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This study delves into the dynamic evolution of solids content in a pilot-scale through air drying tissue machine, aiming to enhance comprehension and refine optimization strategies for tissue manufacturing. It focuses on development of solids content throughout the process. Analyzing the interaction between process parameters and solids content provides deeper insights into water removal dynamics. The research employs solid contents monitoring techniques in pilot scale, offering a detailed view of solids content evolution from the wet web's inception to the final tissue product. It significantly highlights the impact of key parameters, particularly pulp refining, on solids content across all positions of the pilot machine. Additionally, the study emphasizes the influence of vacuum system configurations, identifying the relationship between energy expended in the vacuum system and evaporation energy. Furthermore, the results indicate that compression and air displacement dewatering in vacuum boxes are less speed-sensitive compared to heat-driven evaporation in the TAD- and Yankee cylinders. Solid contents at all positions in the tissue machine correlates between various dewatering elements. Optimizing site-specific dewatering and evaporation strategies can potentially save drying energy in the Yankee drying phase which hold promise for enhanced energy and resource utilization, aligning with demands for sustainable manufacturing practices. [ABSTRACT FROM AUTHOR]

The pulp and paper industry (PPI) has several opportunities to contribute to meeting prevailing climate targets. It can cut its own CO2 emissions, which currently account for 2% of global industrial fossil CO2 emissions, and it has an opportunity to produce renewable energy, fuels, and materials for other sectors. The purpose of this study is to improve understanding of the decarbonization prospects of the PPI. The study provides insights on the magnitude of needed annual renewal rates for several possible net-zero target years of industrial fossil CO2 emissions in the PPI and discusses decarbonization opportunities, namely, energy and material efficiency improvement, fuel switching, electrification, renewable energy production, carbon capture, and new products. The effects of climate policies on the decarbonization opportunities are critically evaluated to provide an overview of the current and future business environment of the European PPI. The focus is on Europe, but other regions are analyzed briefly to widen the view. The analysis shows that there are no major technical barriers to the fossil-free operation of the PPI, but the sector renovates slowly, and many new opportunities are not implemented on a large scale due to immature technology, poor economic feasibility, or unclear political environment. [ABSTRACT FROM AUTHOR]

State-of-the-art manufacturing of tissue paper by Through Air Drying provides excellent product performance, although at a high production cost and energy use. In this work, a laboratory scale vacuum suction box was used to mimic the initial dewatering and the Through Air Drying molding, together with a pilot-scale trial. The purpose was to investigate both how the laboratory scale corresponds to pilot scale testing and investigate how fabric design, basis weight, beating, and fibers affect dewatering and sheet caliper. This study reevaluates dewatering mechanisms during molding, challenging the previous hypothesis of pure air displacement dewatering. Results show a parallel mechanism of compression dewatering and air displacement. The influence of rush transfer is examined, impacting the sheets' visual appearance, thickness, and solids content. Correlations between molding box solids content and headbox freeness emphasize significance of fibers and beating levels. Pilot results confirm the link between former solids and molding box solids. Pilot trials validate the laboratory results, facilitating comprehensive simulation of full-scale manufacturing. This research reveals dewatering mechanisms, highlights operational parameters, and enables effective Through Air Drying process design and refinement. [ABSTRACT FROM AUTHOR]

Producing tissue paper is an energy-demanding process; a significant amount of energy is expended when removing water by vacuum, mechanical pressing, and thermal drying. Because the water is most energy-demanding to remove in drying, making the preceding step of vacuum dewatering more efficient would benefit the whole process. This article focuses on developing a laboratory-scale method for verifying the nature of diminishing returns of water removal and investigating efficiency strategies of the vacuum dewatering. The theoretical concept of successive vacuum suction boxes with progressing vacuum levels was tested at the laboratory scale in order to show quantifiable results of the previously solely theoretical concept. The results confirmed that vacuum dewatering can be improved by adding progressively higher vacuum levels and that such a practice can benefit both outgoing dryness levels and expended vacuum pump energy. To truly examine the power of progression of vacuum levels, rewetting can be included in the calculations, by using an approximate value collected from pilot or fullscale measurements. For any new fiber mix, pulp type, vacuum level setup, basis weight, etc. the vacuum levels, rewetting, and dwell times need to be tuned to that specific case. [ABSTRACT FROM AUTHOR]

This paper investigates the connection between climate change and energy security in Europe and provides empirical evidence that these issues are the two faces of the same coin. Using a panel of 39 European countries during the period 1980–2020, the empirical analysis presented in this paper indicates that increasing the share of nuclear, renewables, and other nonhydrocarbon energy and improving energy efficiency could lead to a significant reduction in carbon emissions and improve energy security throughout Europe. Accordingly, policies and reforms aimed at shifting away from hydrocarbons and increasing energy efficiency in distribution and consumption are key to mitigating climate change, reducing energy dependence, and minimizing exposure to energy price volatility. [ABSTRACT FROM AUTHOR]

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks' energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks' energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices' energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future. [ABSTRACT FROM AUTHOR]

The pulp and paper industry faces several challenges linked to climate and environmental impact, resource efficiency, rising energy prices, increased competition for biomass resources, and declining demand for traditional printed paper products. However, these challenges also offer strategic opportunities for the industry to develop into a competitive, resource-efficient, and low-carbon industry in line with a biobased economy. Against this background, this paper aims to analyse current energy strategies in the pulp and paper industry in Sweden. Specifically, the paper analyses how companies combine continuous process efficiency to reduce energy costs with activities that could be developed into new energy-related products to increase revenue. Most of the analysed companies work to various degrees with both these strategies, employing methods that include improving energy efficiency, energy security, and energy conversion, as well as developing a wide range of biobased energy products. However, our study indicates that there is an untapped potential associated with energy product development, and we conclude that energy efficiency measures can free up resources, enabling the development of new energy products. Finally, several potential managerial outcomes and implications are outlined. • The paper analyses the energy strategies in the pulp and paper industry in Sweden. • Energy strategies focus both to improve energy efficiency and develop new energy-products. • The companies' energy strategies combined exploitation and exploration. • A number of managerial implications are presented. [ABSTRACT FROM AUTHOR]

