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Increasing energy efficiency in manufacturing has significant environmental and cost benefits. Turning on or off a machine dynamically while considering the production rate requirements can offer substantial energy savings. In this work, we examine the optimal policies to control production and turn on and off a machine that operates in working, idle, off, and warmup modes for the case where demand inter-arrival, production, and warmup times have phase-type distributions. The optimal control problem that minimises the expected costs associated with the energy usage in different energy modes and the inventory and backlog costs is solved using a linear program associated with the underlying Markov Decision Process. We also present a matrix-geometric method to evaluate the steady-state performance of the system under a given threshold control policy. We show that when the inter-arrival time distribution is not exponential, the optimal control policy depends on both the current phase of the inter-arrival time and inventory position. The phase-dependent policy implemented by estimating the current phase based on the time elapsed since the last arrival yields a buffer- and time-based policy to control the energy mode and production. We show that policies that only use the inventory position information can be effective if the control parameters are chosen appropriately. However, the control policies that use both the inventory and time information further improve the performance. [ABSTRACT FROM AUTHOR]

Adoption of residential behind-the-meter solar photovoltaic-plus-storage systems (PVESS) is driven, in part, by customer demand for backup power. However, there is limited understanding of how these systems perform over a range of building stock conditions that will evolve with future efficiency and electrification trends, posing challenges for identifying optimal electric resiliency investments. This study quantifies how residential energy consumption impacts the capability of PVESS to provide home backup power during long-duration power interruptions. We model statistically representative distributions of the residential building stock and estimate storage sizes required to provide backup power as a series of building envelope efficiency, load flexibility, and electrification measures are applied. For the baseline building stock, median storage size requirements range from 10 kWh in temperate weather conditions to 90 kWh in hot climates for a 3-day power interruption. Applying energy efficiency and temperature set-point adjustments reduce storage size requirements by 2–45 kWh (16%–53 %). In hot locations, heat pump retrofits reduce median storage sizing by an additional 10–30 kWh while in cold locations, they drive 10–50 kWh of storage capacity increase. Our results suggest that bi-directional EV charging may be essential to enabling PVESS backup of heating and cooling, given their typically large kWh sizes.

Energy efficiency is crucial in contemporary industry and controlling the resource power state by switching off/on commands is a promising measure. The control problem of deciding when to switch off/on the machines depending on the state of the system at a given time is not trivial due to the effect the control might have on the system production rate. Threshold-based policies using buffer occupancy information to control the machines can be effectively used to reduce energy consumption. Nevertheless, highly complex control policies are difficult to be applied and costly to be managed in practice. Buffer-based threshold policies to control multiple machines simultaneously in a serial production line for energy efficiency purposes are analysed in this work. The optimal control minimises the energy consumption while assuring a certain target production rate for the system. The effects of controlling different combinations of machines simultaneously with different number of thresholds have been investigated through numerical experiments with discrete event simulation. Insights regarding the trade-off between the complexity of the control and the performance gains are provided. The proposed policy works effectively and the effect of a proper selection of the controlled machines or thresholds is significant. [ABSTRACT FROM AUTHOR]

Conventional wisdom and standard industry practice is to setback zone temperature setpoints when commercial buildings are unoccupied at night. The HVAC systems then operate in warmup mode to recover zone temperatures prior to the start of occupancy, sometimes with an optimal start algorithm. These strategies were intended to reduce HVAC energy consumption when originally developed decades ago but are due for re-examination given the significant changes in HVAC systems that have since occurred. In particular, the changes currently underway with the movement toward electrification present new design considerations and priorities. Warming up a building as fast as possible may not be the best strategy in terms of energy use, operating cost, or carbon emissions. This article discusses some of the downfalls of conventional morning warmup practices, suggests an improved strategy, and shows the results from a pilot field demonstration test.

The compound impacts of heatwaves and power outages pose a serious indoor heat-related health risk for residents living in disadvantaged communities (DACs) with limited or no air conditioning. In this study we selected 13 heat vulnerable multifamily buildings in El Monte, in Los Angeles County, and employed CityBES to evaluate their energy and thermal resilience performance. A retrofit package with seven passive and low-power active measures—cool roof, cool wall, window solar film, air sealing, internal blinds, natural ventilation, and ceiling fan—was evaluated under 2018 weather conditions and projected 2058 future weather conditions. Results show: (1) under the 2018 weather conditions, the retrofit package reduces the peak electricity load by 19 % and reduces the annual energy cost by $183 per housing unit; (2) the housing units without air conditioning would face heat danger conditions throughout the heatwave period. Although the retrofit package could reduce the heat danger hours by 50 % in 2018 and 34 % in 2058, air conditioning is a life-essential need for residents during heatwaves. These results indicate that, during the decision making of energy and climate retrofits for housing in DACs, policymakers and building owners should consider the co-benefits of reducing indoor heat-related mortality while reducing energy cost.

