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In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building's energy consumption. [ABSTRACT FROM AUTHOR]

Connected and automated vehicle platoons (CAVPs) are considered an effective way to alleviate traffic congestion, reduce the incidence of traffic accidents, and improve vehicle economy in the intelligent transportation system (ITS). Vehicles in the CAVPs can communicate with each other through V2X technology, which could optimize the economy of the platoon. Cooperative adaptive cruise control (CACC) can make effective use of the characteristics of CAVPs and contribute to resource conservation, ecological driving, and traffic system development. In this paper, a two-stage CACC method is proposed for CAVPs to reduce fuel consumption in the multislope road section. In the first stage, the optimal velocity profiles for the leader based on dynamic programming (DP) are planned according to the road information and the fuel consumption model. In the second stage, a vehicle longitudinal third-order differential dynamics model is utilized to build the platoon time-delay system considering communication delay and actuator delay. A feedback controller is developed for each vehicle considering the internal stability and the string stability of the CAVPs. Results show that the proposed method can save 5.33% of fuel consumption compared to the constant speed cooperative adaptive cruise control (CS-CACC) method and has a better tracking performance compared to the model predictive control (MPC) method. The CACC method proposed in this paper can provide a theoretical basis and data support for building an ecological CACC strategy for CAVPs. [ABSTRACT FROM AUTHOR]

Compared with most dynamic tests, point load test is widely used in engineering practice because of its wide requirements for test conditions. In offshore mining engineering, the surrounding rock is affected by dynamic load impact and corrosion of various aqueous solutions for a long time. Therefore, it is of great significance to study the influence of hydrochemical corrosion on dynamic mechanical properties of rock to ensure the stability of surrounding rock. In this paper, a large number of dynamic point load and dynamic uniaxial compression tests were carried out on the granite mined from a large mine by using the Split Hopkinson pressure bar test device after the corrosion of pH values of 2, 5, and 7 solutions, respectively. Based on the energy theory, the crushing energy consumption in the test process is analyzed, and finally the fitting formula of the dynamic compressive strength and the crushing energy consumption in the dynamic point load test is obtained through fitting analysis. The results show that when the crushing energy consumption in the dynamic point load test is used to estimate the dynamic compressive strength, a certain crushing energy consumption (the final value of this test is 70 J in this paper) is more accurate, and it can estimate the dynamic compressive strength under various corrosion conditions under this crushing energy consumption. However, with the change of crushing energy consumption in the dynamic point load test, this transformation relationship is no longer accurate, and it is necessary to modify this transformation relationship with corrosion condition as the influencing factor. This conclusion can provide reference for obtaining the dynamic compressive strength of rocks under different corrosion conditions in engineering field in the future. [ABSTRACT FROM AUTHOR]

This paper focuses on the active cell balancing of lithium-ion battery packs. An improved single-input, multioutput, bi-switch flyback converter was proposed to achieve effective balancing. The proposed topology simplifies the control logic by utilising a single MOSFET switch for energy transfer and two complementary pulses to control the cell-selecting switches. The proposed topology can decrease the number of switching devices as well as the size and cost of the system. The bi-switch flyback converter eliminates the need for a separate buffer circuit to minimise leakage and electromagnetic inductance. Losses and energy efficiency were analysed at each end of the proposed topology. The appropriate MATLAB simulations investigated the balancing characteristics of various state of charge (SOC) imbalances. A comparison is made between the balancing speed and energy transfer efficiency of the proposed topology and a conventional topology that employs a multi-input and multi-output flyback converter in a static mode. The results of the MATLAB simulation were validated by the OPAL-RT (OP5700) real-time simulator. The balancing data of the proposed topology were compared using MATLAB simulation and real-time simulation. This work may reduce the time required to assemble and commission the hardware for the proposed topology's real-time implementation. [ABSTRACT FROM AUTHOR]

To address the issue of multiobjective control in multienergy systems with diverse operational objectives, a two-stage optimization framework based on expected point tolerance has been proposed in this paper. In the first stage, a single objective function is used for optimization control to obtain the expected point of the multiobjective optimization problem. Then, in the second stage, by defining the allowable deviation between each optimization objective and the expected point, the original multiobjective optimization problem is transformed into a single objective optimization problem solution with tolerance measurement. Finally, in the simulation scene of a multienergy system, it is demonstrated that compared with the optimal results under each single objective method, the proposed method increases power line loss, maximum voltage deviation, new energy consumption, and economy by 2.22, 2.30, 1.02, and 2.45 times, respectively. Compared with the suboptimal results, the proposed method reduces power line loss by 22.26, 1.74, 1.09, and 0.97 times, respectively. Combining the shape of the Pareto frontier, it is demonstrated that the proposed method can comprehensively consider the needs of multiple power optimization objectives for forming a more reasonable and effective system optimization scheduling and also provide a new approach for solving multiobjective optimization problems. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) has inexpensive, small, and less energy sensor nodes, which are allocated in random ways in particular areas for measuring the phenomenon or events in that field. In recent days, WSN has played a vital role in various applications, like industrial monitoring, medical treatments, agricultural monitoring, and military operations. However, the security challenges and network lifetime are the main issues in the existing methods. In order to overcome these issues, the Taylor-Spotted Cat Optimization (Taylor-SCO) approach is devised in this paper. Here, the Cluster Heads (CHs) are selected based on the developed optimization method, named Taylor CSO. Moreover, the delay, distance, and energy parameters are considered for effective Cluster Head Selection (CHS). Here, route maintenance is also done for increasing network lifetime and reducing complexities. In addition, the Modified K-Vertex Disjoint Paths Routing (KVDPR) model is established for routing. The modification of KVDPR is carried out using several factors, such as link reliability, throughput, and various trust factors. Moreover, the developed Taylor-SCO algorithm is developed by combining the Spotted Hyena Optimizer (SHO), Cat Swarm Optimization (CSO) algorithm, and Taylor series. The Taylor-SCO achieved better performance with energy consumption, trust, and throughput of 0.00037 J, 0.51, and 793160 kbps. [ABSTRACT FROM AUTHOR]

For the characteristics of channel instability in wireless sensor networks, this paper proposes an intrusion detection algorithm based on FedAvg (federated averaging) and XGBoost (extreme gradient boosting) wireless sensor networks using fog computing architecture. First, the network edge is extended by introducing fog computing nodes to reduce the communication delay. It reduces the transmission bandwidth and privacy leakage risk while improving the accuracy of jointly learned global and local models. Then, the histogram-based approximation calculation method is improved to adapt to the unbalanced data characteristics of wireless sensor networks. Finally, by introducing TOP-K gradient selection, the number of model parameter uploads is minimized, and the efficiency of model parameter interaction is improved. The experimental results show that this algorithm has superior detection performance and low energy consumption. It is also compared with other algorithms to demonstrate the high detection rate and low computational complexity of this algorithm. [ABSTRACT FROM AUTHOR]

