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Deep Reinforcement Learning (DRL) has emerged as a promising approach to address the trade-off between energy efficiency and indoor comfort in buildings, potentially outperforming conventional Rule-Based Controllers (RBC). This paper explores the real-world application of a Soft-Actor Critic (SAC) DRL controller in a building's Thermally Activated Building System (TABS), focusing on optimising energy consumption and maintaining comfortable indoor temperatures. Our approach involves pre-training the DRL agent using a simplified Resistance-Capacitance (RC) model calibrated with real building data. The study first benchmarks the DRL controller against three RBCs, two Proportional-Integral (PI) controllers and a Model Predictive Controller (MPC) in a simulated environment. In the simulation study, DRL reduces energy consumption by 15% to 50% and decreases temperature violations by 25% compared to RBCs, reducing also energy consumption and temperature violations compared to PI controllers by respectively 23% and 5%. Moreover, DRL achieves comparable performance in terms of temperature control but consuming 29% more energy than an ideal MPC. When implemented in a real building during a two-month cooling season, the DRL controller performances were compared with those of the best-performing RBC, enhancing indoor temperature control by 68% without increasing energy consumption. This research demonstrates an effective strategy for training and deploying DRL controllers in real building energy systems, highlighting the potential of DRL in practical energy management applications. [Display omitted] • Implementation of SAC as a continuous action thermostatic controller. • SAC pre-trained in simulation before the deployment in a real-world office. • Development of a benchmarking strategy to compare DRL and RBC in real deployment. • Reduced temperature violations by 68% maintaining the same energy consumption. • Discussing practical DRL controller deployment challenges. [ABSTRACT FROM AUTHOR]

The average electricity consumption per capita in China still has significant room for growth. Under the current rigid residential electricity pricing, it is very important to study how factors other than price can promote electricity conservation among residents. This paper uses micro-survey data of 2018 and 2020 to analyze energy efficiency and the rebound effect through a stochastic energy demand frontier model, as well as to examine the role of news propaganda in this context. The research conclusions are as follows: (1) News propaganda promoting electricity saving can weaken the rebound effect of electricity use, which can lead to better electricity savings. (2) The energy efficiency of residential electricity has improved significantly compared to previous years, and the rebound effect has decreased. (3) Electricity prices have a negative impact on electricity consumption, while income, household size, home area, and extreme temperatures have a positive impact. Based on these conclusions, we propose policy recommendations to address the current inflexibility in residential electricity pricing and to improve the efficiency of electricity use among residents. • News propaganda promoting electricity saving can weaken the rebound effect. • The energy efficiency of residential electricity has improved. • The rebound effect of residential electricity has decreased. • News propaganda can be a complement to the inflexibility of pricing policy. [ABSTRACT FROM AUTHOR]

Using the unique micro-data of industrial enterprises' Nitrogen oxides (NOx) and Sulfur oxides (SOx) emissions covering 58,725 enterprise-year observations, this paper investigates how digitalization affect energy efficiency. The empirical results show that regional digitalization can significantly promote the improvement of energy efficiency of industrial enterprises. Specifically, the result of PSM-DID method indicates that the impact of digitalization on energy efficiency is significantly enhanced by the "National innovative pilot cities" policy, while the dynamic result validates the continuity of industrial enterprises' energy efficiency. Moreover, regional digitalization has superior impact on enterprises within eastern region enterprises, the manufacturing sector, and with elevated liquidity and profitability levels. Finally, moderating effect suggests that technological innovation expand the positive impact of digitalization on enterprises' energy efficiency. This study confirms regional digitalization can promote the energy efficiency of Chinese industrial enterprises, providing the policy suggestions to the energy transition in China. • Measure the regional digitalization level of Prefecture-level city and the energy efficiency at Industrial enterprise-level. • Findings demonstrated that digitalization increases energy efficiency. • The impact of digitalization on energy efficiency can be enhanced by the "National innovative pilot cities" policy. • Technological innovation expands the positive impact of digitalization on enterprises' energy efficiency. [ABSTRACT FROM AUTHOR]

Against the background of global warming, the low-carbon energy transition (LCET) has become one of the top concerns of governments around the world. Artificial intelligence (AI) is serving an increasingly relevant role in the energy sector by facilitating the development of cleaner energy. Thus, based on the panel data of 44 countries from 2000 to 2022, this study employs the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) methods to explore the impact of AI technology innovation on LCET. Moreover, we explore the moderating and spatial spillover effects between AI technology innovation and LCET. The main results show that: (1) AI technology innovation significantly promotes LCET. A 1 % increase in the AI technology innovation index causes a 0.176 % increase in the level of LCET using the AMG method, and a 0.198 % increase using the CCEMG method. (2) Financial incentives and energy efficiency effectively amplify the positive influence of AI technology innovation on LCET. (3) AI technology innovation generates discernible spillover effects on LCET through bilateral trade influence, particularly in countries with closer "trade distances." This study recommends that countries adequately strengthen AI technology resources to realize new situations for the synergistic development of technology and green energy. [Display omitted] • This paper explores the impact of AI technological innovation on low-carbon energy transition (LCET) in 44 countries. • The characteristics of AI technological innovation are assessed. • AI technological innovation significantly promotes LCET. • Financial incentives and energy efficiency have positive moderating effects. • The spatial spillover effect of AI technological innovation on LCET is assessed. [ABSTRACT FROM AUTHOR]

Clustering in the Internet of Things (IoT) involves organizing devices into groups to streamline network management and optimize resource utilization, including Internet connections, energy usage, coverage, quality of service, and connectivity. DCOPA (A Distributed Clustering Based on Objects Performances Aggregation for Hierarchical Communications in IoT Applications) is a recent distributed clustering protocol based on a timer for cluster formation where the election of Cluster Heads (CHs) is modeled as a multicriteria problem. In this paper, three contributions are presented. Firstly, the DCOPA protocol is analyzed with a focus on its multi-criteria aggregation function T(i) which directly contributes to the election of the CHs and the formation of the network's clusters. This is then followed by an in-depth analysis of the impact and the variation of the weights assigned to the two aggregated criteria which are the energy and the distance from the base station. A verification of the scalability, load balancing and distribution of the clusters and CHs will follow. Secondly, a new formal notation for the performance analysis, specifically focusing on the mortality and lifetime based on the Vector of Performance Indicators (VPI), will be introduced for IoT. As a third contribution, a revised version of DCOPA is introduced called ADCOPA (Adaptive DCOPA Using Dynamic Weighting for Vector of Performances Indicators Optimization of IoT Networks). ADCOPA is based on a new property which is the dynamicity or the variability of the weights of the criteria used in the election function of CHs. The simulation results show that the ADCOPA algorithm, which dynamically adjusts the weights of the criteria during the network's lifetime, outperforms the DCOPA algorithm. The latter uses static weights for the criteria that remain unchanged for the entire lifetime of the network. This confirms that the ability to dynamically adjust the weighting of the criteria is an important factor in achieving better performance. • In-depth performance analysis of the DCOPA protocol. • The impact of varying the criteria weights on the DCOPA lifetime parameters. • A novel formal notation for analyzing the performance of an IoT network. • A novel Adaptive DCOPA protocol that uses dynamic weighting is proposed. • Simulation results show that the ADCOPA protocol performs better. [ABSTRACT FROM AUTHOR]

• The established deep learning model is capable of considering the impacts of 3D heterogeneity of oil shale on production and energy consumption. • The optimization procedure simultaneously optimized well constraints and well locations. • The evolution patterns of optimal parameter values across varying levels of energy constraints are identified. The in-situ conversion process (ICP) involves complicated thermal-reactive-compositional flow processes where the production parameters are closely interdependent. Therefore, manual optimization of the production parameters of ICP through numerical simulation is arduous and time-consuming. This paper introduces a computational framework that integrates deep learning techniques and particle swarm optimization (PSO) to automatically optimize the values of production parameters of ICP, and thus locating the highest energy efficiency and the optimum energy usage. This approach utilizes a 3D CNN model to predict key metrics. The prediction process considers the detailed 3D heterogeneity of oil shale, resulting in remarkably high prediction accuracy, as evidenced by determination coefficients (R2) above 0.99. Subsequently, the trained CNN model is integrated to the PSO algorithm to automatically fine-tune the production parameters to optimize the energy efficiency of ICP. The optimization results yield three significant findings. First, as the energy consumption limit increases, the optimal number of heaters, well spacing, and heating temperature exhibit an upward trend, but stabilized beyond the threshold of 7 × 105 kWh. Secondly, the optimal input energy (7 × 105 kWh) is found for the given ICP model. Lastly, the analysis reveals that variations in initial reservoir pressure or bottomhole pressure have limited impact on cumulative oil production and energy usage. [ABSTRACT FROM AUTHOR]

