN (Go-Back-N). On the one hand, the wake-up cycle of the LPNs is determined by the time series prediction of the surrounding load. On the other, LPNs exchange messages through lightweight GBN, which improves the window and ACK mechanisms. Simulation results validate the effectiveness of the Time series Prediction and LlightWeight GBN (TP-LW) algorithm in energy consumption and throughput. Compared with the original algorithm of BLE Mesh, when fewer packets are transmitted, the throughput is increased by 214.71%, and the energy consumption is reduced by 65.14%. [ABSTRACT FROM AUTHOR]

Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize energy consumption or improve the occupant's comfort index. The energy management problem is a multi-objective problem where the user wants to reduce energy consumption while keeping the occupant's comfort index intact. To address the multi-objective problem this paper proposed an energy control system for a green environment called PMC (Power Management and Control). The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The combination of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is performed to make a fusion methodology to improve the occupant comfort index (OCI) and decrease energy utilization. The proposed framework gives a better OCI when compared with its counterparts, the Ant Bee Colony Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the PMC gives practically the same OCI but consumes less energy. The PMC framework additionally accomplished the ideal OCI (i-e 1) when compared with the existing model, FA–GA (i-e 0.98). The PMC model consumed less energy as compared to existing models such as the ABCKB, GAP, PSO, and AEO. The PMC model consumed a little bit more energy than the SOHP but provided a better OCI. The comparative outcomes show the capability of the PMC framework to reduce energy utilization and improve the OCI. Unlike other existing methodologies except for the AEO framework, the PMC technique is additionally confirmed through a simulation by controlling the indoor environment using actuators, such as fan, light, AC, and boiler. [ABSTRACT FROM AUTHOR]

In recent years, attention to the realization and characterization of wetsuits for scuba diving and other sea sports or activities has increased. The research has aimed to establish reliable and standardized measurement methods to objectively assess wetsuit quality, particularly focusing on their mechanical and thermal properties. In this work, we describe and compare two different measurement methods for the characterization of neoprene wetsuit thermal resistivity. The first method follows the existing regulations in the field, while the second one, which we are originally proposing in this paper, offers an alternative yet accurate way based on a simplified experimental set-up and easier measurements. In both cases, the wetsuit sample under testing was shaped in the form of a cylindrical sleeve of proper dimensions and wrapped around a phantom containing water at a higher temperature and surrounded by water at a lower temperature. The wetsuit's cylindrical surface allows heat flow from the warmer water on the inside to the colder water on the outside through the wetsuit area. In the first case, a thermal steady state was achieved, with constant heat flow from the phantom to the exterior. This was obtained with a power balance between two homogenous quantities. Electrically supplied thermal heating within the phantom was used to balance the thermal energy naturally flowing through the wetsuit's surface. In this first case, a stable and fixed temperature difference was obtained between the inner and the outer surfaces of the wetsuit sample. In the second case, a thermal transient was analyzed during the cooling process of the phantom, and the thermal time constant was measured, providing the sample thermal resistance once the phantom thermal capacity was known. In both cases and methods, the heat flow and thermal resistance of other elements than the wetsuit must be evaluated and compensated for if they are not negligible. Finally, the thermal resistivity per unit area of the wetsuit material was obtained with the product of the wetsuit sample's thermal resistance and the wetsuit area. The measurements, conducted until now by immersing the phantom in a free surface tank, show that both methods—under stationary and under transient temperature conditions—were valid to assess the wetsuit's thermal resistivity. The stationary method somehow provided better accuracy while involving less well-known parameters but at the expense of a more complicated experimental set-up and additional energy consumption. The transitory method, on the other hand, is quite easy to implement and, after careful characterization of the phantom's parameters, it provided similar results to the stationary one. An uncertainty budget was evaluated for both methods, and they did provide highly compatible measurement results, with resistivity values of 0.104(9) m2·K/W (stationary method) and 0.095(9) K·m2/W (transient method) for the same wetsuit sample under testing, which is also consistent with the values in the literature. We finally propose that the novel method is a valid alternative for characterization of the thermal insulation properties of a scuba diving wetsuit. [ABSTRACT FROM AUTHOR]

As a kind of space robot, the two-arm cascade combination system (TACCS) has been applied to perform auxiliary operations at different locations outside space cabins. The motion coupling relation of two arms and complex surrounding obstacles make the collision-free trajectory planning optimization of TACCS more difficult, which has become an urgent problem to be solved. For the above problem, this paper proposed collision-free and time–energy–minimum trajectory planning optimization algorithms, considering the motion coupling of two arms. In this method, the screw-based inverse kinematics (IK) model of TACCS is established to provide the basis for the motion planning in joint space by decoupling the whole IK problem into two IK sub-problems of two arms; the minimum distance calculation model is established based on the hybrid geometric enveloping way and basic distance functions, which can provide the efficient and accurate data basis for the obstacle-avoidance constraint condition of the trajectory optimization. Moreover, the single and bi-layer optimization algorithms are presented by taking motion time and energy consumption as objectives and considering obstacle-avoidance and kinematics constraints. Finally, through example cases, the results indicate that the bi-layer optimization has higher convergence efficiency under the premise of ensuring the optimization effect by separating variables and constraint terms. This work can provide theoretical and methodological support for the efficient and intelligent applications of TACCS in the space arena. [ABSTRACT FROM AUTHOR]

