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Highlights: What are the main findings? The use of machine learning algorithms in energy management services can lead to a significant increase in the implementation rate of energy performance, power quality and renewable energy sources projects; Integrating machine learning algorithms in the process of assessing the energy saving potential can accelerate the deployment of energy performance contracting; What is the implication of the main finding? Digitization of the energy services sector could support end-users in achieving their targets regarding the transition towards environmental sustainability Policy makers could also use the proposed methodology to evaluate the global energy performance of the relevant energy sectors, thus increasing the performance of the available financing mechanisms. Current targets, which have been set at both the European and the international level, for reducing environmental impacts and moving towards a sustainable circular economy make energy efficiency and digitization key elements of all sectors of human activity. The authors proposed, developed, and tested a complex methodology for real-time statistical analysis and forecasting of the following main elements contributing to the energy and economic performance of an end user: energy performance indicators, power quality indices, and the potential to implement actions to improve these indicators, in an economically sustainable manner, for the end user. The proposed methodology is based on machine learning algorithms, and it has been tested on six different energy boundaries. It was thus proven that, by implementing an advanced energy management system (AEMS), end users can achieve significant energy savings and thus contribute to the transition towards environmental sustainability. [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]

This research addresses the increasing importance of understanding how Artificial Intelligence can facilitate the transition of companies to a Circular Economy model. This study focuses on energy management, examining its impact on efficiency and emissions across a multi-case analysis of 18 projects in diverse sectors. The findings indicate that Artificial Intelligence positively influences both variables, with variations across applications and sectors. Notably, Artificial Intelligence significantly enhances energy efficiency in four out of six sectors, achieving over 5% improvement in half of the projects. Regarding emissions, positive effects are observed in 15 out of 18 projects, resulting in over 5% reductions in seven cases. Artificial Intelligence plays a pivotal role in emissions reduction in the Design and Energy sectors, with some projects achieving over 20% reductions. Additionally, this study explores how improved energy efficiency positively affects strategic business variables, such as cost, quality, and delivery time. The impact on emissions contributes to reducing occupational risks, particularly those associated with chemical and biological agents. Although managers are satisfied, measures need to be taken to overcome the lack of employee acceptance. These findings are of great interest to the stakeholders involved in the integration of Artificial Intelligence into companies. [ABSTRACT FROM AUTHOR]

As autonomous vehicle technology advances, the development of energy-efficient control methodologies emerges as a critical area in the literature. This includes the behavior control of vehicles near signalized intersections, which still needs comprehensive exploration. Through connectivity, the adoption of promising eco-driving approaches can manage a vehicle's speed profile to improve energy consumption. This study focuses on controlling the speed of an autonomous electric vehicle (AEV) both up and downstream of a signalized intersection in the presence of preceding vehicles. In order to achieve this, a dynamic pro-active predictive cruise control eco-driving (eco-PPCC) framework is developed that, instead of merely reacting to the preceding vehicle's speed changes, uses the preceding vehicle's upcoming data to actively adjust and optimize the speed profile of the AEV. The proposed algorithm is compared to the conventional Gipps and eco-PCC models for benchmarking and performance analysis through numerous scenarios. Additionally, real-world measurements are performed and taken to consider practical use cases. The results demonstrate that when compared to the two baseline methods, the proposed framework can add significant value to reducing energy consumption, preventing unnecessary stops at intersections, and improving travel time. [ABSTRACT FROM AUTHOR]

The evaluation of the energy efficiency of vehicles in operating conditions is used to solve management and control tasks in intelligent transport systems. The modern world fleet is characterized by an increase in the share of vehicles with alternative power plants (hybrid, electric, and hydrogen fuel cells). At the same time, vehicles with conventional power plants (internal combustion engines) remain in operation. A wide range of modern power plants determines the relevance of studying the advantages and limitations of existing methods of evaluating the vehicle energy efficiency, delineating the application scope and highlighting promising directions for their further development. The article systematizes the methods of evaluation and management of the energy efficiency of vehicles with conventional and alternative power plants. Special attention is paid to the assessment of energy consumption per unit of transport work at the stage of vehicle operation, taking into account various operational factors. The concept of a 3D morphological model of the transport system for evaluating the energy efficiency of vehicles is presented. An algorithm for the optimization of the current transport system configuration according to the criterion of an increase in the energy efficiency indicator is given. [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]

Constructing energy-efficient database systems to reduce economic costs and environmental impact has been studied for 10 years. With the emergence of the big data age, along with the data-centric and data-intensive computing trend, the great amount of energy consumed by database systems has become a major concern in a society that pursues Green IT. However, to the best of our knowledge, despite the importance of this matter in Green IT, there have been few comprehensive or systematic studies conducted in this field. Therefore, the objective of this article is to present a literature survey with breadth and depth on existing energy management techniques for database systems. The existing literature is organized hierarchically with two major branches focusing separately on energy consumption models and energy-saving techniques. Under each branch, we first introduce some basic knowledge, then we classify, discuss, and compare existing research according to their core ideas, basic approaches, and main characteristics. Finally, based on these observations through our study, we identify multiple open issues and challenges, and provide insights for future research. It is our hope that our outcome of this work will help researchers develop more energy-efficient database systems. [ABSTRACT FROM AUTHOR]

Vietnam's economy has been growing rapidly in the last 20 years, leading to significant increases in energy consumption as well as in carbon emissions. Most electricity is consumed by loads of industry and construction due to the country's socio-economic development strategy. An energy saving strategy cannot be achieved if the industry factories lack energy consumption data. The installation of energy monitoring systems can help to improve energy efficiency by supplying daily, monthly, and yearly energy consumption reports. Moreover, major energy-consuming enterprises in Vietnam must implement solutions for energy-efficient use as prescribed in the Law on Energy Efficient Use. Therefore, this study aimed to determine the impact of an energy monitoring system as an improvement solution for energy efficiency in a typical major energy-consuming enterprise in Vietnam. The study's results, after six months, show that the total saved electricity after installing the power monitoring system was 191,923 kWh. The company saved approximately 19.584 USD and reduced emission to the environment by 139 tons of CO2. In addition, the return on investment time of power monitoring systems is about 14 months, while the annual energy costs of the factory can be reduced by about 9.62% per year. Therefore, power monitoring systems should be promoted in factories with different scales to control energy wastage in the domestic industry field. [ABSTRACT FROM AUTHOR]

