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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]

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.

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.

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]

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.

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.

The rise in lightweight materials directly connects to efforts to boost fuel efficiency, cut emissions, and meet strict environmental standards. Over the past decade, automakers have significantly increased the use [...]

The adhesion of gases to the electrodes is a major reason for reductions in energy efficiency, and hinders the production of hydrogen in alkaline water electrolysis (AWE). The development of an effective solution to this problem will be crucial for the design and optimization of AWE. In this paper, we present a novel structure for an electrolyser in which baffles are inserted into the channel. The flow process of the electrolyte and the electrochemical reactions in the electrolyser with the new structure are simulated, and the effects of the added baffles on the distribution of the gas phase production and the efficiency of energy conversion are analysed. Several important structural parameters are considered, such as the angle, number and length of the baffles, and corresponding numerical calculations are performed. In order to carry out a comprehensive evaluation of the performance of each electrolyser, the energy efficiency of AWE is evaluated and the current effect, ohmic overvoltage effect and bubble overvoltage effect caused by gas adhesion are quantitatively investigated. The results show that the flow of the electrolyte can be divided into three phases when baffles are added to the channel of the electrolyser, and that the baffles play a crucial role in both the transition phase and the full development phase. The maximum reduction in gas coverage at the electrodes reaches 60% and the maximum increase in energy efficiency reaches more than 3.0% with the addition of baffles. The addition of baffles has significant effects on the current and ohmic overpotential, and the balance between the gas coverage and ohmic overpotential can allow the maximum energy efficiency of the AWE to be achieved. [Display omitted] • A novel design for an electrolyser with baffles optimises the electrolyte flow. • The mechanism of transport in an electrolyser with baffles is analysed. • The effects of the angle, number and location of baffles are studied. • The thermodynamics performance of electrolyser is evaluated. [ABSTRACT FROM AUTHOR]

Recently, wireless body area network (WBAN) becomes a hot research topic in the advanced healthcare system. The WBAN plays a vital role in monitoring the physiological parameters of the human body with sensors. The sensors are small in size, and it has a small-sized battery with limited life. Hence, the energy is limited in the multi-hop routing process. The patient data is collected by the sensor, and the data are transmitted with high energy consumption. It causes failure in the data transmission path. To avoid this, the data transmission process should be optimized. This paper presents an advanced authentication and energy-efficient routing protocol (AAERP) for optimal routing paths in WBAN. Patients' data are aggregated from the WBAN through the IoMT devices in the initial stage. To secure the patient's private data, a hybrid mechanism of the elliptic curve cryptosystem (ECC) and Paillier cryptosystem is proposed for the data encryption process. Data security is improved by authenticating the data before transmission using an encryption algorithm. Before the routing process, the data encryption approach converts the original plain text data into ciphertext data. This encryption approach assists in avoiding intrusions in the network system. The encrypted data are optimally routed with the help of the teamwork optimization algorithm (TOA) approach. The optimal path selection using this optimization technique improves the effectiveness and robustness of the system. The experimental setup is performed by using Python software. The efficacy of the proposed model is evaluated by solving parameters like network lifetime, network throughput, residual energy, success rate, number of packets received, number of packets sent, and number of packets dropped. The performance of the proposed model is measured by comparing the obtained results with several existing models. [ABSTRACT FROM AUTHOR]

Automated Storage and Retrieval System (AS/RS) is driven by multiple motors for loading and unloading the items (z-axis) onto the fork or stacker, then moving the items horizontally (x-axis) and vertically (y-axis) at a time. Thus, it is practical to determine the speed configuration for each movement. To be responsive, it is reasonable to set the speed as fast as possible. However, high speed leads to high energy consumption which is undesirable in the context of green warehousing. Given that the speed changes dynamically, it is important to have an advanced optimization model for balancing both objectives and providing accurate estimation. This study proposed metamodel-based simulation–optimization (MSO) allowing to jointly optimize four speed-related variables namely horizontal speed (x), vertical speed (y), fork or depth speed (z), and acceleration/deceleration under the dynamicity of AS/RS. A case study was given in a warehouse comprising five cranes and ten racks. Using Desirability Function Analysis, the optimal speed configuration is obtained efficiently for minimizing travel time and energy consumption of AS/RS. The result also shows that row-based storage provides better responsiveness and energy efficiency than random-based storage. Further, rack design also indicates a significant impact on the AS/RS speed configuration. [ABSTRACT FROM AUTHOR]

This review offers an extensive examination of the evolving landscape of Long Range (LoRa) Microstrip Patch Antennas (MPAs), highlighting their crucial role in optimizing LoRa systems within the Internet of Things (IoT). As the demand for energy-efficient standards like LoRa grows with the expanding IoT market, this research becomes increasingly relevant. This comprehensive review, the first of its kind, serves as a foundational resource for researchers seeking to optimize LoRa systems within the IoT. The study has categorized these LoRa MPAs – including monopole, Planar Inverted F Antenna (PIFA), dipole, yagi-uda, and array – into single band, dual-band, multiband, and wearable antennas, thus providing substantial viewpoints on their diverse design architectures and performance characteristics. Through systematic tabulation, the review facilitates a thorough comparison of antenna advancements. Notably, the review addresses inherent challenges in LoRa MPAs, emphasizing critical aspects that necessitate attention, including the need for miniaturization and integration, advancements in substrate materials and fabrication techniques, and the imperative for reconfigurable and adaptive antennas. Various approaches to enhance antenna performance are explored, including the metamaterial incorporation, slot-based enhancements, Electromagnetic Band Gap (EBG), dielectric resonator antennas (DRAs), substrate material considerations, and corrugation techniques. Looking ahead, the paper explores the future trends and subtle considerations that are poised to shape the trajectory of LoRa MPAs. To the best of our knowledge, this paper represents the first comprehensive review on the multifaceted topic of LoRa MPAs, serving as a foundational resource for researchers in the field. [ABSTRACT FROM AUTHOR]

