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Additive manufacturing (AM) offers unique capabilities in terms of design freedom and customization, contributing to sustainable manufacturing. However, energy efficiency remains a challenge in the widespread adoption of AM processes. In this paper, we present a comprehensive review of the current research on energy efficiency in AM, addressing challenges, opportunities, and future directions. Our analysis reveals a lack of standardization in the measurement and reporting of energy consumption, making it difficult to evaluate and compare the energy performance of various systems. We propose a holistic framework to address energy efficiency throughout the entire life cycle of the AM process, highlighting the importance of design optimization, material selection, advanced control systems, and energy management strategies. The paper also emphasizes the need for further research on the interactions between process parameters, along with the potential of integrating renewable energy sources into AM systems. This review offers valuable insights for both academics and industry professionals, calling for standardized methodologies and a focus on energy management to optimize energy efficiency in AM processes, ultimately enhancing competitiveness and sustainability in modern manufacturing. [ABSTRACT FROM AUTHOR]

The article offers information on advancements and future directions in process design for sustainable energy systems. Topics discussed include optimizing energy management strategies for fuel-cell hybrid vehicles; reviewing energy-efficient and renewable systems in educational institutions; and developing automated methods for enhancing energy efficiency in industrial robotics.

The pursuit of climate neutrality requires global systemic actions involving the use of solutions aimed at reducing emissions. Changes must be introduced in all sectors affecting climate change, namely power engineering and district heating, construction, transport, and industry, as well as agriculture and forestry. Analyzing the structure of final energy consumption in the EU by sector, it can be stated that households account for 27% of the total energy consumption. Comprehensive actions are needed to increase the energy efficiency of buildings. The aim of this paper was to indicate aspects of improving energy efficiency in buildings and their equipment, taking into account the striving for climate neutrality. Analyzed possibilities and conditions of using various solutions of energy-efficient systems aimed at increasing energy resilience and security and preventing environmental degradation. Particular attention was paid to construction and material solutions, as well as installation solutions, which increased the accumulation and energy efficiency of the building. These activities are closely related to the conditions and dynamics of the heat exchange process in the applied solutions and are also related to the factors influencing thermal comfort and energy consumption in buildings. Due to the growing popularity of modern information technologies and artificial intelligence in energy management in recent years, this article reviews the latest research in this area. One of the directions of future research indicated by scientists is autonomous building control in real time, adapting to the momentary needs of users. The analysis of the possibilities of using modern energy efficiency solutions in buildings conducted in this work may be useful for optimizing heat and energy management models and models of society's consumption as an element of energy transformation towards climate neutrality and counteracting the deepening of energy poverty. [ABSTRACT FROM AUTHOR]

In the effort to limit global warming to 1.5°C above the pre-industrial level, decarbonization of supply chains is crucial. Companies will need to reduce not only their direct and indirect emissions (scope 1 and 2) but also their indirect emissions (scope 3). In some sectors, the upstream scope 3 emissions can actually be larger than the companies' direct emissions. Therefore, a systematic literature review was carried out to understand how previous research in the field of supply chain management (SCM) approached energy efficiency and energy management in the upstream supply chain. The results indicate that only a few studies have addressed energy efficiency within SCM, and the majority assess energy solely as total energy use, without exploring opportunities to reduce final energy use among upstream suppliers. Moreover, results show that there is a lack of standards and methods to assess suppliers' energy use, making it difficult for suppliers, particularly small and medium-sized enterprises, to overcome barriers to implementing energy management. In conclusion, even though energy efficiency is a cost-effective means to reduce CO2 emissions, only a limited number of studies in the field of SCM have integrated energy efficiency, revealing a vast knowledge gap in this field. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to uncover how the process of managing electricity in buildings based on the Internet of Things occurs. In particular, the work seeks to depict the factors affecting electricity consumption and management, as well as the application of the Internet of Things in energy management. A systematic literature review is used to examine the breadth of the electric energy management literature, encompassing bibliometric and thematic analysis based on an established procedure. The findings show the evolution of this field within key research networks with a few papers covering important elements of energy management, such as energy use, consumption and monitoring, assessment, and planning, in an integrative manner. Within this field, lacking in theory and practice, the originality of the work is the assembly of electric energy management into a conceptual framework based on real-time consumption and the Internet of Things (IoT). Indeed, the framework brings together the breadth of factors affecting consumption, energy use, and improvements that have been dispersed across the literature into one place. This framework, therefore, represents a stage towards an integrative view of IoT electric energy management and subsequent enhancement of theory and energy efficiency adoption. [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]

Purpose: This paper reviews the literature on maintenance strategies for energy efficiency as a potential maintenance approach. The purpose of this paper is to identify the main concept and common principle for each maintenance strategy for energy efficiency. Design/methodology/approach: A literature review has been carried out on maintenance and energy efficiency. The paper systematically classified the literature into three maintenance strategies (e.g. inspection-based maintenance [IBM], time-based maintenance [TBM] and condition-based maintenance [CBM]). The concept and principle of each maintenance strategy are identified, compared and discussed. Findings: Each maintenance strategy's main concept and principle are identified based on the following criteria: data required and collection, data analysis/modeling and decision-making. IBM relies on human senses and common senses to detect energy faults. Any detected energy losses are quantified to energy cost. A payback period analysis is commonly used to justify corrective actions. On the other hand, CBM monitors relevant parameters that indicate energy performance indicators (EnPIs). Data analysis or deterioration modeling is needed to identify energy degradation. For the diagnostics approach, the energy degradation is compared with the threshold to justify corrective maintenance. The prognostics approach estimates when energy degradation reaches its threshold; therefore, proper maintenance tasks can be planned. On the other hand, TBM uses historical data from energy monitoring. Data analysis or deterioration modeling is required to identify degradation. Further analysis is performed to find the optimal time to perform a maintenance task. The comparison between housekeeping, IBM and CBM is also discussed and presented. Practical implications: The literature on the classification of maintenance strategies for energy efficiency has been limited. On the other hand, the ISO 50001 energy management systems standard shows the importance of maintenance for energy efficiency (MFEE). Therefore, to bridge the gap between research and industry, the proposed concept and principle of maintenance strategies will be helpful for practitioners to apply maintenance strategies as energy conservation measures in implementing ISO 50001 standard. Originality/value: The novelty of this paper is in-depth discussion on the concept and principle of each maintenance strategy (e.g. housekeeping or IBM, TBM and CBM) for energy efficiency. The relevant literature for each maintenance strategy was also summarized. In addition, basic rules for maintenance strategy selection are also proposed. [ABSTRACT FROM AUTHOR]

