Short-term load forecasting predetermines how power systems operate because electricity production needs to sustain demand at all times and costs. Most load forecasts for the non-residential consumers are empirically done either by a customer’s employee or supplier personnel based on experience and historical data, which is frequently not consistent. Our objective is to develop viable and market-oriented machine learning models for short-term forecasting for non-residential consumers. Multiple algorithms were implemented and compared to identify the best model for a cluster of industrial and commercial consumers. The article concludes that the sliding window approach for supervised learning with recurrent neural networks can learn short and long-term dependencies in time series. The best method implemented for the 24 h forecast is a Gated Recurrent Unit (GRU) applied for aggregated loads over three months of testing data resulted in 5.28% MAPE and minimized the cost with 5326.17 € comp... [more]

The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options.

The influences of superellipse shapes on natural convection in a horizontally subdivided non-Darcy porous cavity populated by Cu-water nanofluid are inspected in this paper. The impacts of the inner geometries (n=0.5,1,1.5,4), Rayleigh number (103≤Ra≤106), Darcy number (10−5≤Da≤10−2), porosity (0.2≤ϵ≤0.8), and solid volume fraction (0.01≤∅≤0.05) on nanofluid heat transport and streamlines were examined. The hot superellipse shapes were placed in the cavity’s bottom and top, while the adiabatic boundaries on the flat walls of the cavity were considered. The governing equations were numerically solved using the finite volume method (FVM). It was found that the movement of the nanofluid upsurged as Ra boosted. The temperature distributions in the cavity’s core had an inverse relationship with increasing Rayleigh number. An extra porous resistance at lower Darcy numbers limited the nanofluid’s movement within the porous layers. The mean Nusselt number decreased as the porous resistance inc... [more]

The main purpose of this study is to investigate the relationships between key sources of air pollutant emissions (sources of energy production, factories which are particularly harmful to the environment, the fleets of cars, environmental protection expenditure) and the main environmental air pollution (SO2, NOx, CO and PM) in Poland. Models based on MLP neural networks were used as predictive models. Global sensitivity analysis was used to demonstrate the significant impact of individual network input variables on the output variable. To verify the effectiveness of the models created, the actual data were compared with the data obtained through modelling. Projected courses of changes in the variables under study correspond with the real data, which confirms that the proposed models generalize acquired knowledge well. The high MLP network quality parameters of 0.99−0.85 indicate that the network generalizes the acquired knowledge accurately. The sensitivity analysis for NOx, CO and PM... [more]

The content of heavy metals Cd, Cr, Cu, Fe, Ni, Pb and Zn in ash samples from miscanthus, oak, pine, sunflower husk, wheat straw, and willow ashes burned at 500, 600, 700, 800, 900, and 1000 °C, respectively, was determined. The statistical analysis of the results was based on multivariate methods: hierarchical cluster analysis (HCA), and principal component analysis (PCA), which made it possible to classify the raw materials ashed at different temperatures into the most similar groups, and to study the structure of data variability. Using PCA, three principal components were extracted, which explain more than 88% of the variability of the studied elements. Therefore, it can be concluded that the application of multivariate statistical techniques to the analysis of the results of the study of heavy metal content allowed us to draw conclusions about the influence of biomass properties on its chemical characteristics during combustion.

The paper determines the stationary thermal field in an elliptical cross-section electric conductor coated with insulation. Heat is generated by the flow of alternating current (AC) through the conducting core, and then dissipated from the insulation surface via convection and radiation. The authors have developed an original method for hybrid (analytical−numerical) modeling of a field. This method has been used to solve the relevant boundary problem of Poisson’s equation. While the eigenfunctions of the Laplace operator were determined analytically, the coefficients of the eigenfunctions were calculated by iteratively solving an appropriate system of algebraic equations. The proposed method enables the analysis of systems with an elliptical geometry and a heterogeneous layered structure (e.g., air, aluminum alloy, PCV), and does not require area discretization (grid). The developed analytical−numerical (AN) method has been positively verified using finite elements (FEs). The determine... [more]

The study of the source rocks was carried out with the use of various analytical methods in order to assess their generation potential and to predict the decomposition products of organic matter. The selected samples from the Menilite Beds from the Silesian and Dukla units, as well as the Istebna layers from the Silesian unit, which are classified as weak and medium source rocks in the Carpathian oil system, were examined. The generation potential and type of the products obtained from the pyrolysis of the analyzed source rocks, despite the often comparable overall content of organic matter, are significantly different. Menilite shale generated a higher abundance of hydrocarbons (alkanes, alkenes, and isoalkanes) by stage pyrolysis, which suggested that the organic matter of Menilite shale is different from the Istebna source rocks. Moreover, the thermogravimetric analysis showed a two-stage weight loss in the case of Menilite shales, while the Istebna shales were characterized by a on... [more]

