• Energy Research

The article assesses an impact of thermal retrofitting on an improvement of the energy quality of public buildings in terms of their heating. The analysis covered a group of 14 buildings, including schools, kindergartens or offices, while energy audits were carried out for 12 of them. The indications of individual gas meters were the source of actual data for the assessment of changes in energy consumption indexes in operating conditions. The analysis showed a clear improvement in the energy quality of buildings; however, the actual effects were much lower than forecasted. The average forecasted decrease in energy consumption was supposed to be 64.3%, but the measured data showed only 37.1%. The investigation confirmed that the most complex refurbishing provided the most satisfactory decrease in energy consumption (51.4% of real decrease in energy consumption), while objects with partial thermal refurbishing reached an efficiency of only 21.8%. It was stated that in operating conditions, special attention should be paid to the manner of energy use, since different indicators of energy consumption can be obtained with the same parameters of building’s balance cover. The results obtained can be further utilized in thermal-refurbishment implementation procedures. Follow-up investigations on the impact of selected parameters on energy consumption are planned.

This paper focuses on reducing energy consumption in ultrasound tomography by utilizing machine learning techniques. The core idea is to investigate the feasibility of minimizing the number of measurement sensors without sacrificing prediction accuracy. This article evaluates the quality of reconstructions derived from data collected through two or three measurement channels. In subsequent steps, machine learning models are developed to predict the number, location, and size of the objects. A reliable object detection method is introduced, requiring less information than traditional signal analysis from multiple channels. Various machine learning models were tested and compared to validate the approach, with most demonstrating high accuracy or R2 scores in their respective tasks. By reducing the number of sensors, the goal is to lower energy usage while maintaining high precision in localization. This study contributes to the ongoing research on energy efficiency in sensing and localization, especially in environments where resource optimization is crucial, such as remote or resource-limited settings.

The following article presents a proprietary real-time localization system using temporal analysis techniques and detection and localization algorithms supported by machine learning mechanisms. It covers both the technological aspects, such as proprietary electronics, and the overall architecture of the system for managing human and fixed assets. Its origins lie in the ever-increasing degree of automation in the management of company processes and the energy optimization associated with reducing the execution time of tasks in an intelligent building supported by in-building navigation. The positioning and tracking of objects in the presented system was realized using ultra-wideband radio tag technology. An exceptional focus has been placed on reducing the energy requirements of the components in order to maximize battery runtime, generate savings in terms of more efficient management of other energy consumers in the building and increase the equipment’s overall lifespan.

During the building design phase, a lot of attention is paid to the thermal properties of the external envelopes. New regulations are introduced to improve energy efficiency of a building and impose a reduction of the overall heat transfer coefficient; meanwhile, this efficiency is more influenced by the efficiency of the heating system and the type of fuels used. This article presents a complex analysis including the impact of: heat transfer coefficient of the envelope, efficiency of building service systems, the type of energy source, and the fuel. The analysis was based on the results of simulation tests obtained for an exemplary multi-family residential building located in Poland that is not equipped with a cooling system. The conducted calculations gave quantitative evaluation of the influence of particular parameters on building energy performance and showed that the decrease of heat transfer coefficient of building boundaries, in accordance to the Polish regulation for 2017 and 2021, gave only 11% of reduction on usable energy demand index. On the other hand, it was found that modification of the heating system and heat source can significantly influence the values of the final and primary energy consumption at the level of 70%. The application of heat pumps has a greater influence on the final and primary energy consumption for heating indices than other parameters, such as the building’s envelopes.

The main goal of this study was to investigate the co-digestion of sewage sludge and mature landfill leachate pretreated through hydrodynamic cavitation. The process efficiency was analyzed from the aspects of organics removal, biogas production, kinetics and digestate quality. Energy efficiency of the overall treatment was evaluated as well. A dose of hydrodynamically cavitated leachate of 5% v/v was used as a substrate for co-digestion with sewage sludge at a hydraulic retention time of 20 days. This improved the kinetics, biogas production and corresponding yields, as well as digestate quality. Fourier transform infrared photoacoustic spectroscopy analysis seemed to indicate that no new toxic compounds were formed with co-digestion. The investigated two-stage treatment is recommended as a safe and cost-effective method of utilizing mature leachate.

This study addresses the issue of energy optimization by investigating solutions for the reduction of energy consumption in the diagnostics and monitoring of technological processes. The implementation of advanced process control is identified as a key approach for achieving energy savings and improving product quality, process efficiency, and production flexibility. The goal of this research is to develop a cost-effective system with a minimal number of ultrasound sensors, thus reducing the energy consumption of the overall system. To accomplish this, a novel method for obtaining high-resolution reconstruction in transmission ultrasound tomography (t-UST) is proposed. The method involves utilizing a convolutional neural network to take low-resolution measurements as input and output high-resolution sinograms that are used for tomography image reconstruction. This approach allows for the construction of a super-resolution sinogram by utilizing information hidden in the low-resolution measurement. The model is trained on simulation data and validated on real measurement data. The results of this technique demonstrate significant improvement compared to state-of-the-art methods. The study also highlights that UST measurements contain more information than previously thought, and this hidden information can be extracted and utilized with the use of machine learning techniques to further improve image quality and object recognition.

The paper presented the analysis of heat consumption for heating in multi-family residential buildings before and after thermal retrofitting. The analysis involved four groups of buildings, i.e., 43 buildings in total, located in various localities, belonging to one weather station. The predicted level of energy savings resulting from thermal retrofitting was achieved from the energy audits. The actual heat consumption, following the calculation into so-called external standard conditions, was obtained based on the readouts from heat-meters. For each building, the values of heat consumption over the periods of 6–10 years were read. The performance measurements involved the periods before, during, and after thermal retrofitting. The following statistical tests were used for data analysis: Wilcoxon–Mann–Whitney, Shapiro–Wilk, Bartlett, ANOVA, Kruskal–Wallis, Dunn and Holm post-hoc. The performed analyses showed that the mean value of energy savings predicted by audits reached 38.5% when the real mean value of savings, achieved from heat-meters, equaled 30.3%. The annual energy demand factors for heating were calculated for final energy and non-renewable primary energy factors. It was established that most of the analyzed objects fulfilled the primary energy factor requirements found in the Polish technical and construction regulations, which were valid at the time of investment.

This paper describes the method developed using the Extreme Gradient Boosting (Xgboost) algorithm that allows high-resolution imaging using the ultrasound tomography (UST) signal. More precisely, we can locate, isolate, and use the reflective peaks from the UST signal to achieve high-resolution images with low noise, which are far more useful for the location of points where the reflection occurred inside the experimental tank. Each reconstruction is divided into two parts, estimation of starting points of wave packets of raw signal (SAT—starting arrival time) and image reconstruction via XGBoost algorithm based on SAT matrix. This technology is the basis of a project to design non-invasive monitoring and diagnostics of technological processes. In this paper, we present a method of the complete solution for monitoring industrial processes. The measurements used in the study were obtained with the author’s solution of ultrasound tomography.