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Meeting global energy demands has been a major challenge due to the shortage of primary energy resources. Adding renewable energy resources to the grid has become increasingly popular. During peak hours of energy demand, the sun's irradiance is strong enough to generate a significant amount of energy. This paper will design a photovoltaic energy generating system that harnesses solar energy using the latest technological advancements available. A system like this one is an ideal solution to addressing rising energy demands and reducing carbon footprint. Renewable energy sources can minimize carbon emissions, operating costs, etc., but their intermittency and uncertainty make their reliability a serious concern. The objective of this paper is to improve the reliability of the power grid through the use of solar photovoltaic (PV), battery energy storage systems (BESS), and distributed generation systems (DG). [ABSTRACT FROM AUTHOR]

• Novel damped conformable fractional accumulation method. • Prediction of energy related series with the uncertainties. • Design of damped conformable fractional grey bernoulli model. • Improved prediction accuracy, robustness and applicability. • Prediction of world coal consumption and production in 2025–2026. Energy resources, such as oil, coal are important to people's life and the development of the economy. Predicting the energy consumption and production with uncertainties can help the government and policymakers make the reasonable energy strategy and give constructive guidance. In this paper, a novel damped conformable fractional accumulation operator with two parameters are proposed to have control of both accumulation generation and new information involvement. The new accumulation can be adjusted from linear approximation to high order accumulation. A structure adaptive grey Bernoulli model based on proposed accumulation method is established, with the parameters optimized by particle swarm optimization algorithm. For testifying the effectiveness and the robustness of the model, two validation cases and two application cases are used to build the prediction model of diverse series with unexpected trends or fluctuations in energy fields. Compared with other existing fractional grey models and non-grey models, the proposed model has best prediction accuracy, better model stability and greatly improved applicability. The global coal consumption by industry is predicted as 30.85×106 TJ in 2025 and 30.81×106 TJ in 2026. The world coal production could be 182.93–187.73 exajoules in 2025 and 180.85–182.16 exajoules in 2026. [ABSTRACT FROM AUTHOR]

This research paper presents a novel technique for improving load-shedding in DC microgrids by incorporating a binary number generator (BNG). Load shedding, a controlled strategy utilized to maintain equilibrium between power supply and demand within a microgrid when the aggregate demand surpasses generated power, is widely employed. Nevertheless, conventional load-shedding methods frequently encounter challenges, notably over-shedding and voltage deviations, particularly in scenarios involving numerous controllable loads. To overcome these challenges, an enhanced algorithm is proposed, which integrates the BNG technique. Simulation results conclusively demonstrate the effectiveness of the proposed algorithm in ensuring microgrid stability during periods of supply-demand imbalances. [ABSTRACT FROM AUTHOR]

Cloud computing technology provides access on demand to virtualized resources, services, and applications via a distributed network. In cloud data centers effective energy utilization is a critical concern in today's technology-driven world. Cloud data centers (CDC) are massive facilities that host and manage an enormous amount of data and computing resources. This article addresses the growing significance and energy-intensive nature of data centers. Due to the rapid growth of cloud computing, it offers on-demand access to resources globally and leads to substantial power consumption and carbon impact on the environment. They consume substantial amounts of energy, and optimizing their energy utilization is essential for reducing operational costs, minimizing environmental impact, and ensuring sustainable growth. To combat this, efficient energy-saving approaches using machine learning methods have been researched. ML methods hold great potential for enhancing energy efficiency in CDCs by analysing data, detecting patterns, and optimizing resource usage. The focus areas include CPU usage prediction, overload finding, underload estimation, selection, migration, and relocation of VMs to attain resources and improve energy utilization. The paper compares energy-saving results achieved by different machine learning techniques in data centers to minimize energy usage and meet service level agreements (SLA). The machine learning approaches reduce energy consumption from 1.6% to 88.5% compared to the benchmark approach mentioned, considering various settings and parameters. [ABSTRACT FROM AUTHOR]

A micro-grid (MG) is regarded as a localized small-scale power supply system designed for providing electrical power for a remote community, which may be connected to the grid. The paper considers six participants' sites in a remote community. The peak energy consumption patterns for these participants must be different to ensure coalition for mutual benefits and this would facilitate efficient utilization of energy resources. This community concerned may include rural communities having participants such as schools, housing estates, industrial sites, etc. The propose approach would ensure that many participants and their needs are captured and optimized profit of the participants would ensure mutual benefits. In islanded MG, most of the cost/profit distributions in energy trading problems are done based on the self-interest of the participants. Some consider a fair (acceptable or reasonable) cost/profit distribution using the cooperative game theory (CGT) based on the Nash bargaining solution (NBS). However, this method leads to a high degree of dissatisfaction. To improve the fairness and mutual benefits for all MG participants, a new flexible approach is developed. Specifically, CGT which uses a generalized NBS (GNBS) has been proposed, which relaxes certain axiom of the NBS to ensure that fairness of game theory is achieved. The approach allows the profit of the MG participants to be shared using a mechanism based on the negotiation power (i.e. weighted fairness) so that the MG would attain an improved economic outcome. Empirical evaluation of the approach indicates that overall profits using CGT is higher when compared with an independent approach. Moreover, different in negotiation power is achieved in which higher profit is allocated to any participant based on participant's mutual agreement in achieving fair profit distribution. A robust optimization algorithm called Teaching-Learning-Based-Optimization (TLBO) is developed to obtain optimal results. The heuristics algorithms are used in testing the effectiveness of TLBO, which confirm that the TLBO is indeed powerful and robust enough to give quality solutions in solving energy-trading problems. [ABSTRACT FROM AUTHOR]

Faced with the growing demand for energy worldwide, fossil fuels, like any other resource available in finite quantities, are in the process of disappearing since their production cannot increase indefinitely. On the other hand, Russia's war with Ukraine is driving up fossil fuel prices, which in turn is impacting price increases at several levels. Added to this is the need to limit global warming, which is largely due to CO2 emissions from the consumption of fossil fuels. Faced with all these constraints, it is essential that the world looks to other sources of energy and that Morocco, in particular, can limit its energy dependence on fossil fuel imports by ensuring an internal energy supply; the best solution appears to be the development of biofuel and in particular, biodiesel. Due to their high energy content and compatibility with gasoline supply infrastructure, biodiesel may be utilized in motor vehicles. To achieve the optimal utilization of biodiesel in internal combustion engines, it is first required to choose the best feedstock that will provide both increased engine performance and economic viability. In this perspective, this paper provides a summary of the climate and energy situation in the world and in Morocco in particular, and then addresses the interest of biodiesel and its production. [ABSTRACT FROM AUTHOR]

• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

In most business and residential organizations, Diesel Generators (DG) is a viable supplementary power source for ensuring an undisturbed power supply. The DG is a hybrid machine that generates electrical energy using a Diesel Engine (DE) and an Electric Generator (EG). By routinely monitoring crucial machine parameters, alternative power source efficiency can be improved. Furthermore, Condition Monitoring Systems (CMS) based on the Internet of Things (IoT) have supplanted the traditional equipment maintenance method. Predictive maintenance is also an important building block of Industry 4.0, whose entire process and performance can be fully understood by using IoT-enabled Remote Monitoring (RM) schemes. Firstly, this paper introduces a remote monitoring and data acquisition scheme to realize the concept of predictive maintenance. Secondly, this article discusses a strategy for real-time observation of DG parameters as well as a comprehensive analysis of various metrics. Thirdly, this research article includes a monitoring and analysis scheme of crucial factors in a DG, like the speed of an engine, voltage output, the current produced, power factor, coolant required, fuel consumption, and battery health. Different mathematical models are formulated by correlating experimental data and estimating the coefficients. Finally, to create suitable real-time warnings under critical circumstances, a fuzzy logic-based Decision Support System (DSS) and web-based integration elements are presented. [ABSTRACT FROM AUTHOR]