With the increasing demands in modern communications, the concepts of energy-efficient and low-cost processors have received a lot of attention from researchers in recent years. These cases are also taken into consideration in today's high-speed and high-capacity communications (HSHCC), which is one of the requirements in new generation of wireless networks i.e., 6G. International telecommunication union's (ITU) goal in wireless communications is to deliver higher data rates up to 10 Gbps, along with high spectral efficiency and advanced coverage. In multi-gigabit or terabit-per-second wireless communications, digital signal processing (DSP) faces significant challenges. Therefore, analog signal processing (ASP) and design methods based on it have gained a new importance recently. In today's modern telecommunication systems, ASP is used in areas such as the design of analog-to-digital converter (ADC)-less and also DAC-less transceivers, beamforming, secure communication, deep learning (DL), neural network accelerators (ANN), real-time phasers-based applications, Internet of things (IoT), etc. In this research, we describe and classify the works done in this field. This paper presents a comprehensive review of the ASP concept, challenges, and outlooks in modern and smart communications to pave the path for academic and industrial researchers who aim to work in this area. [Display omitted] [ABSTRACT FROM AUTHOR]

Intelligent Reflecting Surfaces (IRS) have revolutionized wireless communication systems through their adaptive reconfiguration of the radio propagation environment. This paper compares an IRS-assisted non-orthogonal multiple access (NOMA) system and a relay-assisted NOMA system. The investigation delves into the impact of the number of IRS elements on the performance of a two-user NOMA system. A dynamic power allocation scheme is proposed based on the number of IRS elements. Specifically, analytical investigations reveal the range, upper, and lower bounds of power allocation coefficients. The asymptotic values of these bounds are also determined. Furthermore, an optimization problem is formulated to maximize the sum-rate while considering individual minimum rate constraints. The minimum number of passive reflecting elements required by the IRS is determined to meet the minimum target data rates for both users. Analytical calculations determine the optimal number of IRS elements, minimizing total power consumption. Simulation results confirm the superiority of the IRS over the decode-and-forward relay approach. • Dynamic power allocation in IRS assisted NOMA system. • Performance comparison between IRS assisted and relay assisted systems. • Optimal number of IRS elements for energy efficient communication. • Design of a separate phase shift matrix for each user to maximize the system's overall sum-rate. • Optimal power allocation using Karush–Kuhn–Tucker (KKT) optimization technique. [ABSTRACT FROM AUTHOR]

Cabinet and in-store layout design impose high demands on supermarket refrigeration systems, due to temperature requirements and interactions between the ambient environment and the display. It is clear that views diverge on how different barriers should be investigated, and there are reasons to believe that energy efficiency measurements, with regards to both energy efficiency in buildings and energy efficiency in the operations, have weak points. The energy efficiency situation is a paradox that needs to be elaborated on. The paper presents a systematic literature review of research on open (no doors) or closed refrigerated cabinets (with doors) energy efficiency, chilled groceries and the consumer behavior, followed by an analysis of the contemporary research. The paper draws on findings and collects peer-reviewed articles from databases in order to examine how research has reported on this issue. As a result, 31 articles were framed, with different variables, to form a body of this article. Empirical research is needed that uses actual data, such as perceptions, behavioral research with lab experiments, and field measurements. Consumers shopping situation, for chilled groceries, is an extensive and complex topic, and a single study cannot provide a complete overview of all its related aspects. The results focus on two objectives. Firstly to present the review and secondly to analyze and identify knowledge gaps within the energy efficiency and store installations in the grocery context. [ABSTRACT FROM AUTHOR]

This paper focusses on hot underground tunnels and studies the effect of ground source heat pump (GSHP) intermittent operation and changing tunnel air temperature profile on energy tunnel thermal efficiency. The effects of heat pump operation on the tunnel surrounding soil and the soil recovery rate when the heat pump is not in operation were also studied. A 3D numerical model was developed to simulate the transient heat transfer intermittent operation of an energy tunnel. The intermittent operation was reproduced by controlling the convective heat flux at the boundary between the absorber pipe and the tunnel lining. Variation in the tunnel air temperature was defined in the model as a periodic sink amplitude. Results show that in energy tunnels, strategic intermittent operation increases thermal efficiency and allows the surrounding soil to thermally recover and prevent any adverse effect on the system. A high daily intermittent operation ratio increases the average thermal output but might lead to higher operating costs. Therefore for a given site, it is important to determine the optimum intermittent ratio. The paper also shows how the variation in tunnel air temperature affects the thermal performance of the energy tunnel. The importance of including this variation (rather than assuming a constant average value) when estimating the geothermal potential of underground tunnels was also highlighted. Insights are also provided on the soil temperature recovery rates after prolonged operation. These would serve as a basis for working out a seasonal intermittent operation strategy to optimise the use of ground source heat pumps in underground tunnels. • An improved finite element model to simulate heat transfer in energy tunnels. • Effect of intermittent operations on thermal efficiency of energy tunnels. • Influence of seasonal variations in air temperature on thermal performance. • Intermittent operation minimises thermal disturbance on the surrounding soil. • Different operation scenarios serve as a basis for optimum operation. [ABSTRACT FROM AUTHOR]

• Switchable insulation is an effective means of regulating heat through envelopes. • Current research on switchable insulation is fragmented across multiple domains. • We review existing technologies systematically and propose a classification system. • We assess their performance and improvement opportunities for building applications. Switchable thermal insulation, in the form of an opaque panel that alternates between thermally conductive and insulated states, can be an effective means of regulating the thermal environment by selectively transferring heat between the indoor and outdoor environments. Pioneering work has been undertaken by researchers to develop switchable insulation technologies intended for applications in the built environment, automotive, and aerospace, where conventional space heating and cooling technologies are either too bulky or too energy consuming to meet design requirements. Switchable insulation technologies are in their infancy and the emerging research on this topic is unstructured and fragmented across disparate application sectors and very few of the adaptive insulation concepts and technologies are actively being pursued by the buildings research community. The aim of this paper is therefore to advance the understanding of switchable insulation for possible applications in the building envelope, by reviewing and classifying the existing technologies systematically, with a particular focus on their working principles, theoretical performance and improvement opportunities. The paper provides a qualitative and quantitative assessment of the most promising switchable insulation technologies for building envelopes and identifies the opportunities for further research in each of the technologies. [ABSTRACT FROM AUTHOR]

This paper explores whether the changes in energy intensity in Spain have led to improvements in the energy consumption levels or to a backfire effect offsetting the expected decrease. Jevon's paradox or backfire effect happens when a rebound higher than 100% causes energy efficiency improvement to raise energy consumption. To test Jevon's paradox or the backfire effect caused by energy efficiency actions, a Logarithmic Mean Divisia Index I (LMDI-I) is used for the Spanish economy. The period under consideration ranges from 2000 to 2015, when three national action plans were implemented. The main methodological novelty of this paper consists on to block the economic activity effect in a second decomposition round to better explore the effect of energy efficiency on energy consumption. As a whole, our results do not support Jevon's paradox for the sectors analyzed. However, they do warn about a possible backfire effect in the industry, transport and service sectors. Major findings follow these same findings when the activity effect is blocked. • Backfire effect caused by energy intensity is explored. • Focus is on the Spanish case from 2000 to 2015. • Main methodological novelty is to block the economic activity in a LMDI approach. • Results do not support Jevon's paradox for the sectors analyzed. [ABSTRACT FROM AUTHOR]