The need for sustainable integrated energy systems to mitigate environmental impact is hindered by challenges in fluctuating demand, trading reliability, and trustworthiness. This paper proposes an innovative approach to tackle these challenges by introducing a blockchain-based integrated energy system trading model with smart contracts. It is intricately linked with the operation of carbon capture and storage (CCS) technology and power-to-gas (P2G) equipment. The CCS-P2G-coupled operation principle is first outlined, followed by the presentation of a comprehensive system model. The peer-to-peer (P2P) energy trading algorithm is enhanced using the Bloom filtering technique. Leveraging smart contracts, a distributed energy trading mechanism is employed, resulting in a meticulously constructed integrated energy system trading model for CCS-P2G-coupled operation. The proposed model is rigorously evaluated for energy trading efficiency and system performance, revealing significant improvements compared to prior studies and showcasing substantial cumulative benefits. Three operation scenarios are examined, with experimental results highlighting the model's superior carbon emission reduction capacity. This study introduces an innovative paradigm for trading and managing integrated energy systems, holding potential implications for the sustainable development and decarbonization transition of future energy systems. [ABSTRACT FROM AUTHOR]

Stochastic programming has become increasingly vital in energy applications, especially in the context of the growing need for renewable energy solutions. This paper presents a significant advancement in this field by introducing an efficient and robust algorithm for optimally sizing hybrid renewable energy systems. Utilizing a two-stage stochastic programming approach, the proposed algorithm addresses the challenges posed by the unpredictability of renewable energy sources. The proposed solution leverages the three-block alternating direction method of multipliers (ADMM), a cutting-edge technique that facilitates parallel computation and enhances computational efficiency. The distinctiveness of this method lies in its ability to solve complex stochastic optimization problems without compromising the mathematical integrity of the model. This is achieved by applying first-order optimality conditions, ensuring both robustness and efficacy. To demonstrate the practical applicability and superiority of the algorithm, a case study was conducted in a rural area of South Africa. The proposed algorithm was applied to design an optimal hybrid renewable energy system, and its performance was compared against traditional methods such as progressive hedging and Monte Carlo techniques. Results affirm the superiority of the approach, saving approximately 8.16% capital cost when compared to progressive hedging. In addition, the proposed algorithm outperforms the Monte Carlo method both in terms of CPU time and the number of cost function evaluations. [ABSTRACT FROM AUTHOR]

Low energy consumption and green transformation of automobile product portfolio is the trend of the times. Automotive manufacturers make product portfolio decisions by setting multiple criteria such as fuel consumption, sales, and volume. It is also important to take into account the symbiotic interaction effects between automotive products. In order to achieve the above research objectives, this paper constructs the Lotka–Volterra MCGP model to make multicriteria decisions on automobile product portfolio design from the perspective of energy sustainability with BMW Brilliance is taken as an example to illustrate the process of using the multicriteria model. The empirical analysis successively measures product growth using the logistic model, analyzes the symbiotic relationship of product portfolios using the Lotka–Volterra model, and finally performs multicriteria evaluation using the MCGP model. In order to verify the reliability of the model, this paper verifies the robustness of the model from the perspectives of parameter dynamics, system boundaries, and model scalability. The results of empirical analysis and robustness analysis show that the Lotka–Volterra MCGP model proposed in this paper is applicable to the multicriteria decision-making of automobile product portfolio design from the perspective of energy sustainability. [ABSTRACT FROM AUTHOR]

This paper assesses the transport system of Addis Ababa, Ethiopia, taking factors such as the number of vehicles, roadway width, speed of vehicles, longitudinal grade, and proportion of both fuel and electrical vehicles by dividing vehicles into seven classes, namely, car, minibus, small bus, coach, small truck, heavy truck, and truck trailer, to determine CO2 emission, CO emission fuel consumption, and electric consumption in addition to the percent to replace ICE vehicles. After selecting eight representative road sections in Addis Ababa city, input data was collected from both primary and secondary sources. Simulation of urban mobility (SUMO) is used to model the existing road transport system and two other scenarios, cases being 20% and 40% replacement of internal combustion engine vehicles by electric vehicles. Among the vehicle types studied under this paper, the SUMO results show that coaches are with the highest CO2 emission, releasing an average amount of 28.442 grams of CO2 every time step, while cars are with the lowest CO2 emission value of 6.542 grams. Minibuses are the top CO emitters, releasing an average of 0.420 grams of CO every time step, and truck trailers emit the smallest CO emission, 0.025 grams. Regarding electric consumption, the truck trailer is the vehicle type with the highest electric consumption, with a value of 2.282 kwh (watthour) consumption every time step, and cars are the least electricity-consuming vehicles, with a value of 0.151 kwh. The fourth point is fuel consumption; besides the high CO2 emission, coaches' consumption of fuel is leading by 8.946 grams, and cars use 2.087 grams of fuel every time step. Totally, public transport vehicles are responsible for higher emissions and huge fuel consumption. Therefore, if our transport system encourages the penetration of electric vehicles into the road transport system, a healthy and energy-efficient environment is reserved. Again, from a financial and environmental standpoint, the replacement of 40% of ICE vehicles by EVs enhances us with reduced costs and a green environment. [ABSTRACT FROM AUTHOR]

Currently, coal is still the main energy source for China's economic development, and the environmental issues caused by coal consumption have aroused widespread concern. Predicting and analyzing coal consumption will help to formulate a higher level of energy consumption planning and economic development strategy. The main purpose of this study is to develop a new fractal time-series prediction (FTSP) model to predict coal consumption, providing reference for formulating effective energy consumption planning and economic development strategies. Hurst index is an important indicator of whether time-series data have long memory and fractal characteristics. This paper uses the rescaled range (R/S) method to calculate Hurst index of the coal consumption per-10000-Yuan-GDP (CC/GDP). The Hurst index is 0.8025, exceeding 0.5, indicating that the time-series data CC/GDP has a long memory and fractal characteristic. Based on this, this paper uses the FTSP model to predict China's coal consumption (CC/GDP). The prediction results show that the FTSP model accurately predicted China's coal consumption with a relative error rate (RER) of less than 5% and a mean absolute percentage error (MAPE) of 6.62%. The model predicts a decrease in China's coal consumption from 0.25 tons of standard coal in 2021 to 0.11 tons of standard coal in 2030, a decrease of 44%. To sum up, the FTSP model provides a new and accurate way to predict coal consumption, and the results suggest that China's coal consumption is sustainable and will decrease significantly in the coming years. This study has important implications for energy consumption planning and economic development strategies. [ABSTRACT FROM AUTHOR]

