The diversification of energy sources in buildings and the interdependence as well as communication between HVAC installations in the building have resulted in the growing interest in energy load prediction systems that enable proper management of energy resources. In addition, energy storage and the creation of energy buffers are also important in terms of proper resource management, for which it is necessary to correctly determine energy consumption over time. It is obvious that the consumption of cooling energy depends on meteorological conditions. Knowing the parameters of the outside air and the number of users, it is, therefore, possible to determine the hourly energy consumption of a cooling system in a building with some accuracy. The article presents models of cooling energy prediction in summer for a hotel building in southern Poland. The paper presents two methods that are often used for energy prediction: neural networks and support vector machines. Meteorological data, time data, and occupancy level were used as input parameters. Based on the collected input and output data, various configurations were tested to identify the model with the best accuracy. As the analysis showed, higher prediction accuracy was obtained thanks to the use of neural networks. The best of the proposed models was characterized by the WAPE and CV coefficients of 19.93% and 27.03%, respectively.

With the development of deep mining, the problem of heat damage is becoming more and more serious. The deep mine has a longer ventilation route, which will cause the tunnel exposed area is very large, and the surrounding rock with a high ground temperature could heat the ventilation air. Therefore, in this study, a facility to conduct a series of physical similarity simulation experiments for studying the heat conduction and transfer in mine roadway’s surrounding rock is designed. The facility is applied to analyze, systematically and quantitatively, the effect of physical parameters on temperature field of the surrounding rock as well as its dynamic changes. The experimental results show that: the airflow velocity is strongly related to the temperature distribution of the surrounding rock when the temperature field is becoming stable. The maximum of the influence thickness can be up to 7 meters; the rock thermal diffusion coefficient of the rock also has a great effect on the curve of the rock’s temperature when the curve is becoming stable but has a slight effect on the curve’s shape; the rock’s moisture content has a great effect on the range of the temperature field. But the effect does not last long; The roadway with the structure of thermal resistance circle that is a sign of the increasing of the medium’s total thermal resistance has a large effect on heat transfer, which prolongs the period for the rock to reach a new thermal equilibrium state.

Ensuring the comfort and health of occupants is the main objective of properly functioning building systems. Regardless of the season and building types, it is the priority of the designers and building managers. The indoor air parameters affect both the well-being and health of users. Furthermore, it could impact the effectiveness of their work and concentration abilities. In hotel facilities, the guests’ comfort is related directly to positive opinions or customer complaints, which is related to financial benefits or losses. The main goal of this study is the analysis of the indoor environmental quality in guests’ rooms, based on the example of a hotel in Poland. The article assesses the variability of air parameters, including temperature, humidity, and carbon dioxide concentrations, and the acceptability of indoor conditions. The research was carried out in November 2020. Based on the collected data, the dynamics of changes of selected air parameters were analyzed. The article analyzes the comfort indicators inside guest rooms, including the Predicted Mean Vote (PMV) and Predicted Percentage of the Dissatisfied (PPD) index. The obtained results were compared with the optimal conditions of use to ensure the guests’ comfort. As the analysis showed, the temperature and humidity conditions are maintained at a satisfactory level for most of the time. It was noticed that the CO₂ concentrations temporarily exceeded the value of 2000 ppm in two of the analyzed guests’ rooms, which could cause discomfort to hotel guests. In these rooms, the increase in the volume of ventilation airflow should be considered. The measured parameters dynamically varied over time, and there was no repeatability or clear patterns of variation. This is due to the individual preferences and behavior of users. A detailed analysis is extremely difficult due to the possibility of opening windows by users, the irregular presence of hotel guests in the rooms, and the inability to verify the exact number of users in the room during the measurements.

Methane emissions from coal mines represent a significant environmental and atmospheric challenge, contributing to global greenhouse gas accumulation and impacting local air quality. This study aimed to quantify methane emissions from Polish coal mines, analyze their environmental and economic impacts, and evaluate advanced mitigation technologies to inform sustainable practices and policy alignment with global climate objectives. The study examined methane emissions from hard coal mining in Poland, emphasizing their scale, sources, and implications for the sector’s carbon footprint. A comprehensive overview of measurement methodologies, including direct sampling and advanced monitoring systems, is provided to highlight current capabilities and limitations. Furthermore, innovative capture technologies, such as ventilation air methane oxidation systems and methane drainage techniques, are explored alongside utilization pathways for energy production, including electricity generation and hydrogen synthesis. By integrating quantitative analyses and case studies, the article evaluates the effectiveness of these strategies in reducing methane emissions and improving air quality. The findings underscore the critical role of methane management in transitioning the coal industry toward more sustainable practices and achieving carbon neutrality goals. This study aims to inform policymakers, industry stakeholders, and researchers by presenting actionable insights into mitigating methane emissions, while fostering the dual objectives of environmental protection and resource efficiency.

Greenhouse gases such as carbon dioxide and methane cause global warming and consequently climate change. Great efforts are being made to reduce greenhouse gas emissions with the objective of addressing this problem, hence the popularity of technologies conductive to reducing greenhouse gas emissions. CO₂ emissions can be reduced by improving the thermal efficiency of combustion engines, for example, by using cogeneration systems. Coal mine methane (CMM) emerges due to mining activities as methane released from the coal and surrounding rock strata. The amount of methane produced is primarily influenced by the productivity of the coal mine and the gassiness of the coal seam. The gassiness of the formation around the coal seam and geological conditions are also important. Methane can be extracted to the surface using methane drainage installations and along with ventilation air. The large amounts of methane captured by methane drainage installations can be used for energy production. This article presents a quarterly summary of the hourly values of methane capture, its concentration in the methane–air mixture, and electricity production in the cogeneration system for electricity and heat production. On this basis, neural network models have been proposed in order to predict electricity production based on known values of methane capture, its concentration, pressure, and parameters determining the time and day of the week. A prediction model has been established on the basis of a multilayer perceptron network (MLP).