Turbine as a key power unit is vital to the novel supercritical carbon dioxide cycle (sCO2-BC). At the same time, the turbine design and optimization process for the sCO2-BC is complicated, and its relevant investigations are still absent in the literature due to the behavior of supercritical fluid in the vicinity of the critical point. In this regard, the current study entails a multifaceted approach for designing and optimizing a radial turbine system for an 8 MW sCO2 power cycle. Initially, a base design of the turbine is calculated utilizing an in-house radial turbine design and analysis code (RTDC), where sharp variations in the properties of CO2 are implemented by coupling the code with NIST’s Refprop. Later, 600 variants of the base geometry of the turbine are constructed by changing the selected turbine design geometric parameters, i.e., shroud ratio (rs4r3), hub ratio (rs4r3), speed ratio (νs) and inlet flow angle (α3) and are investigated numerically through 3D-RANS simulatio... [more]

The modernization and optimization of current power systems are the objectives of research and development in the energy sector, which is motivated by the ever-increasing electricity demands. The goal of such research and development is to render power electronic equipment more controllable, to ensure maximal use of current circuits, system flexibility and efficiency, as well as the relatively easy integration of renewable energy resources at all voltage levels. The current revolution in communication technologies and the Internet of Things (IoT) offers us an opportunity to supervise and regulate the power grid, in order to achieve more reliable, efficient, and cost-effective services. One of the most critical aspects of efficient power system operation is the ability to predict energy load requirements, i.e., load forecasting. Load forecasting is essential for balancing demand and supply and for determining electricity prices. Typically, load forecasting has been supported through the... [more]

This paper analyzes the mathematical model of electrohydrodynamic (EHD) fluid flow in a circular cylindrical conduit with an ion drag configuration. The phenomenon was modelled as a nonlinear differential equation. Furthermore, an application of artificial neural networks (ANNs) with a generalized normal distribution optimization algorithm (GNDO) and sequential quadratic programming (SQP) were utilized to suggest approximate solutions for the velocity, displacements, and acceleration profiles of the fluid by varying the Hartmann electric number (Ha2) and the strength of nonlinearity (α). ANNs were used to model the fitness function for the governing equation in terms of mean square error (MSE), which was further optimized initially by GNDO to exploit the global search. Then SQP was implemented to complement its local convergence. Numerical solutions obtained by the design scheme were compared with RK-4, the least square method (LSM), and the orthonormal Bernstein collocation method (OB... [more]

It is the tradition for the fluid community to study fluid dynamics problems via numerical simulations such as finite-element, finite-difference and finite-volume methods. These approaches use various mesh techniques to discretize a complicated geometry and eventually convert governing equations into finite-dimensional algebraic systems. To date, many attempts have been made by exploiting machine learning to solve flow problems. However, conventional data-driven machine learning algorithms require heavy inputs of large labeled data, which is computationally expensive for complex and multi-physics problems. In this paper, we proposed a data-free, physics-driven deep learning approach to solve various low-speed flow problems and demonstrated its robustness in generating reliable solutions. Instead of feeding neural networks large labeled data, we exploited the known physical laws and incorporated this physics into a neural network to relax the strict requirement of big data and improve p... [more]

One of the key challenges on the road to sustainable mobility is the development of low/zero emission urban public transport (UPT). This is crucial in order to meet environmental requirements aiming at reducing greenhouse gas (GHG) emission. In some countries (e.g., Poland) reduction of air pollution is also an important reason behind the implementation of low/zero emission UPT. The aim of this study is to investigate the factors and mechanisms influencing the development of modern electromobility in Polish UPT. We have examined all 242 UPT systems in the country in terms of the characteristics of the relevant urban municipalities, such as size, economic prosperity, level of human and social capital, development paths of urban public transport in the long term as well as the institutional context and proximity and connections to other cities with experience in electromobility. Classification and statistical methods are used based on a variety of approaches, as assigning a score to vari... [more]

