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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]

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]

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.

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 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]

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]

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]

[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]

Energy efficiency improvement in Chinese construction has progressed rapidly over the past two decades. Nearly zero energy buildings (NZEBs), as an integrated solution for energy-efficient construction, have gained significant attention during China's 13th Five-Year Plan period, with continuous maturation of the technical system. In this study, a research framework built upon the accomplishments of China's National Key Research and Development Program is developed, and an in-depth analysis of the most cutting-edge research is provided by thoroughly reviewing the work conducted earlier. Developing NZEB in China has been categorized into three stages based on the characteristics of technological development: (1) definition and standards, (2) demonstration and promotion, and (3) cross-domain integration. This study discerns four noteworthy development trends by examining comprehensive data spanning the last decade from 100 NZEB and zero energy building. Further, a comprehensive analysis of essential technology advancements in line with these identified trends is performed. The issues and challenges arising from the increased application of renewable energy in the context of China's carbon peak and carbon neutrality goals have also been discussed. Finally, based on this analysis, the challenges and corresponding suggestions for future research directions were proposed to help guide future studies exploring emerging trends in the NZEB field. [ABSTRACT FROM AUTHOR]

Global warming is a challenge to the sustainable development of all humanity. Innovation is the main driver of the world's development and a powerful response to economic development and environmental protection. In this paper, a bidirectional fixed effect model and spatial Durbin model are used to explore the impact mechanism, heterogeneity and spatial impact of the low-carbon effect of technological innovation based on panel data of 279 Chinese cities from 2010 to 2019. The findings indicate that (1) technological innovation significantly negatively impacts carbon intensity, while the impact of high-quality technological innovation is greater. After the robustness test with the instrumental variable method and DID method, the conclusion is still valid. (2) The low-carbon effect is stronger in cities where governments pay more attention to green development and in central China. (3) Technological innovation improves energy efficiency (direct mechanism) and optimizes energy and industrial structures (indirect mechanism), and the digital economy strengthens this effect. (4) Furthermore, innovation can negatively impact the carbon intensity of adjacent cities. This paper provides new evidence and perspectives on the impact of technological innovation on carbon emissions more precisely at the city level. Additionally, this study provides an essential reference for countries to cope with global climate change and formulate differentiated policies among regions. [ABSTRACT FROM AUTHOR]

With the rapid economic growth, the energy demand is increasing in China. Coal accounts for the largest proportion of China's energy consumption, and low coal use efficiency will generate more undesirable output like CO2 and soot, which inevitably lead to environmental problems and human diseases. This paper aims to measure the impact of coal-fired, soot on industrial production efficiency and total factor energy production efficiency of China through directional Slacks-based Measure model (directional-SBM) and Total Factor Energy Efficiency model (TFEE), and pay more attention to the relationship between soot emissions and industrial production efficiency. The results show that China's industrial production efficiency and factor efficiency are underperforming, and there exist huge room for improvement in soot emissions and coal usage. Only a few developed provinces and provinces with small industrial scale are at the efficient frontier, and less than half of the regions exceeds 0.9. Moreover, from 2012 to 2016, while China's coal use has been increasing, the efficiency has been declining. The adjustment value of coal consumption has increased from 16 to 20%, resulting in serious pollution and waste of resources. In general, there is a big gap in industrial production and total factor energy production efficiency between China and developed economies. Therefore, it is necessary for China to accelerate technological reform and optimize the energy consumption structure to improve energy efficiency and reduce pollution, and provide a reference for the coal industry in other developing countries. [ABSTRACT FROM AUTHOR]

In the face of the rapid rise in global waste production and the pressing need to shift towards sustainable energy options, advanced Waste-to-Energy (WtE) technologies have emerged as a highly promising solution. These innovative technologies effectively utilize waste as a valuable resource, presenting a viable pathway for sustainable energy recovery and making a substantial contribution to the principles of the circular economy paradigm. This review provides a comprehensive overview of advanced WtE technologies, including thermal, biological, and chemical methods, such as gasification, pyrolysis, plasma arc gasification, anaerobic digestion, fermentation, transesterification, and hydrothermal carbonization. The efficiency of these technologies is evaluated based on their energy recovery potential, environmental impact, and economic feasibility. Case studies on successful implementations of advanced WtE technologies are analyzed to highlight their practicality and effectiveness. Finally, the paper addresses technical, regulatory, and policy challenges in this field and provides future perspectives. The objective is to underscore the role of advanced WtE technologies in achieving a sustainable and resource-efficient circular economy. [ABSTRACT FROM AUTHOR]