As an advanced energy storage technology, the compressed CO2 energy storage system (CCES) has been widely studied for its advantages of high efficiency and low investment cost. However, the current literature has been mainly focused on the TC-CCES and SC-CCES, which operate in high-pressure conditions, increasing investment costs and bringing operation risks. Meanwhile, some studies based on the phase-change CO2 energy storage system also have had the disadvantages of low efficiency and the extra necessity of heat or cooling sources. To overcome the above problems, this paper proposes an innovative compressed CO2 phase-change energy storage system. During the energy charge process, molten salt and water are used to store heat with a smaller temperature difference in heat exchangers, and high-pressure CO2 is reserved in liquid form. During the energy discharge process, throttle expansion is applied to realize the evaporation at room temperature, and CO2 absorbs the reserved heat to improve the power capacity in the turbine and the system energy storage efficiency. The thermodynamic and exergoeconomic studies are performed firstly by using MATLAB. Then, the parametric study based on energy storage efficiency, system unit product cost, and exergy destruction is analyzed. The results show that energy storage efficiency can be improved by lifting liquid CO2 pressure as well as compressor and turbine isentropic efficiencies, and CO2 evaporation pressure has the optimal pressure point. The system unit product cost can be reduced by decreasing liquid CO2 pressure and compressor isentropic efficiency, while CO2 evaporation pressure and turbine isentropic efficiency both have optimal points. Finally, the optimization of two performances is performed by NSGA-II, and they can reach 75.30% and 41.17 $/GJ, respectively. Moreover, the optimal energy storage efficiency is obviously higher than that of other energy storage technologies, indicating the great advantage of the proposed system. This study provides an innovative research method for a new type of large-scale energy storage system. [ABSTRACT FROM AUTHOR]

Forecasting energy demand is critical to ensure the steady operation of the power system. However, present approaches to estimating power load are still unsatisfactory in terms of accuracy, precision, and efficiency. In this paper, we propose a novel method, named ELFNet, for estimating short-term electricity consumption, based on the deep convolutional neural network model with a double-attention mechanism. The Gramian Angular Field method is utilized to convert electrical load time series into 2D image data for input into the proposed model. The prediction accuracy is greatly improved through the use of a convolutional neural network to extract the intrinsic characteristics from the input data, along with channel attention and spatial attention modules, to enhance the crucial features and suppress the irrelevant ones. The present ELFNet method is compared to several classic deep learning networks across different prediction horizons using publicly available data on real power demands from the Belgian grid firm Elia. The results show that the suggested approach is competitive and effective for short-term power load forecasting. [ABSTRACT FROM AUTHOR]

The improvement of efficiency in new and existing buildings is one of the key aspects in achieving the climate change targets promoted by international regulatory and technical bodies, and among the measures that deserve renewed attention is the balancing of hydronic systems. However, the balancing procedures currently applied have not been updated for decades and are still largely unimplemented, as they are mainly based on cumbersome and iterative procedures. This paper deals with the proposal and advanced adaptation of a non-iterative balancing method previously developed for air systems, known as the progressive flow method (PFM). The application to water systems of the PFM's concepts includes some aspects of an existing empirical method called the compensated method (CM) and overcomes its main limitations; moreover, the original PFM has been radically rethought in its implementation aspects, taking advantage of the tightness of water distribution systems, minimising instrumentation and the number of measurement operations, to definitively overcome the iterative nature of the currently applied methods. Experimental validation was carried out. Compared with a standard method, the enhanced PFM reduced the number of measurements by 48% and the number of balancing operations by 41%, achieving final flow rates within tolerances and the same configuration of balancing devices. [ABSTRACT FROM AUTHOR]

Robotic manipulators play a key role in modern industrial manufacturing processes. Monitoring their operational health is of paramount importance. In this paper, a novel anomaly detection framework named U-TFF is introduced for energy consumption auditing of robotic manipulators. It comprises a cascade of Time–Frequency Fusion (TFF) blocks to extract both time and frequency domain features from time series data. The block applies the Fast Fourier Transform to convert the input to the frequency domain, followed by two separate dense layers to process the resulting real and imaginary components, respectively. The frequency and time features are then combined to reconstruct the input. A U-shaped architecture is implemented to link corresponding TFF blocks of the encoder and decoder at the same level through skip connections. The semi-supervised model is trained using data exclusively from normal operations. Significant errors were generated during testing for anomalies with data distributions deviating from the training samples. Consequently, a threshold based on the magnitude of reconstruction errors was implemented to identify anomalies. Experimental validation was conducted using a custom dataset, including physical attacks as abnormal cases. The proposed framework achieved an accuracy and recall of approximately 0.93 and 0.83, respectively. A comparison with other benchmark models further verified its superior performance. [ABSTRACT FROM AUTHOR]

