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

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%.

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]

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

Agriculture in Poland plays an important social and environmental role. Accession to the EU resulted in structural and modernization changes, following adjustments to CAP obligations. In 2019, the European Green Deal and "From Farm to Fork" strategies called for circularity, zero emissions, and food and energy security. The purpose of this study was to assess the consumption and energy efficiency of Polish farms, identify challenges in energy management, and formulate recommendations. This study used data from Polish Statistics, FADN, and other public bodies collecting relevant data. The assessment of energy intensity was carried out based on the concept of technical efficiency by Farell and Debreu, defined as the ratio of effects to inputs. In addition, methods of comparative and descriptive statistics were used. The average annual dynamics of energy consumption and CO2 emissions were determined using the compound percentage formula. The results of this research indicate positive changes in the energy management in Polish agriculture, including a decrease in production energy intensity, CO2 emissions, and the amount of waste generated by the investments made. It is necessary to improve farm energy efficiency further and to increase the use of renewable energy to maintain cost competitiveness and meet environmental requirements. [ABSTRACT FROM AUTHOR]

The global aluminium industry faces a serious challenge in reducing greenhouse gas emissions as the demand for aluminium continues to increase. The aluminium industry has a responsibility to streamline its energy consumption, especially in the production process. There have been many studies discussing energy consumption performance in the industry. However, most of them only discuss energy saving partially, without involving energy consumption with various items produced. This paper proposes an energy savings measurement in the manufacturing industry. An energy baseline consumed per unit volume has been developed using the equivalent volume method with an energy management control system (EMCS). The study takes a case example from the automotiveâaluminium component industry. The steps taken in the study are: examining the production process, converting the production volume to equivalent, calculating the energy consumption ratio, developing an energy baseline, simulating the savings performance, and then proposing an EMCS with key performance indicators (KPI) for sustainability. The results show that the development of a baseline using the ratio of energy and an equivalent volume of production gives a better data correlation with an R2 value close to one. From the baseline, the best­demonstrated performance (BDP) can be used as a reference to set energy goals. Furthermore, the data and the deviant deleted have the same baseline value. They differ in energy reduction goal percentages. The practical application of this study is not only in the manufacturing industry but in other industries as well, such as building management. This study contributes to energy savings achieved with EMCS.

Agriculture in Poland plays an important social and environmental role. Accession to the EU resulted in structural and modernization changes, following adjustments to CAP obligations. In 2019, the European Green Deal and “From Farm to Fork” strategies called for circularity, zero emissions, and food and energy security. The purpose of this study was to assess the consumption and energy efficiency of Polish farms, identify challenges in energy management, and formulate recommendations. This study used data from Polish Statistics, FADN, and other public bodies collecting relevant data. The assessment of energy intensity was carried out based on the concept of technical efficiency by Farell and Debreu, defined as the ratio of effects to inputs. In addition, methods of comparative and descriptive statistics were used. The average annual dynamics of energy consumption and CO2 emissions were determined using the compound percentage formula. The results of this research indicate positive changes in the energy management in Polish agriculture, including a decrease in production energy intensity, CO2 emissions, and the amount of waste generated by the investments made. It is necessary to improve farm energy efficiency further and to increase the use of renewable energy to maintain cost competitiveness and meet environmental requirements.

This paper developed an intelligent multi-agent system (MAS) with a multi-layer framework for multi-microgrids (MMGs) using robust and modern communication patterns for deployed agents to achieve distributed tasks. The MMG paradigm introduces three microgrids (MGs) based on the type of load, working environment, and living habitat: residential, commercial, and industrial. In addition, a day-ahead and real-time model is proposed for day-ahead and real-time signals. Intelligent agents in the multi-layer MAS framework make smart decisions based on multiple algorithms to optimize schedule power and minimize costs, considering demand dispatch and demand response as core components. Maximum renewable energy utilization aims to increase user comfort and reduce greenhouse gas (GHG) emissions. Load agents deployed in each MG ensure maximum efficiency. The proposed framework recommends various tariff rates and tariff adjustment strategies to promote and offer an economic evaluation across the respective indices. To minimize the monopoly of the energy market, an efficient energy market model is developed for the proposed MMG paradigm to maximize the competition by incorporating future and spot-market trading schemes for day-ahead and real-time signals. The comparative analysis indicates optimized results based upon the cost-benefit analysis, cost reduction, power transaction in the market, and maximum utilization of renewable energy resources (RERs).