Natural gas combustion to serve space heating hot water systems causes approximately one third of large commercial building energy use in California. This project evaluated an innovative set of non-proprietary, cost-effective methods to reduce energy consumption and associated emissions from these systems. The project demonstrated 70% natural gas savings and substantial electricity savings in two large office buildings, yielding total utility cost savings of approximately $110,000 (or $0.5/ft²) per year. The project also conducted detailed studies on distribution losses and boiler efficiency in several buildings; measured performance of key components in laboratory tests; gathered and analyzed data from hundreds of buildings to evaluate actual performance of these systems; and provided a public dataset to inform future retrofits, research, and code development. The research also highlighted characteristics that make a building a good candidate for retrofit so these results can be scaled. Market transformation activities included 10 journal and conference publications, policy recommendations and a design guide. Based on these findings and other recent work, the opportunity for similarly large emissions reductions appears to be common within the existing large commercial building stock. The resources provided by this project can aid stakeholders in achieving California’s goals to decarbonize buildings.

Approximately 20% of primary energy consumed in the U.S. is attributed to HVAC use. Ideally, HVAC operation would be driven by actual building occupancy, but lack of reliable occupancy information often results in the use of conservative static schedules. This disparity is even more pronounced in a college campus, where the function of each space differs by building (classrooms, offices, libraries) and the class schedules change frequently -- every semester, day of week, and hour. While several research papers propose the use of counts of the Wi-Fi connections (e.g., phones, computers) as a proxy for occupancy, few real-world implementationsexist. This paper describes the development and deployment of an open-sourceWi-Fi-to-Occupancy software library in 65 buildings of a college campus, and the plannedintegration with the building energy management and control system at the building scale. Overa year of Wi-Fi data was gathered into distinct academic periods, including fall and springsemester, academic breaks, and summer sessions. Patterns such as students moving between classrooms, closing laptops before exams, etc., can be visualized from the data. Approximating occupancy from Wi-Fi data presents challenges which we address in this project -- for example, identifying static devices, or estimating the ratio of devices per person. Utilizing real-time occupancy data to inform optimal HVAC schedules and ventilation rates creates the potential to identify and reduce energy waste. Other potential applications include forecasting occupancy, and using Wi-Fi data to predict peak demands. Finally, the paper discusses how to easily scale these tools to other buildings.

Community and city leaders are interested in achieving sustainability goals, providing resilient energy infrastructure, and improving economic competitiveness. Community-level data acquisition and analysis can provide energy and associated benefits that are not possible at the single building level. However, there is a lack of organizational structure, common semantic data models, interoperable systems, and methods to support data-driven decision-making for community-scale energy supply and demand systems. We explored the need and opportunity for a Community Energy Operations and Planning System (Community EOPS), a potential data exchange platform. We conducted “customer discovery” interviews, and reviewed literature, public tools, and technology platforms to identify key energy data “users” and use cases in communities. The key users of the Community EOPS could be developers of mixed-use districts, corporate, defense and university campus energy managers, and city managers of cities that own their energy utility. The value could be for community planning and reporting (for energy data-integrated land use planning and community infrastructure investments in microgrids, storage, district heating and cooling), energy efficiency (leveraging optimizations for community scale energy supply and demand), flexible load management (grid-edge load management to offset, shift, and flatten loads for multiple buildings and EV fleets), cost savings and revenue generation (participating in grid services), and social benefits such as energy resilience, equity, and awareness. We developed a conceptual Community EOPS architecture with recommendations for streamlined and prioritized data acquisition, sharing, and integration driven by prioritized use cases, common metrics, and actionable visualizations that can provide value to a community’s users.

The time when energy-related carbon emissions come overwhelminglyfrom developed countries is coming to a close.China will soon overtake the United States as the world’s leadingemitter of greenhouse gas emissions. Meanwhile, India alsoseems to be on track to experience rapid long-term economicexpansion. With this growth will surely come continued massivegrowth in energy demand. This paper explores the dynamicsof that demand growth for one sector – residential electricity– and the realistic potential for coping with it throughefficiency.

Hydrometallurgy is a novel metallurgical methodology that employs hydrogen as a reducing agent, thereby facilitating efficient, low energy consumption, and eco-friendly metal smelting processes. This study establishes a new fluidized bed hydrogen direction reduction system, which encompasses hydrogen production by water electrolysis, hydrogen direct reduction iron, waste heat utilization, and hydrogen recovery with Aspen Plus. The impact of the hydro-to-iron ratio and reaction temperature on the iron reduction and the energy and exergy efficiencies of the system is investigated. The results indicate that, the higher the hydro-to-iron ratio or reaction temperature, the higher the reduction rate. The energy efficiency of the system exhibits a non-linear trend with respect to temperature and the exergy efficiency demonstrates an overall increase with temperature or the hydro-to-iron ratio. • A new fluidized bed hydrogen reduction system with hydrogen recycling is established. • Increasing temperature and hydro-to-iron ratio enhances iron oxide reduction by hydrogen. • The energy efficiency of the system shows a non-linear trend with temperature. • The exergy efficiency increases with temperature or the hydro-to-iron ratio. [ABSTRACT FROM AUTHOR]

