Search

Global demand for fossil fuels is highly increasing, necessitating energy efficiency to be enhanced in transitioning to low-carbon energy systems. Electric railways are highly efficient in reducing the transportation demand for fossil fuels as they are lightweight and their energy demand can be fed by renewable energy resources. Further, the regenerative braking energy of decelerating trains can be fed to accelerating trains and stored in onboard energy storage systems (ESSs) and stationary ESSs. It is fundamental to model electric railways accurately before investigating approaches to enhancing their energy efficiency. However, electric railways are challenging to model as they are nonlinear, resulting from the rectifier substations, overvoltage protection circuits, and the unpredictability and uncertainty of the load according to the train position. There have been few studies that have examined the ESS location's impact on improving the energy efficiency of electric railways while using specialised simulation tools in electric railways. However, no single study exists that has studied the location impact of stationary ESSs on the energy efficiency of electric railways while the trains are supported by onboard ESSs. Given these goals and challenges, the main objective of this work is to develop a model using commercial software used by industry practitioners. Further, the energy saving is aimed to be maximised using stationary ESSs installed in optimal locations while trains are supported by onboard ESSs. The model includes trains, onboard ESSs, rail tracks, passenger stations, stationary ESSs, and traction power systems involving power lines, connectors, switches, sectioning, and isolators. In this article, a test scenario is presented comprising two trains running on a 20 km with three passenger stations and two substations. The trains and track are modelled in OpenTrack simulation software (Version 1.9) while the power system is modelled in OpenPowerNet simulation software (Version 1.11). The two simulation tools are used in the railway industry and can produce realistic results by taking into account the entire electrical network structure. A stationary ESS is added on the wayside and moved in steps of 1 km to obtain the optimal location before investigating the impact of stationary ESSs on the performance and energy management of onboard ESSs. It is found that the energy saving when installing a stationary ESS at the optimal location is 56.05%, the peak-power reduction of Substation 1 is 4.37%, and the peak-power reduction of Substation 2 is 18.67%. [ABSTRACT FROM AUTHOR]

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 research, conducted throughout the years 2022 and 2023, examines the role of blockchain technology in optimizing Demand Response (DR) within Smart Grids (SGs). It critically assesses a range of blockchain architectures, evaluating their impact on enhancing DR's efficiency, security, and consumer engagement. Concurrently, it addresses challenges like scalability, interoperability, and regulatory complexities inherent in merging blockchain with existing energy systems. By integrating theoretical and practical viewpoints, it reveals the potential of blockchain technology to revolutionize Demand Response (DR). Findings affirm that integrating blockchain technology into SGs effectively enhances the efficiency and security of DR, and empirical data illustrate substantial improvements in both cases. Furthermore, key challenges include scalability and interoperability, and also identifying opportunities to enhance consumer engagement and foster system transparency in the adoption of blockchain within DR and SGs. Finally, this work emphasizes the necessity for further investigation to address development hurdles and enhance the effectiveness of blockchain technology in sustainable energy management in SGs. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms are now part of everyday life, as many technological devices use these algorithms. The spectrum of uses is wide, but it is evident that ML represents a revolution that may change almost every human activity. However, as for all innovations, it comes with challenges. One of the most critical of these challenges is providing users with an understanding of how models' output is related to input data. This is called "interpretability", and it is focused on explaining what feature influences a model's output. Some algorithms have a simple and easy-to-understand relationship between input and output, while other models are "black boxes" that return an output without giving the user information as to what influenced it. The lack of this knowledge creates a truthfulness issue when the output is inspected by a human, especially when the operator is not a data scientist. The Building and Construction sector is starting to face this innovation, and its scientific community is working to define best practices and models. This work is intended for developing a deep analysis to determine how interpretable ML models could be among the most promising future technologies for the energy management in built environments. [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]

ANNs have become a cornerstone in efficiently managing building energy management systems (BEMSs) as they offer advanced capabilities for prediction, control, and optimization. This paper offers a detailed review of recent, significant research in this domain, highlighting the use of ANNs in optimizing key energy systems, such as HVAC systems, domestic water heating (DHW) systems, lighting systems (LSs), and renewable energy sources (RESs), which have been integrated into the building environment. After illustrating the conceptual background of the most common ANN architectures for controlling BEMSs, the current work dives deep into relative research applications, thereby exhibiting their methodology and outcomes. By summarizing the numerous impactful applications during 2015–2023, this paper categorizes the predominant ANN-based techniques according to their methodological approach, specific energy equipment, and experimental setups. Grounded in the different perspectives that the integrated studies illustrate, the primary focus of this paper is to evaluate the overall status of ANN-driven control in building energy management, as well as to offer a deep understanding of the prevailing trends at the building level. Leveraging detailed graphical depictions and comparisons between different concepts, future directions, and fruitful conclusions are drawn, and the upcoming innovations of ANN-based control frameworks in BEMSs are highlighted. [ABSTRACT FROM AUTHOR]