The management of municipal solid waste presents significant challenges globally. This study investigates the potential of an integrated waste-to-energy system based on circulating fluidized bed gasification technology to address these challenges, while also contributing to renewable energy generation. Using a MATLAB-based simulation model, the study determines the optimal operational parameters for various units within the system, including waste processing, gasification, ash handling, syngas treatment, and emission control. The proposed waste-to-energy system demonstrates a remarkable energy efficiency of 70% under these optimal conditions, notably outperforming conventional waste-to-energy technologies. Sensitivity and uncertainty analyses reveal that waste composition, gasification temperature, and the oxygen-to-solid recovered fuel ratio are key determinants of the system's output and performance. The system's performance remained robust despite variations in these parameters, underscoring its potential as a reliable solution for waste management and energy generation. While the findings are promising, future research should focus on comprehensive lifecycle assessment and consider regional factors for practical implementation. This study contributes to the ongoing development of efficient waste-to-energy systems and highlights their potential in promising sustainable waste management and renewable energy production. [ABSTRACT FROM AUTHOR]

European manufacturing companies are currently in a very difficult situation. On the one hand, they have to strive for sustainable development. On the other hand, they have to face problems related to the geopolitical situation and the pursuit of cleaner production (including restrictive regulations on emissions). A significant increase in electricity prices means that companies have to take steps to use electricity rationally without reducing the level of production or incurring other additional costs. In this study, attention was paid to the influence of quality tools (QTs) in production process (PP) management on increasing energy efficiency (EE). Based on the literature review, it was found that the most frequently described QTs, i.e., SMED, TPM, Six Sigma, SPC, FMEA, 5S, or Poka-Yoke, used to improve PP did not take into account their impact on the EE of the enterprises. Therefore, the main purpose of this study was to examine this correlation and show that QTs can be helpful in shaping the energy management system (EMS). Achieving the research goal was possible thanks to surveys carried out among manufacturing companies. Using statistical tests, the existence of a statistically significant correlation was proved between QTs and EMS elements. [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]

Two prototypes with energy efficiency calculations have been developed to enable real-time efficiency assessment and data collection. The results of the experiment demonstrate that the use of microprocessor technology and the Internet of Things can significantly improve the efficiency and accuracy of energy audits in power transformers. The prototypes developed in this study provide real-time efficiency assessment and data collection, enabling more effective energy management and cost savings for industrial users. During the experiment, it was found that resonance can cause the same losses as a poor power factor of the system, highlighting the importance of addressing energy quality issues in addition to energy efficiency. These findings have important implications for energy efficiency policies and practices in the context of climate change mitigation and rising energy prices. [ABSTRACT FROM AUTHOR]

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]

This work, it was carried out with the design and application of a procedure for the diagnosis of energy management in the Company. The procedure is described starting from 7 steps that go from the theoretical conception of 6 variables and 21 elements of the capacity of energy management in the company; the description of the mathematical model based on compensatory diffuse logic, the design of the checkup list until the application and the valuation of the results. The application and valuation of the results in the company’s Acinox Commercial and DIVEP allowed obtaining the evaluation of the energy management capabilities. The variables are analyzed to identify the elements with the lowest performance: projection of actions of energy saving, use and/or generation of clean energy, and energy planning. [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]

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]

Building energy efficiency has grown strong in a context of soaring energy prices, especially in Europe. The use of energy-saving devices strongly influences its improvement, but in many cases, it is far from sufficient., especially if the energy comes from renewable sources with forced production. In the case of buildings, these are usually photovoltaic (PV) sources. For this reason, energy management systems (EMS) are becoming increasingly popular as they allow the increase in self-consumption and reduce the size of energy storage. This article presents analyses of historical energy consumption profiles in selected small- and medium-sized buildings powered by renewable energy sources. The implementation limitations of this type of systems, depending on the profile of the building, were identified and guidelines were presented to assess low-cost solutions dedicated to small buildings and considering the actual conditions of existing systems. Statistical analyzes were conducted for the energy demand profiles of 15 different buildings. The analyzes consisted of the preparation of box plots for each hour of working days and the calculation of the relative standard deviation (RSD) index for annual profiles of 60 min periods. The analyzes showed that the RSD index has low values for commercial buildings (e.g., hospital 7% and bank 15%) and very high values for residential buildings—even over 100%. On this basis, it can be concluded about the usefulness of energy profiles for demand forecasting. The novelty of the proposed method is to examine the possibility of using measurement data as data to forecast energy consumption based on statistical analysis, dedicated to low-cost EMS system solutions. [ABSTRACT FROM AUTHOR]

In recent years, shipborne emissions have become a growing environmental threat. The International Maritime Organization has implemented various rules and regulations to resolve this concern. The Ship Energy Efficiency Management Plan, Energy Efficiency Design Index, and Energy Efficiency Operational Indicator are examples of guidelines that increase energy efficiency and reduce shipborne emissions. The main engine shaft power (MESP) and fuel consumption (FC) are the critical components used in ship energy efficiency calculations. Errors in ship energy efficiency calculation methodologies are also caused by misinterpretation of these values. This study aims to predict the MESP and FC of a container ship with the help of data-driven methodologies utilizing actual voyage data to assist in the calculation process of the ship's energy efficiency indexes appropriately. The algorithms' prediction success was measured using the RMSE, MAE, and R2 error metrics. When the simulation results were analyzed, the Deep Neural Network and Bayes algorithms predicted MESP best with 0.000001 and 0.000002 RMSE, 0.000987 and 0.000991 MAE, and 0.999999 R2, respectively, while the Multiple-Linear Regression and Kernel Ridge algorithms estimated FC best with 0.000208 and 0.000216 RMSE, 0.001375 and 0.001471 MAE, and 0.999999 R2, respectively. [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]