Energy efficiency in drives is an important issue. In converter-supplied variable-speed drives, switching losses can amount to a significant portion of all losses. This has been considered in Predictive Stator Current Control (PSCC), considering commutations at the power converter. However, in multi-phase drives, the computational burden limits the application of said techniques. Recent fast predictive algorithms have enabled shorter application times with enhanced tracking results. However, the switching frequency becomes larger with diminishing sampling periods. This paper presents a method that retains the fast computation of recent methods while reducing the switching frequency. The proposal revolves around a modification of the cost function to penalize commutations in a nonlinear way. For this task, a novel, gradual penalization is introduced. The method is experimentally applied to a five-phase induction motor. Experimental results show a significant reduction in switching frequency without compromising other control objectives. This results in an enhanced PSCC with a small sampling period and reduced switching losses. [ABSTRACT FROM AUTHOR]

This study introduces a new method for real-time efficiency tracking and stable output power of Dynamic Wireless Power Transfer (DWPT) systems using variable capacitors. A preliminary detailed discussion and an analysis of the DWPT system are carried out to show how the system can optimize power transmission and efficiency when the relative positions of transmitter and receiver change using a dynamic real-time control of the variable capacitors belonging to the compensation networks. This paper shows a detailed model of the DWPT system, including magnetic coupling analysis, circuit dynamics analysis, and efficiency characteristics analysis, in order to modify the control input values as needed. By utilizing a group optimization strategy, the transmission efficiency can be quickly maximized without using a position detection module. Simulation results demonstrate the effectiveness of the proposed method under various dynamic conditions, achieving significant improvements in energy efficiency and transmission reliability of the DWPT system. This research provides a powerful method to increase the overall performances of DWPT systems, which will help the development of future wireless charging technology. [ABSTRACT FROM AUTHOR]

Massive MIMO (Multiple Input Multiple Output) systems impose significant processing burdens along with strict latency requirements. The combination of large-scale antenna arrays and wide bandwidth requirements for next-generation wireless systems creates an exponential increase in frontend to backend data. Balancing the processing latency and reliability is critical for baseband processing tasks such as QAM detection. While linear detection algorithms have low computational complexity, their use in Massive MIMO scenario has heavy degradation in error performance. Nonlinear detection methods such as Maximum Likelihood and Sphere Decoding have good error performance, but they suffer from high, variable, and uncontrollable computational complexity. For such cases, the K-best QAM detection algorithm can provide required control over the system performance while maintaining near-ML error performance. In this paper, hard-output, as well as soft-output K-best QAM detection, is implemented in a CPU by utilizing the multiple cores combined with vector processing. Similarly, hard-output detection in a GPU is implemented by leveraging the SIMD (Single Instruction, Multiple Data) architecture and Warp-based execution model. The processing time per bit and the energy consumption per bit are compared for CPU and GPU implementations for QAM constellation density and MIMO array size. The GPU implementation shows up to 5× processing latency per bit improvement and up to 120× energy consumption per bit improvement over the CPU implementation for typical QAM constellations such as 4, 16, and 64 QAM. GPU implementation also shows up to 125× improvement over CPU implementation in energy consumption per bit for larger MIMO configurations such as 24 × 24 and 32 × 32. Finally, the soft-output detector is combined with a LDPC (Low-Density Parity Check) decoder to obtain the FER (Frame Error Rate) performance for CPU implementation. The FER is then combined with frame processing latency to form a Goodput metric to demonstrate the latency and reliability tradeoff. [ABSTRACT FROM AUTHOR]

Efficient energy management is a significant task in Internet-of-Things (IoT) devices because typical IoT devices have the constraint of a limited power supply. In particular, energy harvesting IoT devices must be tolerant of complex and varying temporal/spatial environments for energy availability. Several schemes have been proposed to manage energy usage in IoT devices, such as duty-cycle control, transmission power control, and task scheduling. However, these approaches need to deal with the operating conditions particular to energy harvesting devices, e.g., power depletion according to energy harvesting conditions. In this paper, regarding a wireless sensor network (WSN) as a representative IoT device, we propose an Energy Intelligence Platform Module (EIPM) for energy harvesting WSNs. The EIPM provides harvested energy status prediction, checkpointing, and task execution control to ensure continuous operation according to energy harvesting conditions while minimizing required hardware/software overheads such as additional measurement components and computations. Our experiment results demonstrate that the EIPM successfully enables a device to cope with energy insufficiency under various harvesting conditions. [ABSTRACT FROM AUTHOR]