World Cities contribute to a massive amount of carbon emissions. About 80% of the global greenhouse gasses are emitted from these cities. The sustainable practices should be adopted in built environment for these cities. As an example, a residential neighbourhoods in arid climate has been discussed as a case study in practice in this paper. It has been developed to implement the sustainable strategies and concepts. It demonstrates a mixed-use community which integrates the sustainability dimensions to improve the quality toward sustainable living in a small community (about 4000 persons), allocated in Dubai land area. This paper aims to highlight the sustainable practices in the arid zones and to give an overview about the performance of the clean energy and renewable in these areas. The studied development has been selected to evaluate such techniques and strategies in the gulf region (Dubai). Various environmental categories such as; on energy, water, mobility, energy consumption of the buildings and infrastructure, urban green spaces, solid waste management and urban farming, have been appraised in this study. It has been noticed that the urban design proposed reduces its potable water consumption by 40% and for energy demands by 57% compared to conventional households. The solar production can cover around 38% of the energy consumption on average. Many challenges face the sustainability movement in the region, especially, the affordability and feasibility in such harsh climate zones areas. This manuscript opens new perspectives about the prioritization that should be considered for adopting the sustainable strategies in arid climate. Image 1 [ABSTRACT FROM AUTHOR]

The possibility to upgrade biomass and intermittent power from renewable energy sources generating stable power and substitute natural gas (SNG) has been discussed in previous papers. Such papers focussed mainly on the choice and design of the most suitable power units to generate power and a high purity carbon dioxide stream to feed the methanation section; moreover, heat recovery strategies to improve the plant efficiency were investigated and implemented. In the proposed plant, power for electrolysis potentially comes from renewable energy sources (RES), thus arises the need to introduce gas storages in order to fully decouple the outputs (SNG flow and stable electric power) from the input intermittency. This paper analyses some different possible layouts for intermediate gas storages and compares them from an energy consumption point of view. • A continuous SNG production from intermittent RES is achieved. • Several gas storage configurations are modelled and compared. • Optimal waste heat recovery solutions prevent storages from penalising efficiency. [ABSTRACT FROM AUTHOR]

Energy utilization attributed technological advancement and social development for the mankind. The population growth and large scale energy use boosted demand for energy across the globe. Design of Cost effective energy system can provide solution for energy crisis with sustainable growth for industry and society. In present paper radical parameters have been selected from literature for heat exchanger investigation. In shell and tube heat exchanger fouling rate, exchange efficiency and cost majorly affects exchanger performance. The objective of the research is improving exchanger performance analyzing cost, exchange efficiency and fouling rate of heat exchanger using marvellous meta-heuristic optimization techniques. In this paper bio-inspired Kril Herd (KH)) strategy is utilized to optimize hidden layers. Advance, the execution of KHO algorithm is contrasted with different algorithms illustrative of state-of-the- art territory. The results of various algorithms are poor down and emerged from comparative frameworks. The finest outcomes achieved by KH approach for minimum cost & fouling rate and maximum energy efficiency in the exchange process. This proposed model compared with existing optimization techniques in the heat exchanger with energy analysis and KH strategy outperforms well-known methods in the literature. • This paper highlights development in energy research by improving performance of energy system, heat exchanger. • The model architecture developed with neural network consists of input, output and hidden layers. • Bio-inspired Kril Herd (KH)) strategy is utilized to optimize hidden layers and contrasted with few high performance optimization techniques. • The cost, energy efficiency and fouling factor of heat exchanger depicted significant improvement by proposed KH technique. [ABSTRACT FROM AUTHOR]

An inrush of groundwater will absorb the heat injected in oil shale in situ pyrolysis exploitation, which will reduce the pyrolysis efficiency and generate a large amount of oil-containing water. This paper proposed a novel method of water-stopping by gas flooding to solve the problem of groundwater infiltration into the in situ pyrolysis zone of oil shale. In this paper, numerical simulation by TOUGH2/EOS3 and field experiment studies were conducted, with the general aim of this study focused on the establishment of the inflatable area and the long-term water-stopping mechanism. The simulation results showed that the resistance of gas to groundwater flow and the pressure balance of the gas-water interface at the inflatable area front were the main mechanisms of water-stopping. The water yield in the pyrolysis zone decreased significantly from 6 m3/h to nearly 0 m3/h after the inflatable area was established, and the effective water-stopping radius exceeded 50 m around a single well. Furthermore, a field-scale water-stopping by gas flooding experiment was carried out in the National Pilot Project for Oil Shale in situ Exploitation of China (NPOSE), with the results demonstrating that the water yield could be reduced from 6 m3/h to 0.5 m3/h after gas injection, which was basically consistent with the numerical simulation results. Cracked oil (2.85 tons) was successfully obtained at high energy efficiency of approximately 6.1. Consequently, it is verified that the method proposed in this paper has the desired effect of water-stopping and practical application value, which provides technical support for large-scale oil shale in situ pyrolysis exploitation around the world. • A novel, effective method is proposed for preventing groundwater infiltration into the pyrolysis zone of oil shale. • The resistance of gas to groundwater flow and the pressure balance of the gas-water interface are the main mechanisms of water-stopping. • The water yield in the pyrolysis zone has decreased significantly after the establishment of the inflatable area. • Cracked oil (2.85 tons) was successfully obtained at high energy efficiency about 6.1 with the aid of the method. [ABSTRACT FROM AUTHOR]

The landscape of Demand-Side Energy Management (DSM) research is rapidly evolving, shaped by technological innovations and policy developments. This paper presents an exhaustive bibliometric analysis and methodological framework to explore the research trends within the DSM domain. By synthesizing data from Scopus and OpenAlex, we compile a comprehensive dataset of DSM publications that serve as the basis for our analysis. Through rigorous data acquisition and cleaning, we ensure the reliability and relevance of our dataset. We employ state-of-the-art Large Language Models (LLMs) and topic modeling techniques, including GPT and BERTopic, to perform semantic analysis and uncover thematic structures within the literature. Statistical analysis of the literature dataset reveals a steady increase in DSM publications, with significant contributions from prestigious journals and institutions worldwide. We observe that articles are the predominant publication type, while reviews often cite more references and receive higher citation counts. The distribution of publications over time indicates a growing interest in DSM, particularly since 2014. Geographical mapping of institutions highlights key regions contributing to DSM research, with notable outputs from Europe, North America, and East Asia. Coupled with citation network analysis, our approach reveals the influential works and emerging trends that define the scientific progression of DSM research. Our unsupervised topic modeling, powered by BERTopic, clusters the publications into distinct themes, while our advanced visualization techniques using UMAP and t-SNE provide insights into the semantic space of DSM literature. The resulting thematic classification is presented in a hierarchical structure, offering a comprehensive understanding of the field's focus areas. Our citation network analysis, utilizing force-directed graph computation and edge-bundling algorithms, maps the interconnectivity and impact of research contributions, providing a dynamic view of the field's evolution. This study not only charts the landscape of DSM research but also offers a methodological blueprint for future bibliometric analyses. The insights gained from this multi-faceted exploration serve as a valuable resource for researchers, policymakers, and industry practitioners looking to navigate the complexities of DSM and contribute to its scientific advancement. • Reviews energy demand-side management literature using bibliometric methods. • Uses citation network to reveal research clusters and interdisciplinary connections. • Uses GPT embedding and BERTopic model to cluster literature into ten themes. • Develops software tool for bibliometric analysis. [ABSTRACT FROM AUTHOR]

With the facilitation of agricultural modernization, China's agricultural energy consumption continues to rise. This paper empirically tests the agricultural energy rebound effect and its influencing factors in China during 1999–2020. It is believed that the improvement of energy efficiency has not led to the expected energy-saving effect in agriculture due to the "Jevons paradox" of energy efficiency and energy consumption. The average energy efficiency of China's agricultural energy consumption was 0.446, and the average energy rebound effect was 68.9 %. The level of agricultural mechanization, the effective irrigated area of farmland and agricultural investment have a positive impact on the rebound effect of agricultural energy, and the direct effects are 0.062, 0.008 and 0.064, respectively, while agricultural labor force and energy price have a negative impact on the rebound effect of agricultural energy. In particular, technology spillover and scale spillover led by agricultural technology progress have strong negative space spillover effect. As it is argued, 1 % of agricultural technology progress can reduce 3.5 % of agricultural energy rebound effect in surrounding areas. In order to reduce the energy rebound effect, it is suggested to increase the subsidies to agricultural labors and rationally formulate regional differentiated energy regulation policies. • The internal mechanism of rebound effect is expounded from the view of producers and consumers. • The average energy efficiency of China's agricultural energy consumption was 68.9 % during 1999–2020. • The determinants of the agricultural energy rebound effect were further discussed by considering the spatial spillover effect. [ABSTRACT FROM AUTHOR]