This paper investigates the performance and fuel-saving effect of a velocity control algorithm on modern 50 cc scooters (Euro 5). The European Parliament has adopted major CO 2 emission reductions by 2030. But modern combustion-powered scooters are inefficiently restricted and emit unnecessary amounts of CO 2. Replacing the original restriction method with the system presented in this paper, the engine's operating point is being improved significantly. A Throttle-by-Wire-System senses the rider's throttle command and manipulates the throttle valve. A redundant wheel speed sensor measures the precise vehicle velocity using the Hall effect. The entire system is managed by a central ECU, executing the actual velocity control, fail-safe functions, power supply and handling inputs/outputs. For velocity control, an adaptive PI-controller has been simulated, virtually tuned and implemented, limiting the max. velocity regulated by legal constraints (45 km/h). In this way, the environmentally harmful restrictors used today can be bypassed. By implementing a human–machine interface, including a virtual dashboard, the system is capable of interfacing with the rider. For evaluation purposes a measurement box has been developed, logging vehicle orientation, system/control variables and engine parameters. A Peugeot Kisbee 50 4T (Euro 5) is serving as test vehicle. Finally, the system has been evaluated regarding performance and fuel efficiency both through simulation and road testing. Fuel savings of 13.6% in real-world test scenarios were achieved while maintaining vehicle performance. • Efficiency is increased and the throttle valve opening is reduced by 50%. • The controlled air supply minimizes the fuel injected by up to 25%. • Significant improvements of fuel consumption and CO 2 emissions by 14%. • Consistent performance is demonstrated by using energy more effectively. [ABSTRACT FROM AUTHOR]

• This paper proposes the research on dimension reduction for visualization of simplified security region of integrated energy system considering renewable energy access. • The imbalance between energy demand and supply caused by the renewable energy uncertainty is defined as a special N-1 fault. • An interval security region model of integrated energy system is constructed, according to the N-1 safety constraints of equipment and pipeline. • An engineering simplification approach for interval security region model is proposed, in order to quantify the security boundary, security distance, and interval total supply capability. • A dimension reduction observation method based on observation variable optimization is proposed, which can enlarge the volume unaffected by renewable energy uncertainty. This paper proposes the research on dimension reduction for visualization of simplified security region of integrated energy system considering renewable energy access. Reason: Integrated energy system is conducive to achieving low-carbon transition and efficient utilization of energy. As the penetration of renewable energy increases, the security of integrated energy system faces severe challenges. It holds significance for comprehensively studying N-1 security test method in the operation scenario of integrated energy system, the impact of renewable energy on the IES security region model, and the optimal selection for observation variables. Methods: Firstly, a model for interval security region of integrated energy system is constructed, according to the N-1 safety constraints of energy hub key equipment and pipeline. The imbalance between energy demand and supply caused by the renewable energy uncertainty is defined as a special N-1 fault. Then, an engineering simplification approach for interval security region model is proposed, in order to quantify the security boundary, security distance, and interval total supply capability. On the basis, a dimension reduction observation method based on observation variable optimization is proposed, the selection of observation variables help to expand the volume of area unaffected by renewable energy uncertainty. Results: To the existed problem in current research, an example is presented to verify the effectiveness of proposed method, which brings forward N-1 security test method in the operation scenario of integrated energy system, analyzes the impact of renewable energy on the security region model, and realizes the optimal selection for observation variables of simplified security region. Conclusions: Interval security region of integrated energy system can intuitively estimate the security status of operating points and observe the security boundaries. The simplified model can satisfy the needs of calculating precision. Optimal selection of observation variables is necessary, and the volume of area unaffected by renewable energy uncertainty can be effectively increased. [ABSTRACT FROM AUTHOR]

Transistor threshold voltage (V t) scaling causes higher power consumption by increasing the subthreshold leakage and short-circuit currents in CMOS circuits. Leakage currents are significant contributors to the overall power consumption of digital systems-on-chip as threshold voltage, channel length, and gate oxide thickness are reduced with CMOS technology scaling. A new dual-pullup/dual-pulldown (DPU/DPD) repeater is proposed in this paper for higher energy efficiency in low-voltage and low-frequency applications. The standby mode leakage power consumption is reduced by 59.11% with the proposed clock tree as compared to the conventional 3 level H-tree operating with a power supply voltage of 1.0V in a 45 nm CMOS technology. The short-circuit currents are suppressed by selectively employing high-V t transistors in the repeaters. The clock network with the proposed buffer lowers the active mode energy consumption by up to 24.91% as compared to a conventional clock tree under equal silicon area constraint. Post layout results reveal that the statistical spread of clock skew in the DPU/DPD H-tree is also 20.60% lower than the conventional H-tree network. • A novel dual-pullup/dual-pulldown (DPU/DPD) repeater is presented for low leakage and high frequency clock distribution. • The minimum operating voltage of a clock tree is determined for achieving the lowest total energy consumption while satisfying the clock frequency and signal transition time (10% of clock cycle time) requirements at the leaves. • The influences of leakage currents on the total energy consumption of the clock trees designed with three different repeaters (conventional CMOS inverter, previously published SPLIT-IO, and the new DPU/DPD) are benchmarked at various supply voltages and frequencies. • The leakage power reduction provided with the DPU/DPD buffers is evaluated. • The impact of process parameter fluctuations on the clock skew of the novel clock distribution network is also characterized in this paper. [ABSTRACT FROM AUTHOR]

Our paper analyzes the optimal capacity investment in solar panels system for consumers. During the daytime, solar panel system absorbs energy from the sun and supplies the energy generated to the building structure. Our model takes into consideration the variations in the daily consumption of electricity and the variations in the daily available sun energy. We model these variations in our paper as uncertainty in energy demand and uncertainty in energy supply. Excessive usage of electricity that is beyond the energy generation of the solar panels are bought at a higher price from the utility company than the payment received from the utility company for excess energy sold to the grid. We analytically solve for the optimal quantity of solar panels to purchase and install. Our paper fills the research gap in quantifying the impact changes in solar energy yield rate, consumption, and sell-back rate has on the consumer's optimal investment decision in solar panels. We find that the optimal quantity of solar panels increases with the cost of energy during the daytime and payment for excess energy sold to the grid. Interestingly, when solar yield increases, the optimal solution only increases initially, and then it will eventually decrease. Furthermore, our analytical models provide consumers with a decision support system to determine the optimal number of solar panels to purchase and install while taking into consideration the random yield of solar panels, variations in energy consumption, and potential for sell back of excess unused energy generated under complex billing structures from the utility company. Our mathematical models are adaptable to policy changes such as rebates and purchase and sell back rates for electricity. Our numerical study validates the mathematical models and further analyzes the impact changes in solar energy yield rate, energy consumption, and sell-back coefficient have on the consumer's capacity investment decision in solar panels. Our numerical studies find that the lower the reliability factor of solar yield, the more impact the daytime energy cost has on the optimal quantity. When the sell-back coefficient exceeds a certain threshold, the optimal quantity of solar panels increases exponentially. [Display omitted] • We model the optimal quantity of solar panels to purchase and install for consumers. • Optimal quantity increases with consumption, but the rate of increase is decreasing. • Optimal quantity increases with energy daytime cost and sell-back rate. • When solar yield is high, the impact of payment coefficient is significant. • Increased variability in consumption, optimal quantity increases. [ABSTRACT FROM AUTHOR]