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]

Artificial intelligence (AI)-enabled energy efficiency in buildings leverages AI to optimize energy consumption, enhancing sustainability and aiding decision-makers in selecting cost-effective and environmentally friendly solutions. Pythagorean fuzzy Z ̃ number sets, capable of handling high uncertainty, play a crucial role in representing fuzzy information in decision-making. This research introduces novel operational laws and weighted aggregation operators, focusing on neutral addition and scalar multiplication. Comprehensive investigations into the properties of these laws lead to groundbreaking aggregation operators tailored for Pythagorean fuzzy Z ̃ number information. These include weighted, ordered weighted, and hybrid neutral averaging aggregation operators, along with a novel approach for calculating attribute weights. Relationships and characteristics of these operators are explored, and an algorithm for solving multiple attribute group decision-making problems is provided. A practical example illustrates the approach's superiority, supported by comparative results. • The advanced Pythagorean fuzzy Z-number-based neutrality aggregation model is given. • Fundamental operational guidelines for neutrality AOs for PFZNs are introduced. • The theoretic algorithm to tackle MAGDM problems in the PFZN environment is proposed. • The problem of AI-enabled energy efficiency is addressed from the MAGDM aspect. • Guidance for experts when evaluating energy-efficient building solutions is offered. [ABSTRACT FROM AUTHOR]

Achieving climate neutrality requires reducing energy consumption and CO2 emissions in the building sector, which has prompted increasing attention towards nearly zero energy, zero energy, and positive energy communities of buildings; there is a need to determine how individual buildings up to communities of buildings can become more energy efficient. This study addresses the scientific problem of optimizing energy efficiency strategies in building areas and identifies gaps in existing theories related to passive design strategies, active energy systems, and renewable energy integration. This study delineates boundaries at the building and community scales to examine the challenges of attaining energy efficiency goals and to emphasize the intricate processes of selecting, integrating, and optimizing energy systems in buildings. The four boundaries describe: (B1) energy flows through the building envelope; (B2) energy flows through heating, ventilation, air conditioning and energy systems; (B3) energy flows through individual buildings; (B4) energy flows through a community of buildings. Current theories often treat these elements in isolation, and significant gaps exist in interdisciplinary integration, scalable frameworks, and the consideration of behavioral and socioeconomic factors. Achieving nearly zero energy, zero energy, and positive energy communities requires seamless integration of renewable energy sources, energy storage systems, and energy management systems. The proposed boundaries B1–B4 can help not only in analyzing the various challenges for achieving high energy efficiency in building communities but also in defining and evaluating these communities and establishing fair methods for energy distribution within them. The results demonstrate that these boundaries provide a comprehensive framework for energy-efficient designs, constructions, and operational practices across multiple buildings, ensuring equitable energy distribution and optimized performance. In addition, the definition of boundaries as B1-B4 contributes to providing an interface for energy-efficient designs, constructions and operational practices across multiple buildings. [ABSTRACT FROM AUTHOR]

This study presents a reinforcement-learning-based approach for energy management in hybrid electric vehicles (HEVs). Traditional energy management methods often fall short in simultaneously optimizing fuel economy, passenger comfort, and engine efficiency under diverse driving conditions. To address this, we employed a Q-learning-based algorithm to optimize the activation and torque variation of the internal combustion engine (ICE). In addition, the algorithm underwent a rigorous parameter optimization process, ensuring its robustness and efficiency in varying driving scenarios. Following this, we proposed a comparative analysis of the algorithm's performance against a traditional offline control strategy, namely dynamic programming. The results in the testing phase performed over ARTEMIS driving cycles demonstrate that our approach not only maintains effective charge-sustaining operations but achieves an average 5% increase in fuel economy compared to the benchmark algorithm. Moreover, our method effectively manages ICE activations, maintaining them at less than two per minute. [ABSTRACT FROM AUTHOR]

The global aluminium industry faces a serious challenge in reducing greenhouse gas emissions as the demand for aluminium continues to increase. The aluminium industry has a responsibility to streamline its energy consumption, especially in the production process. There have been many studies discussing energy consumption performance in the industry. However, most of them only discuss energy saving partially, without involving energy consumption with various items produced. This paper proposes an energy savings measurement in the manufacturing industry. An energy baseline consumed per unit volume has been developed using the equivalent volume method with an energy management control system (EMCS). The study takes a case example from the automotive--aluminium component industry. The steps taken in the study are: examining the production process, converting the production volume to equivalent, calculating the energy consumption ratio, developing an energy baseline, simulating the savings performance, and then proposing an EMCS with key performance indicators (KPI) for sustainability. The results show that the development of a baseline using the ratio of energy and an equivalent volume of production gives a better data correlation with an R² value close to one. From the baseline, the bestdemonstrated performance (BDP) can be used as a reference to set energy goals. Furthermore, the data and the deviant deleted have the same baseline value. They differ in energy reduction goal percentages. The practical application of this study is not only in the manufacturing industry but in other industries as well, such as building management. This study contributes to energy savings achieved with EMCS. [ABSTRACT FROM AUTHOR]

Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research. [ABSTRACT FROM AUTHOR]

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

[Display omitted] • Technical characteristics and management strategies for energy efficiency measures are presented. • Energy performances of energy efficiency measures are evaluated using behind-meter data. • Fully exploiting energy efficiency calls for more energy flexibility from coordinated demand management. • Deep residential decarbonization needs coordination of low-carbon efforts from a whole system perspective. Improving energy efficiency presents great potential to reduce energy consumption and carbon emission of building sector. This work summarizes existing technological innovations, energy management efforts and demonstration effect for decentralized and decarbonized energy transition in Japanese residential sector. Additionally, the study provides a comprehensive understanding of how various energy-efficient technologies and energy management strategies perform using behind-meter data. Measurement analysis shows that electrifying hot water demand would significantly reshape electricity load pattern, overall energy efficiency of cogeneration system highly depends on patterns of household energy demand. Going beyond generation efficiency of on-site energy generators, practical experiences emphasize roles of demand flexibility in improving alignment between distributed energy generation and demand. Demand-side energy management plays crucial roles in fully harnessing benefits of energy efficiency measures in representative residential energy systems. Survey results of demonstration projects verify effectiveness of implementing energy efficiency measures in cost and carbon emission reductions. As penetration of distributed energy resources and building electrification continues to increase, the study identifies impacts of integrating energy resources on public grid, potential grid-interactive interactions. From a holistic system perspective, findings highlight importance of demand-side solutions in exploiting energy flexibility and provide implications for effective decision-making toward energy use efficiency. [ABSTRACT FROM AUTHOR]

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

Microgrids have emerged as a key element in the transition towards sustainable and resilient energy systems by integrating renewable sources and enabling decentralized energy management. This systematic review, conducted using the PRISMA methodology, analyzed 74 peer-reviewed articles from a total of 4205 studies published between 2014 and 2024. This review examines critical areas such as reinforcement learning, multi-agent systems, predictive modeling, energy storage, and optimization algorithms—essential for improving microgrid efficiency and reliability. Emerging technologies like artificial intelligence (AI), the Internet of Things, and flexible power electronics are highlighted for enhancing energy management and operational performance. However, challenges persist in integrating AI into complex, real-time control systems and managing distributed energy resources. This review also identifies key research opportunities to enhance microgrid scalability, resilience, and efficiency, reaffirming their vital role in sustainable energy solutions. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the existing methods of energy efficiency management level evaluation for hydropower stations, this paper introduces Analytic Hierarchy Process (AHP) method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method into the evaluation process. The evaluation index system of energy efficiency management of hydropower station is constructed by considering the three aspects of economy, safety and management comprehensively. In order to overcome the shortcomings of the nine scale method, the paper uses the exponential scale method to determine the weight of each index. What's more, TOPSIS method and expert scoring method are used to score the quantitative indicators and the qualitative indicators, respectively, so as to further realize the comprehensive evaluation of the hydropower station energy efficiency management level. An actual hydropower station is taken as an example to analyze and calculate the score of the energy efficiency management level of the hydropower station from 2018 to 2022.The results show that the energy efficiency management level of the hydropower station is on the rise. [ABSTRACT FROM AUTHOR]

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

This paper developed an intelligent multi-agent system (MAS) with a multi-layer framework for multi-microgrids (MMGs) using robust and modern communication patterns for deployed agents to achieve distributed tasks. The MMG paradigm introduces three microgrids (MGs) based on the type of load, working environment, and living habitat: residential, commercial, and industrial. In addition, a day-ahead and real-time model is proposed for day-ahead and realtime signals. Intelligent agents in the multi-layer MAS framework make smart decisions based on multiple algorithms to optimize schedule power and minimize costs, considering demand dispatch and demand response as core components. Maximum renewable energy utilization aims to increase user comfort and reduce greenhouse gas (GHG) emissions. Load agents deployed in each MG ensure maximum efficiency. The proposed framework recommends various tariff rates and tariff adjustment strategies to promote and offer an economic evaluation across the respective indices. To minimize the monopoly of the energy market, an efficient energy market model is developed for the proposed MMG paradigm to maximize the competition by incorporating future and spot-market trading schemes for day-ahead and real-time signals. The comparative analysis indicates optimized results based upon the cost-benefit analysis, cost reduction, power transaction in the market, and maximum utilization of renewable energy resources (RERs). [ABSTRACT FROM AUTHOR]