This paper applied the advocacy coalition framework to explore and explain the political processes creating policies to enhance energy efficiency of European Union (EU) industry. The paper used legislation on energy audits and energy management systems as a proxy for EU policy on energy efficiency in industry. Based on qualitative text analysis of EU policy documents, including a proposal to recast the energy efficiency directive, amendments to the proposal suggested by Member States, the Council and the European Parliament, and reports from negotiations, the paper identified four advocacy coalitions with different core beliefs, spanning from those that want few companies to implement energy audits or energy management systems, and that recommendations from audits should not be mandatory to implement, to those that advocate that many companies implement energy audits and management systems and that it should be mandatory to implement measures recommended in audits. It was further found that policy change followed an external shock, deliberative negotiations, and policy-oriented learning. The identification of core beliefs and advocacy coalitions will help policymakers and other stakeholders become more aware of their own and others' values on energy efficiency and how these could be changed. As important was the differentiation of deep core beliefs, policy core beliefs and secondary beliefs. Which beliefs can be easily changed, which cannot? [ABSTRACT FROM AUTHOR]

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This paper proposes a multiport energy management system (EMS) and its rule-based expert control strategy for a 150 kW range-extended towing vessel (RETV). The system integrates a diesel generator system, a permanent magnet synchronous motor, a lithium battery, and supercapacitors. To verify its feasibility and effectiveness, the proposed multiport EMS was modelled and tested through MATLAB/Simulink. Simulation results demonstrate that the designed multiport EMS works efficiently under the five typical operating conditions of the 150 kW RETV. In addition, two case studies were conducted and compared to investigate the impact of the battery's initial state of charge (SoC) on the system's energy efficiency. It was found that an overall 85% energy efficiency can be achieved for the RETV when the initial SoC is either 75% or 15%. The battery consistently operates within the optimal SoC range of 20% to 80%, and the supercapacitors effectively meet the instantaneous high-power demand. [ABSTRACT FROM AUTHOR]

The Environmental Protection Agency classifies healthcare as one of the leading energy-consuming industries. Extensive energy is needed around the clock in healthcare institutions for lighting, ventilation, and operating medical equipment. However, there is a growing concern over the sustainability of energy utilization by healthcare institutions worldwide. This narrative review thus seeks to examine energy efficiency and utilization in healthcare institutions and energy management and conservation techniques and make recommendations for future optimal usage. The paper notes that healthcare institutions use different quantities of energy from diverse sources, including hydropower, biomass, solar energy, and wind power. However, energy consumption varies from one institution to another, with the number of beds and intensity of healthcare operations, with an average of 0.27MWh m-2. Moreover, this review also identified various techniques and measures to enhance energy efficiency, such as the variant refrigerant flow technology and the combination of renewable energy sources with diesel generators to reduce the cost of electricity. Overall, healthcare institutions need energy management systems such as automated energy monitoring technologies, to check the systems' efficiency. The same techniques can also help Middle Eastern healthcare institutions with efficient energy utilization. Ultimately, the literature review aims to introduce an approach that focuses on reducing site-level consumption of energy while increasing the quality of the energy used and hence, helping reduce energy costs while conserving the environment. [ABSTRACT FROM AUTHOR]

The implementation of non-intrusive load monitoring has gained significant attention as a promising solution for disaggregating and identifying individual appliances' energy consumption within households and commercial buildings. The issue at the core of non-intrusive load monitoring is the accurate separation of the aggregated energy consumption signal into the energy contributions of distinct appliances without requiring additional sensors or invasive measurements. The erroneous implementation of non-intrusive load monitoring model leads to poor results and a waste of time where the criteria for the implementation are necessary. This paper presents an overview of non-intrusive load monitoring's implementation, addressing the stages of non-intrusive load monitoring and advancements in the field. The overview explores the various techniques, methodologies, and algorithms employed in non-intrusive load monitoring systems to achieve load identification and energy disaggregation. By addressing the problem statement and reviewing the state of the art, this paper sets the stage for understanding the significance of non-intrusive load monitoring in commercial buildings and modern energy-efficient households. Moreover, the limitations, applications, and recommended criteria for the implementation of non-intrusive load monitoring are highlighted as a contribution to the field of load disaggregation. Future research should address scalability, privacy concerns, and the integration of non-intrusive load monitoring with smart grids to propel energy disaggregation towards more efficient, accessible, and sustainable energy management systems. We envision that this endeavour can serve as a guiding beacon, offering a roadmap for researchers and professionals. [ABSTRACT FROM AUTHOR]

Manufacturing facilities use about 35% of the domestic energy in the United States every year. Implementing an effective energy management system (EnMS) is one of the most important approaches to improve energy efficiency. However, the implementation of EnMS is low for many countries (including the US) and even for energy-intensive sectors. The reasons for the low implementation rate of energy management systems had been investigated by multiple researchers, but very few studies have focused on the barriers and challenges of implementing ISO 50001-based energy management systems. To contribute to this understudied area, this paper discusses the implementation and outcomes of the first Better Plants 50001 Ready Virtual In-plant Training. This paper first provides an overview of 50001 Ready and the 50001 Ready Navigator Tool. Then, it provides details on this training event and its outcomes. Finally, it discusses findings from the responses to 40 live polling questions about the status of the 25 tasks of the 50001 Ready Navigator for participating companies, key components of the participating manufacturing companies' energy management systems, and challenges and barriers that these companies are facing. The findings suggest that although many companies understood the importance of an effective energy management system, about half of them do not understand the required resources for building energy management systems, and most of them have only just begun establishing these systems and need more assistance and resources in multiple areas. More specifically, more assistance is necessary for the following: (1) improving corporate management's understanding of the time and resources needed to build an EnMS as well as the benefits; (2) creating linear regression models for more accurate energy performance tracking; (3) understanding energy use, collecting and analyzing energy performance data; (4) optimizing equipment operational controls, and creating action plans. [ABSTRACT FROM AUTHOR]

Hardware viewed in current energy models, but no appropriate attention is given to the network environment and parameters. Components of the traditional design of the network are often viewed individually and separately in terms of power consumption. In this paper we, look at all possible sensor operations and generate a model for energy management in its integrated network. The work proposed divides these tasks into five energy consuming parts. The sensor's energy consumption is then modeled on its energy-consuming parts, parameters, and input operations. Thus, the energy consumption of the sensor can be decreased by effectively balancing and performing the chores of its constituents. The suggested strategy improves energy efficiency and can also be used as guide in setting wireless sensor networks (WSNs) parameters. Further more, It can help in designing an energy efficient WSNs. The serious energy limitations, however, pose special challenges for WSNs applications to establish and project scheduling which is usually has a strong influence on achieving energy efficiency and usage. This paper provides a framework for preparing the tasks, in which each node decides to do next according to the observed portion of the request. Within this Framework, we can exchange the application efficiency and the energy consumption provided by a weighted reward function and thus obtain better results in terms of energy/ performance. We can further improve this energy/performance trade off by exchanging data between neighboring nodes. The suggested approach analyzed in a target monitoring program. The simulation experiments show that cooperative approaches for this type of application are superior to non-cooperative approaches. [ABSTRACT FROM AUTHOR]