The research problem of this paper is related to numerous open questions in the field of energy, its understanding, its use, and the challenges of the energy future. After the introductory part, in which a brief historical overview of energy literacy is provided, the paper focuses on energy literacy, its emergence, and the different approaches to its definition and measurement. The paper analyzes the energy literacy of students at the Faculty of Economics in Rijeka based on their cognitive, affective, and behavioral skills. Their knowledge about energy, energy processes, and the energy efficiency of the objects they encounter on a daily basis is examined. Their personal attitudes and values are examined through various forms of personal initiatives and active participation in energy sustainability projects. Finally, the habits and behavioral patterns that the respondents have in their daily lives and their tendencies to save energy resources and find energy-efficient solutions are examined. Despite some positive findings, the current results are not satisfactory and point to the creation of adequate public policies with a particular focus on education and the role of the corrective mechanisms of the state, but also the need for joint negotiations between policy makers, regulators, scientists, representatives of civil society, and the business community. The particular contribution of this work is reflected in being the first research of its kind conducted among Croatian economics students, as well as among Croatian students in general; in the creation of a research instrument that is conceptually consistent with the findings from the existing literature, but with an original set of questions within each energy literacy skill adapted to the cultural and sociological background of the respondents; and in a kind of progress from previous research by taking into account the quantitative aspect (in addition to the qualitative) assessment of the respondents' energy literacy. [ABSTRACT FROM AUTHOR]

Traditional buildings have a central role to play in Europe's plans to be the first climate-neutral continent by 2050, with the building sector being the single largest energy consumer in the EU with 40 per cent of total energy use. Seventy-five per cent of the existing housing stock is energy inefficient with minute numbers renovated each year. Existing buildings offer huge potential for energy use reduction. Research has shown that a historic retrofit can emit less carbon compared to demolishment and rebuilding. Reductions in carbon emissions of up to 62 per cent were observed across a body of case studies by Historic England, including a 95 per cent reduction in the mews case study described in this paper. Moreover, carefully managed thermal retrofits, taking a 'whole house approach', have many benefits, including improved indoor comfort and air quality as well as reduced operational costs. These are factors which are fundamental in ensuring the continued use of historic constructions while improving their standards of preservation and durability. Thermally upgrading traditional building is still a much-debated topic. Inappropriate thermal interventions with unsuitable materials and finishes can have unforeseen consequences, resulting in building damage and a reduction in internal comfort, health and well-being. This paper aims to contribute to this discussion by providing an overview of key considerations when thermally upgrading the walls of traditional buildings while preserving unique characteristics and functionality. Advantages of adopting thermal mortars such as cork lime are put forward, and their potential efficacy is discussed, concluding with a case study focusing on the deep retrofit of a London mews within a conservation area. [ABSTRACT FROM AUTHOR]

The global energy crisis, which began in 2021 due to the extraordinary economic recovery after the pandemic and intensified after Russia's invasion of Ukraine in February 2022, has changed the conditions of energy management, paying more attention to energy efficiency. Natural gas prices have reached record levels and, consequently, so have electricity prices in some markets. Oil prices have reached their highest level since 2008. Higher energy prices have contributed to sharply increased inflation. Households are again becoming interested in buying coal as a source of heat. High energy and gas prices have pushed many families into poverty and forced some factories to cut production or even close. They have also slowed economic growth to the point where some countries are heading for a serious recession. Paradoxically, the negative effects of the energy crisis may accelerate the introduction of cleaner, sustainable, renewable energy such as wind and solar energy. The energy crisis is comparable to the oil crisis of the 1970s, when it contributed to significant advances in energy efficiency. The current crisis has highlighted the importance of investments in renewable energy resources and initiated the process of integrating regional markets, developing energy efficiency and promoting renewable energies. The aim of this article is to comprehensively explore the complex relationship between energy awareness, consumption patterns, and energy efficiency, with a focus on both individual consumers and industries, during the global energy crisis. This paper is based on a literature review, overarching policy documents, energy reports, and other secondary documents. The primary research method was the systematic literature review method, based on which the impact of the global energy crisis on energy efficiency was evaluated. This study emphasizes the diverse influences on energy awareness, ranging from economic factors to consumer preferences and environmental consciousness. The findings of the paper underscore the significant responsibility of industries in contributing to energy-saving efforts and the active role of consumers in the energy market. The responsibility of industries in contributing to energy efficiency is highlighted, with a call for a comprehensive approach that integrates energy-saving criteria into product development and corporate social responsibility. [ABSTRACT FROM AUTHOR]

The object of the research of the article is transport system of a city. The paper reviews the necessity to assess the greenhouse emissions in the city and proposes the methods for greenhouse emissions inventory of the urban transport system. The proposed approach is aligned with guidelines for the development of Sustainable Energy and Climate Action Plan (SECAP) of Mayor Covenants of European Union (EU). The methodologies outlined in the paper allow to estimate annual greenhouse emissions from transport sector. The SECAP defines transport sectors based on ownership and functioning as following: municipal transport fleet, public transport, and private and commercial transport. The paper proposes the methodology to estimate direct and indirect emissions in each of the described sector based on the information that is typicaly available to municipalities in Ukraine. The assessment is conducted on disaggregated level for different fuel types (diesel, petroleum, natural gas, biofuel, electricity, etc.) and separately for each fleet type (buses, trucks, passenger vehicles, specialized machinery). Total CO2 emissions are then estimated by multiplying the amount of fuel consumed by the emission factor for each fuel type and vehicle type. Information of fuel consumption is estimated based on annual milage and estimated based on available data and number of assumptions proposed in the paper. The proposed methodology for greenhouse emissions inventory for the transport sector allows to analyze available data, recommendations for data collection and a methodology for determining CO2 emissions from the operation of the transport system with sufficient accuracy of calculations. Based on these results, it is possible to forecast changes in energy consumption and emissions in the transport sector as a result of various interventions. For example, in Zhytomyr city the results of inventory were used to develop a set of measures, which include updating the rolling stock of electric transport; increasing the energy efficiency of the power grids of the transport system, developing cycling infrastructure. [ABSTRACT FROM AUTHOR]

A wireless body area network (WBAN) is a type of wireless sensor network that plays a crucial role in monitoring patient healthcare. In this network, sensor nodes are typically placed inside or outside the patient's body. These nodes are capable of transmitting data to the sink node when any functional changes in the patient are detected. The routing and energy efficiency of the network nodes are challenging tasks in WBAN, as each sensor node operates on a battery. Therefore, in multi-hop routing designing a robust routing protocol has a significant impact on reducing energy consumption during the selection of the next hop. In this paper, a simple novel routing protocol named simple energy-aware and reliable (SEAR) routing protocol is proposed to transmit reliable data packets in a WBAN. The proposed routing scheme considers the remaining energy of sensor nodes, priority data, and hop count to the sink node as significant metrics for dynamically selecting the best forwarder node. Furthermore, the proposed protocol utilizes the route reliability factor (RRF) to select the optimal route among all possible routes between the source sensor node and the sink node. RRF selects the route with the maximum route residual energy and minimum hop count. As a result, SEAR has the ability to provide effective single-hop and multi-hop routing data transmission to improve the reliability of data transmission, decrease the energy consumption of the sensor nodes, and prolong the lifetime of the network. The simulation results show that the performance of the SEAR routing protocol outperforms the existing routing protocols for the metrics: packet loss ratio, throughput, end-to-end delay, normalized routing load, energy consumption, and network lifetime. The results indicate that the proposed protocol improves total energy consumption by 18.76% and 10.89% when compared to EERR-RLFL and AMCRP respectively. Meanwhile, SEAR reduces the average end-to-end delay by 17% and 9%, packet loss ratio by 29.48% and 17.69%, and normalized routing load by 31.17% and 20.91%. Additionally, SEAR achieves up to 16% and 9.71% higher throughput compared to EERR-RLFL and AMCRP routing protocols respectively. Overall, the results obtained indicate that the proposed SEAR routing scheme significantly enhances the overall performance of the network. [ABSTRACT FROM AUTHOR]