Sustainability is one of the most multi- and inter-disciplinary fields of study, as it encapsulates three principle dimensions: social/ethical, economic and environmental. These dimensions can also be found within the context of climate change with similar implications. Whilst there is an understanding of the relationship between sustainability and climate change, there appears to be limited literature drawing upon this relationship, particularly from the perspective of energy science, and more specifically in connection to carbon emissions and energy management. On the other hand, all three issues – sustainability, climate change and energy (implications) – have already been the subject of legislation in different countries in varying capacities, though still, predominantly individually and lacking an integrated approach. For instance, the Climate Change Act (2008) legally binds the UK to achieving reductions in emissions of CO2, one of the greenhouse gases (GHG). In this paper, this Act is considered as a platform to depict the relationship propositions between sustainability and climate change via the lens of energy implications. This paper establishes the aspects of this relationship by employing graphical and numerical approaches, and exploring the potential of thermal insulation of the UK housing stock in contributing to achieving the legal carbon-cutting targets set by the Act. Using the UK housing sector as a case study, this paper demonstrates, via numerical modelling, that by even partly insulating the existing UK housing stock the 2020 target set by the Act was achievable for that sector. This case is presented as in retrospect. Links are clearly drawn between energy, sustainability and climate change (mitigation), showing how addressing climate change issues via energy management of the UK housing stock can directly and indirectly assist in meeting the sustainability agenda. The unique, conceptual, numerical model innovatively designed, developed and applied in this paper, can be reproduced to cater for other climate change targets and benchmarks, not only in the UK but also in other countries.

To solve the problems of the low driving efficiency of a fuel cell tractor power source and the high hydrogen consumption caused by the irrational power allocation of the energy source, the power system was divided into two parts, power source and energy source, and a dual-source cooperative optimization energy management strategy was proposed. Firstly, a general energy efficiency optimization method was designed for the power source composed of a traction motor and PTO motor, and the energy source was composed of a fuel cell and power battery. Secondly, the unified objective function and constraint conditions were established, and the instantaneous optimization algorithm was used to construct the weight factor. The instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were designed, respectively. Finally, with the demand power as the transfer parameter, the instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were integrated to form a dual-source collaborative optimal energy management strategy. In order to verify the effectiveness of the proposed strategy, a rule-based energy management strategy was developed as a comparison strategy and tested in an HIL test under plowing and rotary plowing conditions. The results show that the average fuel cell efficiency of the proposed strategy increased by 7.86% and 8.17%, respectively, and the proposed strategy’s equivalent hydrogen consumption decreased by 24.21% and 9.82%, respectively, compared with the comparison strategy under the two conditions. It can significantly reduce the SOC fluctuation of the power battery and extend the service life of the power battery.

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

Sustainability continues to be a key field of study, encapsulating three principle dimensions: social, economic and environmental, which are also found within the context of climate change. However, there appears to be limited literature drawing upon the between sustainability and climate change. relationship, particularly in connection to carbon emissions and energy management. These issues have already been the subject of legislation in different countries, though still, predominantly individually rather than from an integrated perspective. The Climate Change At (2008) provides a platform through which the relationship between sustainability and climate change can be considered. This paper establishes this relationship aspects of this relationship by employing the increased use of insulation within the UK housing stock to contribute to achieving the carbon reduction set by the Act. Taking a retrospective view through theoretical numerical modelling, this paper demonstrates that CO2 reductions were achievable. The results demonstrate that links can be drawn between sustainability and climate change and identifies that significant CO2 savings, through robust energy management of the UK housing stock, these results can be achieved. It is also suggested that the theoretical mole developed can be reproduced to consider climate change targets and provide benchmarks, not only in the UK but in other countries.

This research aims to implement mall operations that are comfortable, efficient, and effective in energy use and determine the value of Energy Consumption Intensity (IKE). This research uses a qualitative method with a literature study approach. Research data was obtained from measurements carried out by researchers at mall buildings in South Jakarta. Instrument data is obtained from the installed Kwh meter. Data analysis technique by comparing measurement results with data results standardized by the Ministry of Energy and Mineral Resources for IKE and Indonesian National Standards for thermal comfort levels. The research results obtained show that the IKE value is classified as efficient. The IKE value during 2023 ranges from 8,79 – 11,37 kWh/m2/month with the total annual IKE value being 263,16 kWh/m2/year. In terms of air conditioning, the average comfort standard in 2023 will be 25℃ and RH (humidity) at 62.9%.

In the face of escalating climate challenges, environmental sustainability has greatly become an urgent and non-negotiable priority, necessitating revolutionary advancements in energy management to reduce carbon footprints and drive profound improvements in overall sustainability. This paper presents an advanced optimization framework, PST-CESS, for managing power-sharing among multiple tenants within the centralized energy storage system (ESS). Our thorough evaluation demonstrates that the centralized ESS facilitated by PST-CESS substantially exceeds the performance of individualized ESS systems in pivotal areas such as peak load reduction, variability mitigation, and financial profitability. Specifically, the centralized ESS model achieves up to a 44.05% reduction in annual peak load for certain tenants and reduces electricity consumption variability by up to 57.67%. From a financial perspective, the centralized ESS model delivers remarkable advantages, reaching a break-even point in just 2.48 years, compared to the 5.08 years required for individualized ESS systems, even when accounting for battery capacity loss costs, also known as battery degradation costs. These results highlight the centralized ESS approach as a more economically advantageous and efficient solution, providing superior financial returns and optimized energy management for multi-tenant buildings. The strategic benefits and compelling evidence presented in this study strongly support the widespread adoption of centralized ESS models to maximize both economic and environmental benefits, establishing a new standard for sustainable energy management.