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

The depletion of fossil fuels necessitates the efficient utilization of solar energy and the urgent resolution of its instability, intermittency, and storage challenges. Photo‐rechargeable batteries, which integrate solar cells and energy storage batteries to convert solar energy into electricity and store it as chemical energy, have gradually emerged as a novel research direction to meet the energy demands of various standalone applications such as building facades, mobile transportation devices, and outdoor settings. This review elucidates the device structure, working principles, and key parameters of photo‐rechargeable batteries. Furthermore, various photo‐rechargeable battery systems such as lithium‐ion battery, lithium‐sulfur battery, sodium‐ion battery, zinc‐ion battery, and aluminum‐ion battery are categorized and summarized, detailing their composition, operational mechanisms, and photoelectric performance. Finally, the future research directions of photo‐rechargeable batteries are delineated, advocating for the exploration of dual‐functional materials that integrate light conversion and energy storage. Specifically, emphasis is placed on studying the compatibility between optical and energy storage materials, investigating new battery operation mechanisms under illumination conditions, considering the imperative of achieving high stability and overall efficiency to enhance device performance, and elucidating the future application pathways of these technologies. [ABSTRACT FROM AUTHOR]

Energy efficiency is considered critical towards sustainability for all businesses, given that traditional energy intensification is not only an expenditure but also a proportional source of carbon emissions. However, it is unclear how investors consider a firm's energy efficiency while deciding on its cost of debt. This study aims to analyse the relationship between firms' energy efficiency and the cost of debt using panel data regression with a fixed effect on 17,175 firm-year observations of 2284 companies. The endogeneity effects of variables have been controlled using instrumental variables. The study found evidence that energy efficiency leads to a reduction in the cost of debt. This study also analyses the impact of energy efficiency on investments made in research and development. The findings suggest that efficient energy firms are technologically advanced through higher investments in research and development, effectively improving the firm's values and helping to reduce their cost of debt. The results support that energy-efficient firms are technologically advanced and better positioned regarding firm value. Thus, they are regarded as secure funding with reduced costs of debts. This research demonstrates that lenders must be responsive to modern-day cultures' social concerns through better social engagements in the context of global warming and climate change. [ABSTRACT FROM AUTHOR]

Summary The sensor networks are the primary and essential components on which the world of Internet of Things (IoT) is built. IoT empowers smart communication, computation, and sensing capabilities. In sensor networks, the data are collected by the sensor nodes and sent to the sink along a communication path. These communication paths are collaboratively established by the nodes and the sink. By incorporating energy‐efficient data gathering techniques, the lifetime of these networks is improved. The major contribution of the study in this work is to provide a survey of various techniques for data aggregation (DA) and the employed algorithmic strategies that facilitate and influence network lifetime (NL) in these environments. DA in wireless sensor networks (WSN), IoTs, and cloud computing extend the lifetime of these networks since it enables efficient merging of traffic flows, thus reducing transmissions and energy consumption of devices. In sensor networks, data aggregation tree (DAT)‐based routing facilitates energy‐efficient routing that extends NL. NL maximization using DATs constructs DATs with optimal NL and is a known NP‐complete problem. Subsequently, the study in this work surveys the various approaches employed by researchers to construct DATs and discusses techniques for DAT scheduling. This work further explores various sensor deployment techniques and discusses real world scenario in which NL is influenced by uncertainty in communication links. Finally, the study in this survey highlights the achievements in realizing NL improvement using DAT and identifies the limitations and research challenges. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) struggle with energy efficiency because of limited node power. This paper presents an approach that uses evolutionary algorithms to choose the Cluster Head (CH) and optimize routing in wireless sensor networks (WSNs) using grid-based topologies. The proposed method repeatedly develops solutions based on criteria for node density, distance, and energy level by using the evolutionary capabilities of the genetic algorithm. A fitness function that considers latency, coverage, and energy efficiency is used to evaluate the solutions. The process selects CHs dynamically and uses GA-guided optimization to construct paths. Simulation results indicate improved network performance and energy efficiency over existing protocols. Evolutionary algorithm integration enables flexibility and optimization for energy-efficient CH selection and routing in WSNs with a grid-based design. [ABSTRACT FROM AUTHOR]

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

AbstractTo elucidate the effects of energy input modes on energy efficiency and berry quality in continuous microwave drying, four energy input modes with variational working ways of magnetrons were investigated by using numerical simulation and bench experiments in a continuous microwave dryer. Energy efficiency was divided into absorption and conversion efficiency to study the change rule of drying process, and the retention rate of anthocyanins of berry’s representative nutrition ingredient was studied considering both of the temperature and its uniformity. Results indicate that the energy input modes affect the absorption efficiency by changing the distribution uniformity of electric field of material layer, and the conversion efficiency by altering the matching degree between the energy allocation and drying characteristics of berry, which are both determined by the ways of working magnetrons of different energy input modes. The conversion efficiency has a greater influence on the final energy efficiency, so the energy input mode should be paid attention to the provision of microwave energy by selecting proper way of working magnetrons according to the drying characteristics. The effect of temperature uniformity on anthocyanin degradation is more obvious with average temperature increasing gradually, where better uniformity of electric field strength should be provided to guarantee the quality of dried berry by adjusting the energy input mode. The research results provide significant insights to optimize the energy input mode of taking into account both the energy efficiency and dried quality of material. [ABSTRACT FROM AUTHOR]