The development of the vehicle-road cooperative intelligence can effectively resolve the current technical impediment and cost quandary associated with high-level autonomous driving. Nevertheless, the intelligent infrastructure entails initial deployment costs and ongoing energy consumption and maintenance costs, necessitating a comprehensive and quantitative analysis of the costs of intelligent infrastructure and the corresponding changes in comprehensive costs. The cost evaluation model for the cooperative intelligent system is designed in this paper, considering the corresponding intelligent infrastructure layout scheme for different road types within the technical framework. The intelligent configuration and corresponding cost transfer from roadside to vehicle side under the synergy effect is also analyzed. Using Beijing as a case study, the results indicate that the deployment of intelligent infrastructure will effectively reduce acquisition and usage costs of high-level intelligent vehicles and achieve a greater "reuse" effect by serving more intelligent connected vehicles (ICVs). Compared to the vehicle intelligence, collaborative intelligence will reduce cumulative total costs by more than ¥200 billion from 2023 to 2050, even with the inclusion of intelligent infrastructure's costs. [ABSTRACT FROM AUTHOR]

Optimal energy-efficient train operation optimization is one of the widely studied areas in transportation science, which can significantly reduce energy consumption that accounts for a large proportion of operating costs. In order to adapt to the complex and changeable railway line conditions such as gradient, slope length, and speed limit and avoid the error in tracking speed curve, an optimal driving strategy decision-making (ODSD) model is proposed in this paper. The model considers the non-fixed sequence of driving regimes, and the regimes are directly selected in the discrete micro-subsegments of an equal time-division pattern. To solve this model efficiently, an improved ant colony system algorithm with the difference edges (ACSd) is proposed, which takes the heuristic effect of the difference between the best solutions of two adjacent iterations, i.e., "the difference edges," into account. Additionally, energy-efficient heuristic factor and speed heuristic factor are presented to balance energy saving and speed. The results demonstrate that ACSd performs better than the basic ant colony system algorithm in solving traveling salesman problem (TSP) and provides more flexible driving strategies for the ODSD model. [ABSTRACT FROM AUTHOR]

As the integration of distributed energy into the power grid continues to rise, the significance of electricity transactions in promoting renewable energy consumption grows substantially. This necessitates the establishment of a robust electricity trading framework to facilitate reliable and trustworthy transactions for prosumers. This paper introduces an innovative blockchain-based electricity trading framework. Within this framework, we present a decentralized collaborative model training approach aimed at predicting the power generation of distributed photovoltaic power stations. Simultaneously, we propose a graph-based algorithm for efficiently matching producers and consumers. Our experiments, conducted on the Hyperledger Fabric blockchain platform using real-world datasets, demonstrate enhanced prediction accuracy compared to existing models. The framework effectively handles transactions within one second, sustaining a sending rate of up to 200 transactions per second. The outcomes of this study not only surpass the performance of existing prediction models but also provide valuable insights for the development of trading frameworks in actual production environments. The proposed framework stands as a reference point for constructing reliable trading infrastructures, thereby contributing to the acceleration of green electricity trading. [ABSTRACT FROM AUTHOR]

In order to evaluate the effect of market-oriented environmental management measures on regional carbon emission intensity in the pilot areas better, this paper adopts a quasinatural experiment of energy-use rights trading (EURT) policy by using the difference-in-difference method from the perspective of cities in China from 2006 to 2019. The results show that the policy of EURT can significantly reduce regional carbon emission intensity, which varies in different regions and different scales of cities. The main goal of implementing the policy is to reduce regional carbon emission intensity by improving the energy consumption structure and promoting the improvement of industrial structure and green innovation. In addition, the spatial impact of the EURT pilot project is demonstrated by its ability to not only reduce local carbon emission intensity but also decrease carbon intensity in and around the designated areas. [ABSTRACT FROM AUTHOR]

The paper investigates the interplay between clean energy, environmental pollution reduction, and economic growth in the Gulf Cooperation Council (GCC) countries from 1980 to 2019, utilizing the autoregressive distributed lag (ARDL) method. The study underscores the global transition towards cleaner energy sources and its implications for the region. In assessing the determinants of economic growth, the findings reveal a positive and significant short- and long-term impact of energy production, with energy consumption exhibiting a positive and significant effect solely in the long term. The absence of a confirmed relationship in the short term is notable. Causality tests demonstrate a significant link from both energy consumption types to environmental pollution, alongside causal relationships from urbanization, energy production, and consumption to economic growth. Moreover, the results include the pivotal role of energy production in driving GDP growth, caution in short-term energy consumption effects, and significant causal links between energy consumption and environmental pollution. The recommendations to incentivize sustainable energy production, engage in long-term energy consumption planning, and adopt integrated urbanization policies provide actionable insights for policymakers. These suggestions are aimed at guiding the GCC countries in balancing the pursuit of economic growth with environmental sustainability—a delicate equilibrium that necessitates careful consideration of various factors. [ABSTRACT FROM AUTHOR]

The use of decentralized renewable energy systems will continue to play a significant role in electricity generation especially in developing countries where grid expansion to most remote areas is uneconomical. The income levels of these off-grid communities are often low, such that there is a need for the delivery of cost-effective energy solutions through optimum control and sizing of energy system components. This paper aims at minimizing the net present cost (NPC) and the levelised cost of energy (LCOE). The study presents a hybrid power system involving a hydroelectric, solar photovoltaic (PV), and battery system for a rural community in Cameroon. The optimization of the system was done using HOMER Pro and validated using a meta-heuristic algorithm known as genetic algorithm (GA). The GA approach was programmed using the MATLAB software. After the HOMER simulation, the optimal power capacity of 3 kW solar PV, 334.89 Ah battery, and 32.2 kW microhydropower was used to meet the load. The village load profile had a daily energy usage of 431.32 kWh/day and a peak power demand of 38.49 kW. The optimized results showed an NPC and LCOE of $90,469.16 and 0.0453 $/kWh, respectively. The system configuration was tested against an increase in hydropower capacity, and it was observed that increasing the hydropower capacity has the ability to significantly reduce the LCOE as well as the battery and solar PV size. A comparative analysis of the two approaches showed that the optimization using GA was more cost-effective than HOMER Pro with the least LCOE of 0.0344 $/kWh and NPC of $86,990.94 as well as a loss of power supply probability (LPSP) of 0.99%. In addition, the GA method gave more hydropower generation than HOMER Pro. This supports the fact that stochastic methods are more realistic and economically viable. They also accurately predict system operation than deterministic methods. [ABSTRACT FROM AUTHOR]