Rising fossil fuel costs and environmental concerns are driving the search for new energy sources, particularly renewable energy. Among these sources, solar photovoltaic (PV) is the most promising in southern European countries, mainly through the use of decentralised PV systems designed to produce electricity close to the point of demand and primarily to meet local energy needs. In an urban scenario, a decentralised energy system usually operates in parallel with the grid, allowing excess power generated to be injected into the grid. Solar carports and rooftop systems are excellent examples of distributed photovoltaic systems, which are far more sustainable than large centralised systems because they do not compete for land use. Despite their operational advantages, these decentralised photovoltaic production plants, which are in most cases financed by specific energy efficiency programs, present challenges in a regulated market where the injection of energy into the electricity grid is restricted by law and support programs. The aim of this work is to integrate two different photovoltaic systems within an academic campus where the only PV source currently available is a solar car park, a solution designed both to provide shaded space for vehicles and to produce energy to be consumed within the facilities. Due to legal restrictions, surplus electricity cannot be sold to the national grid, and solar batteries to store the generated energy are expensive and have a short lifespan. Therefore, since the campus has two different grid connections and a 102.37 kWp PV system, the newly designed system to be installed on the library roof must be calculated to support the installed electricity system during the most critical working hours, determining the specific angle and orientation of the solar panels. On this basis, the energy management of a school campus is fundamental to creating a collective self-consumption system, the basis of a local energy community that can meet energy, environmental, and social objectives.

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]

Energy efficiency is a cornerstone of climate change mitigation. For buildings, facility management is an essential part of achieving efficient energy use while keeping tenants satisfied. This interview study explores success factors and barriers for facility management in maintaining energy efficiency over time in four approximately 10-year-old non-residential premises built as so-called nearly zero-energy buildings (nZEB) in Sweden. The study highlights the importance of functional digital tools, benchmarks, and building professionals' involvement in ensuring energy efficiency. It also emphasizes the need for involvement communication and strategies to engage facility management in energy efficiency efforts. The study suggests that in-house and public policies can play a crucial role in sustaining high ambitions for energy efficiency. Access to professional support that is self-evident to use is identified as a critical success factor. Additionally, the research presents an analytic model that can be used in future studies to assess facility management organizations' potential for maintaining energy performance in buildings over time. [ABSTRACT FROM AUTHOR]

In the dynamic sphere of building energy systems, this study explores advancements in energy integration, storage technologies, management practices, and occupant behavior, assessing sustainable energy practices, including emerging technologies like fuel cells and energy storage systems. It underscores the significance of efficient energy management, considering both renewable and conventional energy mechanisms. The study comprises four key strata: (i) a thorough literature review of recent energy trends, (ii) a comparative study of global energy patents using the World Intellectual Property Organization (WIPO) database, (iii) a comprehensive analysis of building-energy patents, and (iv) expert-guided Analytic Hierarchy Process (AHP) evaluation. These realms encompass five primary sources: (i) energy-efficient building design, (ii) intelligent building automation, (iii) optimizing energy systems integration, (iv) energy storage, and (v) energy management and optimization. Findings reveal energy storage's dominance, with water energy storage and emerging hydrogen technology leading the trajectory. Global energy patent scrutiny underscores China, the United States, and Japan as influential players in optimizing energy markets. The research shapes energy futures, identifies gaps, and drives sustainable energy practices within the built environment, serving as a compass for policymakers and researchers.

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.

Rising fossil fuel costs and environmental concerns are driving the search for new energy sources, particularly renewable energy. Among these sources, solar photovoltaic (PV) is the most promising in southern European countries, mainly through the use of decentralised PV systems designed to produce electricity close to the point of demand and primarily to meet local energy needs. In an urban scenario, a decentralised energy system usually operates in parallel with the grid, allowing excess power generated to be injected into the grid. Solar carports and rooftop systems are excellent examples of distributed photovoltaic systems, which are far more sustainable than large centralised systems because they do not compete for land use. Despite their operational advantages, these decentralised photovoltaic production plants, which are in most cases financed by specific energy efficiency programs, present challenges in a regulated market where the injection of energy into the electricity grid is restricted by law and support programs. The aim of this work is to integrate two different photovoltaic systems within an academic campus where the only PV source currently available is a solar car park, a solution designed both to provide shaded space for vehicles and to produce energy to be consumed within the facilities. Due to legal restrictions, surplus electricity cannot be sold to the national grid, and solar batteries to store the generated energy are expensive and have a short lifespan. Therefore, since the campus has two different grid connections and a 102.37 kWp PV system, the newly designed system to be installed on the library roof must be calculated to support the installed electricity system during the most critical working hours, determining the specific angle and orientation of the solar panels. On this basis, the energy management of a school campus is fundamental to creating a collective self-consumption system, the basis of a local energy community that can meet energy, environmental, and social objectives. [ABSTRACT FROM AUTHOR]