Forecasting energy demand has become increasingly important due to technological advances, especially new power systems and population growth. Accurate predictions of energy demand and supply are crucial for academics and policymakers. Also, energy consumption is non-stationary and dynamic in time, requiring an adaptive forecasting algorithm. This paper presents a new adaptive hybrid approach for energy time series forecasting using statistical methods. Some of the most popular energy demand time series applications are used to demonstrate the superiority of the proposed algorithm, which covers a wide range of critical energy demand forecasting situations at various scales. The paper's objectives are presented in three phases. In Phase I, ARFIMA is used to forecast the entire original time series as a statistical inspector and later for comparison. Although ARFIMA's fractionality feature helps to capture long and short-term memory behavior in time series, to be more adaptive and become close to a nonlinear algorithm that can handle non-stationarity, and Gaussian and non-Gaussian time series closely, a novel dynamic statistical structural break detection framework is developed, and used in phase II to identify the time series' change points and associated time indexes. Since time series are partitioned based on structural change detections, they approach one in terms of the Hurst exponent and exhibit pure long memory, which is ideal for ARFIMA modeling. Phase III captures non-stationary statistical properties and memory characteristics by applying adaptive ARFIMA modeling to segmented time series. Finally, the predicted partitions are concatenated. Various performance assessment metrics, e.g., Mean Absolute Percentage Error (MAPE), tested on the final achieved results show that the proposed adaptive algorithm outperforms the forecasting capabilities of the existing algorithms on the same energy demand time series used in the current paper. MAPE of the proposed approach for the four utilized real-life cases are 1.72%, 1.35%, 0.22%, and 4.4%, respectively. Therefore, the proposed model is a satisfactory method for energy demand forecasting due to its high accuracy. • A novel adaptive hybrid algorithm is developed for energy demand forecasting. • The prediction system uses ARFIMA and a novel dynamic structural break detection framework. • The adaptive prediction method can forecast energy demand with high accuracy. • The structural break detection works dynamically, allowing ARFIMA to be adaptive. • The prediction method captures the energy time series' nonstationary statistical features. [ABSTRACT FROM AUTHOR]

• A novel technique to trace Sun location upon the lunar sky is proposed. • LOLA databases are used to recreate the lunar topography. • Harvesting solar energy from multi-sites on the Moon south pole is proposed. • The multi-site strategy removes the need of energy storage system. • Algorithm to reproduce time series illumination profiles of the Moon. Recently, space organizations have considered the Moon to host lunar bases. Such bases require power and energy to function. However, the efficient and safe use of the energy resources on the Moon is a huge challenge. Space photovoltaic (PV) power systems are appealing technologies due to their maturity and high solar energy availability at some locations on the Moon. The effectiveness of these PV systems depends on their selenographic location, which might necessitate the deployment of energy storage technologies to cover the base's energy demand. Some analysts have proposed the installation of PV modules on kilometers-tall towers near the lunar poles to harvest more solar energy and limit the need for energy storage systems (ESSs). Alternatively, this paper proposes to harvest the energy from multiple sites in the lunar South Pole region using a novel technique to compute the Sun illumination profile and the LOLA topographic databases to compute the terrain elevations. The proposed algorithm seeks the most optimal configuration of sites and tower heights to minimize the longest night period and total distance between the sites. This study assesses groups of 1 to 6 sites assuming the use of towers having heights of 10, 100, and 500 [m]. The time horizon for the analysis is one Axial Precession Cycle, which is approximately 18.6 years. According to the results, a system of two sites with a separation of 42.05 [km] and towers of 500 [m] height has a maximum darkness period of only 3 [h] while another solution proposes a system of three sites with towers of 10 [m] that removes the need of EES (solar eclipse periods by the Earth are not considered). The proposed technique is suitable for engineering applications, such as base planning and operation management. [ABSTRACT FROM AUTHOR]

The structure of direct current (DC) appliances is simpler and more energy-efficient than that of alternating current (AC) appliances. With the proliferation of novel DC loads (e.g., e-bikes and electric vehicles), DC loads will dominate the loads in buildings. It is necessary to develop a DC power distribution method that better accommodates renewable energy, effectively improves the working efficiency of loads, and significantly enhances energy utilization efficiency. However, there is not yet a unified design for the multi-port converter of DC microgrids. The experience is severely lacking in the overall energy planning for buildings from the perspectives of economy and environmental friendliness. To solve the problems, this paper explores the intelligent decision optimization for energy control in DC power distribution system with multi-port access for intelligent buildings. Firstly, a feature analysis was carried out on the multi-port converter of the DC power distribution system for intelligent buildings. With economy and environmental friendliness as the goals, the authors designed constraints related to energy output power, power balance, and distribution node voltage, and established an intelligent decision optimization model for the energy control of the DC microgrid for intelligent buildings. The model was combined with the multi-attribute decision-making framework into a decision-making framework for the coordinated operation of distributed energy supply units, which is based on the interval evidential reasoning (IER). Finally, the authors proposed an evaluation method for the decision-making of the energy control of DC power distribution for intelligent buildings, based on the Maclaurin symmetric mean (MSM) operator, and the IER-based decision-making framework. The proposed method was proved effective through experiments. [ABSTRACT FROM AUTHOR]

This paper proposes a deep reinforcement learning-based approach to optimally manage the different energy resources within a microgrid. The proposed methodology considers the stochastic behavior of the main elements, which include load profile, generation profile, and pricing signals. The energy management problem is formulated as a finite horizon Markov Decision Process (MDP) by defining the state, action, reward, and objective functions, without prior knowledge of the transition probabilities. Such formulation does not require explicit model of the microgrid, making use of the accumulated data and interaction with the microgrid to derive the optimal policy. An efficient reinforcement learning algorithm based on deep Q-networks is implemented to solve the developed formulation. To confirm the effectiveness of such methodology, a case study based on a real microgrid is implemented. The results of the proposed methodology demonstrate its capability to obtain online scheduling of various energy resources within a microgrid with optimal cost-effective actions under stochastic conditions. The achieved costs of operation are within 2% of those obtained in the optimal schedule. [ABSTRACT FROM AUTHOR]

With the development of electronics and communication techniques, the interest in realizing sensor networks with a large number of end nodes is growing. The main idea is to install devices in remote locations without direct supervision, which requires an uninterrupted power supply and secure communication to the rest of the network. In this paper, an experimental comparative analysis of popular practical LoraWAN end nodes (WisNode RAK811 and Seeeduino SX1301) regarding energy consumption in different security modes, spreading factors, energy consumption in sleep/idle mode as well as the security keys extraction from memory was performed. [ABSTRACT FROM AUTHOR]

This paper investigates the cyber resiliency of future power systems with high penetration of distributed energy resources using advanced distributed and (or) hierarchical control architectures. Specifically, we simulate cyberattacks on three prototypical use cases, and we identify attack scenarios that are the most damaging to the overall system performance. We show that these attacks can have a significant impact on grid operation. Results provide additional insights into the robustness of the system to the most common cyberattacks. • This paper delves into the realm of cyber-resiliency, examining its implications across three distinct use cases encompassing distributed, distributed-hierarchical, and hierarchical control architectures. • Through exhaustive numerical simulations, we discern and highlight the precise signals within the system that exhibit heightened susceptibility to cybersecurity attacks. • The results obtained from our analysis offer valuable insights into the robustness of these systems, shedding light on their ability to withstand potential cybersecurity threats. [ABSTRACT FROM AUTHOR]

The utilization of renewable energy has attracted much attention with the deterioration of the environment. Solar energy is widely distributed and has huge reserves. But, solar energy is also volatile and intermittent. This brings difficulties to the large-scale grid connection of solar energy and the distribution of power resources. The key to solve this problem is to provide accurate and stable short-term prediction for solar power generation. For this purpose, we present an attention-based Bayesian sequence to sequence (Seq2Seq) model within decomposition-ensemble strategy. Firstly, an influential factors analysis is performed on the data about meteorological conditions, operation status of the photovoltaic panels and historical solar power generation sequences to obtain the optimal combination of the influential factors. Secondly, this paper proposes a novel decomposition-ensemble framework based on complete ensemble empirical mode decomposition with adaptive noise and independent component analysis to mine the intrinsic modes of the solar power generation time series. Thirdly, this paper presents an attention-based Bayesian Seq2Seq method for modeling the relationships between solar power generation and influential factors. Using a real-world dataset from State Administration of Electricity Investment Science and Technology of China, a case study, comparative analysis and robustness checks are conducted, through which the contributions of the methods in the proposed model and the superior performance of the model are demonstrated. • Short-term solar power generation prediction model established. • An influential factors analysis is designed to obtain the optimal combination of the influential factors. • A novel decomposition-ensemble framework is proposed to mine the intrinsic modes of the solar power generation sequence. • An attention-based Bayesian Seq2Seq method is presented. [ABSTRACT FROM AUTHOR]