The paper describes a new method for determining the energy efficiency of the life cycles of wind farms by the aggregated data of energy costs. The rationale for the use of aggregated data to determine energy costs during the life cycle of the wind farms is given. The classification of wind turbines and wind farms elements by the parameters and technical characteristics of the elements with subsequent division into groups for which aggregated data of energy costs are determined is given. Within the framework of the method have been developed an algorithm for determining energy costs on the production of elements of wind turbines and wind farms and formulas for their calculation during the life cycle of wind farms. In order to test the method, energy cost was calculated during the life cycle for two wind farms with wind turbines that differ parameters and technical characteristics of the elements and their energy efficiency was determined. It has been shown that the use of a hydrogen storage unit as part of a wind turbine makes it possible to efficiently use energy during down periods and increase the efficiency of the installation by 25–30%. It is noteworthy that a wind-hydrogen farm allows not only to accumulate excess energy during lean periods, but also to save the resource of wind turbines. When the energy reserve in the hydrogen battery reaches close to full, part of the wind turbines of the wind-hydrogen farm can be automatically temporarily stopped in a given order. This technology can provide an extension of the warranty service life for each wind turbine. [ABSTRACT FROM AUTHOR]

This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system's robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology. [ABSTRACT FROM AUTHOR]

This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system's robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology. [ABSTRACT FROM AUTHOR]

Summary: The solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation. [ABSTRACT FROM AUTHOR]

The measures needed to limit global warming pose a particular challenge to current fossil fuel exporters, who must not only decarbonise their local energy systems, but also compensate for the expected decline in fossil fuel revenues. One possibility is seen in the export of green hydrogen. Using Algeria as a case study, this paper analyses how different levels of ambition in hydrogen exports, energy efficiency and fuel switching affect the cost-optimal expansion of the power sector for a given overall emissions reduction path. Despite falling costs for photovoltaics and wind turbines, the results indicate that in countries with very low natural gas prices, such as Algeria, a fully renewable electricity system by 2050 is unlikely without appropriate policy measures. The expansion of renewable energy should therefore start early, given the high annual growth rates required, which will be reinforced by additional green hydrogen exports. In parallel, energy efficiency is a key factor as it directly mitigates CO2 emissions from fossil fuels and reduces domestic electricity demand, which could instead be used for hydrogen production. Integrating electrolysers into the power system could potentially help to reduce specific costs through load shifting. Overall, it seems advisable to analyse hydrogen exports together with local decarbonisation in order to better understand their interactions and to reduce emissions as efficiently as possible. These results and the methodology could be transferred to other countries that want to become green hydrogen exporters in the future and are therefore a useful addition for researchers and policy makers. • The required strong capacity expansion for PV and wind is challenging, but feasible. • "Energy efficiency first" benefits both the local energy transition and H2 exports. • Fully renewable power systems require a suitable political framework. • Higher hydrogen exports might help to reduce the specific power system cost. • Fossil capacity expansion must be stopped to avoid lock-ins and higher future costs. [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]

Climate change has emerged as a global focal point, notably affecting vulnerable countries like Pakistan. The country faces heightened risk, prompting concern among many due to its susceptibility to climate-related hazards. An imperative challenge arising from this phenomenon is the necessary shift towards sustainable energy, particularly concerning energy consumption within the building sector. Buildings significantly contribute to daily energy usage and subsequent GHG emissions. In Pakistan, transitioning to energy-efficient building stock holds pivotal importance in mitigating climate change impacts. This hinges on establishing a robust institutional framework and regulatory structure centered around energy efficiency. While Pakistan possesses policies and national guidelines advocating for energy-efficient buildings, their practical enforcement and application remain inadequate. The inception of the Building Energy Code of Pakistan in 1990, based on ASHRAE 90.1 standards, marked a step forward. However, obstacles rooted in institutional weakness and limited capacity building have impeded its effective adoption and adherence. Although the Energy Provisions of 2011 introduced modifications, their scope remained confined mainly to commercial structures. This paper conducts an extensive assessment of Pakistan's existing institutional and regulatory framework for building energy efficiency and transition. Drawing on literature insights and stakeholder perspectives, an in-depth understanding of prevailing policies and institutions uncovers underlying reasons for noncompliance and limited implementation. Additionally, input was gathered from stakeholders through interviews, aiming to identify barriers obstructing the reinforcement of building energy efficiency measures in Pakistan. Discourse and narrative analysis, informed by literature and stakeholder interviews, revealed the significance of awareness, participation, coordination, capacity building, technical expertise, finance, and resources as key factors contributing to deficient compliance and implementation of existing codes and policy guidelines. This paper also proposes actionable recommendations to expedite the transition towards a sustainable, energy-efficient building stock in Pakistan. [ABSTRACT FROM AUTHOR]

This research introduces an innovative analytical framework for studying the behavior of electric vehicles (EVs) in power grids. The approach primarily examines the dynamic State of Charge (SOC) and energy reserves of EV batteries. By thoroughly examining patterns of EV charging demand and operational states (charging, discharging, idling, and waiting) across various time intervals, including day and night periods, this study aims to provide a comprehensive understanding of EV dynamic behavior in power networks. The proposed model goes beyond traditional assessments by incorporating human behavioral patterns and responsiveness to economic signals, specifically investigating the impact of electricity pricing on user behavior. The core analysis centers on exploring the Vehicle-to-Grid (V2G) concept, viewing EVs not only as energy consumers but also as potential contributors to grid stability and efficiency. The study delves into how the interplay of dynamic EV behavioral characteristics and owner responses to pricing influences the overall energy consumption curve of the grid, thereby shaping the capability of EVs in providing grid support services. To quantitatively validate the effectiveness of the model, a detailed case study is conducted, offering empirical evidence of its practical utility. The research findings are quantified, highlighting the significant implications for decision-makers, grid managers, and stakeholders in the energy sector. The paper emphasizes the main outcomes, demonstrating how the proposed analytical framework can empower stakeholders in making informed decisions about policy and infrastructure development. The robust methodological approach presented in this paper serves as a valuable tool for evaluating the impacts of adopting V2G, providing strategic insights for a more sustainable and economically viable paradigm [ABSTRACT FROM AUTHOR]