With the intensifying global energy crisis and environmental issues, electric vehicles (EVs) have emerged as a crucial direction for future transportation development due to their high efficiency and zero emissions. However, EVs still face numerous technical challenges in practical use, particularly in braking energy management and thermal management system optimization. This paper aims to deeply analyze the regenerative braking process of EVs based on the second law of thermodynamics to uncover the irreversible losses and optimization pathways in energy conversion. Furthermore, a comprehensive vehicle thermal management system model is established, with detailed parameter settings and optimization to ensure efficient operation under various conditions. The research results indicate that the thermodynamics-based braking energy management strategy can significantly enhance the energy efficiency and range of EVs. Additionally, optimizing the vehicle's thermal management system contributes to improving overall vehicle performance and reliability. [ABSTRACT FROM AUTHOR]

The industrial manufacturing sector is the biggest final usage sector when speaking of both final energy consumption and emission of greenhouse gases (accounting for over 30% of the total); the industry's expansion is fast modifying the climate of the whole globe. Energy conservation is one of the key components of success for sustainable production because of the pressing need to reduce the negative effects that industrial operations have on the surrounding environment. As a direct consequence of this, the scientific community's involvement in energy management has significantly increased, which has resulted in a number of literature evaluations being conducted on methodologies. However, there is a dearth of both a detailed study of the techniques and tools that attempt to improve energy awareness as well as an assessment of the impact that these methods and tools have on energy efficiency. To try to fill this void, the author of this work conducts an extensive literature study on the various energy assessment methodologies and tools with their microstructures. After examining the databases of scientific literature, a total of 1366 publications were retrieved; however, it might be of use to industry practitioners in the field of energy management. In accordance with the guidelines provided by ISO 50001, the procedures and instruments were categorized into three primary areas (namely, AAM which means analysis, assessment, and saving measures), and the particular results important to each category were then synthesized as the outcome of study. In its concluding section, the article discusses problems and topics that still need to be addressed and offers proposals for new lines of inquiry.

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]

In the era of society's ongoing digitization and the exponential growth in data volume, alongside a growing energy demand, energy management plays an integral role in data centers (DCs) and is a key factor in the quest for decarbonization. In light of the complex nature of DCs, traditional energy management strategies are inadequate. This research introduces a data-driven decision-making framework for DCs, grounded in the OODA (Observation, Orientation, Decision, and Action) loop and based on insights from an Ericsson-operated DC in Linkoping, Sweden. The developed framework enables DCs to enhance energy efficiency effectively. Rooted in the OODA loop and leveraging extensive datasets from DCs' building management systems, this framework aids in decreasing cooling energy usage through strategic, data-driven decision-making. By adopting AI methods, specifically K-means clustering in this research, for continuous monitoring and fine-tuning (Proportional, Integral, Derivative) PID parameters, the framework aids in improving operational efficiency. [ABSTRACT FROM AUTHOR]

This study focuses on the design and optimization of property energy management systems, aiming to improve energy efficiency, reduce waste, and enhance user comfort and satisfaction through intelligent means. The research background is based on the urgency of energy conservation and emission reduction, and the rise of smart property management models on a global scale, especially the increasing demand for energy efficiency monitoring, predictive analysis, automated control, and user engagement. To address the urgent need for energy conservation and emission reduction, particularly in the realm of property management, this study designed and optimized a property energy management system. The core of the research is a systematic energy management framework that encompasses efficient monitoring, intelligent predictive analytics using techniques such as Long Short-Term Memory (LSTM) networks for energy consumption forecasting, automated control, user-friendly interfaces, and system safety. An empirical case study was conducted at a large-scale commercial complex, confirming the effectiveness of the system. Through intelligent transformation, specifically the optimization of air conditioning and lighting systems using advanced technologies like frequency modulation and LED lighting, a total energy saving rate of 25% was achieved. The annual economic savings exceeded 1.25 million yuan, and user satisfaction was significantly improved. During the research process, several limitations and challenges were encountered, including data quality issues and scalability concerns. These limitations were addressed through rigorous data preprocessing and validation, ensuring the robustness of the findings and their applicability to similar environments. The results demonstrate the potential of integrating artificial intelligence and machine learning techniques into property energy management systems, paving the way for more sustainable and efficient buildings. This revised abstract includes more specific details about the technologies used, such as LSTM networks, and mentions the limitations and challenges faced during the research. It also emphasizes the practical application and scalability of the system. [ABSTRACT FROM AUTHOR]