Under the "carbon peak and carbon neutrality" goal, the construction of an efficient, low-carbon, and economical energy supply system is of great significance for advancing a dual carbon strategy. In allusion to the integrated energy systems (IES) with hydrogen energy coupling, a hydrogen energy coupling IES low-carbon optimization operation strategy that took account of green certificate and ladder-type carbon joint trading and dual-incentive demand response was proposed in this paper. First, a hydrogen energy multiuse system composed of an electrolyzer, a hydrogen fuel cell, a methane reactor, and hydrogen energy storage was constructed to make full use of the low-carbon cleaning characteristics of hydrogen energy. Besides, a combined model of hydrogen mixed with natural gas was established to improve the utilization efficiency of hydrogen energy. Second, a dual-incentive demand response model including price incentives and subsidy incentives was constructed to fully use the ability to adjust demand-side resources. Next, in view of the carbon emission reduction mechanism of the green certificate, a green certificate and ladder-type carbon joint trading mechanism was constructed. In addition, a green certificate trading mechanism and a reward and punishment tiered carbon trading mechanism had been introduced separately in the IES optimization operation model to reduce carbon emissions of the system. The calculation simulation sets up different scenarios for comparative analysis. As shown by the results, the proposed model could effectively improve renewable energy consumption capacity and energy utilization efficiency. The effectiveness of hydrogen energy utilization, demand respond, and green certification carbon trading mechanism in improving system economy and low-carbon efficiency is verified. [ABSTRACT FROM AUTHOR]

The academic and policy-oriented literature increasingly quantifies the wider effects of energy policy on the macroeconomy. However, the spillovers from economic policies to energy use are less frequently recognised, meaning that many policymakers who strive to improve productivity whilst simultaneously targeting emission reduction fail to consider these interactions. This paper addresses this issue by using simulation results generated by introducing an exogenous increase in labour productivity in a computable general equilibrium model calibrated with data from the United Kingdom. Theoretical analysis suggests that increasing labour productivity can have positive or negative effects on employment levels and energy use. However, the simulation results show that in the context of a developed open economy, improved labour efficiency will increase employment, a key policy objective, but simultaneously increase energy use. A key policy implication is that this work highlights the need for policy frameworks that explicitly acknowledge and quantify the interconnections between national economic policy strategies and energy policy objectives. Secondly, it shows that in practice, policies to reduce carbon emissions need to be strengthened alongside policies to improve productivity successfully implemented. [ABSTRACT FROM AUTHOR]

With the percentage of embedded SRAM increasing in SoC chips, low-power design such as the near-threshold SRAM technique are getting increasing attention to reduce the entire chip energy consumption. However, the descending operating voltage will lead to longer write latency and a higher failure rate. In this paper, we present a novel low Vth ultradynamic voltage scaling (UDVS) 9T subthreshold SRAM cell to improve the write ability of SRAM cells. The proposed Low Vth UDVS SRAM cell is demonstrated with a low threshold voltage speed-up transistor and an ultradynamic voltage scaling circuit implemented in 16 nm low-leakage CMOS technology. This wide supply range was made possible by a combination of circuits optimized for both subthreshold and abovethreshold regimes. This write assist technique can be operated selectively to provide write capability at very low voltage levels while avoiding excessive power overhead. The simulation findings reveal that with 16 nm technology, the write ability is improved by 33% over the normal case at 0.9 V supply voltage. [ABSTRACT FROM AUTHOR]

The nature of construction and formulation of the multi-objective functions in the flexible job shop (FJS) provides great insights about the study of the technique and the mechanism in scheduling helping to prioritize efforts for optimizing the manufacturing execution performance, which draw the focus of the researchers at home and abroad. From the perspective of the model, the solution method and the objective function, this paper reviews and analyzes the related academic papers and conferences articles during past few years. As a result, the construction forms of the multi-objective function and the scheduling scenarios show strongly positive correlation with the Makespan, the delivery tardiness, the energy consumption or the cost. Consequently, facing with the characteristic in a general way, the detailed construction analysis on the function for the flexible job shop scheduling problem (FJSP) is critical reviewed, and a total 6 types with 49 kinds classification are discussed. Obviously, the main goals are to summarize, analyze, discuss, and synthesize the existing achievements, the current research status, and the ongoing studies on the FJSP functions construction, and additionally to give useful insight into future research. Combined with the development trend of the intelligent manufacturing and the operations research, arguing for the formulation of the multi-obj. function, this review is expected to contribute to the future research of FJSP. [ABSTRACT FROM AUTHOR]

The choice and the dimensioning of the controller for the maximum power point tracking (MPPT) are determined for the ideal energy efficiency of the photovoltaic (PV) systems. Many works have been developed in the field of MPPT methods, especially fuzzy logic controllers. However, these are robust if the parameters of the membership functions have been well designed. In this paper, the performances of an intelligent fuzzy logic controller (FLC)-based MPPT method have been optimized by an evolutionary genetic algorithm (GA). The works presented in the literature have shown the efficiency of the proposed method compared to classical methods. In our paper, the validation of the experimental results obtained is given with respect to a reference signal. The control of the simulated PV source and the proposed method are built using the Simulink/Matlab environment and implemented on the dSPACE DS1104 controller to validate the practical execution of the suggested method. The standalone PV system has been tested in an emulated test bench experimentation. Experimental results confirm the efficiency of the proposed method and its high accuracy in handling fast varying load conditions. [ABSTRACT FROM AUTHOR]

With the establishment and rapid development of the national carbon emission trading market, new energy system participates in the carbon emission trading market. Analysing the potentiality of virtual power plant trading in carbon emission trading market, this paper designs a two-stage joint trading mechanism for electricity and carbon market with a weekly cycle according to their characteristics, which contain multiple transaction types for both markets. In addition, this paper introduces a carbon capture system (CCS) in gas turbine, which reduces the actual carbon emissions and increases the carbon market income of virtual power plant. Furthermore, it improves the comprehensive and flexible operation capability by adjusting the operation level of CCS, which is conducive to the timely consumption of renewable energy. Aiming at the uncertainty of renewable energy output and electricity price, the paper adopts a multiscenario analysis method to deal with it and establishes a stochastic optimization model to maximize joint earnings. Finally, through example analysis with GAMS, the effectiveness of the scheduling model is verified with simulation results. The overall income of the virtual power plant is improved, and the low carbon power is realized. [ABSTRACT FROM AUTHOR]

The efficiency of on-site consumption of new energy and the economy of dispatching strategy for that in modern microgrids are increasingly concerning, which are closely related to the microgrid control model with source-load uncertainty. To this end, this paper proposes the multiagent hierarchical IQ (λ)-HDQC regulation strategy to realize the source-load-storage-charging collaborative control of the microgrid model with high-permeable new energy. The first layer adopts the IQ (λ) strategy, which avoids the overestimation and underestimation problems of traditional reinforcement learning by the coupled estimation method. The second layer adopts the HDQC allocation strategy, which solves the problem of low utilization of new energy in the proportional allocation method and improves the adaptability of the regulation strategy in the complex stochastic environment. The interaction of the two-layer strategies realizes the source-network-load-storage-charge global dynamic interactive regulation of microgrids. Indicators of energy efficiency are constructed in this paper to measure the simulation results. And the superiority of the proposed strategy is verified through the simulation results of the microgrid system. [ABSTRACT FROM AUTHOR]