This paper proposes an energy-efficient multi-level sleep mode control for periodic transmission (MSC-PUT) in private fifth-generation (5G) networks. In general, private 5G networks meet IIoT requirements but face rising energy consumption due to dense base station (BS) deployment, particularly impacting operating expenses (OPEX). An approach of BS sleep mode has been studied to reduce energy consumption, but there has been insufficient consideration for the periodic uplink transmission of industrial Internet of Things (IIoT) devices. Additionally, 5G New Reno's synchronization signal interval limits the effectiveness of the deepest sleep mode in reducing BS energy consumption. By addressing this issue, the aim of this paper is to propose an energy-efficient multi-level sleep mode control for periodic uplink transmission to improve the energy efficiency of BSs. In advance, we develop an energy-efficient model that considers the trade-off between throughput impairment caused by increased latency and energy saving by sleep mode operation for IIoT's periodic uplink transmission. Then, we propose an approach based on proximal policy optimization (PPO) to determine the deep sleep mode of BSs, considering throughput impairment and energy efficiency. Our simulation results verify the proposed MSC-PUT algorithm's effectiveness in terms of throughput, energy saving, and energy efficiency. Specifically, we verify that our proposed MSC-PUT enhances energy efficiency by nearly 27.5 % when compared to conventional multi-level sleep operation and consumes less energy at 75.21 % of the energy consumed by the conventional method while incurring a throughput impairment of nearly 4.2 % . Numerical results show that the proposed algorithm can significantly reduce the energy consumption of BSs accounting for periodic uplink transmission of IIoT devices. [ABSTRACT FROM AUTHOR]

In offshore winch drive applications, determining the required number of pistons in digital displacement motors is critical for minimizing torque ripples. Digital displacement motors have shown promise for improving energy efficiency for offshore operations, such as placing equipment on the seabed or mineral drilling. However, they are known for exhibiting significant torque ripples, which can affect load-handling precision. This paper estimates the required number of pistons for realizing a digital hydraulic winch drive based on information from a commercial winch. The proposed drive employs full-stroke displacement strategies at high speeds and partial-stroke at low speeds. By simulating steady-state operations, this study correlates torque output with position oscillations. The results show that 37 pistons are required to keep position oscillations below a benchmark threshold of 10 mm throughout the drive's operating range to avoid hindering the drive's performance. However, such a high piston count could result in high costs due to the large, expensive valves required for partial-stroke operations. Therefore, this paper suggests an alternative drive topology for future research, which could potentially reduce the number of pistons that are operated with partial strokes. [ABSTRACT FROM AUTHOR]

Under the dual pressure of energy consumption and environmental pollution, mankind hopes to develop clean and renewable alternative energy, and the rapid development of fuel cells meets people's demand for energy-efficient power systems. The emergence of portable micro energy systems represented by microfluidic fuel cells, such as paper-based microfluidic fuel cells, has greatly enriched the means of medical detection to better cope with the threat of disease transmission. In this work, the numerical simulation method is innovatively introduced to study the paper-based microfluidic fuel cells. Both transient and steady-state modes are employed to demonstrate the whole operation process of the paper-based microfluidic fuel cell. In addition, the different structural parameters, including electrode spacing, the distance between electrode and inlet, channel thickness, and electrode length, are also investigated their influence mechanisms on cell performance. Results show that the increase of most structural parameters decreases cell output power in different degrees. Even on the premise that increasing channel thickness has a positive impact on the output power, the fuel utilization still shows a downward trend. These conclusions provide theoretical support and reference for future optimization work and accelerate the development of microfluidic fuel cells. • Numerical simulation is employed to study paper-based microfluidic fuel cell. • The effects of structure parameters on cell performance are discussed. • Through fuel concentration distribution to reveal cell performance mechanism. • Fuel utilization is used to evaluate energy efficiency. [ABSTRACT FROM AUTHOR]

Carbon neutrality is the only way for green low-carbon development. Technological progress is an important starting point for improving energy efficiency. Through scientific and technological innovation, improving energy efficiency is a practical way to achieve the goal of carbon neutralization. Due to the limitation of data and index selection, there is no qualitative analysis of carbon neutrality in this paper. Compared with previous studies, the paper has the following contributions: Firstly, the energy efficiency score of China's provincial level is measured with the DEA method and it is found that the average energy efficiency score of the eastern region is the highest, followed by the western region, and finally the central region. The results also show that the energy efficiency is not in accordance with the economic growth. The regions with higher energy efficiency scores are not always economically developed because of huge energy consumption supporting the economic development. Secondly, the Tobit method is used to study the impact of the reverse technology spillover effect of OFDI on China's energy efficiency. The results show that the spillover effect has a significant positive effect on the improvement of China's energy efficiency, and has a positive impact on the eastern and western regions, but has no significant impact on the western region. Finally, we put forward some policy recommendations to increase energy efficiency to approach carbon neutrality and thus promote green economic development. This paper enriches the content of energy economy, the research method is innovative, and the conclusion is more scientific. [ABSTRACT FROM AUTHOR]

The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces its evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we improve networks' spectral efficiency (SE), extend networks' lifetime, and maximize networks' energy efficiency (EE) of two-way full-duplex (FD) relay networks. Firstly, to improve networks' SE and to extend networks' lifetime simultaneously, we design a two-way FD relay transmission strategy with simultaneous wireless information and power transfer and direct links (DLs). The designed transmission strategy can complete a bidirectional communication in only one time slot with the exists of DLs and the energy-constrained relay node. With the designed transmission strategy, we further give the characteristics of relay amplification factor, the analysis of the designed transmission strategy, and the EE analysis of traditional half-duplex two-way amplify-and-forward relaying. Secondly, to maximize networks' EE, we present both the EE maximization problems and analyses of the designed transmission strategy with equal power allocation and optimal power allocation. To solve the EE maximization problems, we further propose the alternating optimal algorithm and give complexity analysis of the algorithm. Simulations show that our designed transmission strategy can improve the SE and EE of the networks. [ABSTRACT FROM AUTHOR]

Cooling equipment is widely used in industry, commerce and households. Due to their widespread use, they are responsible for the consumption of a significant amount of electricity. They are subject to degradation and various types of damage. Most often, their energy efficiency decreases and electricity consumption increases. Practice shows that even a specialist service is unable to diagnose damage at an early stage of its development. The paper presents a comparison of continuous monitoring of the temperature of the cooling chamber as a utility standard, with constant monitoring of the temperature of the cooling chamber and electricity consumption of a professional refrigeration cabinet with a built-in condensing unit. The comparative analysis was intended to confirm the thesis about unconscious waste resulting from assessing the correct operation of the device based on limited information. The experiment showed an increase in daily electricity consumption on average by over 30% during the period of unconscious exploitation of the device in a state of failure and an increase in daily electricity consumption on average above 300% during the period of conscious exploitation of the device in a state of failure, but still at an acceptable level of temperature of cooling chamber. [ABSTRACT FROM AUTHOR]