A hall of residence with low energy performance was subjected to an extensive retrofitting program due to its high energy consumption, to enhance the building's energy efficiency and reduce its energy costs and greenhouse gas emissions. The retrofitting program included the integration of a solar photovoltaic (PV) system installed on the building's flat roof, among other factors. Nevertheless, the electricity supply provided by the system during the daytime proved insufficient to cover the building's energy demand. Based on this, a study was implemented to analyze the contribution of the electricity produced by the solar PV system throughout the year to reduce power consumption under distinct solar radiation conditions, and to define a strategy to optimize renewable energy use by drawing up a set of organizational measures to be implemented to manage the PV solar system energy strategically. The strategic measures are mainly related to selecting the residence tasks with higher energy consumption to fit into higher PV energy production periods. Additionally, it is fundamental to raise the residence's occupants' education and awareness of energy efficiency, optimize the excess PV energy produced in specific periods by converting it into other energy forms, and install complementary storage systems for surplus production. [ABSTRACT FROM AUTHOR]

Edge devices and their associated computing techniques require energy efficiency to improve sustainability over time. The operating edge devices are timed to swap between different states to achieve stabilized energy efficiency. This article introduces a Cognitive Energy Management Scheme (CEMS) by considering the offloading and computational states for energy efficacy. The proposed scheme employs state learning for swapping the computing intervals for scheduling or offloading depending on the load. The edge devices are distributed at the time of scheduling and organized for first come, first serve for offloading features. In state learning, the reward is allocated for successful scheduling over offloading to prevent device exhaustion. The computation is therefore swapped for energy-reserved scheduling or offloading based on the previous computed reward. This cognitive management induces device allocation based on energy availability and computing time to prevent energy convergence. Cognitive management is limited in recent works due to non-linear swapping and missing features. The proposed CEMS addresses this issue through precise scheduling and earlier device exhaustion identification. The convergence issue is addressed using rewards assigned to post the state transitions. In the transition process, multiple device energy levels are considered. This consideration prevents early detection of exhaustive devices, unlike conventional wireless networks. The proposed scheme's performance is compared using the metrics computing rate and time, energy efficacy, offloading ratio, and scheduling failures. The experimental results show that this scheme improves the computing rate and energy efficacy by 7.2% and 9.32%, respectively, for the varying edge devices. It reduces the offloading ratio, scheduling failures, and computing time by 14.97%, 7.27%, and 14.48%, respectively. [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]

In the last few years, deep reinforcement learning has been proposed as a method to perform online learning in energy-efficiency scenarios such as HVAC control, electric car energy management, or building energy management, just to mention a few. On the other hand, quantum machine learning was born during the last decade to extend classic machine learning to a quantum level. In this work, we propose to study the benefits and limitations of quantum reinforcement learning to solve energy-efficiency scenarios. As a testbed, we use existing energy-efficiency-based reinforcement learning simulators and compare classic algorithms with the quantum proposal. Results in HVAC control, electric vehicle fuel consumption, and profit optimization of electrical charging stations applications suggest that quantum neural networks are able to solve problems in reinforcement learning scenarios with better accuracy than their classical counterpart, obtaining a better cumulative reward with fewer parameters to be learned. [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]

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]

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]