Biodiesel, an eco-friendly fuel with lower greenhouse gas emissions, is a crucial alternative to traditional fossil fuels. Adding hydrogen and nanoparticles to biodiesel enhances diesel engine performance, reduces emissions, and improves fuel efficiency through better combustion and fuel atomization. In this study, various nanoparticles were experimented, and their performance, emission, and acoustic outcomes were systematically evaluated, resulting in a comprehensive ranking and clustering. The k-means clustering-Entropy-TOPSIS method is applied to weigh performance outcomes, rank the nanoparticles, and create clusters among them among best and worst, considering multiple criteria's in this study. This study further divides diverse nanoparticles into clusters, employing a hybrid machine learning k-means clustering method. The CO parameter carries the highest weight (57%), followed by UBHC (22%), owing to their significant variation induced by the addition of nanoparticles, surpassing the variability observed in other parameters. Manganese oxide nanoparticles exhibited superior performance across various critical parameters among all nanoparticles with a minimum centroidal distance of 25.97. Moreover, enrichment with hydrogen gas, combined with nanoparticles, significantly enhanced diesel engine performance, manifesting in an approximate 8 % increase in Brake Thermal Efficiency (BTE) and 23 % reduction in CO emissions. Identifying the optimal nanoparticles is crucial for enhancing biodiesel properties, and the paramount importance of hydrogen mixing lies in its potential to significantly improve combustion efficiency, emissions, and overall diesel engine performance. • This study experimented with various nanoparticles in biodiesel-hydrogen blends within a diesel engine. • The study employs K-means clustering-Entropy-TOPSIS method to cluster nanoparticles based on performance, emissions, and acoustic outcomes. • The CO parameter carries the highest weight (57%), followed by UBHC (22%). • Manganese oxide nanoparticles exhibited superior performance across other nanoparticles. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) in recent times offers a greater amount of flexible design during the deployment of it in the form of network. The interfacing of IoT with a wireless sensor network (WSN) requires an optimal solution to preserve the consumption of energy while the data has been transmitted via nodes. In this paper, we develop machine learning (ML)-assisted routing to route the data packets of IoT sensors from the real-time environment over WSN. In this study, we use three different planes for optimal routing of packets from the source node to the destination node. The source node is the IoT sensors that involve data collection, and the intermediate nodes are the WSN sensor nodes that route the packets. Optimal routing decisions based on the pre-trained data in an artificial neural network (ANN) stabilize the routing of data packets without congestion. The simulation is conducted to test the efficacy of the ANN routing in the integrated network, and the results show that the ANN-based routing achieves higher energy efficiency and throughput than other models. [ABSTRACT FROM AUTHOR]

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

The majority of research predicted heating demand using linear regression models, but they did not give current building features enough context. Model problems such as Multicollinearity need to be checked and appropriate features must be chosen based on their significance to produce accurate load predictions and inferences. Numerous building energy efficiency features correlate with each other and with heating load in the energy efficiency dataset. The standard Ordinary Least Square regression has a problem when the dataset shows Multicollinearity. Bayesian supervised machine learning is a popular method for parameter estimation and inference when frequentist statistical assumptions fail. The prediction of the heating load as the energy efficiency output with Bayesian inference in multiple regression with a collinearity problem needs careful data analysis. The parameter estimates and hypothesis tests were significantly impacted by the Multicollinearity problem that occurred among the features in the building energy efficiency dataset. This study demonstrated several shrinkage and informative priors on likelihood in the Bayesian framework as alternative solutions or remedies to reduce the collinearity problem in multiple regression analysis. This manuscript tried to model the standard Ordinary Least Square regression and four distinct Bayesian regression models with several prior distributions using the Hamiltonian Monte Carlo algorithm in Bayesian Regression Modeling using Stan and the package used to fit linear models. Several model comparison and assessment methods were used to select the best-fit regression model for the dataset. The Bayesian regression model with weakly informative prior is the best-fitted model compared to the standard Ordinary Least Squares regression and other Bayesian regression models with shrinkage priors for collinear energy efficiency data. The numerical findings of collinearity were checked using variance inflation factor, estimates of regression coefficient and standard errors, and sensitivity of priors and likelihoods. It is suggested that applied research in science, engineering, agriculture, health, and other disciplines needs to check the Multicollinearity effect for regression modeling for better estimation and inference. [ABSTRACT FROM AUTHOR]

For sustainable development, the roles of energy efficiency and renewable energy is undeniable. We are passing through the era of globalization and economies are expanding their economic activities across borders. In this scenario, nations are striving for sustainable economic development without hurting the climatic conditions. Therefore, this study employs Bai and Carrion-i-Silvestre unit root test and Westerlund cointegration tests. The variables were found to have mix order of integration and Westerlund test shows cointegration in the panel data. The Method of Moments Quantile Regression (MMQR) is used to ascertain the effects of energy efficiency and economic, environmental, and globalization factors on the load capacity factor (LCF), a measure of environmental quality in G-20 countries. Key findings supporting the Load Capability Curve (LCC) theory show that while initial economic growth (GDP) has a negative impact (coefficient of -0.035 at the median quantile), advanced economic growth (GDP2) improves LCF (coefficient of 0.513 at the 90th quantile and 0.388 at the median). The median quantile coefficient of 0.055 indicates a positive association between the consumption of renewable energy and LCF. Natural resources exhibit a coefficient of 0.061 at the 90th quantile and 0.037 at the median. However, there are drawbacks to both financial globalization and contemporary environmental technology; their coefficients at the median quantile are -0.021 and -0.058, respectively. The work suggests targeted strategies, including more stringent environmental legislation, backing for renewable energy, sustainable resource management, advancements in environmental technologies, regulation of financial globalization, and bolstering of international cooperation. [ABSTRACT FROM AUTHOR]