The focus of this paper is the relationship between sustainable energy sources and financial development. The main research hypothesis assumes a positive link between these areas, with inevitable differences across countries and business sectors. The following research questions were asked: Is the impact of financial development on sustainable energy resources the same in different EU countries advanced in green transition processes? How is transition towards renewable energy sources progressing in different economic sectors? Does financial development influence sectoral transition in particular countries? This study uses the TOPSIS method and 25 variables for EU countries from 2013 to 2021. Key findings reveal that the link between sustainable energy sources and financial development varies across EU countries, country size affects energy autonomy, and the transition also differs by business sector. Surprisingly, higher financial development correlates with less progress in sustainable energy initiatives. The results of our research may be useful for government decision-makers in the process of designing and controlling the country's transition to sustainable energy. The original contribution of the study is expressed in its the diagnosis of the relationship between financial development and sustainable energy sources, while most studies have focused on the relationship between the energy market and financial development. [ABSTRACT FROM AUTHOR]

In this paper, four probability functions are compared with the purpose of establishing a methodology to improve the accuracy of wind energy estimations in a desert city in Northwestern Mexico. Three time series of wind speed data corresponding to 2017, 2018, and 2019 were used for statistical modeling. These series were recorded with a sonic anemometer at a sampling frequency of 10 Hz. Analyses based on these data were performed at different stationarity periods (5, 30, 60, and 600 s). The estimation of the parameters characterizing the probability density functions (PDFs) was carried out using different methods; the statistical models were evaluated by the coefficient of determination and Nash–Sutcliffe efficiency coefficient, and their accuracy was estimated by the measured quadratic error, mean square error, mean absolute error, and mean absolute percentage error. Weibull, using the energy pattern factor method, and Gamma, using the Method of Moments, were the probability density functions that best described the statistical behavior of wind speed and were better at estimating the generated energy. We conclude that the proposed methodology will increase the confidence of both wind speed estimation and the energy supplied by small-scale wind installations. [ABSTRACT FROM AUTHOR]

For the variable speed variable displacement power unit (VSVDPU), achieving power matching between the permanent magnet synchronous motor (PMSM) and the variable displacement plunger pump (VDPP) is the key to reducing system energy consumption. The control method of adjusting the speed of the PMSM and the displacement of the VDPP is the mainstay of current research and application of the VSVDPU. However, the dynamic properties of the PMSM and VDPP have not been balanced, which affects the control effect of the VSVDPU. This paper proposes a control method of variable speed and variable displacement with low energy consumption and high dynamics. The main idea is based on the efficiency model and dynamic response model of the PMSM and VDPP, and the factors that affect the efficiency and dynamic characteristics of the VSVDPU are analyzed. Guided by the multi-objective optimization algorithm, the optimal combination of speed and displacement under specific working conditions is derived. Simulation and experiment results show that the proposed control method is feasible to improve the efficiency and dynamic characteristics of the VSVDPU. [ABSTRACT FROM AUTHOR]

This paper explores the predictive power of economic and energy policy uncertainty indices and geopolitical risks for bitcoin's energy consumption. Three machine learning tools, SVR (scikit-learn 1.5.0),CatBoost 1.2.5 and XGboost 2.1.0, are used to evaluate the complex relationship between uncertainty indices and bitcoin's energy consumption. Results reveal that the XGboost model outperforms both SVR and CatBoost in terms of accuracy and convergence. Furthermore, the feature importance analysis performed by the Shapley additive explanation (SHAP) method indicates that all uncertainty indices exhibit a significant capacity to predict bitcoin's future energy consumption. Moreover, SHAP values suggest that economic policy uncertainty captures valuable predictive information from the energy uncertainty indices and geopolitical risks that affect bitcoin's energy consumption. [ABSTRACT FROM AUTHOR]

The management of large enterprises influences their efficiency and profitability. One of the important aspects is the appropriate management of electricity consumption used for production and daily operation. The problem becomes more complicated when you need to manage not one but a large complex of buildings with heterogeneous purposes. In the paper, we examine real-time series data of electrical energy consumption in a complex of heterogeneous buildings, including offices and warehouses, using time series analysis methods such as the Holt–Winters model and ARIMA/SARIMA model, and neural networks (Deep Neural Network, Recurrent Neural Network, and Long Short-Term Memory). Experimental research was performed on a dataset obtained from an energy consumption meter placed in the building complex, built in different periods, and equipped with a variety of automation devices. The data were collected over a period of four years 2018–2021 in the form of time series. Results show that classic models are good at predicting energy consumption in the mentioned type of buildings. The ARIMA model gave the best results—for buildings characterized by seasonality and trends the forecasts were almost perfect with actual values. [ABSTRACT FROM AUTHOR]