This paper presents the findings of fifteen energy audits conducted on industrial sites in Canada's Greater Toronto Area (GTA). The audits covered a range of industries including food processing, packaged goods, and finishing processes (powder-coating). The primary focus of the audits was to analyze the natural gas consumption and the performance of major-gas-consuming equipment. The audits identified natural-gas-consuming equipment that could be optimized to yield energy and operational cost savings and greenhouse gas (GHG) reduction opportunities. Food production plants' energy intensity ranges from 5.59 m3/ft2 to 17.73 m3/ft2. Therefore, there is a significant opportunity to improve energy consumption through better technology integration. The results of the audits indicate a trend of an increase in the percentage of non-productive consumption with non-productive time. The proposed energy-saving measures include reducing non-productive natural gas consumption, gas-fired equipment tune-up, optimizing boiler loads, and reducing oven exhaust by using variable frequency drives (VFDs). The findings of this study could be used to develop a demand-side management program specifically for small- and medium-sized industrial facilities in the Greater Toronto Area and other parts of Canada. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering the complex dynamics of energy demand on a city-like load. The methodology is applied to a case study at the Federal University of Pará, Brazil, where energy efficiency and demand response initiatives as well as renewable energy projects are underway. The findings highlight the significance of tailored demand management strategies in achieving energy-related cost reduction for large-scale consumers, with implications for economic efficiency in energy consumption. [ABSTRACT FROM AUTHOR]

This paper offers a theoretical and experimental examination of the concurrent production of electricity and heat using photovoltaic thermal (PV/T) technology. The efficiency performance of the PV/T system is meticulously analyzed using MATLAB/Simulink software environments. Notably, the proposed PV/T system shows reliable performance, and its validity is confirmed through experimental validation on a test stand with a single PV/T panel positioned at a 45-degree angle to the horizontal and a 0-degree azimuth angle. The measurements were conducted during the summer season, and two models were suggested to calculate the overall efficiency of the PV/T system. The variance between the results obtained from the two models was minimal, below 5%. For the examined panel type, the following average values were derived: electrical efficiency = 12.01%, thermal efficiency = 47.21%, and overall efficiency = 59.23%. [ABSTRACT FROM AUTHOR]

In buildings, air conditioning and mechanical ventilation (ACMV) systems are the major shareholders of overall energy consumption. Energy-efficient designs for ACMV systems in building applications are therefore needed. While designing an efficient ACMV system, consideration must be given to the growing concerns of enhanced thermal comfort and improved indoor air quality. The variable refrigerant flow (VRF) air-conditioning system is a widely adopted alternative to the existing building cooling systems due to the higher energy efficiency and individualized temperature control feature. However, it still suffers from shortcomings such as no outdoor air induction for ventilation and higher initial cost. Therefore, this paper reviewed the variable refrigerant flow and mechanical ventilation/air distribution systems, their integrated designs for non-residential buildings, performance evaluation and control optimization of the integrated systems, VRF systems' faults detection and diagnosis, current application of the VRF systems, and associated challenges. Together with these all, some advanced buildings' cooling techniques and improvements toward nearly/net-zero energy buildings are briefly discussed. Indoor thermal comfort models and criteria for different climates are also presented for an in-depth understanding of the VRF integrated mechanical ventilation designs. The literature survey shows that the supply air temperature and airflow rate are foremost in parameters that can be optimized in VRF integrated ventilation design as they greatly reduce the energy consumption. Further, policies on elevated indoor temperatures in air-conditioned buildings to mitigate their carbon footprint are strictly being implemented. Therefore, this review provides an insight to the researchers for further improvement in the integrated design and control optimization of the parameters involved. A paradigm shifts from the conventional compression-based electric-powered air conditioning systems to the renewable energy driven advanced air conditioning technologies which is also an emerging research area to be focused on achieving the target of nearly/net-zero energy buildings. [ABSTRACT FROM AUTHOR]

Wireless Body Area Network (WBAN) is an interconnection of tiny biosensors that are organized in/on several parts of the body. The developed WBAN is used to sense and transmit health-related data over the wireless medium. Energy efficiency is the primary challenges for increasing the life expectancy of the network. To address the issue of energy efficiency, one of the essential approaches i.e., the selection of an appropriate relay node is modelled as an optimization problem. In this paper, energy efficient routing optimization using Multiobjective-Energy Centric Honey Badger Optimization (M-ECHBA) is proposed to improve life expectancy. The proposed M-ECHBA is optimized by using the energy, distance, delay and node degree. Moreover, the Time Division Multiple Access (TDMA) is used to perform the node scheduling at transmission. Therefore, the M-ECHBA method is used to discover the optimal routing path for enhancing energy efficiency while minimizing the transmission delay of WBAN. The performances of the M-ECHBA are analyzed using life expectancy, dead nodes, residual energy, delay, packets received by the Base Station (BS), Packet Loss Ratio (PLR) and routing overhead. The M-ECHBA is evaluated with some classical approaches namely SIMPLE, ATTEMPT and RE-ATTEMPT. Further, this M-ECHBA is compared with the existing routing approach Novel Energy Efficient hybrid Meta-heuristic Approach (NEEMA), hybrid Particle Swarm Optimization-Simulated Annealing (hPSO-SA), Energy Balanced Routing (EBR), Threshold-based Energy-Efficient Routing Protocol for physiological Critical Data Transmission (T-EERPDCT), Clustering and Cooperative Routing Protocol (CCRP), Intelligent-Routing Algorithm for WBANs namely I-RAW, distributed energy-efficient two-hop-based clustering and routing namely DECR and Modified Power Line System (M-POLC). The dead nodes of M-ECHBA for scenario 3 at 8000 rounds are 4 which is less when compared to the dead nodes of EBR. [ABSTRACT FROM AUTHOR]

In this paper, we outline a transdisciplinary approach and design thinking methodology (TADTM) to tackle complex problems. Our premise is that these problems need a fundamental understanding of technological solutions and those for human interactions, business operations, financing, socioeconomic governance, legislation, and regulations. They must be approached by different decisionmakers from different disciplines to establish seamless interactions and structured teamwork. In this regard, we emphasize the need for a transdisciplinary framework that accounts for personal preferences based on human behavior as well as the traditional interdisciplinary frameworks. To test and prove our hypothesis, three case studies are discussed. Case Study 1 is based on our studies at a major medical establishment, and Case Study 2 is about the integrated engineering and architecture approach we used at our university campus. Case Study 3 is based on an ongoing project to lead industrial corporations to change their energy policies with practical energy efficiency measures and by adapting renewable/alternative energy adaptations for their operations. Developing creative solutions and strategies to decrease atmospheric greenhouse gas emissions requires such an energy transition framework and should involve every person, company, entity, and all governments. It can only be achieved with efforts on both local and global levels, which needs to convince (a) industries to change their traditional operation modalities, (b) people to alter their consumption behaviors, and (c) governments to change their rules, regulations, and incentives. The complexity and magnitude of this enormous task demand the coordination and collaboration of all stakeholders, as well as the need for technological innovations. [ABSTRACT FROM AUTHOR]