Summary: Wireless body area networks (WBANs) are becoming widely considered in remote healthcare monitoring applications. However, WBAN nodes have limited resources; therefore, effective and reliable routing protocols are pivotal research challenges. Furthermore, balancing the traffic load among sensor nodes is also highly required to increase network stability. In recent years, many interesting routing solutions have been proposed for WBANs; however, the significant feature in terms of stability and energy in these solutions has not been sufficiently addressed. Therefore, in this context, the Hot‐Sspot Aaware Multicost‐based Energy‐efficient Routing (H‐SAMER) protocol is proposed in this paper. The suggested protocol used multieffective cost function for next‐hop node selection based on the data type, where the patient's data are categorized into three classes: normal data, on‐demand data, and emergency data. Furthermore, the H‐SAMER protocol adds awareness to the transmission of control packets by adding the cost value to the RREQ packets. Thus, the simulation scenario shows that the H‐SAMER saves energy 8.57%, 88%, 98%, 97%, and 100% greater than E‐HARP, EH‐RCP, CO‐LAEEBA, EECBSR, and ELR‐W, respectively. Moreover, the first node death of the proposed H‐SAMER is delayed to round number 7500, which proves H‐SAMER to be a stable and reliable effective solution for WBANs. [ABSTRACT FROM AUTHOR]

Summary: Internet of Things (IoT) based sensor networks have been established as a pillar in intelligent communication systems for efficiently handling roadside congestion and accidents. These IoT networks sense, collect, and process data on a real‐time basis. However, IoT based sensor network clustering has various energy constraints such as inefficient routing due to long‐haul transmission, hot spot problem, network overhead, and unstable network whenever deployed along with the roadside that affect their architecture. In such networks, clustering techniques play a crucial role in extending the lifespan and optimizing the routes by integrating sensor devices through clusters. Therefore, a meta‐heuristic algorithm for clustering in IoT sensor networks for an intelligent transportation system is proposed. In this work, the seagull optimization algorithm is applied for clustering by considering residual and average energy, node spacing, and distance fitness parameters. Moreover, this work also considers the dynamic communication range of the cluster heads for increasing the stability period and lifetime of the proposed networks. The experiment results demonstrate that the proposed Seagull optimization algorithm for clustering in IoT networks (SOAC‐IoTNs) and Seagull optimization algorithm for clustering in IoT networks with dynamic communication range (SOAC‐IoTNs‐DR) achieve a significant increase in the stability period and network lifetime, with percentage increments of 55.68% and 71.47%, and 10.03% and 88.66% respectively, compared to the existing optimized genetic algorithm for cluster head selection with single static sink (OptiGACHS‐StSS). [ABSTRACT FROM AUTHOR]

A Wireless Sensor Network (WSN) is usually made up of a large number of discrete sensor nodes, each of which requires restricted resources, including memory, computing power, and energy. To extend the network lifetime, these limited resources must be used effectively. In WSN, clustering constitutes one of the best methods for optimizing network longevity and energy conservation. In this work, we proposed a novel Energy and Throughput Aware Adaptive Routing (ETAAR) algorithm based on Cooperative Game Theory (CGT). To achieve the energy efficient and improved data rate routing in WSN, we are applied two game theories of CGT and coalition game. The main part of this routing mechanism is cluster head selection and clustering the nodes to perform energy efficient and throughput effective communication between the nodes. In first stage, CGT based utility function which adopts both energy and throughput is utilized to handpick the CH nodes. In the second stage, along with the energy and throughput, average end-to-end delay is considered for the adaptive time slot transmission to avoid collision in the coalition game approach. MATLAB tool is used for simulation. The simulation results shows that the proposed ETAAR protocol is outperforms than earlier works of routing in terms of residual energy, PDR, energy due ratio, average end-to-end delay, dead nodes. The network lifetime of 48% extension, energy saving of 60% and 52.5% of delay shortage attained in ETAAR. [ABSTRACT FROM AUTHOR]