Recent trends have shown that autonomous agents, such as Autonomous Ground Vehicles (AGVs), Unmanned Aerial Vehicles (UAVs), and mobile robots, effectively improve human productivity in solving diverse tasks. However, since these agents are typically powered by portable batteries, they require extremely low power/energy consumption to operate in a long lifespan. To solve this challenge, neuromorphic computing has emerged as a promising solution, where bio-inspired Spiking Neural Networks (SNNs) use spikes from event-based cameras or data conversion pre-processing to perform sparse computations efficiently. However, the studies of SNN deployments for autonomous agents are still at an early stage. Hence, the optimization stages for enabling efficient embodied SNN deployments for autonomous agents have not been defined systematically. Toward this, we propose a novel framework called SNN4Agents that consists of a set of optimization techniques for designing energy-efficient embodied SNNs targeting autonomous agent applications. Our SNN4Agents employs weight quantization, timestep reduction, and attention window reduction to jointly improve the energy efficiency, reduce the memory footprint, optimize the processing latency, while maintaining high accuracy. In the evaluation, we investigate use cases of event-based car recognition, and explore the trade-offs among accuracy, latency, memory, and energy consumption. The experimental results show that our proposed framework can maintain high accuracy (i.e., 84.12% accuracy) with 68.75% memory saving, 3.58x speed-up, and 4.03x energy efficiency improvement as compared to the state-of-the-art work for the NCARS dataset. In this manner, our SNN4Agents framework paves the way toward enabling energy-efficient embodied SNN deployments for autonomous agents. [ABSTRACT FROM AUTHOR]

Traditional biomass boiler operations predominantly rely on manual control, with adjustments made based on operator experience. This approach not only demands considerable manpower but also leads to suboptimal resource utilization. Addressing the specific operational dynamics of biomass boilers, this study introduces a controller designed around the STM32 microcontroller. This device employs a modular architecture and incorporates cost-effective chips and peripheral components. To enhance energy efficiency and the overall user experience, the controller utilizes pulse width modulation pulse modulation alongside a fuzzy PID control strategy. Capable of toggling between manual and automatic modes for local operations, it also supports data logging directly on the device or in the cloud. Tailored to meet diverse user requirements, the controller facilitates remote monitoring and management. It employs a strategic control mechanism for various motors, ensuring precise hot water temperature measurements within the boiler and enabling data uploads to cloud platforms. This not only conserves energy and reduces labor costs but also promotes environmental sustainability and operational efficiency. Through ongoing refinement and rigorous testing, the controller has achieved a level of maturity that underscores its readiness for widespread implementation. [ABSTRACT FROM AUTHOR]

AbstractThis paper was conducted to model the drying kinetics of Capparis spinosa buds using dragonfly swarm optimized nonlinear regression and to compare the impact of drying treatments on their quality. Experiments included hot-air convective drying (HACD) from 40 to 120 °C, vacuum drying (VD) at 40, 60, and 80 °C, microwave drying (MD) ranging from 200 to 1000 W. Out of 30 models tested, the drying kinetics fitted best with Jena Das model in both HACD and VD and modified Midilli for MD. The comparison between the drying treatments used indicated that when taking into account the quality of the dried caper, VD at 80 °C was the most effective, resulting in a high-quality caper with a well-preserved color (ΔE = 7.47), high bioactives (TPC = 30.18mgGAE/gDW and TFC = 10.27 mg QE/gDW) and radical scavenging abilities (DPPH = 0.249 and ABTS = 4.448 mg/mL) at the expense of long duration (6h) and high specific energy consumption (50.667 kWh/kg). On the other hand, when considering the drying behavior MD ranked best with samples dried at 1000 W showing high diffusivities 2.04e−8 m2s−1 low energy consumption (MER 0.1216 kg/h), and short drying times (12 mn). This research is critical in selecting better drying conditions for increased usage in the culinary, cosmetic, and pharmaceutical sectors. [ABSTRACT FROM AUTHOR]

Efficient use of energy and other resources, as the basic postulates of the circular economy, is a prerequisite for the green transition to more sustainable cities in the future. The main scientific goal of the paper is the development of a new approach to city governance when it comes to the inefficient use of energy, predominantly fossil fuels, mainly in developing and poor countries. Energy efficiency problems faced by these countries require the introduction of urgent, applicable, and realistically achievable solutions. A prerequisite for adequate analysis and modeling of energy efficiency performance, measures, policies, outcomes, and impacts is the introduction and functioning of the big data management system, which should begin with data mining. On the other hand, adequate data collection has been neglected in many of these countries. The study shows a way to reduce this gap, but in accordance with realistic and limited possibilities for countries with less favorable conditions. In that respect, a conceptual model for the Analytical Service for facilitating energy efficiency in city governance was developed and presented as a driver that can enable cities to manage energy more efficiently. The model is based on an interdisciplinary approach and on the needs of cities in the Republic of Serbia. However, it is designed to allow upgrading in accordance with the capabilities and resources of cities, primarily applicable in developing and poor countries. [ABSTRACT FROM AUTHOR]