Separate sensible and latent cooling (SSLC) is a promising technology for enhancing the energy efficiency of space cooling systems. In this paper, we propose an SSLC configuration consisting of an indirect evaporative cooling (IEC) device and vapor compression cycle (VCC) for handling space sensible load, and a desiccant wheel for handling space latent load. The performance of the proposed configuration is investigated using physics-based or empirical models for each component. A year-round simulation study of the proposed SSLC system is performed for 12 different cities with diverse climatic conditions. This study considers electrical, thermal, or solar energy for desiccant wheel regeneration. Parameters governing the system performance such as the air flowrate ratio in the IEC and the DW, DW regeneration air temperature, and supply temperature are studied. The proposed SSLC configuration is superior to the conventional system in all cities. The enhancement of seasonal coefficient of performance (sCOP) ranges between 50 and 261 %, 67 and 387 %, and 60 and 2072 % with yearly electrical energy savings of 33 and 72 %, 40 and 79 %, and 38 and 95 % for electrical, thermal, and solar energy for regeneration of DW, respectively. [ABSTRACT FROM AUTHOR]

In the context of an insulated area with a subtropical climate, such as La Réunion island, it is crucial to reduce the energy consumption of buildings and develop local renewable energy sources to achieve energy autonomy. Direct current (DC) nanogrids could facilitate this by reducing the energy conversion steps, especially for solar energy. This article presents the deployment and efficiency evaluation of a 48 VDC low-voltage direct current (LVDC) nanogrid, from conception to real-world installation within a company. The nanogrid consists of a photovoltaic power plant, a lithium–iron–phosphate (LFP) battery, and DC end-use equipment, such as LED lighting and DC fans, for two individual offices. For identical test conditions, which are at an equivalent cabling distance and with the same final power demand, the total power consumed by the installation is measured for several stages from 50 to 400 W, according to a 100% DC configuration or a conventional DC/AC/DC PV configuration incorporating an inverter and AC/DC converter. The methodology used enables a critical view to be taken of the installation, assessing both its efficiency and its limitations. Energy savings of between 23% and 40% are measured in DC for a power limit identified at 150 W for a distance of 25 m. These results show that it is possible to supply 48 VDC in an innovative way to terminal equipment consuming no more than 100 W, such as lighting and air fans, using the IEEE 802.3 bt power over ethernet (PoE) protocol, while at the same time saving energy. The nanogrid hardware and software infrastructure, the methodology employed for efficiency quantification, and the measurement results are described in the paper. [ABSTRACT FROM AUTHOR]

It is well known that buildings have a sizeable energy and environmental footprint. In particular, in environments like university campuses, the occupants as well as occupancy in shared spaces varies over time. Systems for cooling in such environments that are centrally controlled are typically threshold driven and do not account for occupant feedback and thus are often relying on a reactive approach (fix after identifying problems). Therefore, having a fixed thermal operating set point may not be optimal in such cases—both from an occupant comfort and well-being as well as an energy efficiency perspective. To address this issue, a study was conducted which involved development and deployment of an experimental Internet of Things (IoT) prototype system and an Android application that facilitated people engagement on a university campus located in the UAE which typically exhibits hot climatic conditions. This paper showcases data driven insights obtained from this study, and in particular, how to achieve a balance between the conflicting goals of improving occupant comfort and energy efficiency. Findings from this study underscore the need for regular reassessments and adaptation. The proposed solution is low cost and easy to deploy and has the potential to reap significant savings through a reduction in energy consumption with estimates indicating around 50–100 kWh/day of savings per building and the resulting environmental impact. These findings would appeal to stakeholders who are keen to improve energy efficiency and reduce their operating expenses and environmental footprint in such climatic conditions. Furthermore, collective action from a large number of entities could result in significant impact through this cumulative effect. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model. [ABSTRACT FROM AUTHOR]

In a scenario characterised by mitigation concerns and calls for greater resilience in the energy sector, energy audits (EAs) emerge as an essential mean for enhancing end-use energy consumption awareness and efficiency. Such a tool allows us to assess the different energy carriers consumed in a productive sector, offering insight into existing energy efficiency improvement opportunities. This opens avenues for research to devise an econometrics-based methodology that encapsulate production sites and their environmental essentials. This paper contributes to the literature by exploiting the EAs received by the Italian National agency for New technologies, Energy, and Sustainable Economic Development (ENEA) in 2019 from the Italian plastics manufacturing sector, matched with Italian firm-based data extracted from the Analisi Informatizzata delle Aziende Italiane (Italian company information and business intelligence) (AIDA) database. In particular, we investigate how the implementation of energy efficiency measures (EEMs) is influenced by a set of contextual factors, as well as features relating to the companies and EEMs themselves. The empirical investigation focuses on the EAs submitted to ENEA in 2019, which was strategically chosen due to its unique data availability and adequacy for extensive analysis. The selection of 2019 is justified as it constitutes the second mandatory reporting period for energy audits, in contrast to the 2022 data, which are currently undergoing detailed refinement. In line with the literature, the adopted empirical approach involves the use of both the OLS and logistic regression models. Empirical results confirm the relevance of economic and financial factors in guiding the decisions surrounding the sector's energy performance, alongside the analogous influence of the technical characteristics of the measures themselves and of the firms' strategies. In particular, the OLS model with no fixed effects shows that a one-percent variation in investments is associated with an increase in savings performance equal to 0.63%. As for the OLS model, including fixed effects, the elasticity among the two variables concerned reaches 0.87%, while in the logistic regression, if the investment carried out by the production sites increases, the expected percentage change in the probability that the energy-saving performance is above its average is about 187.77%. Contextual factors that prove to be equally influential include the incentive mechanism considered and the traits of the geographical area in which the companies are located. Relevant policy implications derived from this analysis include the importance of reducing informational barriers about EEMs and increasing technical assistance, which can be crucial for identifying and implementing effective energy solutions. [ABSTRACT FROM AUTHOR]