The interest in sustainability and energy efficiency is constantly increasing, and the noticeable effects of climate change and rising energy prices are fueling this development. The residential sector is one of the most energy-intensive sectors and plays an important role in shaping future energy consumption. In this context, modeling has been extensively employed to identify relative key drivers, and to evaluate the impact of different strategies to reduce energy consumption and emissions. This article presents a detailed literature review relative to modeling approaches and techniques in residential energy use, including case studies to assess and predict the energy consumption patterns of the sector. The purpose of this article is not only to review the research to date in this field, but to also identify the possible challenges and opportunities. Mobility, electrical devices, cooling and heating systems, and energy storage and energy production technologies will be the subject of the presented research. Furthermore, the energy upgrades of buildings, their energy classification, as well as the energy labels of the electric appliances will be discussed. Previous research provided valuable insights into the application of modeling techniques to address the complexities of residential energy consumption. This paper offers a thorough resource for researchers, stakeholders, and other parties interested in promoting sustainable energy practices. The information gathered can contribute to the development of effective strategies for reducing energy use, facilitating energy-efficient renovations, and helping to promote a greener and more sustainable future in the residential domain. [ABSTRACT FROM AUTHOR]

This paper presents a literature review of recent research on introducing the Industry 4.0 approach to improving energy efficiency, especially in the wood industry. While researchers focus on processes, service, and customer concepts, the effect on energy consumption is less addressed in these studies and applications. This paper focuses on previous works that discuss how to apply Industry 4.0 concepts to energy issues, such as to achieve better efficiency and performance for the industry in general and the wood industry in particular. The complexity of the study requires a multistep development. First, we define each concept separately and the relationships between them. Second, we apply a search algorithm to find related articles with specific terms, and then use the PRISMA method to select the most important ones, eliminating duplicates and excluding articles that do not mention energy efficiency and Industry 4.0 in the manufacturing or wood industry. Third, we explain and categorize the results and consolidate the study with brief examples from other industries. Finally, we conclude the study by mentioning the limitations and perspectives. [ABSTRACT FROM AUTHOR]

Waste water treatment plants (WWTPs) are considered as energy-intensive industries. A comprehensive assessment of energy efficiency in sewage treatment reveals issues of energy waste, offers insights into the energy consumption structure, fosters optimization of energy management, and enhances overall energy utilization. However, the mathematical modeling for energy efficiency evaluation becomes challenging due to the ambiguity and uncertainty of the parameters regarding energy consumption and various indicators. In this paper, the newly constructed evaluation method was used to quantify the energy efficiency. This method employs dimensional reduction, projecting the numerical values of unit energy consumption for various pollutants onto a lowerdimensional space, and then computing the projected eigenvalues. Larger eigenvalues indicate higher energy efficiency. Based on the evaluation results, the back propagation (BP) neural network is used to simulate the sewage treatment process. The results demonstrated that when the inflow load is small and the concentration of pollutants is high, the energy efficiency of sewage treatment plants performs well. When the inflow load is high and the concentration of pollutants is low, the energy efficiency of sewage treatment plants is poor. Meanwhile, the energy consumption could be decreased by 11.61 %, 14 %, and 15.26 % respectively in different scenarios. [ABSTRACT FROM AUTHOR]

The increasing price of and demand for energy have prompted several organizations to develop intelligent strategies for energy tracking, control, and conservation. Demand side management is a critical strategy for averting substantial supply disruptions and improving energy efficiency. A vital part of demand side management is a smart energy management system that can aid in cutting expenditures while still satisfying energy needs; produce customers' energy consumption patterns; and react to energy-saving algorithms and directives. The Internet of Things is an emerging technology that can be employed to effectively manage energy usage in industrial, commercial, and residential sectors in the smart environment. This paper presents a smart energy management system for smart environments that integrates the Energy Controller and IoT middleware module for efficient demand side management. Each device is connected to an energy controller, which is the inculcation of numerous sensors and actuators with an IoT object, collects the data of energy consumption from each smart device through various time-slots that are designed to optimize the energy consumption of air conditioning systems based on ambient temperature conditions and operational dynamics of buildings and then communicate it to a centralized middleware module (cloud server) for management, processing, and further analysis. Since air conditioning systems contribute more than 50% of the electricity consumption in Pakistan, for validation of the proposed system, the air conditioning units have been taken as a proof of concept. The presented approach offers several advantages over traditional controllers by leveraging real-time monitoring, advanced algorithms, and user-friendly interfaces. The evaluation process involves comparing electricity consumption before and after the installation of the SEMS. The proposed system is tested and implemented in four buildings. The results demonstrate significant energy savings ranging from 15% to 49% and highlight the significant benefits of the system. The smart energy management system offers real-time monitoring, better control over the air conditioning systems, cost savings, environmental benefits, and longer equipment life. The ultimate goal is to provide a practical solution for reducing energy consumption in buildings, which can contribute to sustainable and efficient use of energy resources and goes beyond simpler controllers to address the specific needs of energy management in buildings. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the research is to determine the influence of photovoltaic (PV) installation and the share of façade glazing on the energy profile of nursery buildings in the Baltic Sea region, as well as defining the most favorable configuration in terms of energy efficiency. Design/methodology/approach: The article provides comparative calculations of energy performance indicators (Ep, Ed, Eu) and CO2 emissions (mCO2) made for the defined model of the nursery. It includes calculations concerning energy performance of the building, depending on its PV power (0–60 kWp), PV efficiency (100 and 85%) and façade glazing ratio (GR = 25%, 50% and 75%). Findings: The results of the research indicate that an increase in the PV power exerts proportional impact on the reduction of the Ep and Ed indicators, as well as on the CO2 emissions. Only low GR values (25%) reduce the Eu indicator significantly. Decrease in high range of GR values (over 50%) does not provide proportional effects. In the variant: 60 kWp (100% efficiency) with GR = 25%, the biggest share (99.5%) of RES was obtained. This proves that the concept of energy independent nursery buildings is feasible and reasonable in the examined location. Practical implications: Designing buildings towards environmental neutrality requires laborious pre-design conceptual work before developing the right solutions. The set of results of the relationship between the variables of the building's envelope, energy performance indicators and the required involvement of active RES installations to achieve high energy performance of a building presented in the article is valuable. It allows for a preliminary decision of the direction of the design solutions selection in the design process of public utility buildings, such as nurseries. Thus, it may significantly shorten the pre-design analysis process for the location of the southern part of the Baltic Sea region. Originality/value: The novelty of the paper relies on examining the dependences between PV power and façade glazing ratio in terms of their influence on energy profile of nursery buildings. [ABSTRACT FROM AUTHOR]