In this paper we re-examine the relationship between global Gross Domestic Product (GDP), Primary Energy Use (PEU) and Economic Energy Efficiency (EEE) to explore how investment in energy efficiency causes rebound in energy use at the global scale. Assuming GDP is a measure of final useful work, we construct and fit a biophysics-inspired nonlinear dynamic model to global GDP, PEU and EEE data from 1900—2018 and use it to estimate how energy efficiency investments relate to output growth and hence economy-wide rebound effects. We illustrate the effects of future deployment of enhanced energy efficiency investments using two scenarios through to 2100. The first maximizes GDP growth, requiring energy efficiency investment to rise ~ twofold. Here there is no decrease in PEU growth because economy-wide rebound effects dominate. The second scenario minimizes PEU growth by increasing energy efficiency investment ~ 3.5 fold. Here PEU and GDP growth are near fully decoupled and rebound effects are minimal, although this results in a long run, zero output growth regime. We argue it is this latter regime that is compatible with the deployment of enhanced energy efficiency to meet climate objectives. However, while output growth maximising regimes prevail, efficiency-led pledges on energy use and emissions reduction appear at risk of failure at the global scale. [ABSTRACT FROM AUTHOR]

In this study, we construct heterogeneous agent general equilibrium models to investigate the relative importance of labor endowment in driving structural transformation. We aim to explore the following question: beyond the demand-side and supply-side structural transformation driving forces extensively studied in the existing literature, does labor, as a crucial endowment, play a pivotal role in facilitating structural transformation and the energy economy? In contrast to the prevalent partial equilibrium analyses, our study employs a general equilibrium framework to conduct a policy evaluation of college expansion, a significant policy that has altered the labor endowment structure in China. Our approach begins with developing a multi-sector model that integrates a nested CES production function and incorporates workers with different skill levels to assess the macroeconomic impact of college expansion on structural transformation. We calibrate the base model to reflect labor allocations across sectors and skill levels using the simulated method of moments (SMM), ensuring that the model-generated data align closely with actual labor allocation data. Utilizing this calibrated model, we perform counterfactual experiments to assess the impact and relative importance of the college expansion policy. Our counterfactual analysis demonstrates that the policy has resulted in an average decrease of 7.7% in labor allocation in the agricultural sector, alongside an average increase of 8.9% in the industry sector and 28.7% in the services sector. These results highlight the significant, yet often overlooked, contribution of labor in endowment-driven structural transformation. Furthermore, we extend the base model by constructing an industry-level heterogeneous agent general equilibrium model, enabling us to pinpoint which industries have developed as a result of the college expansion policy and recalibrate it at the industry level. This approach allows us to analyze the impact of changes in labor endowment on the energy economy. Counterfactual experiments conducted show that the college expansion policy has prompted a labor shift from industries with low energy efficiency and high pollution to high-end services. This macroeconomic pattern of structural transformation suggests that the college expansion policy has facilitated a transition toward a low-carbon economy by reducing dependency on high energy-consuming industries and promoting high-end services. [ABSTRACT FROM AUTHOR]

Simple Summary: A dynamic model was created to determine the nutritional requirements of lambs, aiming to simulate and describe their growth, nutrient requirements, and body composition. The model inputs include body mass, standard final mass, age, and dietary energy concentration. The results showed that the model accurately predicts the final body mass based on the inputs, with a coefficient of determination of 0.89 and a mean bias of 0.656 kg. The utility of this model extends to agricultural practitioners and livestock managers, providing valuable insights into forecasting final body mass and estimating the duration required to attain specific weights. Moreover, it serves as an educational tool elucidating the intricate interplay between nutrition, growth dynamics, and body composition in lambs. Thus, its implementation stands to enhance decision-making processes within feedlot lamb production, contributing to more informed and efficient management practices. A dynamic model has been developed to simulate aspects of feedlot lamb growth and body composition, including energy and protein requirements, growth rate, composition of gain, and body mass. Model inputs include initial body mass (kg), standard final mass (kg), age (days), and dietary energy concentration (Mcal·kg−1). The model was assessed as a decision support tool using a dataset of 564 individual measures of final body mass and diet energy. The simulations provide graphical and numerical descriptions of nutrient requirements, composition of gain, and estimates of animal performance over time. The model is accurate and precise, with a root mean squared error of 7.79% of the observed final body mass and a coefficient of determination of 0.89 when simulating the same variable. The model can be used as a reliable decision support tool to estimate final body mass and the days on feed required to reach a certain final mass with precision and accuracy. Moreover, the dynamic model can also serve as a learning tool to illustrate practical principles of animal nutrition, nutrient requirement relationships, and body composition changes. This model holds the potential to enhance livestock management practices and assist producers in making informed decisions about feedlot lamb production. [ABSTRACT FROM AUTHOR]