Energy and water are interdependent. Water is essential for energy production, and energy is indispensable for water processes. This paper develops a Stackelberg game based bi-level multi-objective model for optimal water allocation under uncertainties from an energy-water nexus perspective, which considers energy-related water consumption and water-related energy production. It handles the hierarchical decision-making problem between the river basin authority and the subareas, as well as the tradeoffs between social equality and ecological damage. The upper level of the model makes schemes on water allocation for multiple subareas, and the lower level decides on water allocation for different types of power generation. To generate reasonable and practical strategies for water allocation, ɛ -constraint method and KKT conditions are used. A case study of Minjiang River, China is given to demonstrate the applicability and practicality. Scenarios analysis is also conducted to demonstrate the effectiveness of the model and the effects of the control parameter changes. The results indicate that the river basin authority would allocate larger water resources to the subareas with higher energy demand. It is concluded that the proposed allocation method is advantageous to the subareas with higher water utilization efficiency. The proposed bi-level model demonstrates its reasonableness and superiority to the single-level model. Policy implications are finally provided to support the sustainable development of energy-water nexus. • A bi-level model for water allocation in energy-water nexus system is proposed. • The hierarchical decision sequence between the river basin and subareas is solved. • The tradeoffs between social equality and ecological damage are made. • Optimal water allocation for multiple subareas and energy resources are quantified. • Uncertainties in decision-making environment are handled by fuzzy set theory. [ABSTRACT FROM AUTHOR]

Vision-based displacement measurement has become increasingly popular in bridge health assessment. However, this technology faces challenge that a complicated power-consuming supply system must be installed in the darkness, making it unable to meet the needs of low energy consumption and low cost. This paper proposes a bridge displacement measurement method using power-free target and thermography to address it. First, a thermography measurement system for bridge displacement in the darkness is developed. The power-free target with a hollowed-out circular feature is adopted to form the contrast to the background, and graphene is coated on the target surface to enhance the infrared emissivity and contrast further. Second, a sub-pixel elliptic center positioning algorithm based on thermography enhancement and data optimization is proposed to improve the accuracy and the ability to resist occlusion. The feasibility of the method is verified by laboratory tests and bridge field tests. When the measurement distance is within 15 m, the results meet the engineering error requirements of 5%, with the prospect of popularization in small and medium-sized bridges displacement measurement. • A bridge displacement measurement method in the darkness using thermography is proposed for the first time to address the power supply problem in bridge measurement. • A thermography measurement system for bridge displacement is developed and power-free target with graphene coated is adopted to form the contrast to the background. • A two-step parameter optimization strategy for elliptic center positioning is proposed. [ABSTRACT FROM AUTHOR]

The integration of distributed energy resources (DERs) such as wind, solar, batteries, etc. into the grid presents opportunities as well as challenges with regard to efficient coordination and trading. Virtual power plants (VPPs) have emerged as an important paradigm for aggregating and managing numerous DERs. This paper proposes a decentralized blockchain-based peer-to-peer (P2P) multilayer energy trading framework to facilitate optimized coordination of DERs within a VPP context. A control center, prosumers, and service operators constitute the model's multi-layered architecture. Profits from feed-in tariffs, ancillary services, and P2P sales are optimized for revenue while expenses are minimized using a mixed-integer linear programming formulation. Smart contracts enable prosumers to trade energy units in the Ethereum blockchain with ease via a participatory web platform. The effectiveness of the framework in synchronizing power supply and demand within the VPP by coordinating the charging and discharging of storage systems in accordance with consumption patterns is demonstrated through performance evaluation. In contrast to conventional centralized architectures, the proposed framework utilized decentralized architecture efficiently consolidates and optimizes distributed energy resources (DERs) to improve grid stability and renewable energy utilization. [Display omitted] • A novel multi-layer framework for peer-to-peer (P2P) energy trading, within VPP. • An interactive web-based trading platform using the Ethereum blockchain to facilitate exchange of energy units. • Implementation of MILP-based DER management model for optimal trading. [ABSTRACT FROM AUTHOR]

As the number of transistors on a chip surface increases, power consumption becomes more and more a serious concern. A promising solution to bridge the gap between resource-constrained gadgets and computation-intensive applications could be the approximate computing paradigm. This paper presents four efficient approximate full adder cells based on dynamic logic and carbon nanotube field-effect transistors (CNFETs). To the best of our knowledge, dynamic logic has never been deployed in the design of approximate full adders before. Comprehensive simulations and analyses are conducted to study the efficacy of the new circuits. Simulation results indicate remarkable improvements compared to state-of-the-art circuits. For instance, at 0.9 V power supply, our final proposed design improves the power-delay-area product (PDAP) metric by at least 63% compared to its peers. Moreover, the applicability of the proposed adders in the image sharpening application is examined by measuring peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) using the MATLAB tool. The proposed designs have also a reasonable performance in this regard. • Deploying dynamic logic (DL) in the design of approximate full adders for the first time. • Designing ultra-low area and energy consumption full adder structures. • Applying the proposed adders in the image processing application. [ABSTRACT FROM AUTHOR]

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations' sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong's (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively. [Display omitted] • Priority-ordered incentive-based DR program for the EV and non-EV loads is proposed. • Fast dynamic reconfiguration of the off-grid microgrid is presented. • Multi-objective energy scheduling problem for the off-grid microgrid is modeled. • Utopia-tracking approach is used to solve the conflicting multi-objective problem. [ABSTRACT FROM AUTHOR]

Given the stochastic nature of solar energy, integrating PV arrays with energy storage systems (ESSs) is crucial for a consistent power supply. Effective maximum power point tracking (MPPT) control is of great importance in this context. The significance lies not just in achieving high energy efficiency but also in ensuring overall system safety and reliability, as rapid and stable power tracking control alleviates power stress on ESSs. Challenging factors like parameter uncertainty from the PV array linearization process, dynamic DC bus variations induced by pulsed power loads, and sudden irradiance variation under cloudy weather, make the maximum output power of PV arrays largely dependent on the effectiveness of the control laws. Therefore, this paper presents a comparative study for MPPT control using PV-interfaced DC/DC converters. The aim is to demonstrate the advantages of employing average large-signal model-based sliding mode-type controllers in PV system integration with health-conscious objectives. A comprehensive system-level control-oriented modeling process is presented, followed by the theoretical background of three types of controllers (classical proportional–integral (PI)-type controller, μ -synthesis-based robust controller, and sliding mode controller) for practical applications. Extensive tests with real experimental data reveal sliding mode-type controllers' superiority in various integration scenarios. For example, with random solar irradiance variation over 0.5 s, they achieve a 54.75% reduction in energy loss, outperforming the commonly used PI-type counterparts. [ABSTRACT FROM AUTHOR]