The Renewable Energy Community (REC) in Europe promotes renewable energy sources (RESs), offering social, economic, and environmental benefits. This new entity could alter consumer energy relationships, requiring self-consumption, energy sharing, and full utilization of RESs. Modernizing energy systems within the REC requires addressing self-consumption, energy sharing, demand response, and energy management system initiatives. The paper discusses the role of decentralized energy systems, the scenarios of the REC concept and key aspects, and activities involving energy generation, energy consumption, energy storage systems, energy sharing, and EV technologies. Moreover, the present work highlights the research gap in the existing literature and the necessity of addressing the technological elements. It also highlights that there is no uniform architecture or model for the REC, like in the case of microgrids. Additionally, the present work emphasizes the role and importance of technological elements in RECs, suggesting future recommendations for EMS, DSM, data monitoring and analytics, communication systems, and the software or tools to ensure reliability, efficiency, economic, and environmental measures. The authors also highlight the crucial role of policymakers and relevant policies, which could help in implementing these technological elements and show the importance of the RECs for a sustainable energy shift and transition. [ABSTRACT FROM AUTHOR]

In the context of the "Dual-Carbon Strategy", the seamless integration and optimal utilization of renewable energy sources present a pressing challenge for the emerging power system. The advent of demand-side response technology offers a promising solution to this challenge. This study proposes a two-stage response control strategy for multiple DR clusters based on the specific response time characteristics of industrial and residential loads. The strategy enhances the utilization rate of wind power, harnesses the joint response capability of various types of loads on the demand side, and ensures the overall revenue of the load aggregator (LA). It underscores the importance of industrial loads in large-scale energy consumption control throughout the overall consumption response process, while residential load clusters exhibit quick response flexibility. A homogeneous energy consumption sorting unit response strategy is established from the perspective of a residential load variable-frequency air conditioning cluster unit. This strategy addresses the challenge faced by industrial electrolytic aluminum plants in coping with long-term response intervals amidst significant fluctuations in wind power consumption demand, which may lead to incomplete consumption. This study constructs a response model based on industrial and residential time-sharing tariffs, as well as the aggregator consumption penalty price, with the optimal load energy economy index serving as the evaluation criterion. A series of simulations are conducted to comprehensively evaluate the energy consumption of the two load clusters at all times and the total revenue of the aggregator in the response zone. The objective is to achieve a win–win situation for the total wind power energy consumption rate and the aggregator's economy. The results of the simulations demonstrate that the response control strategy proposed in this study enhances the overall energy consumption rate by nearly 4 percentage points compared to a single industrial cluster. The total benefit of the load aggregator can reach CNY 941,732.09. The consumption response scheduling strategy put forward in this paper bolsters wind power consumption, triggers demand response, and significantly propels the comprehensive construction and development of the dual-high power grid. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative cooperative model for energy efficiency retrofitting that incorporates carbon emission rights, addressing critical financial constraints in Energy Performance Contracting (EPC). By employing the fuzzy analytic hierarchy process (F-AHP) to evaluate risk assessment indicators and stakeholder contributions and utilizing the enhanced Shapley method for equitable benefit distribution, the model demonstrates significant improvements in financing and efficiency for energy conservation projects. The findings are as follows: (1) the energy efficiency retrofit model, which integrates carbon emission rights, effectively alleviates the financial constraints and fosters energy conservation and emission reduction in guaranteed-savings EPC projects; (2) the enhanced Shapley method is deemed appropriate for the equitable distribution of energy-saving benefits among stakeholders; (3) when compared with the traditional model and the benefit allocation-absent carbon rights, the energy-saving benefits of the energy efficiency retrofit model incorporating carbon emission rights are higher in individual and overall terms. The findings of this study offer a viable solution to financing challenges faced by stakeholders in such projects and delineate a pragmatic approach for enterprises to enhance energy efficiency and reduce emissions. [ABSTRACT FROM AUTHOR]

Short-term load forecasting plays a crucial role in managing the energy consumption of buildings in cities. Accurate forecasting enables residents to reduce energy waste and facilitates timely decision-making for power companies' energy management. In this paper, we propose a novel hybrid forecasting model designed to predict load series in multiple households. Our proposed method integrates multivariate variational mode decomposition (MVMD), the whale optimization algorithm (WOA), and a temporal fusion transformer (TFT) to perform one-step forecasts. MVMD is utilized to decompose the load series into intrinsic mode functions (IMFs), extracting characteristics at distinct scales. We use sample entropy to determine the appropriate number of decomposition levels and the penalty factor of MVMD. The WOA is utilized to optimize the hyperparameters of MVMD-TFT to enhance its overall performance. We generate two distinct cases originating from BCHydro. Experimental results show that our method has achieved excellent performance in both cases. [ABSTRACT FROM AUTHOR]

The increasing concerns about CO2 emissions and climate change have pointed out the urgency of promoting sustainability in the building sector. One promising solution to enhance the energy efficiency of buildings and diminish environmental impact is the integration of phase-change materials (PCMs) into ventilated façade systems. This review article critically examines the current state of research on this innovative approach, with a particular focus on fire safety considerations. The paper explores the integration of PCM into ventilated façades, highlighting the potential for significant improvements in energy consumption, thermal comfort, and reductions in CO2 emissions. However, the flammability of PCMs introduces substantial fire safety challenges that must be addressed to ensure the safe application of this solution. The fire safety of both ventilated façades and PCMs is approached, followed by specific fire safety concerns when PCMs are integrated into ventilated façade systems. The conclusion states that while the integration of PCMs into ventilated façades offers substantial environmental benefits, attention to fire safety is essential. This necessitates the implementation of rigorous fire protection measures during the design and construction phases. By addressing both the environmental advantages and fire safety challenges, this review aims to provide a comprehensive understanding of the potential and limitations of PCM-integrated ventilated façades, offering valuable insights for researchers, engineers, and policymakers in the field of sustainable buildings. [ABSTRACT FROM AUTHOR]

