Klaudia Zwolińska

Bio: Klaudia Zwolińska is an academic researcher from AGH University of Science and Technology. The author has contributed to research in topics: Energy consumption & Efficient energy use. The author has an hindex of 2, co-authored 9 publications receiving 13 citations.

Energy Monitoring in a Heating and Cooling System in a Building Based on the Example of the Turówka Hotel

Abstract: The energy consumption of buildings is very important for both economic and environmental reasons. Newly built buildings are characterized by higher insulation and airtightness of the building envelope, and are additionally equipped with technologies that minimize energy consumption in order to meet legal requirements. In existing buildings, the modernization process should be properly planned, taking into account available technologies and implementation possibilities. Hotel buildings are characterized by a large variability of energy demand, both on a daily and a yearly basis. Monitoring systems, therefore, provide the necessary information needed for proper energy management in the building. This article presents an energy analysis of the Turowka hotel located in Wieliczka (southern Poland). The historical hotel facility is being modernized as part of the project to adapt the building to the requirements of a sustainable building. The modernization proposal includes a trigeneration system with a multifunctional reverse regenerator and control module using neural algorithms. The main purpose is to improve the energy efficiency of the building and adapt it to the requirements of low-energy buildings. The implementation of a monitoring system enables energy consumption to be reduced and improves the energy performance of the building, especially through using energy management systems and control modules. The proposed retrofit solution considers the high energy consumption, structure of the energy demand, and limits of retrofit intervention on facades.

The Combustion of Methane from Hard Coal Seams in Gas Engines as a Technology Leading to Reducing Greenhouse Gas Emissions—Electricity Prediction Using ANN

Abstract: 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).

Prediction of Cooling Energy Consumption in Hotel Building Using Machine Learning Techniques

Abstract: 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.

The Security of Energy Supply from Internal Combustion Engines Using Coal Mine Methane—Forecasting of the Electrical Energy Generation

Abstract: Increasing emissions from mining areas and a high global warming potential of methane have caused gas management to become a vital challenge. At the same time, it provides the opportunity to obtain economic benefits. In addition, the use of combined heat and power (CHP) in the case of coalbed methane combustion enables much more efficient use of this fuel. The article analyses the possibility of electricity production using gas engines fueled with methane captured from the Budryk coal mine in Poland. The basic issue concerning the energy production from coalbed methane is the continuity of supply, which is to ensure the required amount and concentration of the gas mixture for combustion. Hence, the reliability of supply for electricity production is of key importance. The analysis included the basic characterization of both the daily and annual methane capture by the mine’s methane drainage system, as well as the development of predictive models to determine electricity production based on hourly capture and time parameters. To forecast electricity production, predictive models that are based on five parameters have been adopted. Models were prepared based on three time variables, i.e., month, day, hour, and two values from the gas drainage system-capture and concentration of the methane. For this purpose, artificial neural networks with different properties were tested. The developed models have a high value of correlation coefficient. but showed deviations concerning the very low values persisting for a short time. The study shows that electricity production forecasting is possible, but it requires data on many variables that directly affect the production capacity of the system.

Airflow Fluctuation from Linear Diffusers in an Office Building: The Thermal Comfort Analysis

Abstract: In buildings, the HVAC systems are responsible for a major part of the energy consumption. Incorrect design or selection of the system and improper installation, operation, and maintenance of the systems’ elements may result in increased energy consumption. It is worth remembering that the main aim of the appropriate system is to maintain the high quality of the indoor environment. Appropriate selection of the HVAC solution ensures both thermal and quality parameters of the air, independently of the internal and external heat loads. The microclimate of a room is affected not only by air temperature, humidity, and purity, but also by air velocity in the occupied zone. The proper air velocity distribution prevents discomfort, particularly at workstations. Based on the measurements in the office building, an analysis of velocity profiles of air supplying two different types of linear diffusers was carried out. The analysis was made based on the results of measurements performed with thermoanemometers in the actual facility. During the study, temperature of the supply air was lower that the air in the room. Analysis was focused on the airflow fluctuation and its impact on the users’ comfort. This is an obvious topic but extremely rarely mentioned in publications related to air diffusers. The results show the importance of air fluctuation and its influence on the users’ comfort. During the measurements, the instantaneous air velocity for one of the analyzed types of the diffuser was up to 0.34 m/s, while the average value from the period of 240 s for the same measuring point was relatively low: it was 0.19 m/s. Only including the airflow variability over time allowed for choosing the type of diffuser, which ensures the comfort of users. The measurements carried out for two linear diffusers showed differences in the operation of these diffusers. The velocity in the occupied zone was much higher for one type (0.36 m/s, 3.00 m from diffusers) than for another one (0.22 m/s, 5.00 m from diffusers). The improper selection of the diffuser’s type and its location may increase the risk of the draft in the occupied zone.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Highly sensitive methane detection based on light-induced thermoelastic spectroscopy with a 2.33 µm diode laser and adaptive Savitzky-Golay filtering