The paper presents the results and analysis of interdisciplinary research concerning electromagnetic field shielding, conductive polymers printed on textiles and numerical simulation using the finite element method (FEM). The use of conductive, layered textiles for shielding electromagnetic interference (EMI) has been proposed. After establishing the optimal conditions for deposition of polyaniline (PANI) and polypyrrole (PPy) on polyacrylonitrile (PAN) fabric, conductive composites were made by means of reactive inkjet printing. For this purpose, polyacrylonitrile (PAN) fabrics were coated with polyaniline or polypyrrole, obtained by chemical oxidation of aniline hydrochloride and pyrrole by ammonium peroxydisulfate. The morphology of the obtained coatings was observed using a scanning electron microscope (SEM). The conductive properties (surface resistance) of the fabrics were measured using the four-wire method, and the tests of the effectiveness of electromagnetic shielding were ca... [more]

The thermodynamic properties of fluid mixtures play a crucial role in designing physically meaningful models and robust algorithms for simulating multi-component multi-phase flow in subsurface, which is needed for many subsurface applications. In this context, the equation-of-state-based flash calculation used to predict the equilibrium properties of each phase for a given fluid mixture going through phase splitting is a crucial component, and often a bottleneck, of multi-phase flow simulations. In this paper, a capillarity-wise Thermodynamics-Informed Neural Network is developed for the first time to propose a fast, accurate and robust approach calculating phase equilibrium properties for unconventional reservoirs. The trained model performs well in both phase stability tests and phase splitting calculations in a large range of reservoir conditions, which enables further multi-component multi-phase flow simulations with a strong thermodynamic basis.

Water flow forecasts are an essential information for energy production, management and hydropower control. Advanced actions to optimize electricity production can be taken based on predicted information. This work proposes an ensemble strategy using recurrent neural networks to generate a forecast of water flow at Jirau Hydroelectric Power Plant (HPP), installed on the Madeira River in Brazil. The ensemble strategy consists of combining three long short-term memory (LSTM) networks that model the Madeira River and two of its tributaries: Mamoré and Abunã rivers. The historical data from streamflow of the Madeira river and its tributaries are used to validate the ensemble LSTM model, where each time series of river tributaries are modeled separated by LSTM models and the result used as input for another LSTM model in order to forecast the streamflow of the main river. The experimental results present low errors for training and test sets for individual LSTM networks and ensemble model.... [more]

Moderate or intense low-oxygen dilution (MILD) combustion is a novel combustion technique that can simultaneously improve thermal efficiency and reduce emissions. This paper focuses on the differences in statistical behaviours of the surface density function (SDF = magnitude of the reaction progress variable gradient) between conventional premixed flames and exhaust gas recirculation (EGR) type homogeneous-mixture combustion under MILD conditions using direct numerical simulations (DNS) data. The mean values of the SDF in the MILD combustion cases were found to be significantly smaller than those in the corresponding premixed flame cases. Moreover, the mean behaviour of the SDF in response to the variations of turbulence intensity were compared between MILD and premixed flame cases, and the differences are explained in terms of the strain rates induced by fluid motion and the ones arising from flame displacement speed. It was found that the effects of dilatation rate were much weaker i... [more]

Accurately identifying the types of insulation defects inside a gas-insulated switchgear (GIS) is of great significance for guiding maintenance work as well as ensuring the safe and stable operation of GIS. By building a set of 220 kV high-voltage direct current (HVDC) GIS experiment platforms and manufacturing four different types of insulation defects (including multiple sizes and positions), 180,828 pulse current signals under multiple voltage levels are successfully measured. Then, the apparent discharge quantity and the discharge time, two inherent physical quantities unaffected by the experimental platform and measurement system, are obtained after the pulse current signal is denoised, according to which 70 statistical features are extracted. In this paper, a pattern recognition method based on generalized discriminant component analysis driven support vector machine (SVM) is detailed and the corresponding selection criterion of involved parameters is established. The results sho... [more]

One of the principal indicators of the methane hazard in coal mines is gas pressure. This parameter directly affects the methane content in the seam as well as the rate of its release resulting from mining operations. Because of limitations in the existing methods for methane seam pressure measuring, primarily technical difficulties associated with direct measurement and the time-consuming nature of indirect measurement, this parameter is often disregarded in the coal and gas outburst forecasts. To overcome the above-mentioned difficulties, an attempt was made to estimate the methane seam pressure with the use of artificial neural networks. Two MLP-based models were developed to estimate the average and maximum methane seam pressure values, respectively. The analyses demonstrated high correlation between the values indicated by the neural models and the reference values determined on the basis of sorption isotherms. According to the adopted fit criterion, the prediction errors for the... [more]