Public buildings are energy-intensive users, especially when energy management is lacking. More than ever, the use of energy efficiency strategies and renewable energy sources (RES) in buildings are a national priority for Morocco in order to improve energy self-sufficiency, replace fossil fuel use and lower energy bills and greenhouse gas emissions. Relating to the exemplarity of the Moroccan government in terms of energy efficiency and sustainable development, the study support that aim and presents results of a deep energy performance analysis of more than 20 university campuses across Morocco, which has concluded that around 80% of the energy consumed in the university campuses is designated for lightning and hot water for sanitary use. Later, this study examined the potential for energy saving and the environmental benefits of implementing actions to reduce energy demand from the grid, considering the use of on-site solar energy. Thereafter, the study aimed to analyze the impact of RES integration in public university campuses, namely the photovoltaic (ESM1) for electricity output and solar thermal system for hot water use (ESM2), to assess the techno-economic-environmental performance on building energy consumption reduction. Hence, the paper reported a detailed energetic-economic and environmental (3E) analysis simulation for campuses by integration of the two Energy Saving Measurements (ESM). The results showed that the integration of ESM1 system can reduce the annual energy demand by 22% and the energy bill by 34%, whereas the integration of ESM2 achieved 67% in energy saving. According to the analysis of the results, the integration of ESM1 is expected to save 6044 MWh of electrical energy annually on the 30222 MWh for all campuses and 2559 MWh for ESM2 which is equivalent to 284 m3/yr of diesel. With the reduced energy consumption, it is possible to cut down fossil fuels for electricity production and offset greenhouse gas emissions by 672 tons of carbon dioxide annually. Besides, the evaluation of results showed that the energy performance indicator was reduced from 530 kWh/bed/yr to 248 kWh/bed/yr, which represents 56% of energy saving. [ABSTRACT FROM AUTHOR]

Paper aims: The present study aims to identify the limitations of the artifacts used in the decision-making process in the adoption of energy efficiency measures in productive systems, using non-intensive energy companies as a delimitation. Originality: Identifies authors and connections, the relationships between them and how these interactions contribute to the advancement of knowledge on the subject. Regarding energy efficiency, studies show that the real investment in initiatives in the industrial sector is below the full potential and that the artifacts used in the decision-making process have severe limitations when used in a complex and dynamic context. Research method: In this paper a systematic literature review was conducted from the Literature-Grounded Theory. Additionally, social network analysis was used. Main findings: It concludes that the approaches are limited to technical and financial factors and does not consider a systemic and dynamic understanding of different internal and external variables to the organization. Implications for theory and practice: The contribution of this study is that it identifies the initiatives that help in the process of decision-making for the adoption of energy efficiency measures in productive systems. Specifically, the focus of this study is on non-intensive energy companies. Scientific articles published in the main databases of management were selected. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce basic principles of benchmarking and explain the methodology to set the specific energy consumption (SEC) target for the beer and beverage industry in Vietnam to evaluate performance, support decisions, and energy efficiency (EE) improvement. Energy benchmarking management method is used to assess EE performance. This study also presents the SEC methodology as a guide for Vietnam enterprises in the brewery sector on how to manage the EE consumption and to comply with the legal energy consumption quota in the beer and beverage production. The study supports the enterprise in how to determine the SEC and support for national EE is essential to the promotion and deployment of practical energy-saving solutions and compliance with the SEC through a suitable energy-saving target. In addition, this paper is introducing the original SEC calculation in three cases of production in the beer and beverage industry including producing beer only, producing beverage only, and producing both beer & beverage in the context of Vietnam. [ABSTRACT FROM AUTHOR]

Energy efficiency is a key factor to meet the ambitious climate targets of the European Union (EU) aligned with the international policy directives. On their own, Small and Medium-sized Enterprises (SMEs) do not consume big amounts of energy, but taking into consideration that they represent about 99% of businesses worldwide, their cumulative energy consumption is remarkable. Even though SMEs experience several barriers in their effort to improve their energy efficiency, their contribution to the EU's energy efficiency improvement targets is crucial through the implementation of measures to improve their energy footprint. The purpose of this paper is to present a comprehensive review of SMEs' energy efficiency and energy footprint management, which has been carried out in the context of the "SMEnergy—Energy Footprint Management for SMEs" EU-funded Erasmus+ project. The correlation between energy conversion and consumption processes and energy efficiency, as well as the current situation of energy footprint management and energy management systems in SMEs, are discussed in detail. Moreover, successful case studies of SMEs that have implemented specific measures to improve their energy footprint and achieve energy efficiency targets are also included. It is highlighted that SMEs exhibit a high potential for energy efficiency enhancement that could directly promote climate change mitigation and sustainable development. [ABSTRACT FROM AUTHOR]

Purpose: There is a great interest in developing eco-friendly operations as alternative uses of resources in the university campus, making the employment of technologies more sustainable. Practices such as energy management and efficiency initiatives have been encouraged to meet these sustainability goals. The purpose of this paper is to describe and discuss the main features of the GENIIOT project, a system of energy management using Internet of Things applied in a Brazilian university campus to support energy education practices and to promote lasting energy efficiency measures. The focus of the study is directed at the energy consumption of the air-conditioning system, which is responsible for 40% of the total electricity consumption. In addition, this project also scrutinizes processes such as the building occupants' comfort demands, lighting levels and energy waste control. Design/methodology/approach: A case study at the Unicamp campus was conducted to illustrate the design procedures through qualitative description. This study comprises an overview of the physical plant, hardware and software development, energy data monitoring, management process and energy education. Findings: The GENIIOT project enables energy efficiency actions by concerning the use of air-conditioning equipment based on monitored data, different types of monitored rooms, user's behaviors and their feedback. This initiative requires the engagement, awareness and actions from users combined with investment in energy efficiency to achieve an efficient use of this type of equipment. The proposed approach can be applied in the future to similar situations, inside the Unicamp campus and at other university campuses. In addition, this project can contribute to building efficiency analysis by using the hardware structure developed to monitor facilities and carry out evaluations, providing valuable information for strategic initiatives in energy efficiency projects and research and development programs based on practical experience and promoting a discussion about sustainability aspects in the context of the university campus for energy efficiency. Originality/value: The GENIIOT project is aligned with some of the sustainable development goals, among which sustainability and responsible consumption are identified. Considering the sustainability issues, the economic dimension can be evaluated through a cost–benefit analysis of energy efficiency projects with prudent investment, while the social attractiveness of the project is ensured by education and awareness practices for the community that interacts with the system and learns about a more efficient way of using resources in the campus. Furthermore, there is a more conscious use of natural resources by minimizing waste from the use of electricity and reducing carbon dioxide emissions. GENIIOT is part of Sustainable Campus Project, a living laboratory which aims at developing energy-efficiency-related research activities in the university campus to reduce costs in higher and federal institutions of education in Brazil. [ABSTRACT FROM AUTHOR]