In recent years, remotely controlling an unmanned aerial vehicle (UAV) to perform coverage search missions has become increasingly popular due to the advantages of the UAV, such as small size, high maneuverability, and low cost. However, due to the distance limitations of the remote control and endurance of a UAV, a single UAV cannot effectively perform a search mission in various and complex regions. Thus, using a group of UAVs to deal with coverage search missions has become a research hotspot in the last decade. In this paper, a differential evolution (DE)-based multi-UAV cooperative coverage algorithm is proposed to deal with the coverage tasks in different regions. In the proposed algorithm, named DECSMU, the entire coverage process is divided into many coverage stages. Before each coverage stage, every UAV automatically plans its flight path based on DE. To obtain a promising flight trajectory for a UAV, a dynamic reward function is designed to evaluate the quality of the planned path in terms of the coverage rate and the energy consumption of the UAV. In each coverage stage, an information interaction between different UAVs is carried out through a communication network, and a distributed model predictive control is used to realize the collaborative coverage of multiple UAVs. The experimental results show that the strategy can achieve high coverage and a low energy consumption index under the constraints of collision avoidance. The favorable performance in DECSMU on different regions also demonstrate that it has outstanding stability and generality. [ABSTRACT FROM AUTHOR]

Urbanization is a significant indicator of city progress, and as urbanization advances, carbon emissions exhibit an increasing trend that must not be disregarded. Therefore, it is imperative to thoroughly examine the spatial and temporal variations as well as the factors influencing carbon emissions during the urbanization process. In this paper, based on the 2009–2019 PRD region, carbon emissions are measured from energy consumption, industrial production process, solid waste, and wastewater according to the IPCC coefficients, and spatial and spatial differences in carbon emissions are combined with spatial analysis and the drivers analyzed using the gray correlation scale. The results show that: (1) The total carbon emissions in the PRD region have been increasing over the study period, and the distribution of total carbon emissions shows a pattern of "strong in the east and weak in the west", with energy consumption accounting for the highest proportion of carbon emissions. (2) The global Moran Index of carbon emissions in the PRD has been decreasing, with low and low clustering concentrated in Shaoguan and Zhaoqing, high and high clustering concentrated in Dongguan and Shenzhen, and low and high clustering concentrated in Shenzhen and Guangzhou, with cold spots mainly concentrated in Zhaoqing and hot spots mainly distributed in Guangzhou, Shenzhen, and Dongguan. (3) The degree of economic growth has a substantial influence on carbon emissions in the PRD cities, and the influence of technical advancement has intensified. Guangzhou City is propelled by low-carbon regulations that have a more equitable influence on all elements. Zhuhai City has a more significant influence on energy intensity, while Foshan City has a more noticeable decrease in the effect of foreign investment. To address the issue of carbon emissions, the government should establish appropriate rules to regulate carbon emissions in areas with high emissions, foster collaborative efforts across cities, and encourage the growth of environmentally friendly enterprises. [ABSTRACT FROM AUTHOR]

Cloud service providers deliver computing services on demand using the Infrastructure as a Service (IaaS) model. In a cloud data center, several virtual machines (VMs) can be hosted on a single physical machine (PM) with the help of virtualization. The virtual machine placement (VMP) involves assigning VMs across various physical machines, which is a crucial process impacting energy draw and resource usage in the cloud data center. Nonetheless, finding an effective settlement is challenging owing to factors like hardware heterogeneity and the scalability of cloud data centers. This paper proposes an efficient algorithm named VMP-ER aimed at optimizing power consumption and reducing resource wastage. Our algorithm achieves this by decreasing the number of running physical machines, and it gives priority to energy-efficient servers. Additionally, it improves resource utilization across physical machines, thus minimizing wastage and ensuring balanced resource allocation. [ABSTRACT FROM AUTHOR]

With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assisted MEC. Specifically, the joint UAV deployment and task offloading problem is first modeled as a large-scale multiobjective optimization problem with the purpose of minimizing energy consumption while improving user satisfaction. Then, a large-scale UAV deployment and task offloading multiobjective optimization method based on the evolutionary algorithm, called LDOMO, is designed to address the above formulated problem. In LDOMO, a CSO-based evolutionary strategy and a MLP-based evolutionary strategy are proposed to explore solution spaces with different features for accelerating convergence and maintaining the diversity of the population, and two local search optimizers are designed to improve the quality of the solution. Finally, simulation results show that our proposed LDOMO outperforms several representative multiobjective evolutionary algorithms. [ABSTRACT FROM AUTHOR]