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 article aims to present changes in the use of electricity by service companies, resulting from regulations within the framework of increasing energy efficiency from the perspective of the implementation of the European Green Deal strategy. To achieve the above goal, the following research question was formulated: to what extent did the COVID-19 pandemic affect the implementation of energy transformation and electricity consumption among the surveyed group of recipients? It should be noted that, so far in the global environment, more and more electricity has been used every year, and this tendency is still continuous and growing. Therefore, in European Union countries, measures have been taken to balance demand and its rational use, resulting from the implementation of the European Green Deal strategy. According to the strategic goal of the indicated policy, EU countries are obliged to implement a sequence of actions enabling their transformation into a modern, resource-efficient, and competitive economy. In particular, the strategy aims to achieve three main goals: 1. Achieving climate neutrality by reducing net greenhouse gas emissions to zero in 2050; 2. Decoupling economic growth from the consumption of natural resources; and 3. Striving for an equal standard of living in all countries. Therefore, the behavior of individual countries should focus on providing the required amount of energy to ensure socioeconomic growth while reducing pollution and environmental devastation caused by traditional methods of energy production and use. There are numerous talks and debates about the defined tasks and mechanisms leading to the achievement of the indicated goals, in which hardly any mention is made of the methods of monitoring the progress and evaluation of individual projects at the stage of building a new green deal. This study aims to fill the research gap observed in the current state of knowledge on energy management in EU countries from the perspective of the European Green Deal strategy and changes in its management resulting from the conditions of the COVID-19 pandemic. Therefore, in line with the aim of the article: 1. Factors influencing electricity consumption in service enterprises operating in EU countries were indicated; 2. Energy consumption variability in these entities was determined; and 3. The correlation between electricity consumption and GDP growth in the service sector located in EU countries was indicated. Therefore, a hybrid research method was used to analyze the data obtained from the databases of Eurostat and Statistics Poland, which consisted of the following analyses: diagnostic-descriptive, main components, and wavelet transform. Based on the conducted research, it should be concluded that energy consumption among service companies operating in the EU market is conditioned by three sources of causes along with the relevant consumption factors. The first group includes energy and technical premises, along with technological determinants. The second is shaped by financial and economic motives, which include socioeconomic factors. The third group is related to environmental sources represented by the natural environment and geographic and meteorological factors. The increase in electricity consumption in service enterprises is related to the average GDP growth of EU countries following a two-way cause-and-effect relationship implemented until 2019. Nevertheless, since 2020, there has been a noticeable decrease in energy consumption by 14.01% by service entities, which results from the limitations caused by the COVID-19 pandemic and the implementation of the European Green Deal strategy. Meanwhile, the structure of electricity consumption growth was dominated by industrial enterprises (increased by 37.7%) and individual consumers (increased by 7.8%). The results of the research may motivate the managers of EU countries and enterprises to analyze the factors of energy consumption, its variability, and dependence on economic growth, which contribute to determining the forecast of future energy demand, in connection with the ongoing energy transformation resulting from the implementation of the European Green Deal strategy, and economic slowdown caused by the COVID-19 pandemic. The issues presented in this article are an attempt to fill the gap indicating practical experience related to the process of electricity management and management in the political, economic, and technological dimensions from the perspective of implementing the European Green Deal strategy and the conditions resulting from the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Drivers for industrial energy efficiency are factors that promote the sustained adoption of energy-efficient measures and practices. Leveraging drivers to overcome barriers and encourage action which improves industrial energy efficiency can contribute to closing the energy efficiency gap. In fossil-fuel-based systems, this will also contribute to greenhouse gas abatement. A systematic literature review was used to investigate how knowledge about drivers is generated and whether prevalent drivers can be mapped to existing taxonomies. The systematic literature review confirmed that surveys and/or interviews with managers from countries who are members of the Organisation for Economic Cooperation and Development (OECD) are the most common way to gather data on drivers for industrial energy efficiency. This means the extant knowledge on drivers may be incomplete because contributions from some stakeholders, industry types and company sizes may be missing. The review also found economic drivers are the most prevalent and that not all the drivers identified during the study can be mapped to contemporary driver taxonomies. Having an agreed-upon comprehensive taxonomy facilitates empirical research and comparison of studies. Further research into the views of frontline workers and the creation of a comprehensive driver taxonomy is recommended. [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]

Traditionally, the energy management objectives of a fuel cell–battery hybrid electric vehicle (FCBHEV) powertrain system is achieved by using the centralized control strategy, which suffers from poor flexibility, scalability, and reliability. What is worse, the system stability can hardly be guaranteed because of the serious interactions between the fuel cell (FC) as well as the battery converters and motor drive loads. In this article, a decentralized energy management strategy (EMS) based on a composite control scheme that combines mixed droop control and disturbance-observer-based control is proposed to achieve the dynamic load power allocation, extend the service life, improve the energy efficiency, enhance the robustness (i.e., realize the disturbance and uncertainty rejection), and guarantee the global stability of the FCBHEV powertrain system in a decentralized way simultaneously. First, the architecture of an FCBHEV powertrain system is described in detail. Then, analyses on the design and operating principle of the proposed EMS are deeply studied. Finally, a hardware-in-the-loop (HIL) real-time simulation system of a 50 kW FCBHEV powertrain system is established using the Typhoon HIL 602 device. The HIL simulation results verified the correctness of the theoretical analysis and effectiveness of the proposed EMS. [ABSTRACT FROM AUTHOR]

Global warming has forced researchers to find an alternative for fossil fuels and to enhance the energy efficiency of processes in industries. Waste heat recovery has a significant potential to reduce fossil fuel consumption and energy performance enhancement. The study cycle is a tri-generation system, heating, electrical power, that can capture carbon dioxide gas. The system works with the solar energy and waste heat of the cement plant. In this study, a model for a completely new system has been developed based on renewable energies. Thermodynamic analysis for the energy system is performed, and the system is based on the organic Rankine cycle, absorption chiller, solar energy, and waste heat recovery from the exhaust gases of the cement plant stacks. The results of the analysis showed that the energy and exergy efficiencies were calculated to be 35.78% and 12.77%, respectively, and the total exergy destruction was calculated 277327 kW. Also, the optimisation result with the direct algorithm method with the objective function of exergy efficiency improved both efficiencies. In this optimisation, the exergy efficiency reached 16.39% and energy efficiency was calculated 49.04%. The optimisation with the objective function of total exergy destruction decreased the value to 216813 kW, which was significantly reduced from the base state of the system; while energy and exergy efficiencies were calculated to be 54.61% and 13.85%, respectively. [ABSTRACT FROM AUTHOR]

• Allowing for energy-efficient controls adaptive to diverse production disturbances. • Modeling the system dynamics of serial production lines using max-plus algebra. • Designing an adaptive energy-efficient control policy based on opportunity windows. • The diversity of production disturbances significantly affects energy waste. • Achieving more energy savings with less throughput loss than other control methods. The energy-efficient control has emerged as one of the most promising measures to reduce the unproductive energy consumption of machines during production disruptions. Diverse production disturbances (PDs) are the fundamental cause of production interruptions that result in unproductive energy consumption. In this scenario, the diversity and randomness of PDs necessitate elaborative consideration in energy-efficient controls. This consideration is crucial to the effectiveness of energy-efficient controls, yet it has been generally overlooked. To this end, this paper presents an adaptive energy-efficient control (AEC) methodology to generate adaptive control decisions when machines encounter diverse PDs in serial production lines. As a first step towards AEC, a PD identification method driven by Markov Logic Network is introduced to identify ongoing PDs. To then estimate the system performances in terms of energy savings and throughputs, a system dynamics model for serial production lines is established based on max-plus algebra. Finally, an AEC policy is formulated to optimize unproductive energy consumption on a nearly global scale while maintaining throughputs. A case study was performed on a die-casting production line in order to validate the effectiveness of the methodology. The results indicated a 96.1% F 1 score for PD identifications. Based on this, the AEC policy resulted in a 61.12% reduction in unproductive energy consumption, with a corresponding decrease in throughputs of only 0.65%. It was confirmed that the AEC methodology achieved greater energy savings while minimizing throughput losses compared to conventional energy-efficient control methods. [ABSTRACT FROM AUTHOR]