Summary Internet of things–enabled wireless sensor networks face challenges like inflexibility, poor scalability, suboptimal cluster head selection, and energy inefficiencies. This is due to the faster data transmission rates between cluster nodes during data packet routing. This creates unnecessary energy consumption burdens for those actively transmitting nodes. Conceptually, an effective cluster formation phase supports better data routing mechanisms, while sustaining the energy efficiency of individual nodes. This paper proposes a Neuro‐Fuzzy based Cluster Formation (NFCF) scheme to facilitate adaptive and energy‐efficient cluster topologies. NFCF utilizes fuzzy logic and neural networks to identify optimal super nodes for flexible cluster formations. This approach enables configurable cluster sizes along with inclusion/exclusion criteria for member nodes based on energy thresholds. Parameters evaluated for node selection include the degree of super node, expected energy per cluster, energy variance, and residual energy. Nodes not meeting the thresholds are excluded. The neural network updates fuzzy rules to guide optimal clustering decisions based on anticipated energy dynamics under different conditions. The performance of the proposed NFCF scheme is evaluated based on objective function changes related to data transmission, individual node energy variation, energy variance before and after transmissions, and averaged end‐to‐end delay across transmission cycles. Results are compared against genetic fuzzy clustering, fuzzy energy‐aware clustering, fuzzy‐based distributed clustering, fuzzy logic‐based multi‐hop clustering, and fuzzy weighted k‐means clustering. [ABSTRACT FROM AUTHOR]

This study presents a novel microwave-assisted dry reforming of methane (MW-DRM) method, significantly enhancing syngas (H 2 +CO) production at a lower temperature of 400 °C. Compared to conventional heating dry reforming of methane (CH-DRM) processes, MW-DRM achieves higher conversion rates of CH 4 (84.6%) and CO 2 (85.7%) at a lower temperature of 500 °C, significantly reducing energy consumption. The CsRu/CeO 2 catalyst exhibited excellent stability under microwave heating, maintaining high conversion rates and selectivity for 8 h. Detailed characterization using XRD, BET surface analysis, Raman spectroscopy, TEM, and CO chemisorption revealed insights into the structural changes of the catalyst, highlighting the selective impact of microwave heating on the catalyst structure. This study provides a promising approach for sustainable and cost-effective syngas and hydrogen production, aligning with greenhouse gas reduction goals. [Display omitted] • Dry reforming of methane is conducted under microwave conditions. • Microwave-enhanced DRM demonstrated high efficiency. • Compared to CH-DRM, MW-DRM achieves the same activity at 200 °C lower. • CsRu/CeO 2 catalyst demonstrated high stability in DRM. [ABSTRACT FROM AUTHOR]

The technology known as organic rankine cycle (ORC) is a dependable method for transforming heat into electricity, whether it is for use in renewable energy sources such as biomass, geothermal, and solar, or for improving industrial energy efficiency. The range of ORC systems spans from small-scale (a few kW) for home cogeneration to sizable multi-megawatt geothermal power facilities. Since the 1970s, technology has undergone significant progress, largely due to increased economic incentives and rising energy costs, despite a slow start initially. Tracking the evolution of the technology worldwide is challenging due to the wide variety of applications, manufacturers, and countries involved. Hence, the present research scrutinizes the ORC technology to evaluate this system from the energy and economic perspectives. Aspen HYSYS, and Aspen Capital Cost Estimator simulations were used for the process, thermodynamic, and financial evaluations, respectively. In this research, the ORC is evaluated using various organic working fluids, specifically seven different types of fluids. The power and heat flow of the expander in all scenarios are considered at 1200 kW and 1.200 Mw, respectively, to determine the most appropriate organic fluid. Organic fluid toluene, due to its highest boiling point among the investigated fluids, was able to generate the required production power using the lowest molar flow rate for both input and output to the expander, considering these values. The results showed that the organic fluid toluene is technically and economically superior to other fluids. However, cyclopentane performs slightly better in terms of energy consumption and carbon dioxide output. However, toluene is chosen over cyclopentane due to current safety concerns. [ABSTRACT FROM AUTHOR]

This study delves into the comprehensive analysis of four conventional mixed refrigerant liquefaction processes, namely C3MR-Linde, C3MR-APCI, SMR-Linde, and SMR-APCI, emphasizing energy and exergy perspectives. According to the energy analysis, C3MR-Linde demonstrates a lower energy consumption than the other systems, at 0.271 kWh kg−1 liquefied natural gas, while SMR-Air Products achieves the highest coefficient of performance (COP) at 2.67 kWh kg−1. The exergy analysis provides insights into the exergy efficiency and destruction of components, highlighting the C3MR-Linde process as the most exergy-efficient process, attaining 47.55%. Notably, compressors are identified as the primary sources of exergy destruction, accounting for 52.11%, 52.51%, and 45.39% of the overall cycle exergy destruction in the C3MR-APCI, C3MR-Linde, and SMR-APCI cycles, respectively. Furthermore, this study investigates how certain operational factors affect the COP, specific energy consumption (SEC), and exergy indices. It is observed that each cycle exhibits an optimal pressure drop in the expansion valves, with deviations resulting in a decreased COP and increased SEC. Additionally, changes in the refrigerant molar flow rates demonstrate an inverse relationship between the exergy efficiency and COP, with the SEC being notably more sensitive to such variations than the COP within the studied parameters. [ABSTRACT FROM AUTHOR]