Air conditioning contributes a high percentage of energy consumption over the world. The efficient prediction of energy consumption can help to reduce energy consumption. Traditionally, multidimensional air conditioning energy consumption data could only be processed sequentially for each dimension, thus resulting in inefficient feature extraction. Furthermore, due to reasons such as implicit correlations between hyperparameters, automatic hyperparameter optimization (HPO) approaches can not be easily achieved. In this paper, we propose an auto-optimization parallel energy consumption prediction approach based on reinforcement learning. It can parallel process multidimensional time series data and achieve the automatic optimization of model hyperparameters, thus yielding an accurate prediction of air conditioning energy consumption. Extensive experiments on real air conditioning datasets from five factories have demonstrated that the proposed approach outperforms existing prediction solutions, with an increase in average accuracy by 11.48% and an average performance improvement of 32.48%. [ABSTRACT FROM AUTHOR]

Energy innovation plays an important role in the transition to a zero-carbon economy. Governments in IEA member countries are investing in the R&D, demonstration, and deployment of new energy technologies as part of their energy and climate policies. However, government subsidies for energy innovation are not always efficient in achieving climate policy goals. This paper proposes a two-stage Data Envelopment Analysis model with shared inputs to determine the optimal allocation of public funds for the energy innovation process. The innovation process is divided into two stages: the R&D stage and the commercialization stage. The inputs to the model (budget expenditures for energy innovations) are distributed between the first and second stages. As intermediate products, we use the number of patents in clean energy and hydrocarbon energy. The outputs of the model are the changes in carbon intensity and energy efficiency. This model can be used to assess the effectiveness of government spending on energy innovation. The results show that some IEA member countries should allocate a large part of the fossil fuel technology budget (more than 70%) to the research and development phase. The proposed model can support decision making at the international level to increase the effectiveness of public policies in achieving decarbonization and energy efficiency goals. [ABSTRACT FROM AUTHOR]

This paper introduces a novel portable multi-layer capsule-shaped triboelectric generator (CP-TEG), aimed at optimizing the performance of triboelectric generator technology in terms of miniaturization, modularity, and efficient energy collection. The CP-TEG utilizes a unique multi-layer, stacked structure and an elliptical cylindrical design to increase the effective frictional area and enhance power generation efficiency. Its portable design allows for flexible application in various environments and scenarios. Experimental results demonstrate that the CP-TEG can maintain stable and efficient electrical output under various motion amplitudes and frequencies, and it shows good adaptability to the direction of motion excitation. With a motion amplitude of 7 cm and a frequency of 1.94 Hz, the CP-TEG can charge a 220 μF capacitor to 1.3 V within 100 s. The power generation unit's output voltage and current are more than three times higher than that of traditional single-layer contact-separation mode triboelectric devices. Particularly, its performance in harvesting energy from human motion underscores its effectiveness as a renewable energy solution for wearable devices. Through its innovative structural design and optimized working mechanism, the CP-TEG demonstrates excellent energy collection efficiency and application potential, offering new options for sustainable energy solutions and development. [ABSTRACT FROM AUTHOR]

This work introduces a blockchain-based secure medical prescription-management system seamlessly integrated with a dynamic Internet of Things (IoT) framework. Notably, this integration constitutes a pivotal challenge in the arena of resource-constrained IoT devices: energy consumption. The choice of a suitable blockchain consensus mechanism emerges as the linchpin in surmounting this hurdle. Thus, this paper conducts a comprehensive comparison of energy consumption between two distinct consensus mechanisms: Proof of Work (PoW) and Quorum-based Byzantine fault tolerance (QBFT). Furthermore, an assessment of the most energy-efficient algorithm is performed across multiple networks and various parameters. This approach ensures the acquisition of reliable and statistically significant data, enabling meaningful conclusions to be drawn about the system's performance in real-world scenarios. The experimental results show that, compared to the PoW, the QBFT consensus mechanism reduced the energy consumption by an average of 5%. This finding underscores the significant advantage of QBFT in addressing the energy consumption challenges posed by resource-constrained IoT devices. In addition to its inherent benefits of privacy and block time efficiency, the Quorum blockchain emerges as a more sustainable choice for IoT applications due to its lower power consumption. [ABSTRACT FROM AUTHOR]