Rising energy costs and the current concern about sustainability issues directly impact the competitiveness and survival of organizations, promoting the search for continuous improvement in industrial production processes. The present study, by identifying the absence in the literature of models based on Value Stream Mapping and integrated with Industry 4.0 technologies that promote better energy efficiency practices in industrial processes, proposes a new Lean Management model called Real-time Energy Flow Mapping. This innovative model, provide capabilities to a visual, digital and sustainable management system of all energy behavior in machines and equipment using fully parameterizable metrics and rules. Also, estimulating stakeholders with the ability to predict and monitor energy costs, as well as identify possible problems due to changes in the behavior of energy consumption in machines, thus helping managers to make decisions faster and more dynamically. As a result, the literature search turned up 17 Value Stream Mapping based models with a focus on energy efficiency, along with 31 metrics grouped on energy, environmental and production processes. After discussions by the Focus Groups, 16 resulting metrics were submitted to expert panel, in which the least relevant were discarded and the 13 most relevant classified using Fuzzy Delphi. An analysis to verify the interrelationships between the metrics is performed using the Fuzzy DEMATEL, being finally applied in the proposed model. A case study of the model was carried out, submitting it operationally at the LabFaber 4.0, empirically validating the study. The Real Time Energy Flow Map fills the gap in the literature on Value Stream Mapping models to improve energy efficiency, monitoring energy consumption through dynamic mapping of the energy flow of production processes, identifying the area with increased potential for energy savings through changes in operating behavior that reduce overall energy consumption and environmental impacts. • Lean model integrated with energy, environmental and production process metrics. • Value Stream Mapping model for best practices in energy efficiency. • Real-time energy consumption modeling of industrial production processes. • Lean model in a real-world case study to analyse the benefits of energy efficiency. • Analysis of the most relevant energy, environmental and production process metrics. [ABSTRACT FROM AUTHOR]

• Energy audit and management of an industrial site (SME) in Egypt are investigated. • Installating VSDs would save 48.477 MWh and mitigate 21 t of CO 2 annually. • Thermal insulation of the mold assembly saves nearly 319 MWh per year. • PV system expansion would satisfy about 50.95 % of the factory's annual consumption. • Energy savings of 535.97 MWh and 232 tons of CO 2 mitigations are expected annually. This study presents an energy audit and management for an industrial site in Borg El-Arab City, Egypt. Notably, the investigated industrial site falls in the category of small and medium-sized enterprises (SMEs), which are characterized by a high share of final energy use and possess a significant untapped energy-efficiency potential. In this work, potential sources of energy wastage in the facility have been investigated following a unique energy audit and analysis road map. Furthermore, tailored energy efficiency recommendations and effective energy management practices have been proffered for the various industrial equipment available for the first time. Besides, the proposed action plans are economically evaluated using the payback method and their environmental benefits are assessed accordingly. The results show that a proposed expansion of the PV system would produce 676.62 MWh/year and satisfy about 50.95 % of the factory's annual electricity consumption. The renewable energy system presents an investment cost of $124,115 with an attractive payback period of about 3 years alongside curbing 293 tons of CO 2 emissions annually. In addition, the installation of variable speed drives in the motor systems would save about 48.477 MWh/year with a payback period of 1.73 years and mitigate about 21 t of CO 2 annually. Nevertheless, thermal insulation of the mold assembly using thermal boards and incorporating an air gap in the mold assembly design results in energy savings of about 319 MWh per year. The energy management of fans, compressors, and lights demonstrates annual energy savings of 121.461 MWh, 14.88 MWh, and 32.154 MWh, respectively. Notwithstanding, replacing fossil fuel-based forklifts with electric forklifts leads to an investment cost of $32,220 with an associated payback period of 5.74 years. Noteworthy, the energy audit and management herein present energy savings and CO 2 emissions mitigations of 535.971 MWh and 232 t annually, respectively. Evidently, the energy audit and management performed will act as a guide for relevant decision-makers towards energy efficiency improvements, consequently achieving economic savings and limiting the carbon footprint. [ABSTRACT FROM AUTHOR]

Energy efficiency improvement in the industrial sector is critical in achieving carbon neutrality by 2050. A discrete-event-based dynamic energy simulation method is suggested to enhance the efficiency of energy-intensive manufacturing processes. The statistical characteristics of an actual manufacturing site were reflected in the simulation using data from the factory energy management system (FEMS) and manufacturing execution system (MES). The proposed method integrates discrete event simulation (DES) and transient thermal analysis. The dynamic behavior resulting from the interaction between sub-processes is simulated in DES. The energy conversion and detailed heat loss occurring at the facilities were modeled in the transient thermal analysis. The concept of process loss was suggested for the analysis to account for the energy that could have been not wasted at the factory level. The simulation results provided three key performance indicators (KPIs) representing the energy usage, production, and product quality of a manufacturing site. Investigation of these KPIs under various operating conditions found that the specific energy consumption could be reduced by 12.6% with a better operating strategy. We expect that the integrated simulation method can provide a practical solution for improving the energy efficiency of manufacturing sites equipped with a FEMS. • The dynamic behavior in energy usage of a manufacturing process is simulated. • Input modeling for the simulation is done using MES and EMS data of a real foundry. • The temperature history of the metal charge is predicted by the transient thermal model. • Process loss concept is suggested to consider factory-level energy efficiency. • SEC reduces by 12.6% while maintaining product quality through better operation. [ABSTRACT FROM AUTHOR]