Understanding the connection between water supply and energy consumption in a circular economy is crucial for managing the demands of these resources. Sankey diagrams have the potential to enhance the visualisation of resource flows. However, there is a knowledge gap regarding the effective use of Sankey diagrams to capture the water-energy nexus and circularity of water. This study focused on effectively utilizing Sankey diagrams to model the nexus of water and energy flows and to assess the circularity of water. A comprehensive literature review was conducted on major studies on water material flows and modelling. This was supported by a review of approaches for modelling circularity, which have evolved from the material circularity index. The newly developed framework in this paper models water resource flows at all stages of the water supply chain, embedding the nexus as an interaction and considering resource intensity and material circularity index at each node. A city-level case study in South Africa was then used to apply the recommendations, model circularity, and the water-energy nexus using Sankey diagrams. In terms of circularity, the results indicate that water loss in the study area was very high, accounting for up to 40% of the total water supply and the water recycle rate was still very low, ranging from 3% to 7%. In terms of the nexus, the residential sector was found to have the highest energy intensity for water supply, primarily due to energy demand for water heating. Exploring the use of renewable or solar energy for water heating will be of interest and value. The new approach presented in this study allows for the simultaneous consideration of objectives, enabling a more robust analysis of energy-effective water use and water circularity along the water supply chain. [Display omitted] • A new framework for modelling the water energy nexus enriched by circularity options, energy intensity factors and confidence modelling. • Options for embedded circularity through the water supply chain. • The urgent challenge to address massive water loses in water supply systems for water scarce regions. • New Sankey diagrams showcasing how to model nexus and circularity. [ABSTRACT FROM AUTHOR]

With the rapid development of global informatization, data center computing tasks are increasing, and the resulting energy consumption problems are particularly prominent in the context of carbon emission reduction, and affect the safe and green operation of data centers. Aiming at the problem of high and large fluctuation energy load of data center servers, this paper presents a method of optimizing data center servers load considering spatial-temporal load distribution of computing resources. Co-optimized allocation of computing resources and load is studied with examples from the property that computing resources is adjustable in time and space dimensions. The results show that the proposed method is able to reduce the peak electric and cooling loads of the data center room by 10.7 %, reduce the total annual electric consumption by 2.66 % and the total annual cooling consumption by 2.46 %, and reduce the economic and environmental costs of energy supply for the data center in the case study, which comprehensively shows that the load optimization of data center servers has been achieved through the optimal allocation of computing resources. [ABSTRACT FROM AUTHOR]

Energy optimization in cloud computing is more attractive and much consideration in resource management, where, energy consumption is also one of the important metrics that should be included to resource management technique apart from performance and utilization of resource metric. The majority of existing methodologies for scheduling of resources are focusing on cost and execution time. In fact, the resource failures also distract the execution of cloud resource provisioning, which results in more operative cost and time. In this paper, the self-optimized energy efficient resource management strategy has been consider, which gives the optimal solution to maximize the resource utilization and identify the faulty resources to avoid misleading of scheduling. This strategy, allocate the resources to the workloads submitted by the cloud user by the fulfillment of the QoS with less SLA violation rate and maximizes the resource utilization at low cost. The novelty of this paper is to applied Antlion Optimization algorithm to find the optimal resource. The experimental results are evidence of utmost performance of proposed work. [ABSTRACT FROM AUTHOR]

Highlights • Review of the application of reinforcement learning (RL) for demand response (DR) • DR is relevant for integrating renewable energy source into the smart grid. • Considering RL/DR from energy generation and storage, to supply and user satisfaction. • Typical algorithms and open research directions are discussed. • Most articles focus on single-agent systems in stationary environments. Abstract Buildings account for about 40% of the global energy consumption. Renewable energy resources are one possibility to mitigate the dependence of residential buildings on the electrical grid. However, their integration into the existing grid infrastructure must be done carefully to avoid instability, and guarantee availability and security of supply. Demand response, or demand-side management, improves grid stability by increasing demand flexibility, and shifts peak demand towards periods of peak renewable energy generation by providing consumers with economic incentives. This paper reviews the use of reinforcement learning, a machine learning algorithm, for demand response applications in the smart grid. Reinforcement learning has been utilized to control diverse energy systems such as electric vehicles, heating ventilation and air conditioning (HVAC) systems, smart appliances, or batteries. The future of demand response greatly depends on its ability to prevent consumer discomfort and integrate human feedback into the control loop. Reinforcement learning is a potentially model-free algorithm that can adapt to its environment, as well as to human preferences by directly integrating user feedback into its control logic. Our review shows that, although many papers consider human comfort and satisfaction, most of them focus on single-agent systems with demand-independent electricity prices and a stationary environment. However, when electricity prices are modelled as demand-dependent variables, there is a risk of shifting the peak demand rather than shaving it. We identify a need to further explore reinforcement learning to coordinate multi-agent systems that can participate in demand response programs under demand-dependent electricity prices. Finally, we discuss directions for future research, e.g., quantifying how RL could adapt to changing urban conditions such as building refurbishment and urban or population growth. [ABSTRACT FROM AUTHOR]

Abstract Software systems have a strong impact on the energy consumption of the hardware they use. This is especially important for cyber-physical systems where power consumption strongly influences the battery life. For this reason, software developers should be more aware of the energy consumed by their systems. Moreover, software systems should be developed to adapt their behavior to minimize the energy consumed during their execution. This can be done by monitoring the usage context of the system and having runtime support to react to those changes that impact the energy footprint negatively. Although both the hardware and the software parts of cyber-physical systems can be adapted to reduce its energy consumption, this paper focuses on software adaptation. Concretely, the paper illustrates how to address the problem of developing context-aware energy-efficient applications using a Green Eco-Assistant that makes use of advanced software engineering methods, such as Dynamic Software Product Lines and Separation of Concerns. The main steps of our approach are illustrated by applying them to a cyber-physical system case study. [ABSTRACT FROM AUTHOR]

Abstract China has encountered the environmental issues and energy pressures in the middle-income stage. Strengthening environmental regulation is a crucial measure to prevent environment deterioration and energy dilemma. This paper investigates the three types of environmental regulations on energy consumption using feasible generalized least squares method during 1997–2015 in direct and indirect path. The main conclusions are as follows. In the direct path, the economical and supervised types of environmental regulations have inhibited energy consumption whilst legal environmental regulation has no significant impact. As for indirect path, the "rebound effect" of energy is greater than the energy conservation generated by technological innovation, which is embodied as an inhibitory effect. The effects of environmental regulation display regional difference. The legal and supervised types of environmental regulations have significant "cost effect" and "rebound effect" on energy consumption in the entire samples. On the contrary, the economical environmental regulation shows the opposite sigh in eastern regions. The counterfactual simulation results show that the net effects of environmental regulations on energy consumption are negative, which is beneficial for relieving energy pressures. Based on the findings, this paper provides pertinent policy implications. Graphical abstract Image 1 Highlights • Environmental regulation can be divided into three types. • Environmental regulation can affect energy consumption in direct and indirect path. • Differernt types of environmental regulation have distinct impacts on energy consumption. • The effect of environmental regulation displays regional difference. • Strengthening environmental regulation can relieve energy pressure. [ABSTRACT FROM AUTHOR]

The research fields in the fields of information science and computer are very extensive, and there are many research hotspots. Intelligent optimization algorithm is one of the research hotspots. One of the key issues found in research papers in this field is how to effectively improve the search quality of intelligent optimization algorithms in a complex work environment. As a multi-functional swarm algorithm, particle swarm optimization has relatively few parameters, good convergence, and relatively fast working speed. It is a popular intelligent algorithm. In the process of studying the optimal operation of the combined cooling, heating and power system, in order to better dispatch the output of each device in the combined cooling, heating and power system, a combination of chaotic search based on Tent mapping and nonlinear adaptive particle swarm optimization is proposed Optimization algorithm. Taking the operating cost, pollutant emission and energy efficiency of the cogeneration system as the goals, a multi-objective optimization model was established. The simulation results show that the proposed cooling, heating and power cogeneration system optimization method can effectively improve economic benefits, reduce pollution emissions, and improve energy utilization. The traditional model cannot accurately analyze the environmental pollution mitigation effect. In order to avoid this problem, a modeling analysis of the impact of wind power photovoltaic clean energy on the mitigation of environmental pollution is proposed, thereby building a wind power photovoltaic clean energy organization structure and maintaining the application of wind power photovoltaic clean energy the stability of the range. Set wind power photovoltaic clean energy to alleviate environmental pollution factors, and determine the degree of environmental pollution limited by natural numbers under continuous use of polynomial constraints. [ABSTRACT FROM AUTHOR]