The extensive utilization of wireless services, coupled with the exponential surge in data traffic, has triggered substantial energy consumption. Thereby, growing attentions have been paid to energy-efficient heterogeneous cellular networks (HCNs). In this paper, we concentrate on the joint resource allocation issue under the framework of the non-orthogonal multiple access (NOMA) assisted HCNs with wireless backhauls. To furthermore improve the total system energy efficiency (EE) while ensuring the quality of service (QoS), we formulate the optimization problem as a hybrid joint resource allocation (HJRA) of integer programming and continuous optimization under the cross-tier interference threshold and minimum data rate constraint. The non-convex nature of the optimization problem arises from the fractional objective function and non-convex constraints, making it intractable to efficiently get an exact solution. In this regard, a quantum-inspired political optimizer (QPO) algorithm is devised to find the optimal solution for the HJRA issue, which realizes synchronous allocation of backhaul bandwidth, sub-channels, and power. Simulation results confirm the superiority of the QPO regarding the total system EE over other strategies. [ABSTRACT FROM AUTHOR]

This article analyzes energy poverty in Latin America and the Caribbean and the importance of energy efficiency to address this issue. The paper also touches upon energy subsidies in Mexico and their negative environmental and social consequences. Then, the article studies the potentially detrimental billing practices of Mexico's state electricity company, which are provoked by the enterprise's growing past-due portfolio and losses, and the possible infringement of electricity consumers' rights. The authors propose three courses of action for consumers to protest the company's possibly perjudicial billing. Finally, the authors suggest that a distributed electricity system in Mexico could combat energy poverty and strengthen electricity users' rights. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) have received a lot of interest because of their ability to cut fuel usage by using electricity from the grid as an alternative energy source. PHEVs differ from typical hybrid electric vehicles (HEVs) in terms of increased battery capacity, the availability of plug-in recharging, and improved battery state of charge (SOC) management systems. Efficient energy consumption and excellent performance are critical issues for hybrid powertrains. The estimation of SOC in PHEVs is a difficult NP-hard problem that is frequently tackled utilising metaheuristic techniques. In this paper, we offer an enhanced dingo optimisation with deep learning-based prediction (EDOA-DLP) model specifically tailored for PHEVs. The EDOA-DLP model includes an efficient SOC estimate algorithm for real-time energy management. The EDOA-DLP technique's performance has been thoroughly proven through considerable experimentation in a variety of circumstances. The results show that the proposed EDOA-DLP method beats other current methods in terms of predicting SOC accurately and reaching the targeted goals for PHEVs. Overall, this research presents a unique EDOA-DLP SOC prediction approach for PHEVs, with promising results in terms of energy management and performance optimisation. The experimental results highlight the EDOA-DLP model's improved performance. Overall, this research presents a unique EDOA-DLP SOC prediction approach for PHEVs, with promising results in terms of energy management and performance optimisation. The experimental results illustrate the EDOA-DLP model's improved performance when compared to competing methodologies, highlighting its potential to improve the efficiency and efficacy of PHEV systems. [ABSTRACT FROM AUTHOR]

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

Intelligent fault diagnosis functions as a necessary tool to prevent substantial damage to industrial products and enhance system reliability. While Artificial Neural Networks (ANNs) have been extensively studied in this context, they still face substantial challenges in resource consumption, robustness, and generalizability. To overcome these limitations, researchers have developed the third-generation neural network based on brain's structure, namely the Spiking Neural Network (SNN), which leverages the concept of time steps and spiking signals for enhanced spatiotemporal feature processing and energy efficiency. This paper proposes the Wide Spiking Residual Grouping Attention Framework (WSRGA-FW), which incorporates the advantages of both ANN and SNN. The WSRGA-FW employs Extended Gramian Representation for signal encoding to reduce noise impact, followed by a tailored ANN with wide convolutional kernels, optimized residual structures, and Grouped Perception Generation (GPG) Layers. These augmentations increase the network's representation and robustness, particularly in noisy environments. The backbone ANN is transformed into an SNN model, allowing deployment in portable and miniaturized devices with improved application prospects. Performance evaluations across various noisy scenarios using bearing fault datasets demonstrate that WSRGA-FW surpasses existing networks. Visualization of firing rates and energy consumption calculation contribute to the interpretability and intrinsic energy efficiency advantages of SNNs. [ABSTRACT FROM AUTHOR]

• Improves energy efficiency in SDN networks. • Effectively balances network load with meta-heuristic algorithms. • Dynamically optimizes resource allocation. • Ensures efficient utilization of network resources. • Minimizes energy consumption. Software-Defined Networks (SDN) represent an efficient approach for managing large-scale networks, aiming to enhance resource use, performance, cost efficiency, and quality of service for dynamic services. By segregating the control plane and utilizing a centralized software controller, SDN offers notable capabilities in software development, programmability, and flexibility. Nevertheless, balancing energy efficiency and processing power remains a key challenge. This paper introduces an interactive scheme within the SDN controller, employing the krill herd meta-heuristic algorithm for optimization-based programming. The proposed method emphasizes the interaction between load balancing (LB) and energy efficiency, leveraging control plane data and virtual machine (VM) updates to derive optimal solutions. Simulation results illustrate an average 13.5 % enhancement in SDN load balancing, a 25.9 % improvement in energy efficiency, and an approximate 20 % performance boost, showcasing the effectiveness of the proposed approach. [Display omitted] [ABSTRACT FROM AUTHOR]

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies. • A normalized energy consumption model is presented, combining two energy usage. • A novel eco-driving strategy is developed based on the proposed energy consumption. • The algorithm suits both free traffic and car-following scenarios with safety. • The characteristics of the four eco-driving strategies are compared. • The proposed strategy is verified by both simulation and vehicle-in-the-loop tests. [ABSTRACT FROM AUTHOR]

Wastewater treatment plants (WWTPs) play a pivotal role in natural water recycling and safeguarding the water security of approximately 42% of the world's population, yet they confront challenges posed by escalating energy consumption and regional energy constraints. In this study, we developed an evaluation framework that integrated regional energy scarcity with the operation conditions of WWTPs to quantify their energy efficiencies by applying multiregional input–output (MRIO) analysis and data envelopment analysis (DEA). A case study using this framework was conducted on 3776 WWTPs in 30 provinces in China. The results showed that the average scarcity-based energy efficiency level of WWTPs in China was low, of which those in northern provinces were higher compared to southeastern provinces. Furthermore, only 24 WWTPs in China attained the highest efficiency levels, while the rest had different degrees of input redundancy, indicating an energy-saving potential of 9.05 × 109 kWh. This paper enables the development of optimal efficiency strategies for WWTPs in different provinces considering the potential energy scarcity risks caused by trading activities. • The DEA and MRIO analyses were combined for 3776 WWTPs in China. • Efficiency evaluation index was modified considering energy scarcity levels. • Scarcity-based energy efficiency assessment framework for WWTPs was developed. [ABSTRACT FROM AUTHOR]

This paper delves into the potential impact of a changing climate on the energy performance of European buildings. Research aims to provide a comprehensive evaluation of current energy requirements focusing on the envelope, considering existing regulations in national policies. Energy simulations are conducted at 94 locations across the European Union to cover the climatic variability and Koppen climate classification. The research analyzes future climate scenarios for the years 2030, 2050, and 2070, using three different Representative Concentration Pathways (RCP 2.6, 4.5, 8.5). According to a comprehensive analysis of heating, cooling, and overall energy performance, climate plays a significant role in buildings' energy balance. In moderately cool climate countries, the demand for air conditioning is projected to decrease in the years ahead. Conversely, in countries with a warm climate, there is a projected increase in the overall energy demand. Consequently, a revision of current energy regulations should be a priority. Providing insights into the relation between building design, energy efficiency, and climate change, the research identifies policy adjustments to ensure buildings can effectively respond to changing climatic conditions. A holistic and dynamic approach can support building design accounting for long-term impacts of climate change to create resilient and energy-efficient structures. • National building energy regulations of European Member States do not tackle climate change. • Over next decades, a decrease in heating demand is forecasted across all climate zones. • There is potential for a future considerable increase of cooling demand in Europe. • A reduction in total building energy demand in colder climates and an increase in warmer climates can be expected. • Revise national energy regulations, especially in warmer zones, to curb the expected rise in Europe's total energy demand. [ABSTRACT FROM AUTHOR]