Addressing the urgent need for sustainable energy sources due to the high costs and environmental impacts of fossil fuels, this research explores the integration of decentralized solar photovoltaic (PV) systems in urban Southern European settings. Specifically, we examine the effectiveness of a solar carport PV installation at a higher education institution, focusing on the challenges of energy surplus management within regulated markets that restrict grid energy injection. Our study employed a mixed-methods approach, combining quantitative data analysis of energy production with qualitative assessments of regulatory frameworks. The innovative aspect of our method lies in the development of a smart management system tailored to circumvent legal barriers, ensuring optimal use of locally produced energy and fostering the creation of an energy community with shared energetic, environmental, and social benefits. Preliminary results demonstrate that our system not only prevents energy loss but also enhances investment return by enabling the institution to become an energy prosumer within its community. A comparative analysis with traditional energy exercises reveals that our approach significantly improves energy self-sufficiency and sustainability, positioning decentralized PV systems as a viable solution in similar urban contexts. [ABSTRACT FROM AUTHOR]

Given its adaptable and efficient energy consuming devices during peak hours, the residential building sector is urged to take part in demand response (DR) initiatives with the use of a building energy management system (BMS). The residents of buildings with BMS enjoy secure, pleasant, and fully managed lifestyles. Although the BMS helps the building consume less energy and encourages occupant engagement in energy-saving initiatives, unwelcome interruptions and harsh instructions from the system are inconvenient for the inhabitants, which further discourages their participation in DR initiatives. Building automation control is a crucial factor for improving buildings' energy efficiency and management, as well as improving the electricity grid's reliability indices. Smart houses that use the right sizing procedure and energy-management techniques can help lower the demand on the entire grid and potentially sell clean energy to the utility. Recently, smart houses have been presented as an alternative to traditional power-system issues including thermal plant emissions and the risk of blackouts brought on by malfunctioning bulk plants or transmission lines. This paper describes the necessary technology requirements and presents the methodology and the decentralized building automation novel algorithm for efficient demand side management in a building management system. Human comfort aspects including thermal comfort and visual comfort were taken into consideration when selecting heating and lighting controls. The suggested BMS relies primarily on a load-shifting technique, which moves controllable loads to low-cost periods to avoid high loading during peak hours. The model aims to minimize the individual household electricity consumption cost while considering customers' comfort and lifestyle. All these are applied in an experimental university microgrid, and the results are presented in terms of energy saving in kWh, money in €, and working hours. The results demonstrated that the proposed approach might successfully lower energy use during the DR period and enhance occupant comfort. [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]

Promoting energy efficiency is a key element of the strategic commitment of the European Community. Prominent among the binding measures established by the 2012 Energy Efficiency Directive to further this vision is the requirement for large companies to conduct energy audits every four years. After receiving the second cycle of energy audit reports in December 2019, a new description of the energy situation of Italian companies was made available. This presented the previously inaccessible possibility of comparing the two situations reported in 2015 and 2019 to assess the development of energy efficiency practices in organizations subject to the legislative obligation of energy audits in the country. To this end, in collaboration with the Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), a project was initiated with the aim of developing the tools and methodologies necessary to assess in more detail the evolution that has occurred in the four years since 2015. In this paper, the findings of the analysis conducted on a significant sample of companies in Italy are presented. Through the design of a maturity model to assess the degree of progress achieved in a company's energy management, the results of the two situations were analyzed. The analysis was deepened by assessing the progress achieved in different aspects of Energy Management: "Strategic Approach", "Awareness, Competence, and Knowledge", "Methodological Approach", "Organizational Structure", "Energy Performance Management and Information System", and "Best Practices". Furthermore, the observed variations were statistically tested using a pairwise t-test to make statistical inferences about the maturity of the total population of Italian enterprises under legislative obligation. The results have shown an increase in overall energy management maturity in each maturity dimension. [ABSTRACT FROM AUTHOR]

The building information modeling (BIM) method is one of the newest methods that has been widely used in many parts of the building business, including energy management. The aim of this research is to analyze the most holy theotokos in order to find the best set of modifications that result in an optimal energy cost. The analysis was conducted through the use of building information modeling (BIM) technology and the associated programmers such as Auto Desk Revit 2020 and Auto Desk Insight 360, in order to determine the optimal strategies by which the most applicable alternative construction materials and procedures are considered in order to obtain an environmentally and economically sustainable most holy theotokos. Applying this analyze to the most holy theotokos revealed that many alternatives are capable of making a tangible reduction in the cost of electrical energy consumption and the cost of fuel for generators. Such reductions are noticed when altering in the optimum manner. The alteration of construction materials for walls and roofs also reduces the cost of electrical energy consumption and fuel for generators. The results show that changing the plug load efficiency in the optimal manner reduces the cost of electrical energy consumption by approximately 933913 US dollar ($), and changing the heating, ventilating, and air conditioning systems (HVAC) reduces the cost of fuel energy consumption by approximately 13522 US Dollar ($). Green building studio (GBS) is a tool that helps in the early stages of a project to find the best ways to save energy. [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]