Energy-consuming rights trading policies (ECRTPs) represent a significant institutional innovation for China aimed at achieving the dual control targets of total energy consumption and energy consumption intensity. However, the effectiveness of these policies in curbing air pollution remains uncertain. This study treats ECRTPs as a quasi-natural experiment to empirically analyze their impact on air pollution, utilizing panel data encompassing 277 prefecture-level cities in China covering the period from 2011 to 2021. Analytical methods applied include a Difference-in-Differences model, a mediation effects model, and a triple differences model to explore the effects of ECRTPs on air pollution. The findings reveal that ECRTP can significantly suppress air pollution, and this conclusion remains valid even after conducting robustness tests. Mechanism analysis indicates that ECRTPs suppress air pollution by boosting energy efficiency, advancing industrial structure upgrading, and facilitating technological innovation. Further heterogeneous studies show that ECRTPs have a more pronounced inhibitory effect on air pollution in cities that are economically and socially developed, exhibit greater energy-saving potential, are characterized as resource-based cities, and serve as key regions for the prevention and control of air pollution. The research conclusion provides empirical evidence and policy implications for evaluating the environmental effects of ECRTPs and further improving China's energy-consuming rights trading system, as well as offering references and guidance for other developing countries to put forward ECRTPs. [ABSTRACT FROM AUTHOR]

The focus on building energy efficiency using alternative materials in structures, especially concrete, and the main technical and environmental challenges therein, aligns with Sustainable Development Goals (SDG). This study proposes a review that analyzes structures made with recycled concrete, relating to the energy efficiency of buildings. A classification structure was proposed, addressing the following questions: (i) What are the various dimensions in which research into energy-efficient recycled concrete is concentrated? (ii) What are the themes and classes of research associated with these dimensions? (iii) What are the main shortcomings of current approaches, and what would be a good research agenda for the future development of energy-efficient recycled concrete? A bibliometric analysis was carried out, presenting geographical and cluster maps to understand different research trends and refine future research. This was followed by a bibliographic analysis, reviewing the most relevant studies from the last five years (2019–2024). The results showed some residual alternative materials (around 45 types from five different industries) used in the production of energy-efficient concrete. And, as a negative effect, as substitution rates increase, porosity is the property with the greatest impact on energy efficiency. The greater the number of pores and the greater their interconnection, the lower the material's thermal insulation. [ABSTRACT FROM AUTHOR]

Oil refineries play a crucial role in meeting global energy demands, and optimizing the efficiency of critical processes is vital for economic feasibility and environmental sustainability. Simulation is an essential tool for the optimization of valuable products. This work presents the rigorous simulation of a vacuum distillation unit (VDU) based on actual data from the vacuum distillation processes using Aspen HYSYS V10. The Peng–Robinson fluid package is used in this simulation, and an input assay with a standard density of 29 API_60 (879.8 kg/m3) is employed. True boiling point (TBP) assay data are the type that is being used. Methane, ethane, propane, i-Butane, n-Butane, i-Pentane, and n-Pentane are the components listed in the simulation. The research determines that achieving a yield capacity of 685 tons/h requires thirty stages in the atmospheric distillation unit and twelve stages in the vacuum distillation unit while operating at 420 °C temperature and 9 kPa pressure. Adjustments in the flash section temperature (FST) and steam flow rate (SFR) are proposed to enhance operational efficiency. Increasing the FST from 370 °C to 400 °C and adjusting SFR from 10 tons/h to 26 tons/h increases the Light Vacuum Gas Oil (LVGO) yield by 7.2% while elevating the FST from 400 °C to 430 °C and adjusting SFR from 10 tons/h to 26 tons/h enhances the High Vacuum Gas Oil (HVGO) yield by 7.4%. These optimization strategies offer a practical and effective approach for refineries to improve the economic benefits of vacuum distillation units. The implications of this research can act as a computational thinking exercise for higher education students considering the case study where only through changing the operational strategies can the yield be enhanced by 10.81% in the vacuum distillation unit of the oil refinery. [ABSTRACT FROM AUTHOR]

The pursuit of energy efficiency in machining processes is a critical aspect of sustainable manufacturing. A significant portion of global energy consumption is by the industrial sector; thus, improving the energy efficiency of machining processes can lead to substantial environmental and economic benefits. The present study reviews the recent advancement made for improving the energy efficiency of machining processes. First the energy consumption of the machining processes was explored and then the key areas and developments in their energy consumption modeling were identified. Following this, the review explores various strategies for achieving energy savings in machining. These strategies include energy-efficient machine tools, the accurate modeling of the energy consumption of machining processes, the implementation of optimization techniques and the application of artificial intelligence (AI). Additionally, the review highlights the potential of AI in further reducing energy consumption within machining operations and achieving energy efficiency. A review of these energy-saving strategies in machining processes reveals impressive potential for significant reductions in energy consumption: energy-efficient design can achieve up to a 45% reduction, optimizing cutting parameters may minimize consumption by around 40%, optimizing tool paths can reduce consumption by approximately 50%, optimizing non-cutting energy consumption and sequencing can lead to savings of about 30% and employing AI shows promising energy efficiency improvements of around 20%. Overall, the present review offers valuable insights into recent advancements in making machining processes more energy-efficient. It identifies key areas where significant energy savings can be achieved. [ABSTRACT FROM AUTHOR]