• PV power generation coupling with hydrogen production system is modeled. • Techno-economic performance of the system for five cities in China is investigated. • The system efficiency and LCOH range from 9.03 to 9.31% and 22.54 to 24.97 CNY/kg. In recent years, photovoltaic power generation technology has developed rapidly, but due to its impact on the stability and security of the power grid, some areas in North and Northwest China have a certain degree of photovoltaic power curtailment phenomenon. Hydrogen is considered a good medium for energy storage, and the photovoltaic power generation system based on hydrogen energy storage has been the focus of research. Therefore, this work established simulation models of a photovoltaic power generation with a rated capacity of 100 MW coupling with hydrogen production system using MATLAB software and performs real-time simulation. Techno-economic performance of the integration system in five cities of Hami, Ordos, Datong, Yinchuan, and Yulin was analyzed. The results show that the coupling system can generate 158484–175675 MWh of electricity annually, which can drive the electrolyzer to produce 2524–2761 tonnes of hydrogen. The energy efficiency of the overall system and levelized cost of hydrogen are 9.03–9.31 % and 25.95/22.54–28.38/24.97 CNY/kg. The total CO 2 emissions reduction and carbon credits earned by the established system over a lifetime of 20 years can reach 1.81–2.00 million tonnes and 92.19–102.19 million CNY. This paper will provide economic and energy efficiency reference for studying the coupling of green hydrogen production systems with the coal chemical industry in the above regions. [ABSTRACT FROM AUTHOR]

Approximate computing is one of the promising techniques in error-resilient applications to overcome high-density integration challenges, such as energy consumption and performance. Multipliers constitute a significant portion of computer arithmetic units, leading to considerable energy and time consumption. In this paper, we propose low-power and compact approximate compressors for composing approximate Dadda multiplier structures, including compressors, half adders, and full adders, which utilize three-phase partial product compression: truncated, approximation, and exact columns. In approximate columns, we consider approximate compressors derived from the truth table of the exact 4:2 compressor and simplified K-map entries based on the probability of each combination of inputs. An error-correcting module (ECM) is designed to distinguish specific cases and reduce the error metrics. All circuits were simulated using ModelSim and then synthesized using Design Compiler with the 15 nm FinFET technology. When compared to state-of-the-art works, our multipliers exhibit approximately 30%, 43%, and 10% reductions in power, area, and delay, respectively. To evaluate their functionality, we conducted image multiplication and implemented a simple multi-layer perceptron (MLP) neural network using the modified National Institute of Standards and Technology (MNIST) dataset in MATLAB with 0.998 mean structural similarity index metric (MSSIM), 51 dB peak-signal noise ratio (PSNR), and 95% classification accuracy. • Introducing novel ultra-compact approximate compressor structures to reduce design metrics. • Proposing an error-correcting module (ECM) for compensating errors of approximation. • Proposing truncated 8 × 8 Dadda multipliers using the proposed approximate compressors and ECM. • Evaluating the accuracy of approximate multipliers in error-tolerant applications. [ABSTRACT FROM AUTHOR]

Forecast uncertainties pose a considerable challenge to the success of model predictive control (MPC) in buildings. Numerous possibilities for considering forecast uncertainties in MPCs are available, but an in-depth comparison is lacking. This paper compares two main approaches to consider uncertainties: robust and stochastic MPC. They are benchmarked against a deterministic MPC and an MPC with perfect forecast. The MPCs utilize a holistic building model to reflect modern smart homes that include photovoltaic power generation and storage, thermally controlled loads, and smart appliances. Real-world data are used to identify the thermal building model. The performance of the various controllers is investigated under three levels of uncertainty for two building models with different envelope performance. For the highly insulated building, the deterministic MPC achieves satisfactory thermal comfort when the forecast error is medium or low, but the thermal comfort is compromised for high forecast errors. In the poorly insulated building, thermal comfort is compromised at medium and high forecast errors. Compared to the deterministic MPC, the robust MPC increases the electricity cost by up to 4.5% and provides complete temperature constraint satisfaction while the stochastic MPC increases the electricity cost by less than 1% and fulfills the thermal comfort requirements. [ABSTRACT FROM AUTHOR]

The startup performance is crucial to the high-efficient operation of the sustainable power generation system. Current literature usually focuses on catalysts and startup strategy to improve startup performance of the proton exchange membrane fuel cell, which ignores the gas foil bearings-rotor system that directly affects startup response. Therefore, in this paper, the effects of bump foil structure, coating, and nominal clearance on the startup response of the bearings-rotor system are first investigated experimentally. Multi-objectives optimization mathematical model considering simultaneously lower power consumption, takeoff speed, and vibration is presented based on grey relational analysis. The optimal bearings-rotor system with radial trisection bump foil, coating MoS 2 , and nominal clearance of 50 μm is identified. Compared to average values, the power consumption of the optimal system is diminished by 27.2% while the takeoff speed and nonlinear vibration are reduced by 16.2% and 47.3%, respectively. These findings can be used to improve energy efficiency and startup performance for fuel cells. The systematic methodology proposed in this study can be extended to other investigations about the startup. • Bump foil type, coating, and nominal clearance are first considered in PEMFCs. • Multi-objective optimization mathematical model is presented. • Optimal gas foil bearings-rotor system is proposed and verified. • Startup performance of the proposed system is improved. • Power consumption of the proposed system is diminished by 27.2%. [ABSTRACT FROM AUTHOR]

Over the years, numerous studies have explored the green synthesis of ethylene. Within this context, the focus of this perspective shifts toward plasma technology, which has demonstrated the capability to convert methane into ethylene. Plasma catalysis creates distinctive physical and chemical environments, particularly at normal temperature and pressure, distinguishing it from alternative methods. Nevertheless, the utilization of atmospheric pressure plasma is intricate, posing scientific challenges in the realms of physics and chemistry. In this viewpoint, various key performance aspects are evaluated, encompassing methane conversion efficiency, ethylene selectivity, and specific energy input. These scientific pros and cons are then assessed for their readiness for industrial-scale implementation. Initially, the potential for small-scale ethylene production is examined, leveraging existing robust process technologies to unlock fresh market and supply chain opportunities. Subsequently, the sustainability of plasma technology for green ethylene production is compared to conventional ethylene production and alternative green ethylene production methods, including biomass-based approaches. Contrary to perhaps optimistic expectations, current literature evidence does not uniformly favor the latter, indicating the potential for plasma-based green ethylene processes. Additionally, this paper underscores the importance of considering Environmental, Social, and Governance factors that influence business decisions. Finally, this review underscores plasma technology as a potentially promising approach for green ethylene synthesis from methane, offering unique advantages under normal conditions while simultaneously presenting scientific challenges. It assesses its viability for small-scale production and benchmarks its sustainability against conventional and alternative methods, emphasizing the importance of a sustainable future for the green petrochemical industry. • Potentials of plasma catalytic methane conversion are compared with other green methods. • Conversion efficiency, selectivity, and sustainability are reviewed in terms of specific energy input. • Small-scale plasma technology enables robust ethylene production and opens new market opportunities for green synthesis. • Green ethylene is analyzed in relation to green hydrogen and ammonia from different aspects. • Plasma-based green ethylene processes are more sustainable than conventional and biomass-based methods. [ABSTRACT FROM AUTHOR]