This research paper presents a novel technique for improving load-shedding in DC microgrids by incorporating a binary number generator (BNG). Load shedding, a controlled strategy utilized to maintain equilibrium between power supply and demand within a microgrid when the aggregate demand surpasses generated power, is widely employed. Nevertheless, conventional load-shedding methods frequently encounter challenges, notably over-shedding and voltage deviations, particularly in scenarios involving numerous controllable loads. To overcome these challenges, an enhanced algorithm is proposed, which integrates the BNG technique. Simulation results conclusively demonstrate the effectiveness of the proposed algorithm in ensuring microgrid stability during periods of supply-demand imbalances. [ABSTRACT FROM AUTHOR]

Owing to the high energy demand of buildings, which accounted for 36% of the global share in 2020, they are one of the core targets for energy-efficiency research and regulations. Hence, coupled with the increasing complexity of decentralized power grids and high renewable energy penetration, the inception of smart buildings is becoming increasingly urgent. Data-driven building energy management systems (BEMS) based on deep reinforcement learning (DRL) have attracted significant research interest, particularly in recent years, primarily owing to their ability to overcome many of the challenges faced by conventional control methods related to real-time building modelling, multi-objective optimization, and the generalization of BEMS for efficient wide deployment. A PRISMA-based systematic assessment of a large database of 470 papers was conducted to review recent advancements in DRL-based BEMS for different building types, their research directions, and knowledge gaps. Five building types were identified: residential, offices, educational, data centres, and other commercial buildings. Their comparative analysis was conducted based on the types of appliances and systems controlled by the BEMS, renewable energy integration, DR, and unique system objectives other than energy, such as cost, and comfort. Moreover, it is worth considering that only approximately 11% of the recent research considers real system implementations. [ABSTRACT FROM AUTHOR]

The energy efficiency gap is known as the difference between optimal level of energy efficiency and the actual level of achieved energy efficiency. Energy management has proven to further close the energy efficiency gap. Energy management may differ depending on whether it concerns a large, energy-intensive company or small and medium-sized enterprises (SMEs). SMEs are of high interest since they form a large share of the economy today. For SMEs, a lighter form of energy management, in the form of energy efficiency network participation, has proven to deliver sound energy efficiency impact, while for larger, energy-intensive firms, a certified energy management system may be more suitable. However, various barriers inhibit adoption of energy efficiency measures. While there is an array of research on barriers to and driving forces for energy efficiency in general, research on barriers to, and driving forces for, energy management is rare, one exception being a study of energy-intensive pulp and paper mills. This holds even more so for industrial SMEs. This paper aims to identify the barriers to, and drivers for, energy management in manufacturing SMEs. Results of this explorative study show that the top four barriers to energy management are lack of time/other priorities, non-energy-related working tasks are prioritized higher, slim organization, and lack of internal expert competences, i.e., mainly organizational barriers. The top four drivers for energy management are to reduce production waste, participation in energy efficiency networks, cost reduction from lower energy use, and commitment from top management. Furthermore, results show that energy management among the studied SMEs seems to not be as mature, even though the companies participated in an energy management capacity building program in the form of energy efficiency networks, which, in turn, shows a still largely untapped potential in the societal aim to reduce the energy efficiency and management gaps. The main contribution of this paper is a first novel attempt to explore barriers to, and drivers for, energy management among SMEs. [ABSTRACT FROM AUTHOR]

Games are energy-intensive applications on mobile devices. Optimizing the energy efficiency of games is hence critical for battery-limited mobile devices. Although the advent of energy-aware scheduling (EAS) integrated in recent devices has provided opportunities for improved energy management, the framework is not specifically tuned for game applications. In this paper, we aim to improve the energy efficiency of game applications running on EAS-enabled mobile devices. To this end, we first analyze the functional characteristics of games, and investigate the source of the energy inefficiency. We then propose a scheme, called System-level Energy-optimization for Game Applications (SEGA), to improve the energy efficiency of games. SEGA governs CPU and GPU power consumption in a tightly coupled manner by employing three key techniques: (1) Lsync-aware GPU DVFS governor, (2) adaptive capacity clamping, and (3) on-demand touch boosting. We implemented SEGA on the latest Android-based smartphones. The evaluation results for 23 popular games showed that SEGA reduced the energy consumption of the Google Pixel 2 XL and Samsung Galaxy S9 Plus smartphones, at the device level, by 6.1–22.3 and 4.0–11.7 percent, respectively, with a quality of service (QoS) degradation of 1.1 and 0.5 percent, on average. [ABSTRACT FROM AUTHOR]

The study explored the legislative database for the maritime sector to be sustainable and have an intact blue economy. The problem for organizations with shipowners that have fishing boats older than 20 years is stated. Decision-making authorities face challenges nowadays. To be in the fleet for the next decade, shipowners for their ships should deploy energy efficiency projects for marine system retrofitting to improve energy efficiency and meet environmental regulations. The Ship Energy Efficiency Management Plan's development principles are discussed due to the current need for its deployment from 2021, so shipowners can contribute to the fast adoption of new regulations by the International Maritime Organization. In this work it is being offered to incorporate energy management issues with guidelines for upcoming regulations as well as those that are currently enforced. To improve energy efficiency, reduce environmental impact and cut fuel consumption costs, marine system retrofitting has been proposed. In this paper the focus is being put on studying energy management strengths and weaknesses which can lead to a qualitative understanding of the performance of the organizational structure, this work also focuses on the management settings concerning energy efficiency. If the organization wants to win by integrating energy policy, high-quality communication or promotion of energy efficiency issues should be employed. Marine plastic debris challenges fishing carriers on the African Union route. A possible beneficial solution for shipowners is discussed in this study. [ABSTRACT FROM AUTHOR]