Starting in 2007, EU set energy efficiency improvement targets in sectors with high energy-saving potential such as buildings. ICT allows innovative opportunities for energy consumption forecast to integrate with new control policies such as Demand/Response and Demand Side Management to reduce energy waste. However, such technologies must overcome challenges such as the lack of accurate historic data required for predictions. This article proposes an innovative methodology supporting the energy management of HVAC systems, through Smart Building indoor air-temperature forecast. The applicability of innovative neural networks for time-series predictions is explored. These neural networks are first trained on an artificial but realistic dataset based on BIM simulations with real meteorological data. The inference phase is then carried out on a second dataset collected by IoT devices. Finally, Transfer Learning techniques are exploited to improve the performances predictions. Fanger's model is applied to validate results, showing consistent levels of accuracy and comfort. • Indoor air-temperature prediction in buildings with lack of large and reliable enough data. • Accurate predictions to forecast the energy required as input for load forecasting problems. • Comparison of SOA NNs in time-series prediction, specifically designed and optimized. • Transfer Learning to improve the NNs performances predictions and their ability to generalize. • Accurate and reliable prediction up to 36 h in the future [ABSTRACT FROM AUTHOR]

Adapting to the instantaneous disturbance in the driving environment and balancing the optimality and computational efficiency of control algorithms are two major challenges for the integrated optimization of active speed planning and powertrain energy management strategy. In this study, utilizing the framework of model predictive control, a simultaneous method (SM) and a hierarchical method (HM) are developed to serve as benchmarks for control optimality and computational efficiency, respectively. Then, by modifying the decoupling strategy of the HM, this study ultimately proposes a modified HM which achieves similar control effectiveness in energy saving as that of the SM and preserves high computational efficiency. The comparative validation demonstrates that, due to fierce acceleration/deceleration operation caused by the heuristic decoupling strategy adopted in the HM, the energy consumption provided by the HM is 221.5% in traffic flow scenarios (and 633.5% in the manually designed scenario) of that generated by the SM. However, by adopting the soft constraint on acceleration magnitudes, the modified HM narrows the sub-optimality in energy consumption to 3.95% compared with the SM, and it also realizes a 55.81% improvement in computation efficiency compared with the original HM. • Model predictive control for coping with driving disturbances is developed. • Co-optimization of speed planning and powertrain control is developed. • An effective and efficient algorithm for solving the co-optimization is engineered. • A new benchmark algorithm for measuring control optimality is found out. [ABSTRACT FROM AUTHOR]

The development of global and regional energy markets is one of the most important problems of the current state of global economic relations and is one of the highest-priority areas in the formation of national and regional strategies for the socio-economic development of any country. The role of energy supply is focused on monitoring and compliance with standards. However, in recent years, the importance of energy supply to the housing stock has increased significantly, which has led to an expansion of the role of management within the framework of the risk management function. Kazakhstan has a Unified electric power system, which is represented by a set of power stations, transmission lines, and substations that provide reliable and high-quality energy supply to consumers. Currently, the main task within the framework of the priority of the development of Kazakhstan's energy sector is to build up the energy base and provide the growing needs of the population and the economy with the necessary energy resources based on the development of modern energy complexes and alternative energy sources in conjunction with the implemented and planned macroprojects. Since the current economic development of Kazakhstan is closely interrelated with the realization of energy resources and their effective use, it acts as one of the fundamental levers for the development of the national economy. According to our research goal, it is necessary to consider sustainable energy supply in the country based on the analysis and assessment of energy consumption volumes and the impact of the country's regional and sectoral policies on their use. The author's approach to the distribution of countries by MNC has been developed and estimates of regression parameters, correlation coefficients, and elasticity coefficients. When constructing the author's approach, the countries of Europe and Central Asia were studied and a sample of estimates was carried out, which has statistically significant effects on the formation of energy consumption volumes in the Republic of Kazakhstan. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) opens opportunities to monitor, optimize, and automate processes into the Agricultural Value Chains (AVC). However, challenges remain in terms of energy consumption. In this paper, we assessed the impact of environmental variables in AVC based on the most influential variables. We developed an adaptive sampling period method to save IoT device energy and to maintain the ideal sensing quality based on these variables, particularly for temperature and humidity monitoring. The evaluation on real scenarios (Coffee Crop) shows that the suggested adaptive algorithm can reduce the current consumption up to 11% compared with a traditional fixed-rate approach, while preserving the accuracy of the data. [ABSTRACT FROM AUTHOR]

Implementing energy efficiency measures is widely regarded as a high priority in the search for carbon dioxide emission reductions and can create competitive advantage for industry. Yet, approximately 50% of industry's cost effective energy efficiency measures are never implemented. The lack of implementation of energy efficiency measures results in the energy efficiency gap, which is generally attributed to barriers. Attempts to close the gap are directed at overcoming barriers and leveraging drivers. However, the current view of barriers and drivers may be incomplete due to the under representation of frontline workers' perspectives in research. Frontline workers strongly influence energy use because they make decisions about how and when industrial equipment is operated and maintained. This study compares survey responses from frontline workers and managers to investigate the barriers and drivers to industrial energy efficiency. Analysis of free text responses found perceived barriers and drivers for energy efficiency are different for frontline workers and managers. Differences between frontline workers' and managers' views are less prominent when only responses to Likert-type questions are considered. In response to this, new taxonomies for barriers and drivers were created. It is proposed these new taxonomies will be useful for empirical research as they can incorporate more complete views of frontline workers and managers. Common methods used to overcome barriers may have limited impact on the barriers found in this study. This more complete understanding of barriers and drivers could be useful for industry to characterise their challenges, contribute meaningfully to policy creation and generate new ways to close the energy efficiency gap. The novelty of this research stems from its investigation and presentation of the frontline workers' perspective. Further research into drivers, barriers and data gathering for frontline workers is needed to validate this exploratory study. • Frontline worker perspectives on barrier and drivers are presented. • Barriers and drivers for frontline workers and managers show some differences. • Common methods to overcome barriers may have limited impact on some of the barriers identified. [ABSTRACT FROM AUTHOR]

