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

This 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]

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]

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

The 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]

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]

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]

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

The digitalization of the forest sector, the increased demand of energy-wood, as well as faster market changes have increased the number of challenges for wood procurement of the forest industry. The aim of the study is to optimize wood procurement (upstream of supply chain) of energy and material production in integrated situations, and to consider the effects of production changes on the management of procurement regions. Three scenarios described integrated production situations in a carbon-neutral forest industry: (1) declining export, (2) energy reform, and (3) extensive energy reform. Time-varying capital-, cost-, and energy efficiency of the dynamic wood-flow model affected the competitiveness of the procurement regions. As a novel contribution, energy efficiency was modeled using the price of emission allowance as a cost parameter. The results show the positive effects of the energy reforms, which partly compensate for the declining exports. In addition, it is possible to change wood procurement in the regions in a market-oriented way. Decision makers should optimize purchases, inventories, and procurement resources as business processes, which are also considered success factors for the forest industry in integrated production situations. The strategic solution of extensive energy reform provides a potential approach for carbon-neutral customer-oriented supply chains, but it needs tactical energy efficiency analysis in future studies of sustainability, if the ultimate goal is to implement a carbon-free forest industry and bioeconomy. [ABSTRACT FROM AUTHOR]