This study investigates the thermohydraulic performance of surface-modified silver nanofluids in turbulent convective heat transfer applications. The primary objective is to evaluate the impact of citrate, lipoic acid, and silica surface modifications on heat transfer coefficients, pressure drops, and friction factors under turbulent flow conditions. Silver nanoparticles (50 nm) with the specified surface modifications were synthesized and dispersed in deionized water, ensuring stable nanofluid preparations. Experimental evaluations were conducted in a smooth brass tube with a uniform heat flux, covering Reynolds numbers from 3400 to 21,800, mass flow rates of 32 to 78 g s−1, and inlet temperatures of 26 °C, 31 °C, and 36 °C. Key findings indicate that the silica-shelled nanofluid (Ag/S) exhibited a significant 35% increase in the heat transfer coefficient compared to DI water, while citrate-coated (Ag/C) and lipoic acid-coated (Ag/L) nanofluids showed slight decreases of 0.2% and 2%, respectively. The mean Nusselt number for Ag/S also increased by 9%, demonstrating enhanced heat transfer capabilities. Surface-modified nanofluids experienced higher pressure drops and friction factors than the base fluid. Ag/C showed a 7.7% increase in pressure drop, Ag/L a 12.3% increase, and Ag/S a 12.5% increase, correlating with an 11.9% rise in viscosity. While surface-modified silver nanofluids, particularly silica-shelled, can significantly improve heat transfer performance, the associated increases in pressure drops and friction factors must be carefully balanced for specific applications. Future research should explore long-term stability, varying nanoparticle concentrations, and more complex geometries to optimize nanofluid formulations for targeted heat transfer applications. [ABSTRACT FROM AUTHOR]

Deploying Cellular Internet of Things (CIoT) devices in urban and remote areas faces significant energy efficiency challenges. This is especially true for Narrowband IoT (NB-IoT) devices, which are expected to function on a single charge for up to 10 years while transmitting small amounts of data daily. The 3rd Generation Partnership Project (3GPP) has introduced energy-saving mechanisms in Releases 13 to 16, including Early Data Transmission (EDT) and Preconfigured Uplink Resources (PURs). These mechanisms extend battery life and reduce latency by enabling data transmission without an active Radio Resource Control (RRC) connection or Random Access Procedure (RAP). This paper examines these mechanisms using the LENA-NB simulator in the ns-3 environment, which is a comprehensive framework for studying various aspects of NB-IoT. The LENA-NB has been extended with PURs, and our analysis shows that PURs significantly enhance battery life and latency efficiency, particularly in high-density environments. Compared to the default RAP method, PURs reduce energy consumption by more than 2.5 times and increases battery life by 1.6 times. Additionally, PURs achieve latency reductions of 2.5–3.5 times. The improvements with PURs are most notable for packets up to 125 bytes. Our findings highlight PURs' potential to enable more efficient and effective CIoT deployments across various scenarios. This study represents a detailed analysis of latency and energy consumption in a simulated environment, advancing the understanding of PURs' benefits. [ABSTRACT FROM AUTHOR]

A Carbon Footprint (CF) is defined as the total emissions of greenhouse gases, primarily carbon dioxide, methane, and nitrous oxide, and is a specific type of Environmental Footprint that measures human impact on the environment. Carbon dioxide emissions are a major contributor to anthropogenic greenhouse gases driving climate change. Wood, as a renewable and ecological material, has relatively low carbon emissions. The study aimed to review and analyze the criteria influencing the feasibility of constructing modern zero-carbon wooden buildings. The review was conducted in two phases: (i) a literature review and (ii) an assessment of existing buildings. The preliminary research led to (i) narrowing the focus to the years 2020–2024 and (ii) identifying key criteria for analysis: sustainable material sourcing, carbon sequestration, energy efficiency, life cycle assessment (LCA), and innovative construction practices. The study's findings indicate that all these criteria play a vital role in the design and construction of new zero-carbon wooden buildings. They highlight the significant potential of wood as a renewable material in achieving zero-carbon buildings (ZCBs), positioning it as a compelling alternative to traditional construction materials. However, the research also underscores that despite wood's numerous potential benefits, its implementation in ZCBs faces several challenges, including social, regulatory, and financial barriers. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between artificial intelligence (AI), industrial robots, and renewable energy consumption, driven by the rapid technological advancements and widespread adoption of AI tools in various industries. This research aims to evaluate the environmental implications of these technologies, specifically their impact on renewable energy usage. Employing a comprehensive analytical framework, this study utilizes advanced methodologies, including regularization factors, to accurately estimate the effects of these variables. Through a thorough data analysis, the research quantifies how AI and industrial robots influence the shift towards renewable energy sources. The findings reveal that investments in AI significantly enhance renewable energy consumption, as demonstrated by both conventional estimation techniques and those that integrate regularization factors. Conversely, the use of industrial robots is found to have a detrimental effect on renewable energy consumption. These results have important implications for policymakers, industry leaders, and sustainability researchers. This study encourages policymakers and investors to prioritize funding for AI solutions that promote renewable energy adoption, while it advises industry managers to strategically modify their use of industrial robots to reduce their environmental impact. Ultimately, this research lays a critical foundation for future inquiries and policy initiatives aimed at aligning technological advancements with sustainable energy practices. [ABSTRACT FROM AUTHOR]