[Display omitted] • Distributed real-time optimal control was developed for typical HVAC systems. • Physical meaning in decomposition was enhanced in the proposed scheme. • Computation load was significantly distributed from BEMS to local computation unit. • Global convergence of the distributed optimal control was investigated. • Distributed and centralized optimal control led to similar energy performance. Real-time optimal control (RtOpt) is an efficient tool to improve the energy efficiency of heating, ventilating and air-conditioning (HVAC) systems. RtOpt is always centralized and solved entirely in building energy management systems (BEMSs). However, centralized optimization is not be an efficient solution because real-time operation data in local loops need to be sent to the central computation unit in BEMS for optimization. This paper, therefore, proposes a distributed RtOpt scheme for typical centralized HVAC systems, which uses a dual decomposition mechanism to break the centralized problem into one mater problem and several sub-problems that can be solved in parallel and independently. The mater problem is converted to a simple mean value calculation using a projected sub-gradient method, thus distributing the computation load from the central computation unit into local computation units. Unlike available decomposition schemes in literature, the proposed method considered the functionality and spatial distribution of the major components of HVAC systems, which could enhance the physical meaning and facilitate the application of the decomposition scheme. The proposed distributed RtOpt scheme was tested in case studies, with numerical analysis being used to demonstrate its potential benefits. [ABSTRACT FROM AUTHOR]

Achieving energy efficiency through adoption of energy management practices remain top priorities among industry. Studies focusing on energy management practices are scarce and this area needs to be focused. Building on the perspective of resource-based view and behavioral theory of corporate governance, the purpose of the study is to develop and test an integrative framework linking manufacturing firm's energy management practices (EMPs) to environmental and financial performance through mediating roles played by energy efficiency and audit. The moderating role played by the top management commitment is further examined. Structural equation modeling was employed to test the hypotheses alongside Hayes' PROCESS to check moderation effects. Results from a survey of 637 employees working in Small- and Medium-sized Enterprises (SMEs) of Indian manufacturing firms indicate that EMPs result into increased environmental as well as financial performance of the firm. It was also found that energy efficiency mediates the relationship between the adoption of EMPs and environmental performance, amplified by top management commitment. Further, energy audit mediates the effect of EMPs on energy efficiency. The study contributes to offering the new research directions to identify alternatives that monetises environmental concepts such as energy efficiency, leading to higher performance of SMEs. • The adoption of EMPs significantly enhance environmental and financial performance of manufacturing SMEs. • The energy audit mediates the relationship between EMPs and energy efficiency. • Energy efficiency enhances the relationship between EMPs and environmental performance positively. • Top management commitment moderates the EMPs.→Energy efficiency→Environmental performance relation. [ABSTRACT FROM AUTHOR]

Increasing energy efficiency is included in the UN Sustainable Development Goals (SDGs) to be achieved by the year 2030. Enhancing energy efficiency is also one of the priority areas for improving the operational efficiency of any oil production enterprise. The energy management system of enterprises has been founded and implemented on the basis of the international standard ISO 50001:2018 and it works successfully. The energy efficiency strategy is formulated in the energy policy and integrated into the business model of the companies. Companies receive significant energy savings in the exploration and production segments through technical, technological, and organizational measures. This article shows the main directions for improving the energy efficiency of the artificial lift well stock and the results of their implementation. The main constraints on the implementation of the energy efficiency policy of oil-producing enterprises have been identified and directions for improvement of energy-saving structure have been proposed. The article proposes strategic-level classification of energy-saving measures, which is based on assessment and comparison of implementation costs, payback period, and takes into account investments into artificial lift technology, therefore allowing investment priorities in the energy management sphere to be distinguished. Advanced directions for investment in oil-production technology have been identified, and an algorithm of development and implementation of key indicators of energy consumption efficiency has been proposed. [ABSTRACT FROM AUTHOR]

Buildings are responsible for approximately 30–40% of energy consumption in Europe, and this is a fact. Along with this fact is also evident the existence of a defined and strict legislation framework regarding energy efficiency, decarbonization, sustainability, and renewable energy systems in building applications. Moreover, information and communication technologies, along with smart metering for efficient monitoring, has come to cooperate with a building's systems (smart buildings) to aim for more advanced and efficient energy management. Furthermore, the well-being in buildings still remains a crucial issue, especially nowadays that health and air quality are top priority goals for occupants. Taking all the above into consideration, this paper aims to analyze ventilation technologies in relation to energy consumption and environmental impact assessment using the life cycle approach. Based on the review analysis of the existing ventilation technologies, the emphasis is given to parameters related to the efficient technical design of ventilation systems and their adequate maintenance under the defined guidelines and standards of mechanical ventilation operation. These criteria can be the answer to the complicated issue of energy efficiency along with indoor air quality targets. The ventilation systems are presented in cooperation with heating and cooling system operations and renewable energy system applications ensuring an energy upgrade and reduced greenhouse gas emissions. Finally, the mechanical ventilation is examined in a non-residential building in Greece. The system is compared with the conventional construction typology of the building and in cooperation with PVs installation in terms of the environmental impact assessment and energy efficiency. The methodology implemented for the environmental evaluation is the Life Cycle Analysis supported by OpenLca software. [ABSTRACT FROM AUTHOR]