The usage of batteries and supercapacitors in the field of electric vehicles is becoming increasingly prominent in both research and development. Due to the complementary advantages of the two systems, high energy density, and high power density, a combined battery/supercapacitor system offers potential. To effectively utilise the potential of such a hybrid energy storage system (HESS), one requires an intelligent energy management system (EMS). The EMS is responsible for controlling the electrical power between the battery and the supercapacitor in such a way that the required power can be optimally distributed at all times (currently and in the future). For this purpose, the energy management system utilises information on the driving route and, based on this information, shall calculate a global strategy for the continuous power distribution. The controlled power distribution should take place in real time and be robust against discrepancies so that unpredictable or unreliably predictable events do not significantly influence the functionality. For the implementation of the concept, a rule-based power distribution is implemented in combination with a predictive energy management. Here, the energy management is combined with a rule-based strategy calculation based on data on the route to be driven with a global optimization for the calculation of a route-specific strategy. Depending on the selected objective, the increase in energy efficiency, or lifetime, the operation of the power control is optimised. Due to the functional separation, the continuous power control can operate in real time, while more computational time can be spent on the calculations of the power management strategy, which accordingly does not need to be executed in real time. The results show that by using the presented EMS, especially in combination with a route-specific parameterisation, a significant effect on the energy efficiency and/or battery lifetime can be achieved. The average battery energy consumption can be reduced by up to 9.14% on urban routes. Regarding battery lifetime, the average battery usage can be reduced up to 13.35% and the battery energy losses even up to 62.72%. [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]

Environmental protection is currently one of the key priority areas of the European Union (EU). The search for effective solutions for the supply and use of energy in cities is currently a key topic. The reduction in gas emissions and the use of renewable energy sources are goals that result from environmental aspects. The purpose of this publication is to conduct a literature review in the area of municipal energy management. Municipal energy management systems integrate many areas, from energy supply systems and the modernization of public transport to the energy demand reduction of residential and commercial facilities. The results of the literature review research have allowed for the classification of articles based on the following criteria: research methodology, research purpose, research data collection method, use of research results article type, and research subject area. [ABSTRACT FROM AUTHOR]

Smart grid plays a vital role in energy management systems. It helps to mitigate the demand side management of electricity by managing the microgrid. In the modern era, the concept of hybrid microgrids emerged which helps the smart grid management of electricity. Additionally, the Internet of Things (IoT) technology is used to integrate the hybrid microgrid. Thus, various policies and topologies are employed to perform the task meticulously. Pakistan being an energy deficient country has recently introduced some new policies such as Energy Wheeling Policy (EWP), Energy Import Policy (EIP), and Net Metering/Distributed Generation Policy (NMP) to manage the electricity demand effectively. In addition, the Energy Efficiency and Conservation Act (EECA) has also been introduced. In this paper, we present the overview and impact of these policies in the context of the local energy market and modern information and communication mechanisms proposed for smart grids. These new policies primarily focus on energy demand–supply for various types of consumers such as the demand for bulk energy for industrial ventures and the distributed production by consumers. The EWP deals with obtaining power from remote areas within the country to ease the energy situation in populated load centers and the EIP highlights energy import guidelines from foreign countries. The NMP deals with the integration of renewable energy resources and EECA is more focused on the measures and standardization for energy efficiency and conservation. The benefits and challenges related to EWP, NMP, and EIP have also been discussed concerning the present energy crisis in Pakistan. The generalized lessons learned and comparison of a few aspects of these policies with some other countries are also presented. [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]

This research presents an optimal scheme for the integration of renewable resources with the utility grid to minimize the operational cost of the residential and industrial microgrids. With the changing paradigm of solar photovoltaic in low-voltage distribution networks, utilities have allowed net metering and feed-in tariff (FiT). These incentives encourage residential and industrial consumers to contribute toward energy generation. However, in conventional mode, the system may underperform if resources are not scheduled optimally. To compensate for the price difference during off-peak and on-peak hours, the energy should be taken from the grid when electricity prices are lower and supplied to the grid when the electricity price is higher. The proposed models will therefore allow optimal resource utilization considering intermittent renewable generation as well as a time-varying utility tariff. A complete comparative analysis of on-grid and off-grid models was carried out. The results indicate that the daily average saving is about 32.0% by using the proposed on-grid scheme, where a feed-in tariff is available. [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]