This paper focuses on the comparative study of vertical and horizontal vacuum membrane distillation (VMD) modules coupled with mechanical vapor recompression (MVR) system to obtain the optimal system layout structure and further improve operation stability and energy efficiency in the wastewater treatment. Firstly, freshwater accumulation characteristic in the vertical VMD module was explored under different working conditions. Then, comparative experiments of vertical and horizontal VMD-MVR systems were conducted. Finally, comprehensive performance of horizontal VMD-MVR system were evaluated. The obtained results showed that freshwater production in the distilling tank and freshwater accumulation in the VMD module were affected by operation time, feed temperature, feed flow rate, feed concentration and vacuum side pressure. Furthermore, the horizontal VMD-MVR system was proved to be a better choice due to lower freshwater accumulation in the VMD module and energy consumption in comparison to the vertical VMD-MVR system. Finally, long term operation experiment of 240 h indicated evaporation rate dropped to 55 kg/h as operation time reached to 180 h owing to membrane fouling. After membrane cleaning, evaporation rate recovered from 55 to 62 kg/h with a higher recovery efficiency of 95.1 %. Compared to conventional wastewater treatment systems, the horizontal VMD-MVR system exhibited excellent comprehensive performance. [Display omitted] • Performance comparison of vertical and horizontal VMD-MVR were conducted. • Freshwater accumulation in the VMD module affected the evaporation rate. • Vertical VMD-MVR system had higher energy consumption than horizontal system. • Separation efficiency of horizontal VMD-MVR system was 99.9 %. • Evaporation rate recovered from 55 kg/h to 62 kg/h after membrane cleaning. [ABSTRACT FROM AUTHOR]

• Summarize sustainability challenges and future trends of energy systems. • Analyze various development options and their impact on the power grid. • Propose multi-disciplinary research steps and challenges that must be addressed. The increased awareness of the climate change has triggered numerous actions and world-wide policies (e.g. the Paris agreement), with strong incentives to stop using fossil fuel-based energy forms in order to achieve a climate neutral economy (OECD, 2019). To realize the political targets, there are numerous challenging technical hurdles to overcome, which call for continued research. Earlier isolated systems need to come together and jointly collaborate to enable many changes in processes, for instance, seeking opportunities for electrification of energy intensive processes (Vogl et al., 2018). In this quest power systems play a crucial role through the increasing electricity transmission volumes, and they are already in the process of fundamental shift towards climate neutrality. The scale and urgency of this transformation is enormous and will affect most industries due to the central role of energy and electricity. In this paper, we discuss the main ongoing developments and future trends that also imply a closer coordination between power systems and various process systems. [ABSTRACT FROM AUTHOR]

The global COVID-19 pandemic has highlighted the importance of indoor air quality and ventilation to mitigate the spread of respiratory viral infections. Schools, in particular, represent a vulnerable environment with high occupancy rates, prolonged exposure times and often inadequately ventilated rooms. This paper evaluates the functionality of different natural and retrofitted mechanical ventilation strategies in this context. An experimental setup, combining empirical measurements with building performance simulation and analytical risk analysis was used to assess key performance characteristics, including the energetic performance, thermal comfort, indoor air quality and the airborne infection risk of SARS-CoV-2. The results of this study underscore the need for a holistic approach to ventilation design in schools, taking into consideration the balance between energy performance, carbon emissions, thermal comfort, indoor air quality and associated health factors. We demonstrate that the risk of one or more long-range airborne infections, with the SARS-CoV-2 Omicron variant, can be reduced by >50% through appropriate use of natural, mechanical or hybrid ventilation in a classroom setting. Analytical modelling demonstrates that this risk can be further reduced, by an order of magnitude, through the use of FFP2 masks. • 5 classroom ventilation strategies were evaluated using measured and modelled data. • Mechanical and hybrid ventilation were compared to natural ventilation strategies. • Energy, thermal comfort, IAQ and SARS-CoV-2 infection risk criteria were evaluated. • None of the ventilation scenarios could eliminate the risk of aerosol transmission. • Hybrid systems provide the best compromise solution for all criteria. [ABSTRACT FROM AUTHOR]

[Display omitted] • Feasibility of using organic wastewater as renewable energy source is verified. • ＞99% COD, TOC removal rate in the experimental platform. • Up to 78.75% of the energy recovery efficiency of the SCWO system. The degradation of high concentration organic wastewater and the recovery of energy using supercritical water oxidation (SCWO) technology have the potential to become viable in the future. High concentration organic wastewater contains a significant amount of energy. This paper focuses on utilizing high concentrated wastewater as a renewable energy source, from an energy system perspective, an experimental rig was designed to determine the optimal working conditions. The experiments were conducted with an oxidation coefficient of 1.1, a flow rate of high concentration organic wastewater of 9 ml/min, a heating temperature of 400 °C, and a reaction pressure of 25 MPa. Under these conditions, the COD and TOC removal rates of the high concentration organic wastewater were found to be 99.383 % and 99.876 % respectively. Additionally, the phenomenon of "Abnormalities in pressure sensitivity" observed during high concentration organic wastewater the experiment was further explained through additional experiments. Finally, by comparing the experimental energy recovery with the simulation results, an energy recovery efficiency of 78.95 % was achieved. The feasibility of the simulated energy recovery in practice was verified, confirming the potential to convert the previous energy-consuming wastewater treatment process into a productive one. This study provides a theoretical and experimental foundation for further related research. [ABSTRACT FROM AUTHOR]

Motor systems are ubiquitous throughout the industrial sector, particularly in fluids applications. It is well known that the efficiency of these operations can be greatly enhanced using variable frequency drive (VFD) controls, but adoption of the technology is far from universal. The goal of this paper is to introduce engineering, economic, and environmental analyses to demonstrate benefits to both individual facilities and the state at large. Individual case studies are presented to describe the variety of opportunities for VFD-enhanced motor control; however, this project seeks to describe the combined impact of VFD implementation at a new scale. Data sourced from a U.S. Department of Energy program is used to perform economic and environmental input output analysis to calculate broad impacts of individual audits and recommendations. Results indicate that widespread implementation of VFD controls on motor systems in the industrial sector will introduce significant economic and environmental benefits. One of the scenarios showed a potential reduction of over 4.7 million tonnes of carbon dioxide from regional manufacturing sector as well as a cascading contribution to the regional economy totaling nearly $1 billion. The results are presented as a demonstration of the magnitude of impact that improving control of industrial motor systems can have, as a guide to industry leaders and policymakers about the value of such investments. [Display omitted] • Variable frequency drive (VFD) modifications to industrial motors are described analytically. • Individual micro-scale carbon and cost savings are combined for input-output analysis. • Leveraging VFD control as a universal practice would result in nearly 5 million avoided tonnes of CO 2. • Similarly, economic benefits across the state of Ohio would total more than $1 billion. [ABSTRACT FROM AUTHOR]

Pressure servo control plays a crucial role in the majority of industrial applications that use compressed air as their power source. How to reduce the energy consumption of the pneumatic system while ensuring the high accuracy of pressure control remains a key problem to be solved. This paper proposes a Gaussian Process (GP) 1 model predictive controller (MPC) applied to a pressure servo control system based on high-speed on-off valves. The smallest quantity of collected data is used to create the GP model. The entire model, which includes some predictive data, is produced once the generated model has been optimized. The GP model is combined with the MPC to perform comparative experiments using an industrial controller with a Field Programmable Gate Array (FPGA). The system responds quickly and with little tracking error in steady-state response studies as well as dynamic response experiments. The GP-MPC achieves the best outcomes in the dynamic comparison tests. The root mean square error (RMSE) is just 2.42 kPa, and the overshoot is less than 59.9% of the Proportional-Integral-Derivative (PID) controller and 68.5% of the sliding mode controller (SMC). Although the compressed air consumption of GP-MPC is basically the same as that of PID, it is significantly better than SMC, with a total saving of about 70.2%. All the experiments prove that the controller proposed in this paper can effectively improve the energy efficiency and tracking accuracy of the pressure servo control system. [Display omitted] • A Gaussian process model for the high-speed on-off valve is developed which is able to predict uncollected data. • A model predictive controller is established by combining the Gaussian process model with rolling optimization method. • The pressure tracking and energy-saving performance are intended to be improved by using the objective function. [ABSTRACT FROM AUTHOR]