With the rapid development of industry, the requirements for the means of resource and energy utilization are increasing. Advanced oxidation processes (AOPs) based on piezoelectric catalysis are environmentally friendly and have high treatment efficiencies, which have received wide attention from researchers. As a typical class of advanced piezoelectric catalytic materials, inorganic piezoelectric fibers have become a popular research direction in recent years due to their better piezoelectric properties. However, improving the carrier separation efficiency of inorganic fibers and subsequently enhancing their catalytic activity are of great interest to researchers. This review outlines the strategies and applications for enhancing the catalytic performance of inorganic piezoelectric fibers in light of the issue that the catalytic performance of piezoelectric fibers is dependent upon the charge energy or the separation efficiency of charge carriers. Firstly, the electrospinning method is introduced as an advanced technique for fabricating inorganic piezoelectric fibers. Next, the catalytic mechanism of inorganic piezoelectric fibers is discussed, and the charge transfer behavior of piezoelectric catalysts is analyzed. This review focuses on summarizing the engineering strategies for catalytic performance enhancement, such as surface modification, construction of heterojunctions and synergistic catalysis, as well as a variety of advanced means, and evaluates and compares the effectiveness of different strategies in practical applications. Finally, given the current challenges and prospects of piezocatalytic technology, this paper looks forward to the development direction of inorganic piezocatalytic fibers in the field of synergistic catalysis for more in-depth explorations and broadening the applications of piezocatalysis. [Display omitted] • Mechanisms of different catalytic enhancement strategies on inorganic piezoelectric fibers. • Preparative molding methods for various inorganic piezoelectric fibers are outlined. • Inorganic piezoelectric fibers are used for water treatment and energy regeneration. • Challenges and perspectives for enhanced inorganic piezoelectric catalytic fibers. [ABSTRACT FROM AUTHOR]

The growing utilization of wind energy resources has been evidenced to have had substantial positive impacts on mitigating climate change consequences. Unlike stationary wind turbines, mobile wind turbines (MWTs) can travel along the local transportation system (TS) via a truck, supplying power to microgrids (MGs). This spatiotemporal flexibility can provide significant benefits, including enhancing system resilience in the aftermath of high-impact low-probability (HILP) events and contributing to energy system decarbonization. In this paper, we propose a two-stage stochastic optimization model for the service restoration problem by coordinating the routing and scheduling of MWTs with hydrogen storage systems (HSSs) in MGs. The first-stage problem focuses on optimizing the deployment of MWTs based on the shortest-path information obtained through Dijkstra's algorithm. The second-stage problem aims to minimize the expected power outage costs while accounting for wind energy uncertainties estimated through a Monte Carlo simulation approach. Case studies on an integrated transportation and energy network – a central Alabama interstate TS and four IEEE 33-node test power systems – demonstrate the effectiveness of the proposed restoration scheme in boosting MGs resilience and reducing carbon emissions. • Design a two-stage model to assign mobile wind turbines in hydrogen-based microgrids. • Use the shortest-path algorithm to enhance efficiency of MWTs' dispatch decision. • Integrate microgrids and a transportation system, enabling use of wind energy. • Discuss insight on striking balance between power outage costs and carbon emissions. [ABSTRACT FROM AUTHOR]

Multiple DC microgrids are interconnected to form a DC microgrid cluster, which can effectively improve the renewable energy consumption capacity and power supply reliability of the microgrid cluster through sharing of energy storage among the participating DC microgrids. However, the existing methods are not able to flexibly coordinate the power flows between DC microgrids connected by a multiport converter. In this paper, a distributed cooperative control method is proposed for a DC microgrid cluster with multiple voltage levels connected by a multi-port interconnected converter. A multi-active-bridge (MAB) converter acts as a bridge for power exchange among microgrids, enabling the sharing of distributed energy storage among microgrids under a sparse communication network. The improved secondary control overcomes the drawbacks of droop control and achieves deviation-free voltage regulation and accurate load/source sharing of the energy storage converters within the cluster. The proposed secondary control can be used to retrofit the existing DC distribution grids to permit access of renewable energy. In addition, the improved voltage observer provides the voltage information of other nodes in a microgrid in addition to the energy storage information, reducing the voltage estimation error caused by the uneven distribution of energy storages. Finally, hardware-in-the-loop (HiL) experiments of the cluster with three DC microgrids verify the control performance, the flexible plug-and-play capability, and the high tolerance of communication failure. • A MAB converter acts as a bridge for power exchange among multiple microgrids. • Achieving DESs sharing in a microgrid cluster under sparse communication networks. • Both the secondary control and the MAB control are fully distributed. • An improved voltage observer enhances the accuracy of microgrid voltage control. [ABSTRACT FROM AUTHOR]

A bi-objective Fuel efficient Green Vehicle Routing Problem (F-GVRP) with varying speed constraint is discussed in this paper as an extension of Green Vehicle Routing Problem (G-VRP). F-GVRP is modelled to minimize both route cost and fuel consumption using goal programming. The problem is solved using Particle Swarm Optimization with Greedy Mutation Operator and Time varying acceleration coefficient (TVa-PSOGMO). The objective of this paper is to study the behaviour of F-GVRP under varying speed environment and its impact on the route cost and fuel consumption. Experiments are conducted with constant and varying speed constraints and it is observed that better routing plan with minimum fuel consumption can be achieved under varying speed environment. [ABSTRACT FROM AUTHOR]

Some scholars have addressed the relationship between the Human Development Index (HDI) and energy consumption in terms of the semi-logarithmic or hyperbolic function. One of the results from this relationship is to divide the world countries into two categories, developed and developing countries. This classification considers pre-developing countries and developing countries into one category (developing) while the proportion of people with fundamental energy needs in the former is far higher than in the latter. The objective of this paper is to allocate a separate class for pre-developing which their fundamental energy needs have not completely satisfied. The methodology of the study is divided into two parts. Initially, based on the longitudinal data (112 country’s data during the period of 2005–2013), a linear Quality of Life (QoL) indicator is proposed in terms of six variables. Then an S-shape (sigmoid) curve is fitted to the QoL indicator data against energy consumption (total primary energy supply as a proxy) per capita (ECpc) and electricity consumption per capita (Elcpc) data. Three types of countries, developed, developing, and pre-developing are identified based on the sigmoid function. The results of the proposed model demonstrate that the “pre-developing” category has different QoL and ECpc as compared to developing and developed classes. Another result of the paper shows the entry of new technologies has influenced the QoL and Elcpc to a greater extent in developing countries than in developed countries. One-way analysis of variance is a method to shed light on the latter consequence. In the case of pre-developing countries, the pertinent analysis shows an insignificant impact. This paper concludes the new classification of countries appropriately addresses variation of QoL against ECpc in each class. [ABSTRACT FROM AUTHOR]

With the rapid expansion of computational infrastructure, the electricity consumption of geographically dispersed Internet Data Centers (IDCs) has imposed a substantial influence on the power grid. Geographically distributed IDCs, which present a versatile electricity demand with significant operational flexibility, hold potential for mitigating cost issues and balancing regional supply and load demand. To exploit this flexibility, this paper has developed a dual-drive coordinated operational strategy, which integrates locational electricity prices and unit carbon emission costs based on the dynamic carbon emission intensity (DCEI) of various regions as interaction signals to improve coordination effectiveness. The upper-level determines the consumption behaviour of IDCs, the lower-level provides feedback on dual-drive signals factoring in IDCs' involvement in grid operation, whilst an iterative algorithm is suggested to solve the bi-level model. Case studies based on real-world datasets demonstrate that the proposed operation strategy can achieve arbitrage through multi-regional carbon-electricity markets, significantly reduce operating costs, and support the rapid development of IDCs. Additionally, this strategy facilitates optimal matching of power load in IDCs with renewable energy sources, thereby mitigating imbalances in regional renewable energy supply and demand. • Analysing the necessity of integrating IDCs into the carbon market. • Proposing a dual-drive coordination to activate spatiotemporal flexibility of IDCs. • An iterative algorithm for solving the bi-level model is designed. • Comparing impacts of carbon policies in IDC costs and renewable energy accommodation. [ABSTRACT FROM AUTHOR]