The energy challenges of overcoming climate change and economic and global political crises require not only the increased integration of renewable energies but also an optimisation of energy generation and use and, as a result, a reduction in energy consumption in various sectors. Thermal energy consumption in buildings in particular accounts for a significant proportion of final energy consumption. With respect to commercial buildings, a central problem in optimising the system settings is the lack of or only limited information about the actual room temperatures as well as the comfort requirements and temperature perception of the users in the rooms on the one hand and the operational management and settings specified by the facility management, for example, the heating curves of the heating circuits, on the other. The aim here is to create a bidirectional flow of data and information so that the compromise between the necessary room temperatures and the comfort of the users and the most energy-efficient operational management possible can be achieved. In this context, the paper presents a developed and tested web platform that makes it possible to optimise the operation of the system technology, particularly the heating system in the building, from an energy point of view and to involve the building user (e.g., office employees) and to pass on information to the facility management, thus pursuing a holistic approach. In the associated Living Lab project (called ComfortLab), it was possible to obtain over 6500 votes on temperature perception and combine this with building operation and the parameters relevant to facility management. This made it possible to bridge the gap between user requirements and room temperatures on the one hand and energy consumption and the inlet temperature of the heating system and supply circuits on the other. The use of the platform makes it possible to optimise the setpoint specification, specifically the inlet temperature of individual heating circuits, considering both regular building operation at times of presence and the setting of weekend and night setback times. The results show a diversified picture regarding temperature perception and possible room temperature reductions of several degrees Celsius and energy savings in the double-digit percentage range. [ABSTRACT FROM AUTHOR]

Energy efficiency is widely recognized as the foundational and most critical strategy for decarbonizing the manufacturing sector. Misconceptions surrounding energy efficiency measures often hinder their widespread adoption. This article aims to debunk five common myths and provides data and resources to help implement efficiency projects faster and more effectively to achieve greater decarbonization. First, the article challenges the myth that organizations have exhausted all possible energy efficiency opportunities by achieving voluntary energy intensity goals or energy performance certification. Second, it also addresses the misconceptions that efficiency projects are capital-intensive, require many qualified specialists, and have long investment return periods. By presenting real-world case studies and referencing commonly found efficiency opportunities, the article illustrates that energy-savings opportunities are ubiquitous. Organizations can use various contracting mechanisms as well as financial and technical resources from utility companies and government programs to lessen their burden. The notion that efficiency measures can be implemented solely in proprietorship facilities is dispelled. This article emphasizes the importance of green leases and explains that aligning decarbonization goals between the lessor and lessee can help drive savings for both parties. Finally, using unbundled renewable energy certificates as the sole pathway to decarbonization is strongly discouraged. By debunking these prevalent myths, this article aims to foster a deeper understanding of energy efficiency's potential as a cornerstone of decarbonization efforts and to embrace it as a critical pathway toward a sustainable future. [ABSTRACT FROM AUTHOR]

Despite the tightening of energy performance standards for buildings in various countries and the increased use of efficient and renewable energy technologies, it is clear that the sector needs to change more rapidly to meet the Net Zero Emissions (NZE) scenario by 2050. One of the problems that have been analyzed intensively in recent years is that buildings in operation use much more energy than they were designed to. This problem, known as the energy performance gap, is found in many countries and buildings and is often attributed to the poor management of building energy systems. The application of Artificial Intelligence (AI) to Building Energy Management Systems (BEMS) has untapped potential to address this problem and lead to more sustainable buildings. This paper reviews different AI-based models that have been proposed for different applications and different buildings with the intention to reduce energy consumption. It compares the performance of the different AI-based models evaluated in the reviewed papers by presenting the accuracy and error rates of model performance and identifies where the greatest potential for energy savings could be achieved, and to what extent. The review showed that offices have the greatest potential for energy savings (up to 37%) when they employ AI models for HVAC control and optimization. In residential and educational buildings, the lower intelligence of the existing BEMS results in smaller energy savings (up to 23% and 21%, respectively). [ABSTRACT FROM AUTHOR]

The market for electric cars (EVs) is growing quickly, which has led to a diversity of models and significant technological advancements, particularly in the areas of energy management, charging, range, and batteries. A thorough analysis of the scientific literature was conducted to determine the operational and technical parameters of EVs' performance and energy efficiency, as well as the factors that influence them. This article addresses the knowledge gap on the analysis of ambient temperature-related parameters' effects on electric garbage trucks operating in particular urban traffic conditions for selective waste collection. To optimize vehicle routes, a computational model based on the Vehicle Routing Problem was used, including the Ant Colony Optimization algorithm, considering not only the load capacity of garbage trucks but also their driving range, depending on the ambient temperature. The results show that the median value of collected bulky waste for electric waste collection vans, depending on the ambient temperature, per route is 7.1 kg/km and 220 kg/h. At a temperature of −10 °C, the number of points served by EVs is 40–64% of the number of points served by conventional vehicles. Waste collection using EVs can be carried out over short distances of up to 150 km, which constitutes 95% of the optimized routes in the analyzed case study. The research contributed to the optimal and energy-efficient use of EVs in variable temperature conditions. [ABSTRACT FROM AUTHOR]