This article explores operational challenges in mining, with a focus on energy management amid depleting ore grades and rising costs. The urgent need for innovative energy management systems and strategies is highlighted by analyzing the unexplored landscape of mine energy budgeting and forecasting and identifying gaps in current practices. Drawing from the literature, this paper offers new insights into energy budgeting and the evaluation of energy efficiency initiatives by integrating traditional and advanced measurement and verification (M&V) techniques. M&V practices are crucial for energy management, particularly in deep-level mines, with a focus on practical knowledge and advanced methodologies. Key findings from this study show that integrating advanced M&V techniques with unplanned events is crucial to improve the financial management of mines. By leveraging these key findings, this article proposes a roadmap of the next seven milestones needed in advanced M&V research to aid effective energy management in a mining environment. If executed successfully, a practical method for applying advanced M&V processes to deep-level mining operations can be constructed. Such a generic method will enhance mining companies' energy efficiency initiatives and improve financial management practices on a global scale. [ABSTRACT FROM AUTHOR]

The global demand for energy is significantly impacted by the consumption patterns within the building sector. As such, the importance of energy simulation and prediction is growing exponentially. This research leverages Building Information Modelling (BIM) methodologies, creating a synergy between traditional software methods and algorithm-driven approaches for comprehensive energy analysis. The study also proposes a method for monitoring select energy management factors, a step that could potentially pave the way for the integration of digital twins in energy management systems. The research is grounded in a case study of a newly constructed educational building in New South Wales, Australia. The digital physical model of the building was created using Autodesk Revit, a conventional software for BIM methodology. EnergyPlus, facilitated by OpenStudio, was employed for the traditional software-based energy analysis. The energy analysis output was then used to develop preliminary algorithm models using regression strategies in Python. In this regression analysis, the temperature and relative humidity of each energy unit were used as independent variables, with their energy consumption being the dependent variable. The sigmoid algorithm model, known for its accuracy and interpretability, was employed for advanced energy simulation. This was combined with sensor data for real-time energy prediction. A basic digital twin (DT) example was created to simulate the dynamic control of air conditioning and lighting, showcasing the adaptability and effectiveness of the system. The study also explores the potential of machine learning, specifically reinforcement learning, in optimizing energy management in response to environmental changes and usage conditions. Despite the current limitations, the study identifies potential future research directions. These include enhancing model accuracy and developing complex algorithms to boost energy efficiency and reduce costs. [ABSTRACT FROM AUTHOR]

Sorting out the requirements for intelligent functions is the prerequisite and foundation of the top-level design for the development of intelligent ships. In light of the development of inland intelligent ships for 2030, 2035, and 2050, based on the analysis of the division of intelligent ship functional modules by international representative classification societies and relevant research institutions, eight necessary functional modules have been proposed: intelligent navigation, intelligent hull, intelligent engine room, intelligent energy efficiency management, intelligent cargo management, intelligent integration platform, remote control, and autonomous operation. Taking the technical realization of each functional module as the goal, this paper analyzes the status quo and development trend of related intelligent technologies and their feasibility and applicability when applied to each functional module. At the same time, it clarifies the composition of specific functional elements of each functional module, puts forward the stage goals of China's inland intelligent ship development and the specific functional requirements of different modules under each stage, and provides reference for the Chinese government to subsequently formulate the top-level design development planning and implementation path of inland waterway intelligent ships. [ABSTRACT FROM AUTHOR]

This article explores the role of energy-efficient buildings in addressing climate change and reducing carbon emissions. It highlights the significant impact that buildings have on global energy consumption and greenhouse gas emissions, emphasizing the need for sustainable construction materials and design techniques. The article also discusses the potential of digital technologies, such as Building Information Modeling (BIM), IoT sensors, and Digital Twins, to optimize energy use and sustainability throughout the building lifecycle. However, challenges such as cost concerns and operational inefficiencies need to be addressed for widespread adoption of these technologies. The article proposes a research roadmap to overcome these barriers and promote the use of digital tools in the building sector. [Extracted from the article]

