This study analyzes the energy management approach as one of the driving factors for sustainable development, not only in enterprises, but also countries. Sustainability of an enterprise is defined as its tendency to meet the need of customers and stakeholders through creative and innovative business strategies as well as activities that have a positive influence on society and environment. In this study, sustainability is measured through the application of an energy management system, but in the wood industry, which has an increasingly significant impact on the economic development in one country. The aim of this research is to determine the current situation regarding the application of energy management practices in production and service systems. The ISO 50001 standard for energy management systems considers the energy performance achieved by an enterprise. The connections between critical factors and the level of application requirements for the energy management system were statistically analyzed in this research. It was found that in enterprises that have a certified system of quality and environmental management, a higher level of energy management system implementation is evident. The research results provide data on the scientific level for the national policy improvement, as well as a developed model based on the Plan Do Check Act (PDCA) cycle for energy management system application.

The study represents a new approach of analyzing energy poverty using an intelligent method based on social factors. Energy poverty represents one of the most vital problems, especially in recent years, with serious implications in the social, economic, environmental, political, and health sphere. Identifying and measuring social energy parameters represents a challenge not only on the national, but also on the regional level. This research represents a new approach to neural network application in energy systems and energy resource planning activities with the aim of analyzing energy poverty with real socio-economic data. The analyzed data are associated with social factors within one country. The newly developed model includes a new data optimization framework for pre-processing and selecting the most important parameters from a raw dataset. In analyzing energy poverty, it is concluded that its weakness is based on the actual energy consumption of households (both electricity and heat consumption). The model presented in this paper uses 15 influential parameters in energy prediction.

The aim of this paper is to propose a model for an energy management system (EnMS) in the hospitality industry in line with the ISO 50001 standard. The new proposed model, specifically developed for service organizations, was based on the Plan–Do–Check–Act cycle and was confirmed in 280 hotels in the Western Balkans 6 (WB6) region. The data were used to investigate if hotel properties represent an influential factor in the wider implementation of EnMS requirements. The proposed model, based on the ISO 50001 standard, was developed into sub-categories, including actual regulations and legislation within the EU and WB6. Hotels, as some of the most energy-demanding buildings, due to their purpose and processes, should provide their guests with electricity, hot water, heating and air-conditioning every hour of every day. The survey was conducted in 280 hotels and lasted from December 2021 to September 2022. The research shows that the average implementation of EnMS requirements was 38.07% for the whole researched sample, i.e., 25.86% in Albania, 30.40% in Bosnia and Herzegovina, 26.11% in Kosovo, 46.34% in Montenegro, 29.86% in North Macedonia and 44.56% in Serbia. In the researched case study, the average implementation of the PLAN phase (phase for establishing basics for energy management) was 35.71%, implementation of the DO phase (phase for implementing energy management processes) was 44.90%, implementation of the CHECK phase (phase for monitoring and the measurement of energy performance) was 45.45%, and finally, implementation of the ACT phase (phase for reviewing and improving energy management) was only 25.36%. The proposed model is significant as it can be used for broader research to investigate other business sectors in hospitality and for policymakers to propose energy consumption strategies and measures for energy savings that would have a minimal impact on service quality. The novelty of this research can be seen not only in the specifically developed model for service organizations in the hospitality sector but it also contains current data from the verified model in one specific region which offers the possibility of benchmarking and evaluating one’s own organization.

Buildings with indoor swimming pools have a large energy footprint. The source of major energy loss is the swimming pool hall where air humidity is increased by evaporation from the pool water surface. This increases energy consumption for heating and ventilation of the pool hall, fresh water supply loss and heat demand for pool water heating. In this paper, a mathematical model of the swimming pool was made to assess energy demands of an indoor swimming pool building. The mathematical model of the swimming pool is used with the created multi-zone building model in TRNSYS software to determine pool hall energy demand and pool losses. Energy loss for pool water and pool hall heating and ventilation are analyzed for different target pool water and air temperatures. The simulation showed that pool water heating accounts for around 22%, whereas heating and ventilation of the pool hall for around 60% of the total pool hall heat demand. With a change of preset controller air and water temperatures in simulations, evaporation loss was in the range 46-54% of the total pool losses. A solar thermal sanitary hot water system was modelled and simulated to analyze it's potential for energy savings of the presented demand side model. The simulation showed that up to 87% of water heating demands could be met by the solar thermal system, while avoiding stagnation.

This paper presents a methodology for applying the Lean Six Sigma method on the educational process. After defining defects that have negative influence on the final quality evaluation of higher education and how these defects can be remedied, the Pareto analysis is done, and that is used for establishing a vital minority of the exams that are critical for examination of faculty. The next step is the Statistical Process Control (SPC) analysis that is performed on the exams that are classified as vital minority in Pareto analysis. Ishikawa diagram shows a relation between considered consequence (small number of passed exams) and all factors that influence this consequence. Based on the results of implementation of the Lean Six Sigma method in the educational process and implementation of all suggested improvements, the comparative overview of Pareto analysis is given for 2009/2010 and 2012/2013 academic year at the Faculty of Mechanical Engineering, University of Niš.

Surging electricity demand, its limited supply, and the pandemic crisis are just some of the key factors that resulted in changes in electricity prices on the power exchanges. This topic brings about a notable economic influence on both producers and consumers. The main purpose of this paper is to explore power price volatility during the four-year period from 1 January 2018 to 31 December 2021, in 28 power exchanges in Europe, measured using daily velocity data. In addition, based on the fixed and chain base index numbers, as well as their relative merits, this paper was designed to measure the gap and convergence in trends. Considering that the price volatility varies depending on the observation period, this paper performs a comparative analysis of electricity price volatility on a daily, monthly, quarterly, and annual level for all examined countries. The obtained results indicate that electricity price volatility is higher on a daily basis. Related to this, convergent trends are demonstrated on all 28 observed markets, and there is a growing trend of hourly spot prices in the analyzed four-year period. The results of this paper also confirm a higher power price volatility during the pandemic period in 2020 and 2021 compared to the prepandemic period during 2018 and 2019.