The pace of good quality retrofit must be urgently scaled up across the UK, in order to tackle fuel poverty, improve the health and comfort of our homes, increase energy security and build climate resilience. Currently, however, incomplete information is used when designing retrofit policies and plans. There are barriers to the scale-up of good quality retrofit including citizen engagement, finance, supply chain uncertainty, a lack of trust and low uptake of quality assurance frameworks. This paper discusses the potential of enhanced, interoperable and democratised building, local and systems data to unlock many of these barriers. It will explore how the adoption of property logbooks across the UK can enable the aggregation of projects and drive access to finance. A retrofit plan for every home has the potential to engage and empower citizens and small to medium enterprise (SME) builders to act locally. This paper describes how the right packages of data can inform better decisions, what work has already been done to date, and how you can get involved in creating transformational change. [ABSTRACT FROM AUTHOR]

With the increasing advancements in the Internet of Things (IoT) and the growing production of tasks by IoT devices, the demand for cloud computing centers has become more critical than ever. The energy consumption in cloud computing servers has a significant impact on the overall costs and environmental pollution. This article addresses the task allocation problem to cloud computing servers with the aim of reducing energy consumption in those servers while maintaining Quality of Service (QoS). Evolutionary algorithms have been employed to solve this NP-hard problem. In this paper, a novel version of Equilibrium Optimization algorithm is defined and used for finding good solutions for this problem. In the proposed algorithm, a tournament operator is introduced to control selection pressure and enhance the algorithm's exploration capability during local optima convergence, added to the EO algorithm. The utilization of this operator in the proposed algorithm eliminates the need for sorting all search agents at each iteration, resulting in reduced execution time. The simulation results indicate that the proposed algorithm has demonstrated a 24% improvement in performance compared to existing algorithms in solving the task allocation problem to servers in cloud computing environments. [ABSTRACT FROM AUTHOR]

• An enhanced ejector-subcooler CO 2 transcritical refrigeration cycle is presented. • A parametric analysis and optimization of the enhanced cycle and the baseline cycle are carried out. • The results suggest the integration of the proposed cycle could achieve more compact and reliable systems. • The maximum COP improvements are computed as 11.7%-17.5% when the gas cooler outlet temperature ranges from 35 °C to 50 °C and the evaporating temperature is -40 °C. The main drawback associated with CO 2 refrigeration systems is related to their performance reduction during transcritical operation at warm climate conditions, which may be compensated by better cycle architectures such as the split-cycle with subcooling or the flash-tank configuration, among others. Specifically, the use of standard gas-ejectors together with parallel compressors provides even better efficiency improvements, not being able to use them with low-temperature evaporators to prevent the triple point inside the ejector. This paper proposes an enhanced cycle with a gas ejector for two-stage compressor architectures with vapor injection from the flash-tank, which is able to operate at low evaporating temperatures and that provides a greater performance improvement the more severe the climate conditions are. The methodology conducted is based on a thermodynamic analysis that includes parametric evaluation and cycle optimization, comparing the results to a conventional CO 2 transcritical cycle with flash-tank and dynamic vapor injection architecture. The main results show that a maximum Coefficient of Performance improvement of 17.5% is achievable for transcritical operation at -40 °C evaporating temperature. The compressor displacement capacity required with the enhanced cycle is up to 9% lower for the same refrigeration demand, reducing the electrical consumption as well as the compressor expenditure. Moreover, greater vapor injection mass flow rates are obtained by the gas-ejector injection with discharge temperature reductions up to 18%, enhancing the system reliability. [ABSTRACT FROM AUTHOR]

• A novel variable stiffness valve based on PEEK and stainless steel was proposed. • Stress-strain behavior and operational characteristics of novel valve were studied. • Impacts of novel valve on compressor performance and noise experimentally. • Compressor performance improved by 2.6 % and noise reduced by 3–5 dB. Improving valve design is crucial for enhancing the performance of rolling piston type rotary compressor. A novel variable stiffness valve based on PEEK and stainless steel was proposed. The stress-strain behavior of the novel valve was studied, and its operational characteristics, including opening and closing angles and over-compression losses, were analyzed in a compressor. It is shown that opening and closing angle of the new-designed valve were advanced compared to that of the original cylinder by about 2∼3°, leading to a general reduction in over-compression losses, with a maximum decrease of 4.15 W. The impacts of new-designed valve on compressor performance were experimentally investigated. The cooling capacity and coefficient of performance of compressors equipped with new-designed compared to original valve were improved by 1.1–1.7 % and 1.1–2.6 %, respectively. In addition, the input electrical power required for the new valve was observed to be 0.9–1.3 % lower. The incorporation of high-damping material in the new valve design significantly reduced compressor noise by 3–5 dB at the critical frequency bands of 630–1250 Hz. These findings offer valuable insights for enhancing the energy efficiency and noise reduction in rolling piston type rotary compressors. [ABSTRACT FROM AUTHOR]

Hydrogen, volatile fatty acids (VFAs), and methane coproduction from sweet sorghum stems using bacterial consortium was investigated as an efficient and sustainable pre-treatment strategy to improve energy recovery. Integrated two-stage dark fermentation and methanization approach aimed to reduce fractionation, juice extraction, and pre-treatment steps to improve the efficiency and sustainability of stalks energy bioconversion. Stems biomass loading did not significantly influence hydrogen and VFAs productivities. Energy recovery yields were (7.07) and (10.01) MJ/kg dry matter (DM), respectively, for raw stem single dark fermentation (DF) and methanization processes, corresponding to 41.22% and 58.37% of raw stalk energy potential. Methanogenic potential increase of 31.9% and energy bioconversion yield of 13.21 MJ/kg DM were reached for solid residues from DF (80.75% of their energy content), suggesting that bacterial consortium efficiently pre-treated sorghum stalk fibers. Coupling process led to 88.74% net biomass energy recovery yield, corresponding respectively to 57.38% and 40.23% more than single DF and methanization. Fiber degradation ability of DF bacterial consortium significantly contributed to improve sorghum stalk energy recovery efficiency and cost-competitiveness. [ABSTRACT FROM AUTHOR]