The fast-moving world relies on intelligent connected networks to support the numerous applications of the expanded Internet-of-Things (IoT). The evolving communication requirements of this connected world require a new sixth generation (6G) radio to enable intelligent interaction with the massive number of connected objects. The energy management of billions of connected devices supporting massive Internet-of-Things (IoT) applications is the main challenge. These IoT devices and connected nodes are energy limited, and hence, energy-aware solutions are needed to enable seamless information flow between these communicating nodes. This paper presents an intelligent network solution for improved energy efficiency in a 6G-enabled expanded IoT network. A cell-free massive multiple input multiple output (mMIMO) technology is utilized for maximum energy efficiency with optimum network resource allocation. A practical power consumption model is proposed for the designed network topology which contains all the power components related to data transmission and circuit power. The proposed scheme aims to achieve maximum energy efficiency by the optimal allocation of pilot reuse factor and access point (AP) density for a given number of antennas at each AP and number of users. It is observed that the maximum energy efficiency of 5.2362 Mbit/Joule is achieved at the AP density of 29 and pilot reuse factor of 4 with PMMSE receive combining. In the end, the role of energy efficiency and area throughput tradeoff on the system performance is also evaluated, which suggests that both the energy efficiency and area throughput can be jointly increased until maximum energy efficiency is reached at a point. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) landscape to cover long-range applications. The LoRa-enabled IoT devices adopt an Adaptive Data Rate-based (ADR) mechanism to assign transmission parameters such as spreading factors, transmission energy, and coding rates. Nevertheless, the energy assessment of these combinations should be considered carefully to select an accurate combination. Accordingly, the computational and transmission energy consumption trade-off should be assessed to guarantee the effectiveness of the physical parameter tuning. This paper provides comprehensive details of LoRa transceiver functioning mechanisms and provides a mathematical model for energy consumption estimation of the end devices EDs. Indeed, in order to select the optimal transmission parameters. We have modeled the LoRa energy optimization and transmission parameter selection problem as a Markov Decision Process (MDP). The dynamic system surveys the environment stats (the residual energy and channel state) and searches for the optimal actions to minimize the long-term average cost at each time slot. The proposed method has been evaluated under different scenarios and then compared to LoRaWAN default ADR in terms of energy efficiency and reliability. The numerical results have shown that our method outperforms the LoRa standard ADR mechanism since it permits the EDs to gain more energy. Besides, it enables the EDs to stand more, consequently performing more transmissions. [ABSTRACT FROM AUTHOR]

Starting from the relationship between urban planning and mobility management, TeMA has gradually expanded the view of the covered topics, always following a rigorous scientific in-depth analysis. This section of the Journal, Review Notes, is the expression of a continuous updating of emerging topics concerning relationships among urban planning, mobility and environment, through a collection of short scientific papers. The Review Notes are made of five parts. Each section examines a specific aspect of the broader information storage within the main interests of TeMA Journal. This section of the Review Notes explores a specific topic, related to cities, within the framework of the European program NextGenerationEU. This contribution deepens the topic of energy efficiency in urban areas, providing an overview of the proposed interventions to improve energy management and save natural resources in the Italian Recovery and Resilience Plan (NRRP). The aim is deepening how these measures are intended to make Italian cities more sustainable, livable and environmental-friendly. Furthermore, it highlights the main strategies, reforms, and interventions which have been activated in Italian cities, thanks to the NRRP investments. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this publication is to present the usage of smartphone application in Smart Cities in energy efficiency management. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The rise of the Internet of Things has made smart devices commonplace in homes and businesses. Smartphone apps play a key role in enabling users to control and monitor these devices, thereby enhancing energy efficiency. Real-time data on energy consumption informs users' decisions, leading to reduced energy waste and costs. These apps not only offer convenience but also support customization and automation for energy-efficient operations. Participation in demand response programs and integration with renewable energy sources further enhance energy efficiency. Smartphone applications also contribute to eco-friendly commuting and optimized waste management, while educating users about their environmental impact and ways to reduce energy consumption sustainably. Despite their advantages, smartphone applications face challenges related to data privacy, adoption, compatibility, user education, reliability, affordability, and access inequalities. Overcoming these challenges is crucial for smart city planners and developers to fully harness the potential of smartphone applications in creating energy-efficient and sustainable smart cities. [ABSTRACT FROM AUTHOR]

Environmental organizations have alerted that rapid weather phenomena have intensified in recent years, such as floods, hurricanes, whirlwinds, droughts, fires and storms, caused by the deteriorating condition of the natural environment. Carbon emissions, which are the main cause of the deteriorating environment condition and dramatic climate change, are largely caused by the use of heavy fuel by shipping companies and generate large quantities of sulfur oxides, nitrogen oxides and particulate matter. These compounds exert a strong negative impact on the environment and human health. Care for improving the marine environment and coastal areas has become the subject of interest of many international institutions, such as the International Maritime Organization, Organization of United Nations, European Sea Port Organization, European Commission and others, which introduce restrictive regulations and guidelines on the level of permissible harmful emissions into the environment as a result of ship operations in ports and at sea. In addition, they propose to monitor the level of pollution through the use of indicators, such as the Energy Efficient Design Index (EEDI), Ship Energy Efficiency Management Plan (SEEMP) and Environmental Ship Index (ESI), among others. The aim of this paper is to assess the Northern Europe seaports' commitment to monitor the level of pollutants emitted by cruise ships in their ports. This article may constitute a subject of interest for seaport authorities and cruise ship operators. [ABSTRACT FROM AUTHOR]

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Industries in different sectors are addressing the emission reductions of their processes. Several energy efficiency proposals have been developed by researchers. However, the complexity of most industrial sectors makes it difficult for modelers, businesses, and policymakers to appreciate and realize the full potential for efficiency-driven energy savings in specific industries. Thus, this paper studies the energy efficiency potentials within a vehicle store in Quito, Ecuador. Data on power consumption, operating hours, and consumption were recorded for each inventory item. The workshop was identified as a problem area when the data were analyzed by agency action areas. It is the part of the company with the most machinery and the most operational hours. Finally, proposals for energy efficiency were developed and examined using the internal rate of return and net present value. The proposals with the best applicability were specified using these two indications, resulting in the changing of lights, the use of a timer for the compressor, and the reduction of pump usage time, and lastly, the energy performance indicator and the energy use intensity of the building were calculated. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless sensor technology, recent progress in wireless sensor and actuator networks (WSANs) with energy harvesting provide the possibility for various real-time applications. Meanwhile, extensive research activities are carried out in the fields of efficient energy allocation and control strategy design. However, the joint design considering physical plant control, energy harvesting, and consumption is rarely concerned in existing works. In this paper, in order to enhance system control stability and promote quality of service for the WSAN energy efficiency, a novel three-step joint optimization algorithm is proposed through control strategy and energy management analysis. First, the optimal sampling interval can be obtained based on energy harvesting, consumption, and remaining conditions. Then, the control gain for each sampling interval is derived by using a backward iteration. Finally, the optimal control strategy is determined as a linear function of the current plant states and previous control strategies. The application of UAV formation flight system demonstrates that better system performance and control stability can be achieved by the proposed joint optimization design for all poor, sufficient, and general energy harvesting scenarios. [ABSTRACT FROM AUTHOR]