Résumé: De manière agrégée, les PME sont de grandes consommatrices d'énergie et ont des impacts environnementaux considérables au niveau mondial. Malgré cela, elles attirent encore peu l'attention des chercheurs et des responsables des politiques publiques. Pourtant, sans leur contribution, l'atteinte des cibles nationales en matière énergétique et environnementale est improbable. En ce sens, il devient essentiel de mieux comprendre ce qui les incite à devenir plus mature au niveau énergétique. La présente étude adopte une analyse multiniveau en considérant les aspects individuel, organisationnel, contextuel pour identifier les déterminants de la maturité énergétique des PME. Une étude menée auprès de 409 PME québécoises permet d'établir que ce sont les facteurs organisationnels intangibles qui sont les principaux déterminants de la maturité énergétique dans les PME québécoises. Les facteurs contextuels n'ont pas d'influence et un seul facteur individuel, l'âge du dirigeant, exerce une influence limitée. Cette reconnaissance de la prépondérance des facteurs organisationnels intangibles est un apport aux écrits sur le sujet qui ne font pas cette distinction lorsqu'ils considèrent les freins et accélérateurs des pratiques d'efficacité énergétique. Cela procure aussi un enseignement pratique important: l'acquisition de compétences et d'attitudes proactives dans les PME envers l'efficacité énergétique est un levier d'action à prioriser. In an aggregate manner, SMEs are very large consumers of energy and have significant environmental impacts worldwide. Nevertheless, they still attract little attention from researchers and public policymakers. Yet, without their contribution, the achievement of national energy and environmental targets is unlikely. In this sense, it becomes essential to better understand what motivates them to become more energetically mature. This study adopts a multi-level analysis that considers individual, organizational, contextual aspects to identify the determinants of SME energy maturity. A study of 409 Quebec SMEs shows that intangible organizational factors are determinants of energy maturity in Quebec SMEs. Contextual factors have no influence and only one individual factor, the age of the leader, has a limited influence. This recognition of the preponderance of intangible organizational factors is a contribution to the literature on the subject, which does not make this distinction when considering the obstacles and the accelerators of energy efficiency practices. It also provides an important practical lesson: the development of proactive skills and attitudes in SMEs towards energy efficiency is a lever of action to prioritize. [ABSTRACT FROM AUTHOR]

Pervasive Wireless Sensor Networks (PWSNs) are essential for collecting and transmitting real-time data from the physical world to various applications. Integrating blockchain technology with PWSNs enables decentralized data integration securely and immutably, although it requires energy-intensive computing processes. In this paper, the proposed model is designed to investigate the potential of blockchain technology to utilize acoustic signals in a pervasive acoustic wireless sensor network (PAWSN) to achieve energy efficiency. Considering the energy limitations of battery-operated sensor nodes, particularly acoustic sensors, and the energy consumption associated with blockchain technology, this study addresses these challenges by dividing the network into centralized and decentralized structures. In the proposed approach, acoustic sensor nodes operate within a centralized network structure, each assigned to a sensor zone with a central sink node. The sink nodes then participate in a decentralized network structure. To enhance energy efficiency, acoustic sensors in sensor zones are equipped with contextual information to minimize event listening. The primary objective of the proposed technique is to collect context-sensitive acoustic sensor data and integrate it into a decentralized blockchain while minimizing energy consumption at leaf nodes. The evaluation of the system architecture will employ blockchain technology, with a specific focus on leaf node energy efficiency (LNEN) as a primary performance metric. Analysis of experimental results reveals a substantial enhancement in energy efficiency, with LNEN approaching approximately 50% ( L N E N ≈ 50 % ), attributed to the proposed model's effectiveness. [ABSTRACT FROM AUTHOR]