The fouling behavior of whey protein concentrate (WPC) in food‐grade polyether ether ketone (PEEK) heat exchangers was compared to benchmark stainless steel (SS) to evaluate if fouling can be better mitigated by using PEEK. No research has been conducted on WPC fouling behavior of PEEK at WPC concentrations of 2–6 g/L and heat flux densities of 45–55 kW/m2. It was found that PEEK materials led to a reduction in heat resistance of up to 40%. At WPC concentrations of 6 g/L, a fouling factor of 0.9 m2 K/kW was measured for PEEK compared to 1.6 m2 K/kW for SS. Despite a constant heat flux, fouling curves for PEEK showed an asymptotic behavior, whereas linear fouling was observed for SS. To achieve a comparable heat resistance between PEEK and SS heat exchangers, the operating time could be extended by 9 h when using PEEK materials. Investigations of the deposit mass showed that even though the heat transfer resistance is limited on PEEK, fouling continued to grow at a decreased rate. It was found that the fluid started to evaporate underneath the fouling layer, which led to a partial detachment of the fouling layer and therefore mitigated the heat resistance effects of fouling. To test whether these results are transferable to larger setups, experiments on a scale‐up apparatus were conducted. A very similar behavior was qualitatively observed; however, measured deposition deviated on average by 18%. PEEK surfaces also showed great promise regarding cleanability, with fouling layers detaching completely after drying for 10 min and restarting the process. This restored the heat transfer coefficient to its clean state. A cleaning in place therefore seems feasible. In contrast, fouling layers on SS did not detach through drying and had to be chemically cleaned to restore its heat transfer capacity. [ABSTRACT FROM AUTHOR]

This paper addresses the research gap concerning the impact of thermal diffusivity on cooling energy consumption in buildings, specifically in the context of Saudi Arabia's hot climate. The objective is to determine the optimal thermal diffusivity of building walls for different climate conditions in Saudi Arabia to minimize the cooling energy consumption of residential buildings. The study analyzes four locations in Saudi Arabia, namely, Riyadh, Jeddah, Abha, and Tabuk, to understand the correlation between optimal thermal diffusivity and cooling degree days. The study includes numerical modeling using the finite-difference method and developing a calibrated building energy model of an existing residential building. Sensitivity analysis is conducted to evaluate the influence of thermal conductivity and heat capacity on cooling energy consumption. Correlation analysis is performed to assess the relationship between optimal thermal diffusivity and cooling degree days. The results highlight the importance of finding a balance between thermal insulation and thermal mass for energy-efficient buildings, considering the unique climate conditions in Saudi Arabia. Finally, this study considered the development of the Saudi Energy Conservation Code (SBC-602), which provides guidelines and regulations for energy-efficient building design in Saudi Arabia by recommending the selection criteria for the optimal thermal properties of building walls while considering the outdoor climate conditions. By aligning the findings of this study with the requirements and recommendations of the SBC-602, the study aims to contribute to the effective implementation of energy-efficient strategies in building design. [ABSTRACT FROM AUTHOR]

Energy demand is a critical contemporary concern, with significant implications for the future. While exploring renewable or sustainable energy sources offers potential solutions, optimizing energy consumption in existing power generation systems is also key. Aviation accounts for a substantial portion of energy demand, underscoring the importance of energy efficiency in this sector. Conventional energy analyses may be misleading; hence, employing exergy-based analyses provides a clearer understanding of energy consumption. Also, most of these analyses do not include the effect of the turbine blade's cooling in calculations. In the present study, exergy analyses have been conducted on a helicopter turboshaft engine with turbine-blades cooling, focusing on design parameters such as ambient temperature, compressor pressure ratio, and turbine inlet temperature. Thermodynamic optimizations are conducted using a genetic algorithm. Results show that increasing pressure ratio and turbine inlet temperature boost performance, yet technical restrictions on compressor and turbine size, and metallurgical constraints on turbine blades' material limit these gains. Sea level scenario prioritizes ambient temperature-drop for enhancing net-work and efficiency, while altitude-gain boosts turboshaft performance. Combustion chambers incur the highest exergy destruction of 74-80%, followed by 16-20% and 4-6%exergy destructions in the turbine and compressor, respectively. Lower air temperatures and higher flight altitudes demand larger fuel consumption for equivalent turbine inlet temperature, albeit enhancing cooling capacity and reducing required cooling air fraction for turbine blades. [ABSTRACT FROM AUTHOR]