This study evaluates the productivity of energy efficiency (EE) measures taken at the regional level employing a set of non-parametric statistical methods. The first aspect is investigating the impact of various energy efficiency measures. A high proportion of the population lives in apartment buildings, with limited opportunities to manage their own energy efficiency. The second research aspect of this paper is the study of the effectiveness of various energy efficiency measures in the context of low energy prices. Our analysis indicates that only two tools exert a long-term positive effect on reducing the economy's energy intensity for all regions. This is the use of combined heat sources in large cities and implementing ISO 50001 in large companies. These two energy efficiency measures work even under low energy prices, which do not sufficiently stimulate the introduction of innovative energy-saving technologies. [ABSTRACT FROM AUTHOR]

Copyright of Scientia et Technica is the property of Scientia et Technica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Edge computing aims to make internet-based services and remote computing power close to the user by placing information technology (IT) infrastructure at the network edges. This proximity provides data centers with low-latency and context-aware services. Edge computing power consumption is mainly caused by data centers, network equipment, and user equipment. With edge computing (EC), energy management platforms for residential, industrial, and commercial sectors are built. Energy efficiency is considered to be one of the key aspects of edge power constraints. This paper provides the state of the art of power consumption and energy management for edge computing, the computation off-loading methods, and more important highlights the power efficiency of edge computing systems. Furthermore, renewable energy and related concepts will also be explored and presented since no human participation is required in replacing or recharging batteries when using such energy sources. Based on such study, a recommendation is to develop a dynamic system for energy management in real-time with the assessment of local renewable energy so that the system be reliable with minimum power consumption. Also, regarding energy management, we recommend providing backup energy sources (or using more than one energy source) or (a hybrid technique). [ABSTRACT FROM AUTHOR]

Introduction. Nowadays, electrical energy is indispensable in industrial, tertiary and domestic appliances. However, its efficiency is becoming affected by the presence of the disturbances that appear in the electrical networks such as harmonics, unbalance, sags/swells, flickers …etc. Indeed, the disturbances cause a decrease in the power factor and an increase in the power losses. In this paper, the harmonic disturbance is considered and a 3-level shunt active power filter powered by a hybrid fuel-cell/battery DC is applied to mitigate current harmonic components from the electrical feeder. Aim. Studying the energy efficiency of a system based on a 3-level shunt active filter powered by a hybrid fuel-cell / battery DC bus. Methodology. It is a matter of finding the suitable formulas that express the efficiency and the relative power losses according to the load factor (which is the ratio between the short-circuit active power and the load active power) and the load power factor. The DC bus energy is controlled using an energy management algorithm that contributes in generating the required reference input currents and output voltages of the fuel-cell and the battery. The DC/DC converters control circuits are performed in a closed loop by means of regulated duty cycles. Results. The simulation results carried-out under MATLAB/Simulink environment show better filtering quality if compared with the case of open loop control of the DC/DC converters and lesser differences between the fuel-cell power, the battery power and their respective reference powers. Which concerns the energy efficiency, the results demonstrate that higher efficiency and lower relative power losses can be achieved only when higher load factor and load power factor are attained. Therefore, the compensating system of the power factor is very important to improve the energy efficiency. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative decision support system for improving ship energy efficiency. It combines an engine optimization model and an artificial neural network (ANN). Real-time data regarding the ship and engine performance is obtained along a specific ship navigation, and an engine optimization model is built using Ricardo Wave software. This model is validated with high accuracy. Ship-specific parameters are derived, and fuel consumption is estimated using ANN models with different structures. The study identifies the most suitable ANN model with 1 hidden layer and 5 neurons based on error metric evaluation where the 0.99697 R2, 0.00035 RMSE, and 0.06470 MAPE scores are calculated. This approach offers a cost-effective solution for shipping companies to monitor critical engine parameters in real-time without investing in sensors or data collection systems. Thus, it contributes to dealing with the problem of the scarcity of analyzable data in the maritime literature, which is one of the significant issues in the papers related to energy efficiency, machine learning, and deep learning. It is an innovative approach since the parameters that are related to real-time operations rather than considering only instruction book information are aggregated, and the produced data is analyzed by an intelligent tool. • A system is proposed to control ship energy efficiency level by combining an optimization model and a neural network • A simulation model is created in the Ricardo Wave tool and the engine model is validated by actual data. • The ship fuel consumption is estimated by an artificial neural network based on the derived input parameters. • The prediction process is evaluated by error metrics to determine its success. [ABSTRACT FROM AUTHOR]

In recent years, the severe environmental degradation and high levels of fossil fuel consumption linked to conventional ship energy systems have drawn attention to the advancement of alternative ship energy systems. Consequently, ship energy systems based on the use of an electrical microgrid are coming to the fore as an increasingly popular alternative solution. However, managing the energy flows within a shipboard microgrid can be highly challenging due to the multiple energy sources (including renewable energy sources) and types of loads involved. Additionally, the integration of an energy storage system has been identified as an effective solution for improving the reliability of shipboard power systems, pointing out the important role of energy storage systems in maritime microgrids and their potential to enhance the energy management process. As such, effective energy management strategies are necessary to optimize performance and ensure that the vessel's energy demands are met while minimizing fuel consumption and reducing cost and environmental impacts. This paper presents a comprehensive review of such strategies and methods recently presented in the literature associated with energy management in shipboard microgrids integrating energy storage systems and examine the different techniques that can be utilized to achieve optimal system performance. Moreover, the paper also sheds light on the future challenges and perspectives imposed by the modern shipboard microgrids operation on the energy/power management of these systems. The aim of this paper is to provide a helpful guideline for researchers and engineers conducting research in the field of electrically powered ships. [Display omitted] • A comprehensive review of EMS for electrical and hybrid ship is presented. • A comprehensive review of ESS which can be used in such ships is presented. • Technologies of ESS and their suitability to ships are discussed and commented. • Future challenges and perspectives imposed by modern SMG operation are presented. [ABSTRACT FROM AUTHOR]

There has been increasing interest in reducing carbon footprints globally in recent years. Hence increasing share of green energy and energy efficiency are promoted by governments. Therefore, optimizing energy consumption is becoming more critical for people, companies, industries, and the environment. Predicting energy consumption more precisely means that future energy management planning can be more effective. To date, most research papers have focused on predicting residential building energy consumption; however, a large portion of the energy is consumed by industrial machines. Prediction of energy consumption of large industrial machines in real time is challenging due to concept drift, in which prediction performance deteriorates over time. In this research, a novel data-driven method multi-regime approach (MRA) was developed to better predict the energy consumption for industrial machines. Whereas most papers have focused on finding an excellent prediction model that contradicts the no-free-lunch theorem, this study concentrated on adding potential concept drift points into the prediction process. A real-world dataset was collected from a semi-autonomous grinding (SAG) mill used as a data source, and a deep neural network was utilized as a prediction model for the MRA method. The results proved that the MRA method enables the detection of multi-regimes over time and provides a highly accurate prediction performance, thanks to the dynamic model approach. [ABSTRACT FROM AUTHOR]