Reliable operation of energy management systems, grid stability, and managing energy demand responses are becoming challenging because of the flickering nature of solar irradiance. Accurate forecasting of global solar irradiance, i.e., global horizontal irradiance (GHI), plays a significant role in energy policy-making and the energy market. This paper proposes a novel global solar irradiance forecasting model based on the ensemble recursive radial basis function neural networks (ERRBFNNs). The various atmospheric inputs based on the built ensemble recursive radial basis function neural networks make the network more stable and robust to climatic uncertainty. This paper statistically investigates the performance of novel feed-forward neural networks based on forecasting models with various hidden nodes for global solar irradiance forecasting applications. We validated the proposed ERRBFNN global solar irradiance forecasting model using real-time data sets. The simulation results confirm that the proposed ensemble recursive radial basis function neural network based on global solar irradiance forecasting improves the accuracy, generalization, and network stability. Furthermore, the proposed ERRBFNN lowers the forecasting error to the least compared to other state-of-the-art forecasting models. [ABSTRACT FROM AUTHOR]

This paper seeks to improve mobile communications performance by integrating artificial intelligence (AI) techniques. The study is centered on device-to-device (D2D) communication, which has emerged as a significant aspect in the 5th generation of mobile networks (5G) and is expected to be extended to the 6th generation (6G). Based on the fuzzy system, the proposed solution tackles the critical issue of energy harvesting in D2D communication. The solution presents a support system that selects the best relay for the transmitter-receiver communication to minimize energy consumption and maximize the network's lifetime. Minimizing energy consumption optimizes the network's lifetime, thus providing a reliable and efficient communication system. The approach taken in this study offers a novel perspective in addressing the energy harvesting challenge in D2D communication. It is expected to have a significant impact on the performance of mobile communications. [ABSTRACT FROM AUTHOR]

This paper introduces the utilization of the International Atomic Energy Agency's toolkit for reactor technology assessment (RTA) application to deploy small modular reactors (SMRs) in the Czech Republic, Egypt, and Poland. The increasing demand for clean energy has led to the prominence of small modular reactors (SMRs) in addressing global energy challenges. The successful integration of SMRs into national energy systems necessitates comprehensive evaluations that take into account each country's specific characteristics and energy requirements. RTA application represents significant progress towards innovative nuclear solutions, advancing a cleaner and more resilient energy future plan. The aim of this study is assessing the feasibility and advantages of SMR implementation in these countries, focusing on energy security, emission reduction, and long-term sustainability. Various SMR technologies, including NuScale, SMART, HTR-PM, BWRX-300, SMR-160, and RITM-200, are comparatively analyzed based on safety, scalability, efficiency, and economic viability. The findings reveal that BWRX-300 suits the needs of the Czech Republic and Poland, while RITM-200 is the optimal choice for Egypt. Moreover, NuScale also stands as a strong alternative for all three countries. This article emphasizes the importance of informed discussions and evidence-based decisions, promoting sustainable energy development and global advancements in nuclear technology. By utilizing SMRs, the Czech Republic, Egypt, and Poland can enhance energy security, reduce emissions, and meet rising energy needs sustainably. [ABSTRACT FROM AUTHOR]

Researchers are paying attention to the current trend of electric vehicles (EVs), but there are not many places to charge them and serve as electric vehicle charging stations (EVCSs). In order to prevent the lack of EVCS for battery EVs (BEVs) on any road with all modes of operation, this paper proposed the design of a novel configuration and control structures for the isolated charging station (ICS) based on the solar PV system for efficient energy utilization to charge battery EVs (BEVs) with flexible double function cascaded converter (MPPT and DC-link voltage regulation). This was done with the aid of a battery energy storage system (BESS) as a backup/auxiliary source. The PV system is reliant on solar radiation and the daily load profile. Therefore, the BESS is employed to provide continuous power to BEVs and overcome PV's intermittent nature. The cascaded converter is used to achieve the MPP and to regulate the DC-link voltage to its reference value (100 V), while the buck-boost matching bidirectional DC-DC voltage converters (MBDVCs) are used for charging and discharging mode/buck and boost mode, respectively, of BEVs and BESSs. Additionally, the MBDVCs are used as an interface between the ICS sources with the load through the developed control systems. The outcomes of various operating modes show that the system functions effectively. To verify the system performance, the MATLAB/Simulink application builds the system setup. [ABSTRACT FROM AUTHOR]

Energy consumption from biofuels against fossil fuels over the past few years has increased. This is due to the availability of these resources for production of different forms of energy, and the environmental benefit in the utilization of these resources. Ethanol fuel production from biomass is a complex process of known challenges in the area of handling, optimizing, and future forecasting. The existence of modelling techniques like artificial intelligence (AI) is, therefore, necessary in the design, handling, and optimization of bioethanol production. The flexibility and high accuracy of artificial neural network (ANN), a machine learning technique, to solve intricate processes is beneficial in modelling pretreatment, fermentation, and conversion stages of a bioethanol production system. This paper reviews various AI techniques in bioethanol production giving emphasis on published articles in the past decade. [ABSTRACT FROM AUTHOR]

Researchers are always looking for the improvement of existing methods. Today, CMOS technology is widely used, which has some advantages and disadvantages. One of the alternative methods for CMOS technology is QCA technology which compared to CMOS, has the advantages of low energy consumption and small occupied area. In this paper, by using the concepts and methods of QCA technology, a digital code converter is presented. In this converter, a new gate is used, which can produce outputs such as 4-input AND, 4-input OR, 4-input NAND, and 4-input NOR. The proposed converter has 10 inputs and 12 outputs. The 10 inputs are decimal numbers from 0 to 9, producing the output equivalent to excess-3, BCD, and gray codes. One of the advantages of this circuit is providing three different codes per input in just one circuit. In addition, due to the use of the new 4-input gate, the occupied area and the number of used cells were minimized. Simulations were performed by using QCADesigner-E version 2.2, and outcomes illustrated that the occupied area is equal to 0.29 μm2 and 380 QCA cells with 7 clock phases are used. The energy dissipation of the presented circuit is 171 meV. Also, given the favorable performance exhibited by the 4-input gate across various measurement parameters, it possesses the capability to be efficiently employed within larger and intricately designed circuits. [ABSTRACT FROM AUTHOR]

Design work was done on a broadband and wide-angle selective pyramid metamaterial emitter. COMSOL Multiphysics software was used to study the emitter, which was made of tungsten and aluminum nitride. The width of the unit cell and the tungsten ground thickness were fixed while other geometric parameters, such as the base length of the pyramid, the height of the pyramid, and the thickness of the dielectric, were tuned to produce the emitter's desired broadband emission. A high average emissivity over 0.96 below the cutoff wavelength (0.1 μm-2.2 μm) was seen in the numerical simulation. The developed metamaterial emitter also had good emissivity across a broad range of incidence angles, from 0° to 60°, and was polarization independent. In addition, the planned emitter for the InGaAs cell has a better spectral efficiency than the blackbody other designed emitter. In general, the planned selective nanopyramid emitter was realized with 75% spectrum efficiency for InGaAs band gap energy (0.55 eV) at 1200 K, which was greater than blackbody and prior papers. [ABSTRACT FROM AUTHOR]