Now-a-days, due to the increasing demand of the energy resources across the world, choosing the best sustainable biomass crop for the production of biofuels is a strategic decision-making problem. This is generally accepted that intuitionistic fuzzy sets (IFSs) are much more efficient in comparison with fuzzy sets at representing and processing the uncertainty in real-life problems. Proceeding on the same line, this paper attempts to introduce an extended combined compromise solution (CoCoSo) framework to analyze the sustainable biomass crop selection (SBCS) problem in an intuitionistic fuzzy environment. In this framework, we suggest a new integration function based on double normalization multiple aggregation approach to overcome the aggregation biases of the original CoCoSo approach and discuss its advantages with some numerical examples. We also develop a combined weighting strategy based on distance measure and decision experts' (DEs) opinions to evaluate the significance of criteria. For this, we propose a novel distance measure (DM) and establish its superiority through some numerical comparisons. Also, the rationality of the suggested measure over the extant measures is justified by the use of an algorithm based on the developed measure for pattern recognition issues. In this framework, the comparison issue of IFSs is resolved by proposing a new score function. Furthermore, a case study of the SBCS is presented for the implementation of the developed CoCoSo approach, which confirms the viability and effectiveness of the new methodology. The results of the sensitivity analysis demonstrate that option "Miscanthus" consistently achieves the highest rank and is independent of variations of trade-off parameter and balancing factor. Finally, a comprehensive comparison is carried out to ensure the steadiness and reliability of the introduced framework. • We propose a new distance measure and establish its validation. • We develop and visualize an improved score function for IFS with its basic properties. • A new integration function is used to overcome the biases of the original CoCoSo method. • We introduce an improved IFS-CoCoSo framework and apply it to multi-criteria decision-making. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have become increasingly important in recent years due to their ability to monitor and collect data in various environments. However, WSNs often face limitations in terms of their energy resources, making energy efficiency a critical concern when designing WSNs. Clustering and multihop routing are effective methods for maximizing the lifetime of WSNs. The Graph Neural Network (GNN) is an emerging architecture in neural networks that has recently gained attention for solving problems in various domains, including wireless networks. In this paper, we propose a method based on GNN to create static clusters of equal size. This approach aims to balance energy consumption among the nodes, which is an important sub-goal in designing an energy-efficient WSN clustering protocol. Additionally, we introduce a distributed cluster head selection scheme that operates independently within each cluster. Another crucial aspect of energy efficiency in WSNs is multi-hop routing, which helps avoid long-distance communication that consumes significant amounts of energy. Our proposed centralized routing protocol establishes exclusive routes to the base station for each cluster. This ensures even energy consumption for relay nodes and prevents the hotspot problem. To evaluate our proposed protocol, we conducted simulations comparing it with several state-of-the-art counterparts. The empirical results demonstrate that our approach outperforms other protocols in terms of lifetime and coverage metrics. [ABSTRACT FROM AUTHOR]

Intermittent embedded devices and systems are widely used in various scenarios, but they often experience power failures due to unstable power supplies. Non-volatile memory (NVM) is gaining popularity in embedded systems due to its byte-addressability, low access latency, and high density. As a result, backups can be stored in NVM, and global data are directly manipulated in NVM in this architecture. However, NVM has limited write endurance and modifications to global data frequently occur which results in a significant number of writes. As a result, the extensive NVM accesses may lead to poor performance for intermittent programs. This paper presents an energy-efficient approach, called shadow-stack to minimize NVM write overhead while ensuring consistency. Furthermore, we propose a series of principles to assist programmers in rewriting the programs. And we prove the correctness of these principles using formal methods. Experimental results demonstrate that shadow-stack achieves an approximately 95.1%, 81.3%, and 76.6% improvement in NVM writes overhead compared with log-based, Coach, and Clank, respectively on average. Simultaneously, shadow-stack also significantly reduces energy and time consumption compared with log-based approach by up to 19.4 × and 6.87 × , Coach by up to 4.2 × and 2.46 × , and Clank by up to 3.4 × and 2.26 × respectively. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) face challenges related to limited energy resources in sensor nodes, making network lifetime and energy consumption critical considerations. In this paper, we introduce a novel approach to extend network lifetime and reduce energy consumption in WSNs by optimizing network architecture selection. We explore various architecture options, including single-layer and multi-layer networks, to determine the most suitable configuration. Several studies have applied knowledge transfer in evolutionary multitasking optimization to maximize network lifetime, achieving state-of-the-art results for the problem. However, they still have some drawbacks, such as redundant representations, i.e., many genotypes represent the same phenotype, and invalid solution generation. To address these limitations, we propose a new evolutionary multitasking algorithm incorporating efficient encoding to represent tree-structure solutions (network architectures) to the problem. Additionally, we tailor new genetic operators, such as recombination and mutation, to meet the specific requirements of our proposed evolutionary multitasking algorithm. The standout feature of our proposal lies in its ability to consistently generate valid solutions for the problem, which significantly reduces redundancy within the genotype search space and effectively satisfies the problem constraints. Additionally, our multitask evolutionary algorithm facilitates the exploration of various network architectures, harnessing knowledge transfer to enhance optimal performance and reduce computation time compared to single-task methods. We conduct comprehensive experiments on diverse datasets and use statistical tests, e.g., the Wilcoxon signed rank test, to verify the performance of our proposed algorithms. The empirical results demonstrate that our proposed algorithm outperforms existing methods and achieves state-of-the-art results in terms of solution quality, convergence rate, and running time across a wide range of data instances. Specifically, our algorithm provides 49.54% better solution quality on average across all data types compared to the previous state-of-the-art method. [ABSTRACT FROM AUTHOR]

In the realm of economic scheduling and optimal operation within integrated energy systems, integrated energy load aggregation plays a pivotal role. Accurate prediction of integrated energy load aggregation enables the refined management of energy resources, thereby enhancing energy utilization efficiency. This paper merges the advantages of clustering algorithms, prediction algorithms and ensemble learning to propose a short-term prediction approach for integrated energy load aggregation. Firstly, the intrinsic distribution of energy consumption data is analyzed through Affinity Propagation (AP) clustering with multi-dimensional similarity. This analysis yields the division of all samples into clusters based on data characteristics. Subsequently, the Transfer entropy is harnessed to conduct correlation analysis within each cluster. For constructing predictive models for each individual cluster, a prediction algorithm based on the Feature-Temporal Attention-enhanced Bi-directional Simple Recurrent Unit (FTA-Bi-SRU) is employed. The Dynamic Weight Average (DWA)-based multi-task loss equilibrium and Multi-Term Adam (MTAdam) loss function are combined to enhance the efficiency of the forecasting model. Finally, a Light Gradient Boosting Machine (LightGBM) model with Bayesian optimization is employed to integrate the predictive outcomes of each individual cluster to generate the overall predictive outcomes for integrated energy load aggregation. The proposed methodology is experimented on real-world energy consumption data to ascertain the efficacy of the prediction method. The experimental results substantiate the superiority of the suggested forecasting approach. • This marks the inaugural discussion of load forecasting for integrated energy load aggregation is discussed. • A multi-dimensional similarity Affinity Propagation (AP) clustering algorithm is introduced to group multi-energy users. • A method based on transfer entropy for correlation analysis is presented. • An Feature-Temporal Attention based Bi-directional Simple Recurrent Unit (FTA-Bi-SRU) prediction model is introduced. • A Bayes-optimized Light Gradient Boosting Machine (BO-LightGBM) ensemble framework is proposed to enhance overall prediction. [ABSTRACT FROM AUTHOR]