The customers of textile wet processing plants have committed to reduce their greenhouse gas emissions from outsourced manufacturing activities. The textile brands directly use specific energy consumption values to analyze and set steep, time-bound emission intensity reduction targets for the energy intensive textile wet processing plants. However, the direct use of specific energy consumption values to evaluate energy savings can be misleading. In this paper, the authors propose a methodological framework to systematically evaluate energy performance and monitor energy savings in the textile wet processing plants. Further, the approach was extended to analyze the energy performance monitoring practices and identify energy conservation measures. The proposed framework was developed based on thorough analysis of the manufacturing process of textile wet processing plants and the principles of ISO 50001. A textile wet processing plant was chosen as a case study for illustrating the effectiveness of the framework using multiple regression method. Bottom up approach for benchmarking was used and yet significantly simplified by the introduction of an innovative parameter "program mix factor" and thereby evaluate "equivalent specific energy consumption". The predicted values for specific energy consumption were found to be within a close error range of ±10 % of the actual data. The proposed methodological framework is therefore innovative, simple and yet comprehensive to conduct internal benchmarking in textile wet processing plants. The textile brands are suggested to use equivalent specific energy consumption instead of specific energy consumption for energy performance monitoring of their textile wet processing plant suppliers. The authors also conducted a survey in fifteen textile wet processing plants to assess the prevailing energy monitoring practices. The results of the survey suggests that only 20 % of the plants are practicing basic level energy performance monitoring, while the remaining 80 % are at minimal level. • Textile wet processing plants were analyzed to identify the parameters required to normalize specific energy consumption • A simplified methodological framework was developed for the textile wet processing plants and the textile brands • The framework serves as a useful tool to evaluate, monitor the realistic energy performance & identify conservation measures • The textile brands are suggested to use equivalent specific energy consumption to monitor energy performance • The study identified the low levels of energy monitoring practices in textile wet processing plants in South Asian countries [ABSTRACT FROM AUTHOR]

This study aims to develop and evaluate an LSTM neural network for predicting household energy consumption. To conduct the experiment, a testbed was created consisting of five common appliances, namely, a TV, air conditioner, fan, computer, and lamp, each connected to individual smart meters within a Home Energy Management System (HEMS). Additionally, a meter was installed on the distribution board to measure total consumption. Real-time data were collected at 15-min intervals for 30 days in a residence that represented urban energy consumption in Sincelejo, Sucre, inhabited by four people. This setup enabled the capture of detailed and specific energy consumption data, facilitating data analysis and validating the system before large-scale implementation. Using the detailed power consumption information of these devices, an LSTM model was trained to identify temporal connections in power usage. Proper data preparation, including normalisation and feature selection, was essential for the success of the model. The results showed that the LSTM model was effective in predicting energy consumption, achieving a mean squared error (MSE) of 0.0169. This study emphasises the importance of continued research on preferred predictive models and identifies areas for future research, such as the integration of additional contextual data and the development of practical applications for residential energy management. Additionally, it demonstrates the potential of LSTM models in smart-home energy management and serves as a solid foundation for future research in this field. [ABSTRACT FROM AUTHOR]

Over the recent years, multimedia applications have begun to be used over wireless sensor networks (WSN), which hitherto has been primarily used to carry small amount of data. To meet the demand for higher throughput and real-time characteristics of multimedia contents, the routing protocol need to be simplified and optimized. In this paper, we present a routing algorithm that provides a low end-to-end packet delay and low packet loss, referred to as smart, greedy forwarding algorithm based on throughput and energy-awareness (SGFTEM). This algorithm routes multimedia packets across a WSN by choosing high- throughput paths rather than always choosing the shorter path to the sink. When it encounters network holes, a network void-bypass is applied, which enhances the reliability of the network. Energy management is also implemented to extend sensor node lifetime by way of reducing the radio transmission coverage to a suitable distance reachable by the sensor nodes The simulation results shows that, SGFTEM reduces the end-to-end delay by 40% and packet loss ratio by 35% and maintain energy consumption against other algorithms. With energy management, the residual energy of sensor nodes are made to be equally distributed across the entire network which results in load balancing. The performance of SGFTEM are also compared via simulation against AGEM, TPGF, GPSR, and AODV showing superior performance under similar scenarios i.e. low packet end-to-end delay, low packet loss and load balancing of routing paths. [ABSTRACT FROM AUTHOR]

• TIP-EMS enhances EV energy efficiency by integrated prediction and energy management. • Significant driving economy boost; 1.6%-4.2% better than state-of-the-art PEMS. • TIP-EMS shows robust performance with varying prediction weight adjustments. To enhance the energy efficiency of electrified vehicles (EVs), developing effective energy management strategies (EMS) for hybrid storage systems is essential. Predictive EMS (PEMS) that foresee future vehicle speeds have demonstrated substantial potential in boosting EMS performance. However, traditional PEMS models, employing a sequential approach of speed prediction followed by energy allocation, are hampered by cumulative errors. These errors from the initial speed predss this issue, this paper introduces a novel solution: the trainable integrated preiction negatively impact the efficiency of subsequent energy management. To addrediction and energy management strategy (TIP-EMS). Contrasting with conventional sequential PEMS, TIP-EMS features a dual-branch, integrated neural network, which is fully trainable. This network processes driving status inputs via attention layers, with one branch dedicated to energy management objectives using a reinforcement learning (RL) algorithm, and the other to vehicle speed prediction. Both branches are trained simultaneously, but post-training, only the RL branch is activated for energy management. Implemented with a soft actor-critic RL algorithm, TIP-EMS is applied to a fuel cell EV for optimized energy management. The validation involved training TIP-EMS using 27 driving profiles, which developed its prediction and energy management capabilities, followed by tests in untrained scenarios. The results show that TIP-EMS surpasses conventional sequential PEMS by up to 4.2% in scenarios where prediction accuracies are comparable, highlighting the efficacy of the trainable integrated mechanism. In addition, TIP-EMS exhibits superior energy conservation compared to non-predictive RL strategies. Lastly, TIP-EMS exhibits robustness to adjustments in the weight given to the prediction objective, further confirming its practical applicability. [ABSTRACT FROM AUTHOR]