With the construction of a new-type power system under the China "double carbon" target and the increasing diversification of the energy demand on the user side, the short-term load forecasting of the power system is facing new challenges. To fully exploit the massive information contained in data, based on the graph convolutional network (GCN) and long short-term memory network (LSTM), this paper presents a new short-term load forecasting method for power systems considering multiple factors. The Spearman rank correlation coefficient was used to analyse the correlation between load and meteorological factors, and a model including meteorology, dates, and regions was established. Secondly, GCN and LSTM are jointly used to extract the spatial and temporal characteristics of massive data, respectively, and finally achieve short-term power load prediction. Historical electrical load data from 2020 to 2022 public data of a real industrial park in southern China were selected to verify the validity of the proposed method from the aspects of forecasting accuracy, feature dimension, and training time. [ABSTRACT FROM AUTHOR]

This paper studies the complexity analysis in production between competition and R&D for a traditional energy vehicles enterprise and a new energy vehicles enterprise under the introduction of corporate average fuel consumption credits and new energy vehicle credits mandate. From the internal perspective of market complexity evolution, this paper analyzes the internal mechanism of market competition fluctuation and discusses the influence of different corresponding speeds of enterprises on market stability through the nonlinear dynamics' theory. The research results show that the adjustment coefficient of R&D investment and the price coefficient of credit transactions are the key factors affecting the competitive strategies of the two companies. The difference between these two factors will even change the relative R&D investment intensity, relative profitability, and market stability of the two companies in a balanced state. In addition, the numerical analysis showed that the time-delay feedback control method could control the unstable behavior of the dynamic system efficiently and quickly and make the market quickly recover a stable and orderly state, which provided a scientific basis for decision makers to effectively solve the market instability. [ABSTRACT FROM AUTHOR]

Microintegrated energy systems (MIESs) can be disconnected from power distribution systems during power system faults. This paper develops a control scheme for an islanded MIES. The VSC inverter controls the AC bus voltage and frequency using a modified AC voltage regulator and a modified frequency regulator. The control structures of the power-to-gas and PMSG-based microturbine generator (MTG) systems are improved. Renewable generation always runs at the maximum power point. The surplus renewable energy in the MIES can be converted into natural gas using power-to-gas, and the MIES can make full use of renewable energy. The proposed coordinated control scheme of the electrolyzer and the supercapacitor can achieve a power balance of the islanded MIES and reduce the DC-link voltage fluctuation. A micro-gas turbine can provide electric energy to the load and enhance distribution system resilience. A coordinated control scheme of the MTG and the supercapacitor is developed to improve MIES operation. A feature of this paper is the research on fault ride-through of the islanded MIES. A fault ride-through strategy is proposed, where the AC voltage of the VSC inverter is reduced to limit the short-circuit current during AC system faults. Islanded MIES simulations are conducted in a MATLAB/Simulink environment to test the control scheme. The simulation results verify the effectiveness of the control scheme during normal operation and failure. [ABSTRACT FROM AUTHOR]

In order to further improve the accuracy of electric bus energy consumption estimation and reduce the complexity of using data, the paper proposes a new method for estimating electric bus energy consumption based on a deep learning approach with a data-driven model. The method can estimate the single-trip energy consumption of an electric bus by employing CNN (convolutional neural network) to time series prediction, which takes into account easily accessible trip data of electric buses, including initial SOC (state of charge), average speed, average temperature. First, we need to convert the raw data into a trip dataset by preprocessing the collected real-world trip data of an electric bus. Then, the single trip of the bus from the original station to the terminal station is considered the basic unit for energy consumption estimation, and the trip data are processed in a quasi-time series. Following that, the trip data were modified so that the subsequent convolutional operations more closely matched the interactions between adjacent trips, and a time series prediction method based on CNN was used instead of the regression analysis methods used in traditional data-driven models. Finally, single-trip operation energy consumption estimation of electric buses is achieved with time series prediction based on CNN, and this method is compared and analysed with the LSTM (long-short term memory) time series prediction method and multivariate nonlinear regression prediction methods in traditional data-driven models. The results show that the energy consumption estimation model for electric buses developed in this paper has a higher prediction accuracy, which can improve by 3.68 percent over the traditional multivariate nonlinear regression prediction method and by 1.32 percent over the LSTM time series prediction method. [ABSTRACT FROM AUTHOR]

This paper considers the hybrid flow shop scheduling problem with two types of sequence-dependent setup times, one depending on the order of jobs and the other depending on both the order of jobs and the machines where the jobs are allocated. Three constructive heuristic algorithms based on the NEH algorithm are developed with the object of minimizing the total energy consumption cost. The first algorithm, called EPRA, obtains the order of jobs using the energy consumption cost based on the processing time. The second algorithm, called ESRA, obtains the order of jobs using the energy consumption cost based on the setup time. The third algorithm, called ESPRA, takes advantage of both EPRA and ESRA by constructing fictitious jobs with ESRA and acquiring the sequence of fictitious jobs with EPRA. Due to the drawback of NEH algorithm for the FFS problem proposed in this paper, all three heuristic algorithms use EST rules to allocate the jobs to the machines. Two lower bounds are developed to evaluate the heuristic algorithms proposed in this paper. A computational experiment based on the split-plot design is developed to carry out the factor analysis, algorithm analysis, lower bounds analysis, and CPU running time analysis. The analysis reveals that all main factor interactions except the algorithm are significant; the ESPRA algorithm is generally outstanding among the three algorithms; LB1 is better than LB2; the CPU running time of ESPRA algorithm is significantly reduced compared to those of EPRA and ESST algorithms. [ABSTRACT FROM AUTHOR]

This paper uses SketchUp and OpenStudio to design and optimize the multiperformance parameters of the near-zero energy consumption residential building scheme stage. By establishing a basic model, the performance parameters such as insulation thickness, typical outer window, window-to-wall ratio, and their contribution rates within the threshold range are optimized for energy conservation; simulate the energy consumption of the determined multiperformance parameter combination scheme, select the most energy-saving scheme, and calculate its energy-saving contribution rate; calculate the life cycle cost of the above schemes, select the scheme with the lowest cost, and analyze its economic benefits; select energy-saving cost and economic benefit, and calculate energy-saving contribution rate and economic benefit. Finally, through the comparative analysis of the design and test data, this paper concludes that EnergyPlus will optimize the multiperformance parameters of the near-zero energy consumption residential building scheme stage. At the same time, the multiobjective optimization method and process of the passive energy-saving technology of the near-zero energy consumption building proposed in this paper, as well as the technology combination template under different needs, provide a reference for the selection of the technology path. [ABSTRACT FROM AUTHOR]