Energy extraction from renewable sources like hydrokinetic energy from the flowing water of rivers and canals is one of the affordable and clean energy generation technique. The current work is carried out towards performance enhancement of the Darrieus hydrokinetic turbine for open water channel utilization application. The investigation has been done using the flat and concave shape of blocking plate. Both blocking plates have been analyzed for different sizes as well as location at the upstream side of the turbine. The obtained results are reported after considering appropriate velocity correction. The blocking plates with projected width of 76 mm, 89 mm, 101 mm, 127 mm, 152 mm, and 203 mm are selected for the investigation. It is observed that 101 mm (Width Ratio , WR =0.36) with concave blocking plate provides maximum C p of 0.245 at 0.99 TSR. Also, the optimum region is found between 0.143 and 0.286 Distance Ratio (DR) from the top corner of the acrylic plate. The concave blocking plate enhance the performance more compared to flat blocking plate. The conclusion is made that, the performance with flat blocking plate with optimized width and location improve the power coefficient by 10.92% and that of concave blocking plate by 18.45%. • Energy extraction using Darrieus hydrokinetic turbine is one of the affordable and clean energy generation techniques. • The performance of the Darrieus water turbine has been compared with a bare rotor and with using two different types of blocking plates. • The concave and flat blocking plates have been experimentally analyzed, and its effect has been explored in the paper. • The different sizes of concave and flat blocking plates were placed at the upstream side of the Darrieus turbine to enhance the performance. • The paper also includes the optimum region of the optimized shape concave and flat blocking plates placed at the upstream location of the rotor. [ABSTRACT FROM AUTHOR]

• A commuting-pattern-oriented representation of random driving conditions is developed. • Integrated stochastic optimization of powertrain design and control is engineered. • Machine-learning solvers for stochastic optimization are developed. • The stochastic optimization improves the expected energy economy by up to 12%. • Topology modification to EV powertrains improves energy economy by over 17%. The performance of an electric vehicle (EV) powertrain is greatly influenced by the design scheme, control strategy, and driving conditions. Owing to uncertainties in driving conditions and the mutual correlation between design scheme and control strategy, identifying the optimal topology and component parameters of EV powertrains for improving the energy efficiency of EVs remains a challenging task. Oriented to the commuting patterns of individuals, this paper proposes a stochastic optimization method for identifying the superior powertrain design that optimizes the expected energy economy of the EV powertrain and reduces the energy economy variability in random driving conditions. This paper also introduces a machine-learning solution to the complex mutual restrictions among design variables, control strategies, and driving conditions, which determines the feasible design space and powertrain performance. Suggested by massive validations, the stochastic optimization method reduces the expectation and deviation of the energy consumption of EV powertrains up to 12.0% and 5.6%, compared with deterministic optimization methods. Additionally, by comparative evaluation among stochastic optimal design results of different topologies, contributions of topology modifications to the energy efficiency of EV powertrains are clarified, and the superior powertrain topology is identified, which improves 17.1% expected energy economy compared with the current popular topology. [ABSTRACT FROM AUTHOR]

The new roadmap of the European Union (EU), the European Green Deal, aims at tackling climate adaptation, energy, biodiversity, and pollution challenges. To contribute to such aim, the latest EU Renewable Energy Directive defines for the first time renewable energy communities. Among them, Photovoltaic-Green roof Energy Communities (PGECs) emerge as a potential option in urban areas. This paper investigates whether and under which conditions PGECs are capable to meet the objectives of the European Green Deal in an economically convenient manner. Since some conditions are context-specific, this research is showcased using a case study (Luxembourg). First, European legislation was reviewed to determine the suitable legal model for PGECs. Second, a systematic literature review helped identifying lifecycle costs and benefits of photovoltaic-green roofs and their value ranges. Third, a model for probabilistic social and private cost-benefit analyses was developed and tailored to Luxembourg. Lastly, Scenario Discovery was used to identify the ranges of input values leading to desirable net present values. Results show that PGECs can contribute to achieving multiple objectives of the European Green Deal in an economically convenient manner. From the societal perspective, PGECs are found to be economically convenient for any cost, benefit, and discount rate in the case study. From the private perspective, PGECs remain convenient in 62% of the scenarios, with green roofs' installation cost and electricity generation benefit playing pivotal roles. This paper presents a rare combination of probabilistic cost-benefit analyses and scenario discovery, It supports policymakers designing incentive schemes for PGECs, and can be replicated in other countries. [Display omitted] • Review of European legal models for Photovoltaic-Green Roof Energy Communities─PGECs. • The European Renewable Energy Community is the most suitable legal model for PGECs. • Qualitative and quantitative costs-benefit analysis of photovoltaic-green roofs. • PGECs prove economically convenient from private and social cost-benefit approaches. [ABSTRACT FROM AUTHOR]

[Display omitted] • Tungsten oxide doped carbon (WO 3 /C) catalyst was firstly prepared for HQ/BQ RFB. • The WO 3 /C shows outstanding electrocatalytic activity and reversibility. • The WO 3 /C catalyst boosted the energy efficiency of HQ/BQ flow battery (∼70%). The research in organic flow batteries is emerging as the cost of the vanadium electrolyte limits the grid-scale application. Tungsten trioxide doped carbon (WO 3 /C) electrocatalyst is developed to examine its potential in hydroquinone - benzoquinone redox flow battery (HQ/BQ RFB). The catalyst is characterized by a field emission scanning electron microscope (FESEM), X-ray diffraction (XRD), and fourier transform infrared spectroscopy (FTIR) and confirms the formation of active WO 3 /C electrocatalyst. The electrochemical activity of the synthesized WO 3 /C electrocatalyst is inspected by cyclic voltammetry (CV). It shows comparatively high anodic and cathodic peak currents, low charge transfer resistance, and relatively high electrokinetic reversibility. The charge–discharge test on single cell HQ/BQ RFB is conducted using pristine carbon paper (CP), and WO 3 /C coated carbon paper (WO 3 /C-CP). The columbic efficiency (CE), voltage efficiency (VE), and energy efficiency (EE) of the RFB using WO 3 /C-CP are around 90%, 75%, and 70%, respectively, which are significantly higher than the CP. The improvement in results demonstrates that the WO 3 /C electrocatalyst is better at providing active sites and improving the electrochemical reactions kinetics, thus suits the application of HQ/BQ RFB. [ABSTRACT FROM AUTHOR]

Existing pieces of literature on previous studies advocate the research focus by various researchers to reach the benchmark of energy efficiency of Wastewater Treatment Plants (WWTPs). The driving factors to improve the energy efficiency and mitigate the energy self-sufficiency of WWTP have been identified as the increase in population growth, rising energy costs, and tightening effluent discharge requirements. WWTP economy is directly related to energy consumption and thus affects smart grid economy. There has been limited research on energy self-sufficiency and optimizing the energy demand and cost of WWTP, where the significant contributing factors are in development an optimal pumping system and advanced motor technologies. Moreover, high quality WWTP's effluent depends on the concentration level of biochemical oxygen demand (BOD) and total kjedhal nitrogen (TKN). Even so, WWTPs are always subject to evident nonlinearities and uncertainties, making it difficult to define proper optimization objectives. Besides, controllers and control systems have a strong influence on the performance of the plants. Therefore, existing research gaps to achieve optimal efficiency with minimized energy consumption and costs are the design of the WWTP plant, pumping system, motors, selection of suitable controllers and control systems, and their parameter optimizations to get the optimal output from the plant, switching techniques, challenges and uncertainties associated with plant are highlighted in this paper. Thus, as a novel contribution to the literature, this study aims to review and analyze the history, current issues, and future directions of WWTP control technologies in the context of sustainable development. The rigorous study by the authors in this paper will definitely lead the academic researchers and industry partners toward the development of optimal WWTP technologies with improved efficiency and minimized energy consumption and costs. • Effective control techniques and monitoring of the wastewater treatment plant. • Optimal energy-efficient WWTPs with renewable sources and the utility grid. • Uncertainties of WWTP can be considered for improved operation. • Effective policies should be imposed by combining academics and industry. • Reduced energy consumption and cost can be achieved. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of the current regulations and the various technologies as well as the decision support methods for each technology the maritime industry considers to ensure the fleet's sustainability. It covers the period between 2010 and 2022, emphasizing the last four years. It shows the impact of each technology on the reduction of ship resistance and the energy required on board, affecting the amount of fuel consumption and avoiding the transportation of harmful species around the world to achieve a smooth transition towards green shipping by improving the fleet's energy efficiency and achieving the goals of the 2050 plan. The paper covers five main topics: hull design, propulsion systems, new clean fuels and treatment systems, power systems and ship operation; and each topic has different technologies included. This study's findings contribute to mapping the scientific knowledge of each technology in the maritime field, identifying relevant topic areas, visualising the links between the topics, and recognising research gaps and opportunities. This review helps to present holistic approaches in future research supporting the cooperation between maritime industry stakeholders to provide more realistic solutions toward sustainability. • A review is presented of the various technologies considered for the transition to green shipping and ship decarbonization. • The paper covers hull optimization, alternative fuels, engine technologies, slow steaming speed, after-treatment systems. • It covers also propeller selection, waste heat recovery systems, DC distribution systems, wind energy, ship coatings. • Finally it deals with ballast water air lubrication technologies and steering gears. • A bibliometric analysis is performed, showing statistics about the papers on each topic. [ABSTRACT FROM AUTHOR]