Climate change and natural resource depletion have reinvigorated the relevance of technical advancements in energy efficiency, which has been emphasized by the Paris Climate Accord that includes a framework for carbon neutrality. In that effort, the governments have been allocating a considerable amount of budget shares to research and development relating to energy efficiency and green energy. Thus, this study primarily examines the impact and effectiveness of research and development on energy efficiency and energy sources (renewables and fossil fuels) on CO2 emissions in the European region using a novel Method of Moments Quantile Regression (MMQR). Our data cover annual observations of 9 EU countries, including Austria, Denmark, Finland, France, Germany, Netherlands, Norway, Spain, and Sweden, based on the period 1990–2021. Our empirical findings confirm that the U-shaped Environmental Kuznets Curve occurs across all quantiles at 1% significance levels. The findings reveal that energy consumption increases CO2 emissions across the lower to higher quantiles. We discover that (i) R&D on renewable energy cuts CO2 emissions just at the median and higher quantiles, (ii) R&D on Fossil Fuels decreases CO2 emissions just at the lower quantile, and (iii) R&D on Energy efficiency lowers CO2 emissions at both the median and higher quantiles. Therefore, empirical findings suggest that policymakers should maintain their incentives for energy efficiency R&D and renewable R&D to achieve sustainable development goals. [ABSTRACT FROM AUTHOR]

The rental market, notably within the UK, is facing increased scrutiny with the tightening of regulation in relation to energy performance and the increases in energy costs and rental unaffordability. Whilst a sizeable volume of research has examined the pricing effects of EPCs and house prices, scrutiny of this relationship within the private rental sector remains more embryonic. This study, using 2,914 transactions for Northern Ireland, extends the traditional analysis beyond the conditional mean estimate by examining the quantiles of the relationship between EPCs and rental prices. The findings provide evidence of a rental premium of 0.2% for a one-point improvement in energy efficiency. In terms of EPC ratings, we find premiums for B- and C-rated dwellings of 8.2% and 2.4% and also discount effects for E, F and G-rated properties ranging between 3.9%-5.5%. The quantile findings exhibited a parabolic effect across the price distribution demonstrating the lowest and highest priced properties to comprise higher price premiums of 13% and 16% for B-rated dwellings, however more pronounced discount effects for F- and G-rated properties at the lowest and highest deciles. The findings provide evidence to help alleviate the split-incentive problem between landlords and tenants within the UK. [ABSTRACT FROM AUTHOR]

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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]

This study investigates the thermal performance of polymer-modified concrete composites, focusing on their potential to enhance energy efficiency in building envelopes. Four types of recycled polymers—low-density polyethylene (LDPE), high-density polyethylene (HDPE), polypropylene (POLYP), and polyester—were integrated into concrete mixes at varying percentages (10%, 20%, and 30%). Key thermal metrics, including thermal transmittance (U-value), decrement factor (f), decrement delay (ϕ), admittance (Y), optimum thickness, and heat capacity per unit area, were measured using the admittance approach and a MATLAB program conforming to CIBSE standards. Results indicate that higher polymer content generally improves thermal damping and reduces cyclic transmittance. Notably, LDPE 30% exhibited the best performance, achieving a decrement factor of 0.179 and a time lag of 11.76 hours. HDPE 30% demonstrated a decrement factor of 0.206 and a time lag of 10.85 hours. Polyester 30% showed a decrement factor of 0.310 and a time lag of 8.50 hours. Sensitivity analysis revealed that optimal wall thicknesses for polymer-modified concretes are lower than those required for conventional concrete, with LDPE 30% requiring an optimal wall thickness of 0.128 meters compared to 0.269 meters for conventional concrete. This research underscores the dual benefits of waste management and energy performance improvement, advocating for the practical application of polymer-modified concrete in sustainable construction. The study ranks the polymers' efficiency as LDPE, HDPE, POLYP, and Polyester. These findings support the use of recycled polymers in concrete, promoting sustainability through effective waste management and improved thermal efficiency in building envelopes. [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]