Taking into account the contributions of economic performance (GDP), urbanization (URB), industrial structure (IND), and renewable energy consumption (REC), this paper examines the impact of green technology innovation (GTE), energy efficiency (EF), and environmental regulation (ER) on CO2 emissions in Chinese provinces from 2010 to 2020. Using the GMM method for the initial estimation, the MMQR as 2nd generation test for robustness and innovative panel causality presented by the JKS test, we have found: 1) a one percent boom in GDP is linked with a 0.08% upward push in CO2 emissions throughout 30 provinces in China. 2) the renewable energy and energy efficiency data seems to effectively decrease CO2 emissions, with a more pronounced impact observed at the upper quantile. 3) The environmental policy is limited across all quantiles. The study examines novel implications regarding sustainable development and carbon neutrality objectives. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, the expensive and scarce resources are energy and frequency spectrum. To overcome this scarcity of spectrum, cognitive radio has been introduced in WSNs. The licensed band is utilized by the primary users in cognitive radio whereas the secondary users can use the licensed channels. For improving the network lifetime, overall network scalability, and energy consumption, the clustering technique is used to group the sensor nodes into clusters. Clustering algorithms must be energy efficient because of the difficulties in replacing or recharging the batteries of nodes. While designing the algorithms, more constraints result in the clustering for CRSN as the dynamic spectrum access has been addressed. We propose an energy-efficient fuzzy clustering and congestion control algorithm (EFCCA) in this paper to improve energy efficiency. With the consideration of spectrum availability, queue length, and residual energy, the election of cluster head contributes to the energy efficiency of the algorithm. The active Queue Management algorithm is used to monitor and control the congestion rate. The proposed EFCCA's performance evaluates with the help of comparison with other clustering methods and it shows enhanced performance in energy efficiency and lifetime based on the outcomes of experimental investigation. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) play a very important role in providing real-time data access for big data and internet of things applications. Despite this, WSNs’ open deployment makes them highly susceptible to various malicious attacks, energy constraints, and decentralized governance. For mission-critical applications in WSNs, it is crucial to identify rogue sensor devices and remove the sensed data they contain. The resource-constrained nature of sensor devices prevents the direct application of standard cryptography and authentication techniques in WSNs. Low latency and energy-efficient methods are therefore needed. An efficient and safe routing system is created in this study. Initially the outliers are detected from deployed nodes using stacking based ensemble learning approach. Deep neural network (DNN) and long short term memory (LSTM) are two different basic classifiers and multilayer perceptron (MLP) is utilized as a Meta classifier in the ensemble method. The normal nodes are considered for further process. Then, distance, density and residual energy based cluster head selection and cluster formations are done. Sunflower optimization algorithm (SOA) is employed in this approach for routing purpose to improve energy efficiency and load balancing. Superior transmission routing can potentially obtained by taking the shortest way. This proposed method achieves 95% accuracy for the intrusion detection phase and 92% is the packet delivery ratio for energy efficient routing. Consequently, the proposed method is the most effective option for load balancing with intrusion detection. [ABSTRACT FROM AUTHOR]

Through energy and exergy analysis, the present work revisits the combustion processes generated using different fuels (CH4, C3H8, H2, LPG, Natural gas and Biogas) under the same operating conditions. First, numerical computations, in which the turbulent dynamic k-ϵ model coupled with a probability density function (PDF) approach together with different sub-models that ensure the reliability of the results, are used. Then, the First and Second Laws of thermodynamics are applied to all fuel combustion cases considered previously to predict the energy loss, exergy destruction and their corresponding efficiencies. Next, variations in the air factor, the air inlet temperature and the fuel inlet temperatures are considered to analyse the response of combustion system performance as well as energy and exergy losses for the different fuels under investigation. The results show an amelioration of combustion parameters for the mixture of fuel and air using values of λ greater than 1. Also, the preheating of the inlet air temperature has a positive effect on energetic parameters. However, preheating the inlet fuel does not take a considerable role in the efficiency amelioration and loss reduction of the combustion system. It further turns out that biogas features higher energy efficiency compared to other fuels, at approximately 89.64%, while hydrogen fuel exhibits higher exergy efficiency at around 78.70% followed by biogas with 74.96%. [ABSTRACT FROM AUTHOR]

It was last estimated in 2016 that data centers (DCs) comprise approximately 2% of total US electricity consumption. However, this estimate is currently being updated to account for the massive increase in computing needs due to streaming, cryptocurrency, and artificial intelligence (AI). To prevent energy consumption that tracks with increasing computing needs, it is imperative we identify energy efficiency strategies and investments beyond the low-hanging fruit solutions. In a two-phased research approach, we ask: What non-technical barriers still impede energy efficiency (EE) practices and investments in the data center sector, and what can be done to overcome these barriers? In particular, we are focused on social and organizational barriers to EE. In Phase I, we performed a literature review and found that technical solutions are abundant in the literature, but fail to address the top-down cultural shifts that need to take place in order to adapt new energy efficiency strategies. In Phase II, reported here, we interviewed 16 data center operators/experts to ground-truth our literature findings. Our interview protocols focus on three aspects of DC decision-making: procurement practices, metrics and monitoring, and perceived barriers to energy efficiency. We find that vendors are the key drivers of procurement decisions, advanced efficiency metrics are facility-specific, and there is convergence in the design of advanced facilities due to the heat density of parallelized infrastructure. Our ultimate goals for our research are to design DC decarbonization policies that target organizational structure, empower individual staff, and foster a supportive external market. [ABSTRACT FROM AUTHOR]