This paper addresses the design and implementation of a model predictive control framework for temperature control in buildings zones via direct control of their thermal energy inputs. Comfort-centric approach in ensured by selecting building thermal zones to be equal to the physical building rooms. The framework integrates different identification and estimation technologies, machine learning and model predictive control to assure systematic handling of non-modelled disturbances and offset-free control. It is envisioned as the lowest level in the hierarchical decomposition of building subsystems responsible for comfort and shaping the overall thermal energy consumption in building zones. The paper shows how it is deployed on a full scale occupied skyscraper building. To enable optimization of the whole building behaviour a special focus is put on developing the possibility for interaction and coordination with other building subsystems or energy distribution grids. This ensures the scalability of the approach, computational relaxation, technology independency, cost-effective implementation and enables upscaling towards the smart grid and smart city concepts where buildings play decisive roles. [Display omitted] • Direct control of thermal energy per zone. • Enabled interaction with other building subsystems. • Integral part for upscaling towards smart grid and smart city concepts. • Deployment and verification on a scale of the whole skyscraper building. • Modular service built on top of the existing building automation infrastructure. [ABSTRACT FROM AUTHOR]

A new PEMEC system without integrated cooling channels inside the PEMEC stack is analyzed in this paper. An external energy source is integrated into the heat exchanger to control the stable operation of the system. Based on the balance of charge, mass and energy, the models of both the stack and the system are established. The influences of operating parameters, such as inlet water flow rate, inlet water temperature and current density, on the PEMEC stack performance are investigated, and the energy efficiency and exergy efficiency of the PEMEC system under both the heat dissipation condition and the adiabatic condition are discussed. The results show that the purpose of keeping the operating temperature of the stack can be achieved by regulating the inlet water temperature and flow rate. A relatively good operating mode is a low inlet temperature, an insulated stack and a current density in range of 1.0 and 2.0 A/cm2. The present work can provide some guidances for the optimum design of the PEMEC system. [ABSTRACT FROM AUTHOR]

Energy efficiency interventions in Water Supply Systems (WSSs) need a precise evaluation of the available water flow rates for energy recovery interventions; however, flow meters are generally too costly for being installed in all the gravity adduction pipelines of a WSS. This paper presents a methodology for predicting flow rates in gravity adduction pipelines based on the electricity bill consumption. In this study, the predicted average flow rate is 0.0300 m3∗s−1, being 1.64% lower than the real one. A Pelton turbine has been chosen as energy recovery unit for supplying electricity to a pumping station of a preloading tank where the water is treated to make it drinkable. An energy saving of 475.26 (MW∗h)∗year−1 is achieved, which can be also expressed as 88.87 saved Tonnes of Oil Equivalent (TOE) and 204.36 ktCO 2 not released into the atmosphere. The gross economic saving due to the installation of the Pelton turbine is equal to 94.29 k€∗year−1 and it can be further increased up to 116.51 k€∗year−1 if the energy efficiency certificates issued by the Italian Authorities are considered. The Payback Period (PBP) of the intervention corresponds to 3 years, and a Net Present Value (NPV) after twenty years is approximately 1.4 M€. • A methodology to assess the energy recovery in Water Supply Systems is presented. • The methodology is used in gravity adduction pipelines linked to preloading tanks. • Measured flow rates have been used for validating the proposed methodology. • A Pelton turbine was used as energy recovery unit in a Water Supply System. • The achievable energy recovery potential in this study is 475.26 (MW∗h)∗year-1. [ABSTRACT FROM AUTHOR]

• Refrigerator modeling • Incremental capital and recurring cost of alternative designs • Energy efficient refrigerator design options • Energy efficiency – cost tradeoff • Lifecyle emissions reduction Domestic refrigeration appliances are ubiquitous consuming roughly 14% of the global residential electrical consumption. It is estimated that the global installed stock of domestic refrigerators is between 2 and 2.3 billion units. Furthermore, domestic refrigerators employ high global warming potential (GWP) refrigerants (e.g., R-134a). The global goal to limit the global warming to 1.5°C requires rapid reduction in CO 2 emissions and other greenhouse gases (GHGs). Transition towards cost-effective, efficient, and environmentally friendly refrigeration technologies is considered as one of the most effective measures to reduce GHG emissions. In this paper, we worked with 5 manufacturers in 4 developing countries to study energy efficiency (EE) design options and the associated incremental capital cost and incremental recurring cost (IRC). In our study, we have accounted for their product development capabilities, their productions volume, their relationship with original equipment manufacturers (OEMs), and the local product competition in their markets. Our detailed study showed that cost effective energy savings of 9.5 to 19.2% can be achieved while the maximum EE upgrades varied between 30.2 to 50%. Furthermore, the corresponding indirect emissions reduction varied by the local electricity production mix and the baseline technology. The cost-effective EE upgrades result in emission reduction of 65,145 to 612,959 metric tons of CO 2eq while the maximum EE upgrades result in emission reduction of 157,675 to 1,077,005 metric tons of CO 2eq. [ABSTRACT FROM AUTHOR]

In a typical embedded CPU, large on-chip storage is critical to meet high performance requirements. However, the fast increasing size of the on-chip storage based on traditional SRAM cells makes the area cost and energy consumption unsustainable for future embedded applications. Replacing SRAM with DRAM on the CPU's chip is generally considered not worthwhile because DRAM is not compatible with the common CMOS logic and requires additional processing steps beyond what is required for CMOS. However a special DRAM technology, Gain-Cell embedded-DRAM (GC-eDRAM) [1–3] is logic compatible and retains some of the good properties of DRAM (small and low power). In this paper we evaluate the performance of a novel hybrid cache memory where the data array, generally populated with SRAM cells, is replaced with GC-eDRAM cells while the tag array continues to use SRAM cells. Our evaluation of this cache demonstrates that, compared to the conventional SRAM-based designs, our novel architecture exhibits comparable performance with less energy consumption and smaller silicon area, enabling the sustainable on-chip storage scaling for future embedded CPUs. • This work exploits the latest advances in Gain-Cell technology and analyzes its potentials in low Power On-Chip memory implementation. • To our knowledge, this is the first proposal of SRAM and Gain-Cell eDRAM hybrid architecture in first level data (L1D) cache and L2 cache (SRAM tag array and GC-eDRAM data array). • We analyze tradeoffs in performance, area and power of our proposed hybrid architecture in comparison to conventional SRAM baseline. [ABSTRACT FROM AUTHOR]

Abstract: A preliminary bottom-up analysis of the energy use in the chemical industry has been performed, using a model containing datasets on production processes for 52 of the most important bulk chemicals as well as production volumes for these chemicals. The processes analysed are shown to cover between 70% and 100% of the total energy use in the chemical sector. Energy use and the heat effects of the reactions taking place are separately quantified. The processes are also compared with energetically-ideal processes following the stoichometric reactions. The comparison shows that there is significant room for process improvements, both in the direction of more selective processes and in the direction of further energy-savings. [Copyright &y& Elsevier]