Currently, scarcity/security of clean water and energy resources are the most serious problems worldwide. Industries use large volume of ground water and a variety of chemicals to manufacture the products and discharge large volume of wastewater into environment, which causes severe impacts on environment and public health. Fossil fuels are considered as major energy resources for electricity and transportation sectors, which release large amount of CO 2 and micro/macro pollutants, leading to cause the global warming and public health hazards. Therefore, algae-bacterial consortium (A-BC) may be eco-friendly, cost-effective and sustainable alternative way to treat the industrial wastewaters (IWWs) with Bio-H 2 production. A-BC has potential to reduce the global warming and eutrophication. It also protects environment and public health as it converts toxic IWWs into non or less toxic (biomass). It also reduces 94%, 90% and 50% input costs of nutrients, freshwater and energy, respectively during IWWs treatment and Bio-H 2 production. Most importantly, it produce sustainable alternative (Bio-H 2) to replace use of fossil fuels and fill the world's energy demand in eco-friendly manner. Thus, this review paper provides a detailed knowledge on industrial wastewaters, their pollutants and toxic effects on water/soil/plant/humans and animals. It also provides an overview on A-BC, IWWs treatment, Bio-H 2 production, fermentation process and its enhancement methods. Further, various molecular and analytical techniques are also discussed to characterize the A-BC structure, interactions, metabolites and Bio-H 2 yield. The significance of A-BC, recent update, challenges and future prospects are also discussed. [Display omitted] • Scarcity and security of clean water and energy resources are the most critical today's problem. • IWWs are rich in color, nutrients, organic pollutants and heavy metals. • IWWs are highly toxic to human/animal/plant/soil/water and microbes. • A-BC is cost-effective and eco-friendly methods of IWWs treatment with Bio-H 2 production. • Bio-H 2 is a future fuel to fulfil the world's energy demand in eco-friendly manner. [ABSTRACT FROM AUTHOR]

It is a hot topic whether the choice of heating supply is necessary and what is the most suitable form of heating system for hot summer and cold winter regions. Apparently, the difference of climate and building envelope makes it a complex problem, which cannot rely on the northern heating measures directly. At the same time, the form of building envelope is closely related to energy consumption and indoor thermal environment. Thus, this paper looked at the importance of different thickness of building external insulation for residential building in Chongqing. This paper discussed the thickness of building external insulation in Chongqing based on the intermittent heating system, which used Dest to draw the conclusion. Economic and technical analyses were also carried out in order to find the balance point between the growth of cost and the amount of energy saving, due to the different form of building envelopes. [ABSTRACT FROM AUTHOR]

As an important part of green eco-community planning, reasonable community energy planning requires accurate community load prediction. Hence, this paper focuses on the method of community load prediction. Given that community energy planning has great uncertainty and forward-looking requirements, this paper proposes an integrated method of buildings load prediction, which combines building simulation and scenario analysis. We apply scenario analysis to avoid simply adding up the loads which leads to overestimation. Establishment and calibration of the prototypical models are carried out. Using the proposed method, a case study is conducted to predict the cooling and heating load of a CBD in Shanghai. The results show that the energy system operates at relatively low efficiency and adjustment of area ratio of different building types is suggested. [ABSTRACT FROM AUTHOR]

Whether industrial agglomeration can promote an increase in energy efficiency is of great importance to not only China’s energy conservation and emissions reduction plans, but also China’s future sustainable development. This paper uses statistical data from 285 cities, from 2004 to 2013, to analyze the effect of industrial agglomeration on energy efficiency. The spatial autocorrelation tests show that there is significant spatial autocorrelation and spatial heterogeneity in urban energy efficiency. Although dynamic spatial econometric results show that industrial agglomeration can promote an increase in energy efficiency from a country level, there are significant differences at a regional level. In the east, the effect of industrial agglomeration on energy efficiency has a threshold effect, but in central and western areas, industrial agglomeration can promote the increase of energy efficiency, with the positive effect in western areas greater than that in central areas. Finally, based on the research, this paper puts forward concrete suggestions to enhance energy efficiency. [ABSTRACT FROM AUTHOR]

Energy resource depletion and environmental degradation have become big challenges for China's sustainable development. Energy efficiency improvement is usually regarded as an major pathway for reducing the pressure on energy and the environment. However, this would be undermined by the rebound effect, because the rebound effect would partially or even totally offset the anticipated of efficiency improvement. Evidence of the rebound effect has important implications for the economic, resource and environmental consequences of improving energy efficiency, as well as enables policy makers to take informed decisions on energy and environmental issues. In this paper, we incorporate the endogenous energy efficiency into the model specification of rebound effect, and build a unified comprehensive framework for analyzing rebound effect and related issues. We applied this framework to conduct empirical analysis and comparison of heavy and light industries, considering heterogeneity across sub-industries. Furthermore, this paper goes beyond simple estimation of rebound effect, and takes additional effort to highlight the policy application via combining energy subsidies and technological progress with rebound effect. [ABSTRACT FROM AUTHOR]

This paper studies the energy-constraint output formation control for swarm systems with leaderless and leader-following topology structures. Most existing results on output formation with the dynamic output feedback protocols focus on the swarm systems without the energy constraint, but it is well known that the energy constraint is critically important for practical applications. In order to analyze the impacts of the energy constraint, a new energy-constraint output formation protocol is proposed. First, by the observable decomposition approach, a dynamic output formation protocol is presented, which contains an energy-constraint term to restrict the whole consumption. Then, sufficient conditions for leaderless energy-constraint output formation are presented via establishing the relationship of the energy constraint and the matrix variables, where it is found that the designed gain matrices of the output formation protocol can ensure that the actual energy consumption is lower than the total energy supply. Especially, a partition checking algorithm is proposed to check those conditions, which can ensure the scalability and solvability of a swarm system. Moreover, the output formation center function is derived to depict the whole macroscopic movement of a swarm system. A nonsingular transformation approach is presented to unify leaderless energy-constraint output formation and energy-constraint output formation tracking into the same framework, which are usually discussed in different theoretical frameworks. Finally, two simulation examples are illustrated to show that the theoretical results about leaderless energy-constraint output formation and energy-constraint output formation tracking are correct. • Firstly, the energy constraint is imposed on the formation protocol to restrict the whole energy consumption, which has extensively potential applications in practical swarm systems. • Secondly, a partition checking algorithm is proposed to deal with the impacts of nonlinear terms in matrix inequality criteria for output formation with the energy constraint, which can guarantee the solvability and the scalability. • Thirdly, leaderless energy-constraint output formation and energy-constraint output formation tracking are unified into the identical framework. [ABSTRACT FROM AUTHOR]

Facing the challage of energy conservation and emission reduction in the building sector, most energy saving strategies of central heating only focus on the performance improvement of building envelope, ignoring the impact of energy use behavior. This paper proposed a building energy conservation strategy based on energy use behavior analysis, which provides a perspective for in-depth exploration of building energy conservation. After investigating a typical university in the northern China, the energy use behaviors were characterized and two different DeST energy consumption models before or after considering energy use behavior were established. By comparing the results calculated by these two models, the impact of energy use behavior analysis on building energy consumption was quantitatively analyzed. The results show that the heating energy consumption and carbon emission can be reduced by 23.28% and 23% respectively through the proposed strategy based on energy use behavior. Therefore, the analysis of energy use behavior can provide a theoretical basis for heating operators to make decisions on energy supply regulation. With central cooling and heating further popularizing in the future, the research methodology of this paper has more extensive reference value for energy conservation and emission reduction strategies in the building sector. [ABSTRACT FROM AUTHOR]