Abstract: In this manuscript, a highly sensitive methane (CH4) sensor based on light-induced thermoelastic spectroscopy (LITES) using a 2.33 µm diode laser with high power is demonstrated for the first time. A quartz tuning fork (QTF) with an intrinsic resonance frequency of 32.768 kHz was used to detect the light-induced thermoelastic signal. A Herriot multi-pass cell with an effective optical path of 10 m was adopted to increase the laser absorption. The laser wavelength modulation depth and concentration response of this CH4-LITES sensor were investigated. The sensor showed excellent long term stability when Allan deviation analysis was performed. An adaptive Savitzky-Golay (S-G) filtering algorithm with χ2 statistical criterion was firstly introduced to the LITES technique. The SNR of this CH4-LITES sensor was improved by a factor of 2.35 and the minimum detection limit (MDL) with an integration time of 0.1 s was optimized to 0.5 ppm. This reported CH4-LITES sensor with sub ppm-level detection ability is of great value in applications such as environmental monitoring and industrial safety.

Design and feasibility study of an on-grid photovoltaic system for green electrification of hotels: a case study of Cedars hotel in Jordan

Abstract: This paper presents a feasibility study of utilizing an on-grid photovoltaic (PV) system for electrification of Cedars hotel located in Amman in Jordan as a case study. The PV system has been designed, keeping in view the required electrical load and energy available from the sun in Jordan. The actual energy consumption of the hotel is estimated (444 MWh/year) for the design and simulation of the on-grid PV system using Photovoltaic Geographical Information System software (PVGIS) and photovoltaic software (PVsyst). The results showed that PV system required 912 panels distributed over 12 inverters, with a required area of 1757.3 m2. In addition, the on-grid PV system produced a total yearly energy of 541 MWh, with an average performance ratio of 0.828. The economic study for the proposed PV system showed that the system’s payback period was 4.1 year. Moreover, life cycle savings (LCS) and levelized cost of energy (LCOE) analysis have been carried out. The LCS and LCOE of the system were found to be $51,493 and $0.0199 /kWh, respectively. Therefore, installing a proposed on-grid PV system on the Cedars hotel will save $38,718/year. It is concluded that an on-grid PV system is a technically and economically viable technology for the electrification of residential hotel applications.

Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining

Abstract: In the process of gas prediction and early warning, outliers in the data series are often discarded. There is also a likelihood of missing key information in the analysis process. To this end, this paper proposes an early warning model of coal face gas multifactor coupling relationship analysis. The model contains the k-means algorithm based on initial cluster center optimization and an Apriori algorithm based on weight optimization. Optimizing the initial cluster center of all data is achieved using the cluster center of the preorder data subset, so as to optimize the k-means algorithm. The optimized algorithm is used to filter out the outliers in the collected data set to obtain the data set of outliers. Then, the Apriori algorithm is optimized so that it can identify more important information that appears less frequently in the events. It is also used to mine and analyze the association rules of abnormal values and obtain interesting association rule events among the gas outliers in different dimensions. Finally, four warning levels of gas risk are set according to different confidence intervals, the truth and reliable warning results are obtained. By mining association rules between abnormal data in different dimensions, the validity and effectiveness of the gas early warning model proposed in this paper are verified. Realizing the classification of early warning of gas risks has important practical significance for improving the safety of coal mines.