The energy management system of a fuel cell electric robot should be highly responsive to provide the required power for various tactical operations, navigation of different routes, and acceleration. This paper presents a new multi-level online energy management strategy for a fuel cell electric robot based on the proposed functions of equivalent hydrogen fuel value evaluation, classification of the battery state of charge via the squared combined efficiency function, identification of the robot maneuver condition based on the proposed operation state of robot function, improvement of the overall energy efficiency based on the proposed function of the battery overcharge control, and separation of the functional points of the fuel cell based on the operational mode control strategy. The simulation study of the proposed online multi-level energy management strategy was carried out with MATLAB R2018b software to verify its superiority by comparing with other strategies. The results indicate a reduction in hydrogen consumption, reduction in fuel cell power fluctuations, prevention of battery overcharging, and incrementation in the total energy efficiency of energy storage systems compared to other energy management strategies. [ABSTRACT FROM AUTHOR]

Directive 2018/844/EU on energy efficiency claims that almost 80% of the final energy consumption in the EU is used within the buildings. The subsequent Directive (EU) 2023/1791 introduced in 2023 expected 68% of the world population to live in urban areas. Both directives enhanced the role of energy management (EM) applied to the city buildings. This paper introduces the development of energy management in Czech municipalities between the years 2009 and 2023. The main goal of this article is to present selected results of the unique national survey focused on the energy management in Czech municipalities conducted in 2023 and compare it with results of national surveys implemented in 2009 and 2018. The main method is the comparing of two surveys conducted among the municipal representatives or energy managers in the past with the new survey conducted in 2023. The results show that EM became a notably more important issue for most Czech municipalities. The number of cities collecting data on energy consumption grew by 288% and the number of city energy managers increased by 57%. On the other hand, the used potential of energy savings triggered by establishing EM was insufficient and only 4% of the respondents claimed high and exactly measured savings because of EM. This article should be applied to the stress power of municipal EM in performing energy efficiency and RES projects and encouraging the government to support municipal energy managers in their complex role. [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]

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]

The proliferation of IoT devices has ushered in a new era of smart homes, where efficient energy management is a paramount concern. Multiagent artificial intelligence-of-things (MAIoT) has emerged as a promising approach to address the complex challenges of smart home energy management. This research study examines MAIoT's components, functioning, benefits, and drawbacks. MAIoT systems improve energy efficiency and user comfort by combining multiagent systems and IoT devices. However, privacy, security, interoperability, scalability, and user acceptability must be addressed. As technology advances, MAIoT in smart home energy management will offer more sophisticated and adaptable solutions to cut energy consumption and promote sustainability. This article describes how energy status and internal pricing signals affect group intelligent decision making and the interaction dynamics between consumers or decision makers. In a multiagent configuration based on the new concept of artificial intelligence-of-things, this intelligent home energy management challenge is simulated and illustrated using software and hardware. Based on sufficient experimental simulations, this paper suggested that residential clients can significantly improve their economic benefit and decisionmaking efficiency. [ABSTRACT FROM AUTHOR]

With the widespread application of drone technology, energy management of drone swarms has become a key challenge in ensuring sustained and efficient operations. To optimize the energy consumption of drone swarms, researchers have proposed the energy balance plane routing algorithm. This algorithm uses ultraviolet light for low visibility communication and has shown promising results in managing the energy of drone swarms. To address the issues of uneven cluster head distribution and reduced energy consumption in other algorithms, an improved non-uniform clustering energy balancing routing algorithm is proposed. Compared to existing algorithms, the improved non-uniform clustering energy balancing routing algorithm achieved the lowest average communication consumption among 5 nodes and prolonged node failure time. The performance of the research method has been verified through simulation experiments, which is of great significance in maintaining energy consumption balance and can improve the energy efficiency and sustainability of the network. This study can provide more effective solutions for the development and application of drone technology, promoting its widespread application and promotion in various fields. [ABSTRACT FROM AUTHOR]

Conservation Voltage Reduction (CVR) is a potential energy management approach for increasing computer system energy efficiency. This study uniquely contributes to the field by thoroughly investigating the impact of CVR on computing devices, filling a significant gap in the existing literature. The research employs a novel experimental approach, considering the temporal variations in energy use behavior, and presents a comprehensive benchmark analysis of desktop PCs and laptops. Notable gains in processing efficiency are observed, with specific instances such as Desktop 1's 1.53% Single-Core performance improvement and Desktop 3's 3.19% total performance boost. Despite variations, the thermal performance of CVR-equipped devices, particularly Desktop 3 and Laptop 3, consistently demonstrates lower temperatures, indicating thermal management enhanced by 3.19% and 1.35%, respectively. Additionally, the study introduces the CVR Performance Enhancement Ratio (%), providing a unique metric for evaluating the trade-offs between energy efficiency and system performance. This research highlights the dual impact of CVR on thermal and computational elements, emphasizing its broad advantages. Integrating CVR emerges as a viable strategy for developing more durable, efficient, and sustainable computing devices, setting the stage for advancements in voltage regulation. [ABSTRACT FROM AUTHOR]