[Display omitted] • Impact of big data and green loans on firm's energy efficiency is analyzed. • Information and transaction costs are paths for big data affecting green loan choices. • Changes in firm's decisions due to improved energy efficiency are discussed. Green digital finance is an instrumental way to promote technological innovation, accelerate the low-carbon transition, and foster sustainable development. With the emergence of green digital finance, how does it affect firms' energy use efficiency? Using big data and green loans as an entry point, the impact of green digital finance on corporate energy efficiency and the role of big data are examined. We provided a simple theoretical model to analyze the green loaning behavior of the banking sector after applying big data and its impact on corporate energy efficiency. Our research finds that: (1) The application of big data can make it easier for the banking sector to obtain loan companies' information and reduce loan delinquency rates. This will reduce the information and transaction costs of the banking sector and expand the scale of optimal green loans. (2) The optimal green loan scale has a negative relationship with the optimal green loan interest rate. (3) The application of green loans by firms can improve energy efficiency and have a range of impacts on firms' decision-making, including an increase in the emission reduction ratio, innovation probability, output, and profit, followed by a decrease in energy consumption and pollution emissions. This paper further clarifies the channels through which green digital finance affects energy efficiency and specifies the role of big data in green digital finance. This could help relevant policymakers design more effective green digital finance policies, contributing to carbon peaking and carbon neutrality goals. [ABSTRACT FROM AUTHOR]

Recently cell-free massive multiple-input multiple-output (CF-MMIMO) systems have attracted a lot of interest. It has been considered one of the main keys of current and next-generation wireless communications networks like 5G and 6G. Effectively, it can handle demand growth and maintain better spectral efficiency (SE). However, the power consumption of a large number of access points (APs) significantly influences the performance which is one of the main points in CF-MMIMO. In this research, improving energy efficiency (EE) is considered while maintaining a high quality of service in the downlink (DL) transmission by optimizing the power allocation of users. This is achieved through the use of the sequential convex approximation method to optimize DL power control coefficients with zero-forcing (ZF) precoding. Moreover, the power consumption of the backhaul lines is minimized by implementing a novel dynamic AP selection method. This method ensures that each user is assigned appropriate APs without quality-of-service degradation and reliable spectral efficiency. Reducing overall power, which leads to improving the EE, is essential for cost savings, better coverage, and carbon emissions. The outcome of the optimized power allocation demonstrates a 43.7% improvement in energy efficiency when comparing the ZF with the conjugate beamforming approach. The results of the suggested AP selection demonstrate a 20.8% improvement in EE compared to the scenario without AP selection. Additionally, there is a 7.2% enhancement in EE compared to a previous study that used fixed AP selection with a signal-to-noise ratio of 10 dB. There is a tradeoff in the total SE when AP selection is used since it tends to decrease. This degradation can be effectively controlled through the careful selection of suitable APs. [ABSTRACT FROM AUTHOR]

Designing an efficient node deployment algorithm in Underwater Acoustic Sensor Networks (UASNs) is crucial to address coverage holes and connectivity issues while meeting the Quality of Service (QoS) requirements of underwater applications. At present, the research enhances underwater communication through random deployment algorithms. However, current research has focused on designing node deployment algorithms for underwater sensors, underwater relays, or surface stations, which leads to poor network performance. Therefore, it is critical to consider all underwater nodes while designing an efficient node deployment algorithm to guarantee meeting the QoS requirements of underwater applications. To address these issues, this paper proposes the Distributed Deployment Optimization algorithm using Grid-based Depth Adjustable (DDOGDA) that relies on the minimum number of underwater nodes to maximize coverage, connectivity, and Energy Efficiency (EE) while reducing the Total Collisions (TC). In DDOGDA, the underwater nodes are placed within their Sensing Range (SR) to maintain network coverage and connectivity. The proposed algorithm takes into account multiple factors, including deployment strategies, Geographical Information System (GIS) data for the Solomon Islands, non-environmental factors, and the unique characteristics of underwater nodes. We find that the grid-based distributed node deployment algorithm can achieve higher performance than random and geometric deployment algorithms. Simulation results confirm that DDOGDA can maximize coverage and connectivity while minimizing TC. Moreover, we compare DDOGDA with random, tetrahedron, cuboid, triangular, and adaptive triangular deployment algorithms in terms of coverage, connectivity, TC, and EE and demonstrate that DDOGDA outperforms state-of-art methods. [ABSTRACT FROM AUTHOR]

In the article, the application potential of the dew-point cooling tower (DPCT) in selected energy-intensive applications in temperate climates was analyzed and discussed. The applications selected for analysis are power generation with natural gas turbines and chilled water air conditioning systems. The study is based on a mathematical model derived from a modified ε-NTU model. The model was validated against experimental results and showed satisfactory agreement with the experimental data. DPCT was compared with a typical cooling tower limited by the wet-bulb temperature (wet-bulb cooling tower, WBCT). The simulation results showed that DPCT is able to provide significant energy savings in energy-intensive applications; therefore, its application potential in temperate climates can be considered justified. In the case of gas turbines, DPCT was able to generate 2 to 10 percentage points more capacity than operating on outdoor air and 1.8 to 5 percentage points more than operating with WBCT. In the case of air conditioning systems, the system equipped with DPCT achieved EERs (energy efficiency ratios) higher by 1 to 7.2 compared to dry cooling and by 0.3 to 5.1 compared to systems equipped with WBCT. The annual energy savings obtained by the system with DPCT were 14.7 MWh compared to WBCT and 30 MWh compared to dry cooling. [ABSTRACT FROM AUTHOR]