Sustainable smart cities (SSCs) target decarbonisation by optimising energy consumption through the emerging capabilities of technology. Nevertheless, the energy consumption behaviour of end users has the potential to compromise the effectiveness of technological interventions, reflecting the importance of active social engagement in realising decarbonisation goals. Although extensive research exists on energy consumption behaviour, little is known about how technology engagement affects it, the nature of these technologies, and their role in SSC. The paper aims to identify, categorise, and investigate the smart technologies that impact household energy consumption behaviours and their integration into the larger SSC system. Following a systematic review of 60 articles from the Scopus database (2013–2023), the study found 45 smart technologies cited, with 49% affecting efficiency behaviour and 51% affecting curtailment behaviour. While these technologies inform the city administration level in the SSC framework, the role of end users remains unclear, suggesting a technocratic approach. The study proposes the Sustainable Smart City Network to facilitate a grassroots approach, identifying five key domains: government policies, smart technology adoption, smart technology engagement, smart city infrastructure, and urban sustainability. The study provides an original contribution to knowledge by unveiling the key technologies affecting energy consumption behaviour and outlining the pragmatic requirements for achieving decarbonisation through a grassroots approach. [ABSTRACT FROM AUTHOR]

Fifth Generation (5G) mobile networks introduce the concept of slicing to ensure isolation among the various supported heterogeneous services. The User Equipment (UE) can be connected to multiple slices simultaneously. Additionally, the notion of a Bandwidth Part (BWP) was also instigated to reduce power consumption. A BWP is a small chunk of the bandwidth scanned by the UE to retrieve its service data. Therefore, a UE connected to multiple services can be configured with multiple BWPs each associated with a given service. Such UEs find themselves scanning multiple BWPs, which can be time consuming and highly energy intensive. Hence, it is paramount to study the appropriate choice of the BWP configuration from an energy-efficiency perspective for multi-slice users depending on their battery level. In this paper, two energy-efficient BWP selection solutions are proposed for users connected to multiple slices. The first solution is based on a centralized approach where UEs are stirred optimally to the best BWP configuration, while the second solution relies on a user-centric distributed approach using non-cooperative game theory. The proposed schemes take into account the users' battery level and their sojourn time in the network as well as the scanned BWP size. Both solutions are compared with one another and against the legacy solution. Intensive simulation results demonstrate the efficiency of our proposition in terms of users' energy efficiency and quality of service. [ABSTRACT FROM AUTHOR]

One of the most challenging problems in cloud datacenters is the degradation of performance and energy efficiency due to the overutilization of hosts and their exposition to excessive workload. Virtual machine (VM) consolidation and migration from one host to another are strategies that have been proven to successfully bring about performance improvements and energy efficiency. These schemes help in energy optimization by moving VMs experiencing difficulty functioning on an overloaded host to another host. Similarly, by migrating VMs from an underloaded host and consolidating them, unnecessary resources have a chance to be shut down. This makes clear why the accurate detection of overloaded and underloaded hosts is of fundamental importance when energy consumption, quality of services, and service level agreements are targeted. In this paper, an energy-aware QoS-based consolidation algorithm is proposed to dynamically manage VMs in cloud datacenters. The proposed algorithm applies reinforcement learning and artificial neural networks. The first method is used to select a suitable VM for migration, while the latter helps to predict the future state of hosts and detect overloaded and underloaded hosts. We simulated the proposed algorithm using the CloudSim framework and compared it to the baselines and state-of-the-art algorithms. The results show that the proposed approach surpasses other methods in what concerns both performance and energy efficiency. [ABSTRACT FROM AUTHOR]

The extensive consumption of energy in manufacturing has led to a large amount of greenhouse gas emissions that have caused an enormous effect on the environment. Therefore, investigating how to reduce energy consumption in manufacturing is of great significance to cleaner production. This paper considers an energy-conscious unrelated parallel batch processing machine scheduling problem under time-of-use (TOU) electricity prices. Under TOU, electricity prices vary for different periods of a day. This problem is grouping jobs into batches, assigning the batches to machines and allocating time to the batches so as to minimize the total electricity cost. A mixed-integer linear programming model and two groups of heuristics are proposed to solve this problem. The first group of heuristics first forms batches, assigns the batches to machines and finally allocates time to the batches, while the second group of heuristics first assigns jobs to machines, batches the jobs on each machine and finally allocates time to each batch. The computational results show that the SPT-FBLPT-P1 heuristic in the second group can provide high-quality solutions for large-scaled instances in a short time, in which the jobs are assigned to the machines based on the shortest processing time rule, the jobs on each machine are batched following the full-batch longest processing time algorithm, and the time is allocated to each batch following an integer programming approach. The MDEC-FBLPT-P1 heuristic that uses the minimum difference of the power consumption algorithm to assign the jobs also performed well. [ABSTRACT FROM AUTHOR]

Reducing the heat demand of existing buildings is an essential prerequisite for achieving a greenhouse gas-neutral energy supply. Numerous studies and open-source tools deal with heat demand mapping. It is not uncommon that estimated heat demands deviate from real heat consumption, so existing approaches should be improved by including in-depth building information. Some tools have recognised this problem and offer built-in functions for factoring various parameters into their assessments. Nevertheless, the necessary information is usually missing and should be obtained first. In this paper, we analyse the impact of thermal refurbishment and climate on building heat demand; hence, generate public datasets with corresponding key figures for each building type in different efficiency states and years. Accounting for already performed refurbishments in methodologies for assessing the actual state heat demand for cities will result in a reduction of at least 8% up to more than 21%, depending on whether conventional or passive house components were installed. As a result of climatic differences within Germany, a building's heat demand can be up to 39% higher or up to 21% lower than the heat demand of an identical building in the reference climate of Germany. By further developing the approaches of the tools Hotmaps and Heat Cadastre Hamburg, we could improve the estimated heat demand of Hamburg to a value approximating the real consumption. [ABSTRACT FROM AUTHOR]

This paper presents innovative machine-learning solutions to enhance energy efficiency in electrical tomography for industrial reactors. Addressing the key challenge of optimizing the neural model's loss function, a classifier tailored to precisely recommend optimal loss functions based on the measurement data is designed. This classifier recommends which model, equipped with given loss functions, should be used to ensure the best reconstruction quality. The novelty of this study lies in the optimal adjustment of the loss function to a specific measurement vector, which allows for better reconstructions than that by traditional models trained based on a constant loss function. This study presents a methodology enabling the development of an optimal loss function classifier to determine the optimal model and loss function for specific datasets. The approach eliminates the randomness inherent in traditional methods, leading to more accurate and reliable reconstructions. In order to achieve the set goal, four models based on a simple LSTM network structure were first trained, each connected with various loss functions: HMSE (half mean squared error), Huber, l1loss (L1 loss for regression tasks—mean absolute error), and l2loss (L2 loss for regression tasks—mean squared error). The best classifier training results were obtained for support vector machines. The quality of the obtained reconstructions was evaluated using three image quality indicators: PSNR, ICC, and MSE. When applied to simulated cases and real measurements from the Netrix S.A. laboratory, the classifier demonstrated effective performance, consistently recommending models that produced reconstructions that closely resembled the real objects. Such a classifier can significantly optimize the use of EIT in industrial reactors by increasing the accuracy and efficiency of imaging, resulting in improved energy management and efficiency. [ABSTRACT FROM AUTHOR]