With the development of green energy, the penetration rate of new energy in the power grid is rising rapidly. Incorporating a large amount of new energy into the power grid will affect the power supply balance of the power grid, and even in severe cases will affect the safe and stable operation of the power grid. The information park has various small and medium data centers. The adjustable load of the data center can be transferred in time and space through the optical fiber and other communication networks, which can enhance the controllability of the power grid to a certain extent and promote the consumption of new energy. This paper proposes a collaborative planning method for information parks that considers the temporal and spatial transfer characteristics of data center loads, builds a load transfer model for data centers and builds a park power grid expansion model with the goal of minimizing the total cost of construction and operation. In this paper, an example of a park is used to verify the calculation example, and it is compared with other schemes to verify the feasibility of the method in this paper. [ABSTRACT FROM AUTHOR]

To cope with the challenge of successful edge offloading brought by the mobility of mobile devices in intelligent factories, this paper studies the optimization problem of the edge offloading strategy of mobile devices based on mobility. Considering the decision task flow executed by priority, the unique offloading mode of a single task, the communication range of the edge server, and the delay constraint of the offloading of a single task, appropriate computing resources are selected according to the real-time location of the mobile device to offload the computing task. Based on the edge computing architecture of an intelligent factory, this paper puts forward five different computation offloading methods. From a global perspective, the energy consumption and delay of tasks offloading in local, edge, cloud center, local-edge collaboration, and local-edge-cloud collaboration are considered. In this paper, the algorithm based on the genetic algorithm and particle swarm optimization is used to design and obtain the decision task flow offloading strategy with the lowest energy consumption and delay. Simulation results show that the proposed algorithm can reduce the computation offloading energy consumption and delay of mobile devices. [ABSTRACT FROM AUTHOR]

This paper considers the hybrid flow shop scheduling problem with two types of sequence-dependent setup times, one depending on the order of jobs and the other depending on both the order of jobs and the machines where the jobs are allocated. Three constructive heuristic algorithms based on the NEH algorithm are developed with the object of minimizing the total energy consumption cost. The first algorithm, called EPRA, obtains the order of jobs using the energy consumption cost based on the processing time. The second algorithm, called ESRA, obtains the order of jobs using the energy consumption cost based on the setup time. The third algorithm, called ESPRA, takes advantage of both EPRA and ESRA by constructing fictitious jobs with ESRA and acquiring the sequence of fictitious jobs with EPRA. Due to the drawback of NEH algorithm for the FFS problem proposed in this paper, all three heuristic algorithms use EST rules to allocate the jobs to the machines. Two lower bounds are developed to evaluate the heuristic algorithms proposed in this paper. A computational experiment based on the split-plot design is developed to carry out the factor analysis, algorithm analysis, lower bounds analysis, and CPU running time analysis. The analysis reveals that all main factor interactions except the algorithm are significant; the ESPRA algorithm is generally outstanding among the three algorithms; LB1 is better than LB2; the CPU running time of ESPRA algorithm is significantly reduced compared to those of EPRA and ESST algorithms. [ABSTRACT FROM AUTHOR]

The high consumption of energy resources but low utilization, low factor allocation, and unreasonable economic structure affect the sustainability of economic development. Based on the energy factor theory from a low-carbon perspective, this paper constructs an impact model of green total point productivity and conducts a detailed analysis of the resource mismatch in the energy industry. Under the premise of efficient resource allocation, the factor input of an enterprise is positively correlated with total point productivity. Second, the model adds environmental constraints, considers the undesired output of carbon dioxide, and considers the input of energy factors, and reduces the undesired output as much as possible while increasing the expected output. The model realizes the update estimation of each component of the stationary part of a class of nonstationary stochastic processes and can use the information at a certain time and its previous time in a cycle to forecast all subsequent times. In the simulation process, in order to make the results more objective and accurate, this paper calculates the index and its decomposition results under two conditions, and uses MaxDEA 6.0 professional software to measure the regional industry total point productivity index. The experimental results show that the effective factor of low-carbon data is 0.91, and the relative error is less than 3.11%, which meets the test threshold and effectively improves the robustness of the model. [ABSTRACT FROM AUTHOR]

With the improvement of the living standards of the Chinese people, the construction of the economic system will change from one that meets the needs of the people to one that is perfect. Under the current environment of building a resource-saving socialist harmonious society, it is very necessary for architectural design practitioners to return to the original architectural design and pay attention to the design and use of building materials during construction. The purpose of this paper is to explore and analyze the application of energy saving and environmentally friendly materials in architectural design. This paper firstly analyzes the building's energy consumption and building's energy efficiency. Every major achievement in building energy efficiency will be an important means of mitigating the Earth's energy. Building energy saving has become a major trend of global development, and then the optimal design of reinforcement selection for reinforced concrete beams based on a multiobjective genetic algorithm is discussed. The area of reinforcement required for the member control section is obtained according to the structural analysis program, the main purpose of reinforcement selection is to select safe, economical, and reasonable reinforcement on the premise of meeting the provisions of the code and the requirements of reinforcement structure. Finally, this paper conducts experimental research on the application of phase change energy storage building material SP28 in building energy conservation. The experimental results in this paper show that when the mass ratio of the phase change energy storage material in the wall is less than 15%, there is no wetting and leakage phenomenon. However, when the mass ratio of the phase change energy storage material contained in the wall is greater than 15%, the phenomenon of wetting and leakage will occur, which will cause greater losses when added to the wall. Therefore, it is not suitable for application and building walls. Regardless of leakage, wetting, or economic considerations, the mass ratio of phase change material of 10% is the most suitable for use in walls. It not only has no wetting and no leakage but also has a good energy storage effect and can effectively adjust the ambient temperature. [ABSTRACT FROM AUTHOR]

Traditional historical buildings carry the culture and spirit of China for thousands of years and have a unique charm that modern buildings do not have. However, traditional historical buildings have gradually declined over time, and their restoration not only takes time and materials but also causes certain harm to the environment. Therefore, this paper has aimed to explore how traditional historical buildings can develop in a sustainable and ecologically adaptive image in modern times. For traditional historical buildings, this paper has taken traditional cave dwellings in northern Shaanxi as an example to analyze their ecological adaptability, and based on the analysis results, traditional cave dwellings have been transformed. Specifically, this paper has taken the comfort of residential houses as the evaluation standard and has selected the thermal stability of the building and the energy consumption of heat supply as indicators to compare and analyze the traditional cave dwellings and the renovated cave dwellings. The experimental results of this paper have found that the traditional historical buildings are the crystallization of the wisdom of the ancients for thousands of years. However, due to the limitation of technology, the lighting and thermal stability of traditional cave dwellings are not high. After scientific design, the lighting and thermal stability of the transformed cave are 100% and 30% higher than those of the traditional cave, respectively, and the heating energy consumption is reduced by 50%. [ABSTRACT FROM AUTHOR]