Improving energy efficiency has important significance for hybrid electric UAVs. This paper aims at improving energy efficiency by maximizing utilization solar energy, reducing demand power and optimizing power allocation. The flight trajectory optimization and energy management are considered synthetically, and the coupled model is established. Adaptive sequential convex programming method (ASCP) is further proposed to solve this coupled model. The adaptive discretization method, fuzzy-based trust region update mechanism and two-stages solving strategy are proposed to improve its optimality and convergence. The optimized flight trajectory and global optimal battery S O C trajectory can be applied to trajectory tracking control and online energy management. Numerical simulation results show that ASCP can improve energy efficiency of hybrid electric UAVs. Compared with uniform discretized sequential convex programming (UDSCP) and Gauss pseudo-spectral method (GPM), it can improve solar energy by 9.3% and 24.1%. Energy management experiment results indicate that ASCP has excellent energy saving effect. Compared with nonlinear model predictive control (NMPC), fuzzy logic state machine (FLSM) and passive energy management strategy (PEMS), it can save hydrogen by 13.5%, 18.3%, and 22.2%, respectively. This work is conducive to promoting the application of renewable energy in UAVs. • Energy efficiency is improved by flight trajectory and energy management coupled optimization. • The flight trajectory optimization and energy management coupled model is established. • Adaptive sequential convex programming method is proposed to solve the coupled model. • Fuzzy logic control is proposed to adaptively adjust the sizes of trust region. [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]

THe energy-efficient operation of microgrids—a localized grouping of consuming loads (domestic appliances, EVs, etc.) with distributed energy sources such as solar photovoltaic panels—suggests the deployment of Energy Management Systems (EMSs) that enable the actuation of controllable microgrid loads coupled with Artificial Intelligence (AI) tools. Such tools are capable of optimizing the aggregated performance of the microgrid in an automated manner, based on an extensive network of Advanced Metering Infrastructure (AMI). Modular adaptable/dynamic building envelope (ADBE) solutions have been proven an effective solution—exploiting free façade areas instead of roof areas—for extending the thermal inertia and energy harvesting capacity in existing buildings of different nature (residential, commercial, industrial, etc.). This study presents the PLUG-N-HARVEST holistic workflow towards the delivery of an automatically controllable microgrid integrating active ADBE technologies (e.g., PVs, HVACs). The digital platform comprises cloud AI services and functionalities for energy-efficient management, data healing/cleansing, flexibility forecasting, and the security-by-design IoT to efficiently optimize the overall performance in near-zero energy buildings and microgrids. The current study presents the effective design and necessary digital integration steps towards the PLUG-N-HARVEST ICT platform alongside real-life verification test results, validating the performance of the platform. [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]

In mixed-criticality systems (MCSs), timely handling of I/O operations is a key for the system being successfully implemented and appropriately functioned. The I/O system for an MCS must simultaneously enable different features, including isolation/separation, timing-predictability, performance, scalability, and energy-efficiency. Moreover, such an I/O system also requires to manage I/O resource in an adaptive manner to facilitate efficient yet safe resource sharing among components of different criticality levels. Existing approaches cannot achieve all of these requirements simultaneously. This article presents a mixed-criticality I/O management framework, termed MCS-IOV. MCS-IOV is based on hardware-assisted virtualization, which provides temporal and spatial isolation and prohibits fault propagation with limited extra overhead. MCS-IOV extends a real-time I/O virtualization system, by supporting the concept of mixed criticalities and customized interfaces for schedulers, which offers good timing predictability and scalability. Finally, we introduce an energy management framework for MCS-IOV, ensuring the power-efficiency of the design. The MCS-IOV is the first systematical solution that fulfills all the requirements as a mixed-criticality I/O system. [ABSTRACT FROM AUTHOR]

Modular building energy management strategy based on a three-level hierarchical model predictive control is proposed in the paper. Building zones, central medium conditioning and microgrid subsystems are controlled independently by individual linear and nonlinear model predictive controllers, and further integrated together as levels of hierarchical coordination control structure based on price-consumption information exchange. The three-level coordination provides a holistic energy management strategy and enables significant demand response ancillary services for buildings as prosumers, while retaining the independence of required expertise in very different building subsystems. The approach is applied for daily operation scheduling of a full-scale building consisting of 248 offices. Models of building subsystems are obtained by identification procedures on measurement data. Compared to rule-based control, detailed realistic simulations show that the overall building operation cost for typical days in summer is reduced by 9-12% for level-by-level energy-optimal and by 15-24% for price-optimal, coordinated operation. The application of predictive control in the proposed way also improves the indoor comfort substantially. [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]