Today's energy challenges are multifaceted. Over the past 30–40 years, energy issues have been discussed and published on an extensive scale. The green transition involves concrete actions related to increasing energy efficiency, replacing fossil fuels with alternative fuels, producing energy using renewable resources, creating various means of transport that use electric motors, identifying technical solutions that generate an increased energy yield in the case of buildings, and waste reduction, reuse and recycling. In order to attain a climate-neutral environment, it is mandatory to impose regulations, measures and actions to help decarbonize the energy sector. The analysis of published articles on these issues is the subject of this large and information-dense review. Concretely, the transition to climate neutrality will generate obvious advantages at an economic, social and technological level, for example, the opportunity for economic growth, new business models and new markets, and the generation of new jobs or technological development. At the same time, this paper underscores the need for a multifaceted approach, integrating technological innovation, policy intervention and global cooperation for an effective energy transformation. The review suggests future issues and research directions, focusing on viable strategies for energy transition and its socio-economic environmental impacts. [ABSTRACT FROM AUTHOR]

The water and sewage sector is responsible for approximately 3.5% of energy consumption in the European Union (EU). Leaks causing water losses in water distribution systems (WDSs) are responsible for approximately 24% of water consumption in the EU, which contributes to additional energy losses and emissions of greenhouse gases (GHGs). The implementation of the Directive of the European Parliament and the EU Council on the quality of drinking water (Directive (EU) 2020/2184) introduces the obligation to report water losses by large water utilities in EU Member States. The reported indicator will be the infrastructure leakage index (ILI) which is the ratio between current annual real loss (CARL) and unavoidable annual real loss (UARL). The paper presents a comparative analysis of selected water loss performance indicators calculated for 12 Polish WDSs. Results show that values of calculated indicators were diverse. The overestimation of both the reported value of operating pressure and total length of service connections may lead to the overestimation of UARL and thus to the underestimation of ILI. Obtaining a satisfactory, but incorrect, value of ILI may result in the abandonment of activities aimed at water loss reduction. Water losses in water distribution systems (WDSs) contribute to a significant increase in both energy consumption and GHG emissions. Total approximated electrical energy related to CARL consumed in 2021 by eleven utilities (except for one company) amounted to 3.276 GWh and total approximated carbon emissions amounted to 2807.84 MgCO2eq. In the case of four WDSs, reduction of ILI to the value of 1.5 may reduce GHG emissions by 31–54%. It can be concluded that the implementation of Directive (EU) 2020/2184 will require unification of methodology for calculation of parameters used in ILI evaluation in all EU Member States. [ABSTRACT FROM AUTHOR]

With the increasing demand for sustainable urban development, smart cities have emerged as a promising solution for optimizing energy usage, reducing emissions, and enhancing the quality of life for citizens. In this context, the combined use of key performance indicators (KPIs) and data analytics has gained significant attention as a powerful tool for promoting energy efficiency and emissions reduction in urban areas. This paper presents a comprehensive conceptual framework in which a series of KPIs are proposed to serve as essential metrics for guiding, monitoring, and assessing energy efficiency and emissions reduction levels in smart cities. Some of the included KPIs in the analysis are 'annual energy consumption per person', 'reduction in greenhouse gas emissions', 'public transport use', and 'adoption of renewable energy'. By incorporating these KPIs, city planners and policymakers can gain valuable insights into the effectiveness of sustainability initiatives. Furthermore, the paper explores how the integration of KPIs with data analytics can be used for monitoring and assessing the overall performance of the city in terms of energy efficiency, emissions reduction, and the enhancement of urban living conditions. Visualization tools, such as radar plots, and time series analysis forecasting methods allow data to be processed and patterns to be identified, enabling informed decision-making and efficient resource allocation. Real-life case studies of ongoing smart city projects are presented in the paper, which also provides a KPI comparison among different European cities, as well as models to forecast the evolution of KPIs related to energy usage and emissions reduction in different European cities. [ABSTRACT FROM AUTHOR]

The highlighted energy consumption of Internet data center (IDC) in China has become a pressing issue with the implementation of the Chinese dual carbon strategic goal. This paper provides a comprehensive review of cooling technologies for IDC, including air cooling, free cooling, liquid cooling, thermal energy storage cooling and building envelope. Firstly, the environmental requirements for the computer room and the main energy consumption items for IDC are analyzed. The evaluation indicators and government policies for promoting green IDC are also summarized. Next, the traditional cooling technology is compared to four new cooling technologies to find effective methods to maximize energy efficiency in IDC. The results show that traditional cooling consumes a significant amount of energy and has low energy efficiency. The application of free cooling can greatly improve the energy efficiency of IDC, but its actual implementation is highly dependent on geographical and climatic conditions. Liquid cooling, on the other hand, has higher energy efficiency and lower PUE compared to other cooling technologies, especially for high heat density servers. However, it is not yet mature and its engineering application is not widespread. In addition, thermal energy storage (TES) based cooling offers higher energy efficiency but must be coupled with other cooling technologies. Energy savings can also be achieved through building envelope improvements. Considering the investment and recovery period for IDC, it is essential to seek efficient cooling solutions that are suitable for IDC and take into account factors such as IDC scale, climate conditions, maintenance requirements, etc. This paper serves as a reference for the construction and development of green IDC in China. [ABSTRACT FROM AUTHOR]

This paper presents the concept of an innovative control of a central heating system in a multifamily building based on the original thermodynamic model, the resulting architecture of the control system, and the originally designed and manufactured wireless temperature sensors for thermal zones. The novelty of this solution is the developed layers of the control system: distributed measurement and correction analysis, which is based on the existing infrastructure and the local HVAC controller. This approach allows for the effective use of the measured temperature data from thermal zones and finally sending the value of the calculated correction of settings to the controller. Moreover, in the analytical layer, a model was also implemented that calculates the necessary amount of energy based on data from the subsystem of temperature sensors located in the thermal zones of the building. The use of the algorithmic strategy presented in this paper extends the functionality and significantly improves the energy efficiency of the existing, classic, reference heating control algorithm by implementing additional control loops. Additionally, it enables integration with demand-side response systems. The presented concept was successfully tested, achieving real energy savings for heating by 12%. These results are described in a case-study format. The authors believe that this concept can be used in other buildings and thus will have a positive impact on the energy savings used to maintain thermal comfort in buildings and significantly reduce CO2 emissions. [ABSTRACT FROM AUTHOR]