Energy-saving driving and regenerative braking energy utilization are two main ways to realize energy-saving optimization of urban rail transit train timetables. On the basis of the more mature energy-saving driving achievements of the predecessors, the absorption utilization rate of regenerative braking energy is improved by adjusting the dwell time of trains in the station so that while a train is braking, other trains in the same electric section are just under traction. In this paper, the overlap time between the traction and braking processes of different trains is used as a measure of the proportion of regenerative braking energy that is absorbed. In order to maximize this overlap time, an energy-saving optimization model of urban rail transit train timetable based on regenerative braking technology was established. To facilitate the solution, the nonlinear constraints are converted to linear at the time of model construction in this paper. In the solution, the spatio-temporal local rolling algorithm and the commercial optimization software ILOG CPLEX are used for the solution. The solution results show that the method in this paper can effectively improve the absorption and utilization of regenerative braking energy. [ABSTRACT FROM AUTHOR]

In recent years, with the development of smart grid, the power systems and other energy systems are gradually forming integrated energy systems. The electric-thermal integrated energy system is a mature integrated energy system at present. The electric-thermal integrated energy system uses modern communication technology to realize the comprehensive regulation of electric energy and thermal energy, which greatly improves the efficiency of energy use. However, this also greatly increases the risk of malicious tampering with the energy dispatch system. In this paper, we study the regulation of electric-thermal integrated energy systems considering false data injection attacks. First, we establish a compromised model of an electric-thermal integrated energy system considering false data injection attacks. Then, we designed vulnerable variable observers for different tampering scenarios to observe the tampered variables. Finally, considering the relationship between the observed data and the measured data, we design a tampering behavior detection method based on relation network. The simulation results verify the effectiveness of the detection method proposed in this paper. [ABSTRACT FROM AUTHOR]

Multipath data transmission is a key problem that needs to be solved urgently in wireless sensor networks. In this paper, sensor node failure, link failure, energy exhaustion, and external interference affect the stability and reliability of network data transmission. A multipath transmission strategy for wireless sensor networks based on improved shuffled frog leaping algorithm is proposed. A mathematical model of multipath transmission in wireless sensor networks is established. In the shuffled frog leaping algorithm, combined with the transition probability in the particle swarm optimization algorithm, random individuals in the subgroup are introduced to assist the search when updating the frog individual position, which improves the algorithm's ability to jump out of the local optimum and improves the quality of the optimization algorithm solution. The model is applied to multipath transmission in wireless sensor networks. Then, the shuffled frog leaping algorithm is used to update, divide, and reorganize the sensor nodes to select the optimal node to establish the optimal transmission path and improve the stability and reliability of the network. Simulation experiments show that the algorithm in this paper can ensure the reliability of data transmission, reduce the network packet loss rate and network energy consumption, and reduce the average delay of data transmission. [ABSTRACT FROM AUTHOR]

The utilization efficiency of China's rural collective resource assets has always been at a low level, and excessive energy consumption and input have become the main limiting factor, which seriously affects the overall improvement of agricultural modernization and rural people's livelihood. In this paper, time measure and space measurement are adopted alternatively, and spatiotemporal vertical-horizontal progressive deduction is built with the improved model, so as to set up a perfect spatiotemporal dynamic effect evaluation system: First of all, improve the U-HDM-GML index model, study on the improvement strategy for input redundancy and unexpected output redundancy in the utilization efficiency of rural collective resources assets in various provinces of China, as well as the equilibrium rupture relation between the two from the perspective of global space and time, and at the same time of fulfilment of time measurement and estimation, provide support for better expressing the spatial effect in terms of improving approach and strategy; secondly, measure the improvement strategy of the total factor productivity of research samples with the spatial expletory ESDA test method, and it is also applicable to global spatial effect evaluation system; finally, apply FE-SDM to further analyze the difference of spatial effect of total factor productivity in rural resource asset utilization. Through analysis of the significant difference characteristics of the research samples with different geographical and spatial characteristics, the strategy deficiency in terms of difference discrimination is made up. Conclusions. This paper carries out the time and spatial effect measurement of total factor productivity for the performance of pilot strategies in various provinces of China in the last ten years and puts forward a more beneficial improved strategy and mechanism, preferentially selected according to the direction indicated by good research indicators, gets the redevelopment strategy for improving the utilization efficiency of areas at a low level of resources utilization currently, and indicates the direction for tapping the full potential of existing rural resources according to the practical conditions at local places, in the hope of establishing the trans-provincial cooperation supply and demand system based on the new rural collective economic organizations (cooperatives) after the reform experimentation and optimizing the spatial spillovers of rural resources assets, so as to make a breakthrough in the barrier due to growth of technical efficiency. [ABSTRACT FROM AUTHOR]

Energy decision-makers have considered solutions to meet the demand for affordable and highly reliable energy due to population growth and technological advancement. One of these possibilities is the utilization of several energy carriers in one system as an energy hub. Instead of optimizing a single energy carrier, the energy hub optimizes a system with many energy carriers, including electricity, natural gas, and local heat, by use of its converters and storages. In this paper, a novel and new general framework is proposed to evaluate and compare both series and parallel connections of n hub to analyze cost and reliability aspects by considering coupling matrices. In order to compare the outcomes of the connections of several comparable hubs in both series and parallel modes, new indices are proposed and evaluated from the aspect of evaluating the amount of energy not supplied and the amount of energy input to each hub. Additionally, simulations are run for a variety of scenarios in order to better assess the proposed model and investigate each type of connection by evaluating the proposed performance indices. The results show that in all the examined scenarios, the total cost of the energy carriers in the series mode (link in the output) is lower than in the parallel mode (link in the input). [ABSTRACT FROM AUTHOR]

Traction energy consumption (TEC) is a critical part of the total energy consumption in urban rail transit (URT) systems. Energy consumption patterns and abnormal analysis of TEC guarantee the energy-saving URT operation. With the rapid development of urbanization, the current energy consumption is becoming more and more prominent with some inherent drawbacks, such as complex original data, complicated statistical analysis, and abnormal energy consumption. This paper proposes a method for time accrual abnormal analysis of TEC. The system architecture of TEC typical values is presented, composed of three elements: research object, evaluation index, and time scale. The time series prediction algorithm calculates the typical values of the cumulative energy consumption index in each energy consumption mode. For the abnormality in TEC mode, the distance of the string vector is used as the similarity measure. Then, the similarity-based anomaly analysis method is used to judge the pattern abnormality. By comparing the advantages and disadvantages of engineering practice and theoretical research methods, we analyze the applicability of traditional anomaly detection algorithms to perform anomaly analysis of TEC in URT systems. The adopted time accrual abnormal analysis achieves a high fault detection rate, outperforming other models. [ABSTRACT FROM AUTHOR]