The higher share of renewable energy sources in the electrical grid and the electrification of significant sectors, such as transport and heating, are imposing a tremendous challenge on the operation of the energy system due to the increase in the complexity, variability and uncertainties associated with these changes. The recent advances of computational technologies and the ever-growing data availability allowed the development of sophisticated and efficient algorithms that can process information at a very fast pace. In this sense, the use of machine learning models has been gaining increased attention from the electricity sector as it can provide accurate forecasts of system behaviour from energy generation to consumption, helping all the stakeholders to optimize their activities. This work develops and proposes a methodology to enhance load demand forecasts using a machine learning model, namely a feed-forward neural network (FFNN), by incorporating an error correction step that i... [more]

This paper refers to an original concept of tomographic measurement of brick wall humidity using an algorithm based on long short-term memory (LSTM) neural networks. The measurement vector was treated as a data sequence with a single time step in the presented study. This approach enabled the use of an algorithm utilising a recurrent deep neural network of the LSTM type as a system for converting the measurement vector into output images. A prototype electrical impedance tomograph was used in the research. The LSTM network, which is often employed for time series classification, was used to tackle the inverse problem. The task of the LSTM network was to convert 448 voltage measurements into spatial images of a selected section of a historical building’s brick wall. The 3D tomographic image mesh consisted of 11,297 finite elements. A novelty is using the measurement vector as a single time step sequence consisting of 448 features (channels). Through the appropriate selection of network... [more]

Given the mounting evidence favoring quantitative and qualitative analyses, prompted by easy access to data, mathematical and statistical models have gained a formal appreciation for their role in the analytical apparatus of contemporary research methodologies in all fields [...]

The decline in small towns is a concern in many countries. The manufacturing and tourism sectors are considered to be important in the revitalisation of towns but could be subject to ‘Dutch disease’. This is a malady in which success in one sector leads to a decline in the other. The importance of, and relationships between, the manufacturing and tourism sectors of more than 500 United States micropolitan statistical areas (micropolitans) were extensively investigated by following settlement scaling theory. Publicly available 2016 datasets were used to test a hypothesis that Dutch disease between the two sectors is important. Both sectors are present and important in virtually all of the micropolitans. Regression analyses, including log−log (power-law) analyses, were used to examine the population-based and enterprise-based orderliness in the micropolitan demographic−socioeconomic−entrepreneurial nexus. There is much orderliness, and non-linear relationships are prevalent. No evidence... [more]

Though the development of China’s civil aviation and the improvement of control ability have strengthened the safety operation and support ability effectively, the airlines are under the pressure of operation costs due to the increase of aircraft fuel price. With the development of optimization controlling methods in flight management systems, it becomes increasingly challenging to cut down flight fuel consumption by control the flight status of the aircraft. Therefore, the airlines both at home and abroad mainly rely on the accurate estimation of aircraft fuel to reduce fuel consumption, and further reduce its carbon emission. The airlines have to take various potential factors into consideration and load more fuel to cope with possible negative situation during the flight. Therefore, the fuel for emergency use is called PBCF (Performance-Based Contingency Fuel). The existing PBCF forecasting method used by China Airlines is not accurate, which fails to take into account various influ... [more]

Flow over a cavity or a gap may induce pressure fluctuations that are emitted as sound waves and perceived by a human as noise. This phenomenon may occur in different kinds of industrial machines or in everyday life devices, e.g., cars. For this reason, it is important to predict the flow conditions that intensify or attenuate the noise. This research paper presents the numerical and experimental analysis of the pressure fluctuations in a deep, ducted cavity. The experimental test stand made it possible to investigate the flow over a cavity with air velocity in the range of 30−80 m/s. The pressure fluctuations were measured using miniature microphones located in the duct and the cavity wall and processed with LabView software. The phenomena were also analysed using the computational fluid dynamics (CFD) technique. The several modelling approaches were tested and validated against the experimental data. The highest sound pressure levels were obtained for 40 and 70 m/s. The sound frequen... [more]

The majority of education buildings in Poland are equipped with natural (gravity) ventilation, where the air inflow depends on the level of window airtightness. A complete statistical urban population of 50 school buildings in Czestochowa have been examined. The main issue to be clarified is the answer to the following questions: Is it theoretically possible to supply enough air to meet the ventilation requirements with gravity ventilation? What is the airtightness of the windows at which it will be possible? The average technical conditions of windows in the analysed buildings were bad. However, only in the case in which high external air leakage coefficient a = 7.0 m3/(h m daPa2/3) (q100KL = 32.4912 m3/(h m) is the amount of air passing through the leaks similar to the quantitative ventilation requirements for classrooms. The quantity of air flowing from the outside through modernized windows that meet the technical requirements (a = 0.6 to 1.0 m3/(m h daPa2/3)) covers on average onl... [more]