The increasing global consumption of energy and its negative impacts on the environment has been an issue discussed both by academics and practitioners, including in the health sector. Hospitals are major consumers of energy, which leads to increased operating costs, decreased competitiveness, and negative environmental impacts. Energy management in hospitals must be understood as a way to contribute to environmental sustainability and as a way to improve competitiveness. Therefore, the objective of this paper is to propose guidelines for efficient and sustainable energy management in hospitals, developed based on the literature review, the challenges faced and the good practices of sustainable energy management in the hospitals studied, the cross analysis of the case studies, and the authors' expertise. As a result, the eight proposed guidelines are related to culture change, energy consumption management, energy management standard, purchase of renewable energy, green technologies, sustainable hospital buildings, social responsibility programs focused on energy use, and sustainable energy public policies. These guidelines contribute in an applied/managerial way to help managers of public and private hospitals overcome the challenges of energy management and contribute to UN's SDG7 due to its potential to improve energy efficiency and environmental performance, therefore, being a movement towards clean energy. This study also theoretically/scientifically contributes to the academic debate since the empirical findings found in the hospitals studied and the proposition of guidelines deepen and expand the key elements identified in the literature on efficient energy management in hospitals, providing the basis for developing new research on the subject. Energy management in hospitals is discussed from the perspective of sustainability. Challenges need to be overcome for sustainability benefits to be achieved. This work can assist public and private hospitals in developing sustainability. Guidelines for sustainable energy management in hospitals are proposed. [ABSTRACT FROM AUTHOR]

In this work, a novel methodology to assess energy performance indicators of productive and economic sectors trough the analysis of the Italian mandatory energy audits database is presented. The updating of sectoral reference energy performance indicators is fundamental for both companies and policy makers—for the formers to evaluate and compare their energy performance with competitors in order to achieve improvements and for the latter to effectively monitor the impact of energy policies. This methodology could be potentially applied to all production sectors, providing key information needed to characterize various production processes from an energy point of view. Awareness of energy efficiency and sectorial benchmarking represent the first necessary steps for companies moving towards energy transition. This paper provides details of the statistical method developed and its application to the NACE 23 division "Manufacturing of other non-metallic mineral products", with a focus on the cement industry. For this sector, results are presented in terms of specific indicators based on energy source. General results, methodological insights, and validation of the proposed case study are discussed. [ABSTRACT FROM AUTHOR]

Saving energy and looking for alternative energy sources are both elements of energy efficiency management, which is still a significant challenge for many companies around the world. Unfortunately, energy efficiency in companies is often equated only with thermo-modernization or the replacement of lighting. However, one of the most important methods of improving energy use in manufacturing companies may be the modernization of the machine park. Therefore, the main purpose of the paper was to investigate the activities of enterprises in the field of the modernization of machines and the impact of this on the actual improvement of energy efficiency. The modernization of machines in production plants is understood as adapting new parts to the old device or rebuilding the machine in such a way that it can cooperate with its new subassemblies. Companies usually decide to modernize the machinery fleet, bearing in mind the benefits of production efficiency, and they do not always combine this modernization with energy efficiency improvement measures. This is confirmed by the presented results of a survey conducted with a group of 111 manufacturing companies. Several important statistical relationships in the studied area were indicated by conducting an analysis based on the chi-square test and, in some cases, by clarifying the results of the z-test with the Bonferroni correction. [ABSTRACT FROM AUTHOR]

A large amount of global power consumption and environmental pollution problems are attributed to manufacturing industries. Grinding is one of the most energy intensive precision machining processes. Stimulating energy-saving potential in grinding to improve energy efficiency will be great helpful to accelerate energy and environmental sustainability. However, the energy utilization and efficiency problem has not been fully considered in traditional grinding strategies for production benefits. Therefore, a Pareto optimal design method is proposed to obtain optimal grinding parameters under the dual drive of energy management and machining performance. A nonparametric model based on the improved adaptive artificial neutral network (aANN) has been built to predict the surface quality, machining time, total and active power consumption. Parametric studies of the aANN model have been performed to achieve more accurate predictions. Accordingly, the multi-objective optimization problem searching a trade-off among energy and time efficient criteria, as well as product quality, has been solved using non-dominated sorting genetic algorithm II (NSGA II). Moreover, an energy efficiency benchmark has been suggested to indicate eco-friendly ability of grinding. Experimental results of the AISI 1045 steel have demonstrated that the optimal solution could improve energy efficiency by 89.52% and reduce machining time by 174.36% keeping the same product quality. Similar procedures will be performed to build the energy efficient grinding database towards sustainable and intelligent manufacturing. [ABSTRACT FROM AUTHOR]

Ongoing climate change and increasing energy consumption are becoming a serious threat to international development efforts. To ensure a secure and sustainable future, local, national and regional authorities, as well as energy companies, need to be involved in improving energy efficiency and promoting rational energy use. The consumer is always at the centre of interest of policy and energy reduction strategies makers. It is their behaviour, motives and beliefs that can lead to optimised and economical energy management. This paper responds to the need to identify the individual preferences of energy consumers. The presented research fills an existing gap in the literature by analysing the strength of the influence of different types of instruments and external stimuli shaping pro-environmental attitudes of consumers of different behavioural types. The analysis presented in this paper is the result of the next stage of the authors' research on energy consumers modelling, their segmentation and comprehensive characteristics. The analysis was conducted on a representative sample of N = 4332 respondents from 8 European countries (Czech Republic, France, Greece, Spain, Germany, Poland, Romania, and the United Kingdom). The study used multivariate statistical methods, such as: Correspondence Analysis, Factor Analysis and Kruskal-Wallis test. These methods are adequate to the assumptions of the research procedure and allowed for the identification of 4 latent factors that link the incentives into groups, where the motivation to save energy is based on: information ("Information and Knowledge"), social norms ("Social Influence"), investment funding ("Investments") and energy price changes ("Energy Price"). In addition, the level of effectiveness of the studied incentives and motivators depends on the behavioural type of energy consumers and increases with rising levels of their intrinsic pro-environmental motivation. [ABSTRACT FROM AUTHOR]