Worldwide, different strategies are being developed in order to ensure optimum conditions for the development and growth of economic competitiveness, as well as for increasing the quality of life and environmental protection. All these strategies are closely linked to the development and modernization of systems for producing energy from clean and renewable sources. In this context, the present paper presents the results of research regarding the evaluation of the sustainability of briquette production using biomass resulting from vine winter pruning as the raw material. An analysis of the scientific literature indicates that nearly 8 Mt of biomass would result from the over 7.4 million hectares of vine plantations in the world, biomass that could be valorized through densification in order to produce solid biofuels with a lower calorific value of more than 17 MJ/kg. This study examines the production of briquettes from vineyard winter pruning with consideration of two types of densification technologies: baling and natural drying of the tendrils, and collection, shredding, and artificial drying of the lignocellulose debris. The quality indices and energy consumption and energy efficiency of the briquettes were evaluated to determine their feasibility as an alternative fuel source. When designing the scientific endeavor, the following aspects were considered: defining the aim and objectives of the research; designing the research algorithm; collecting, preparing, and conditioning the biomass; conducting a chemical analysis of the briquettes; and evaluating the energy consumption and energy efficiency for producing the briquettes, taking into account two drying methods (natural and artificial drying). In the meantime, some specific laboratory equipment was designed and built for the artificial drying of biomass, evaluation of mechanical durability, measurement of energy consumption, etc. Analysis of the experimental data has led to the conclusion that the agricultural waste from vine pruning can constitute an important and sustainable source of energy in the form of briquettes that fulfill most of the requirements imposed by international standards. [ABSTRACT FROM AUTHOR]

This paper describes an empirical experiment of Internet of Things (IoT)'s integration in the Post-Occupancy Evaluation (POE) process. The experiment aimed to trial a novel IoT approach to enabling building user responsiveness to prevalent IEQ for individualised comfort. The purpose is to provide a system that mitigates a common issue of centralised air conditioning that limits occupants' control over their immediate environment. To achieve this, an IoT platform was developed with smart IEQ monitoring sensors and wearable devices and trialled with PhD researchers in a shared university workspace. The findings provided empirical evidence of IoT's enhanced benefits to improving user control over their individual comfort and enabling positive energy behaviour in buildings. Specifically, the IoT system provided real-time insight into CO2 concentration data while enabling responsive occupant interaction with their immediate environment and at-the-moment mitigation actions. Outputs of the experiment showed that the perceptions of participants about the stuffiness of the air, productivity, and healthy environment were significantly better after taking the mitigation action compared to before. Also, we found a significant relationship between measured CO2 concentration readings and perceived air stuffiness (p = 0.004) and productivity (p = 0.006) and a non-significant relationship between CO2 concentration readings and perceived healthy environment (p = 0.058). Interestingly, we observed that irrespective of the similarities in recorded CO2 concentration readings being within acceptable ranges (632–712 ppm), the perception of air stuffiness significantly differed (p = 0.018) before and after the mitigation actions. The effectiveness of the developed IoT platform was evidenced as most of the participants found the process very easy to participate in with little interruptions to their work as little time was consumed. The results are useful in modifying approaches to building occupant comfort and energy behaviour in commercial and residential settings. [ABSTRACT FROM AUTHOR]

Current research on rural houses in China mainly focuses on improving energy efficiency, with relatively few studies addressing energy-saving measures and enhancing thermal comfort for residents. Therefore, this paper focuses on existing rural houses in Nantong City, Jiangsu Province, as the research object. Through on-site measurements and questionnaire surveys, it was found that the average indoor temperature of rural houses is 28.5 °C in summer and below 10 °C in winter, failing to meet the comfort needs of the villagers. To further study human thermal comfort, a linear regression method was used to establish an indoor Mean Thermal Sensation (MTS) model for Nantong's rural houses. The neutral temperature in summer was found to be 26.46 °C, and an adaptive thermal comfort model for rural residents in the Nantong area was established. Through single-factor simulation and orthogonal experiments, the optimal comprehensive energy-saving renovation scheme was proposed. Finally, a typical rural house in Zhangzhuang Village was used as a case for building renovation practice. After the renovation, the number of thermal comfort hours increased by 145 h per year, the thermal comfort compliance rate reached 47.07%, and the overall energy-saving rate was 57.41%. [ABSTRACT FROM AUTHOR]

Particle damping technology is applied in vibration and noise reduction because of its good broadband vibration reduction effect. The energy transfer and loss between particles are keys to the role of damping. This paper investigates the relationship between the thermal energy dissipation caused by the collision of particles and the input energy. The temperature rise characteristics under different vibration states are studied. The results show significant differences in the thermal dissipation characteristics of granular assemblies for different vibration states. Under equivalent excitation amplitudes, the frequency increases, and the thermal loss of the particles increases. At the same frequency, the excitation amplitudes increase, and the thermal loss of the particles decreases. Granular assemblies in strong vibrational states, such as a gas-like state, have intense vibrations and apparent temperature-increasing effects. However, in this vibration state, the input energy to the particles is considerable, and the thermal loss accounts for a small proportion of the total energy. In states such as solid-like states, micro-vibrational states, and intermediate vibrational states, the particles interact closely, and the input energy for the particles is small. Additionally, the movement of the particles is not intense, and the thermal loss accounts for a large proportion of the total energy. The thermal loss of the particles also shows a frequency variation characteristic. According to the different temperature rise characteristics of the particles, the proportion of thermal energy consumption is analyzed. The research shows that the proportion of thermal energy consumption is not more than 70%, so there are other forms of energy consumption in the vibration reduction and energy consumption of the particles. [ABSTRACT FROM AUTHOR]