European Union Allowances (EUAs) are rights to emit CO2 that may be sold or bought by enterprises. They were originally created to try to reduce greenhouse gas emissions, although they have become assets that may be used by financial intermediaries to seek for new business opportunities. Therefore, forecasting the time evolution of their price is very important for agents involved in their selling or buying. Neural Networks, an artificial intelligence paradigm, have been proved to be accurate and reliable tools for time series forecasting, and have been widely used to predict economic and energetic variables; two of them are used in this work, the Multilayer Preceptron (MLP) and the Long Short-Term Memories (LSTM), along with another artificial intelligence algorithm (XGBoost). They are combined with two preprocessing tools, decomposition of the time series into its trend and fluctuation and decomposition into Intrinsic Mode Functions (IMF) by the Empirical Mode Decomposition (EMD). Th... [more]

This article is concerned with the numerical solution of nonlinear hyperbolic Schro¨dinger equations (NHSEs) via an efficient Haar wavelet collocation method (HWCM). The time derivative is approximated in the governing equations by the central difference scheme, while the space derivatives are replaced by finite Haar series, which transform it to full algebraic form. The experimental rate of convergence follows the theoretical statements of convergence and the conservation laws of energy and mass are also presented, which strengthens the proposed method to be convergent and conservative. The Haar wavelets based on numerical results for solitary wave shape of |φ| are discussed in detail. The proposed approach provides a fast convergent approximation to the NHSEs. The reliability and efficiency of the method are illustrated by computing the maximum error norm and the experimental rate of convergence for different problems. Comparisons are performed with various existing methods in re... [more]

Heavy-duty trucks contribute approximately 20% of fuel consumption in the United States of America (USA). The fuel economy of heavy-duty vehicles (HDV) is affected by several real-world parameters like road parameters, driver behavior, weather conditions, and vehicle parameters, etc. Although modern vehicles comply with emissions regulations, potential malfunction of the engine, regular wear and tear, or other factors could affect vehicle performance. Predicting fuel consumption per trip based on dynamic on-road data can help the automotive industry to reduce the cost and time for on-road testing. Data modeling can easily help to diagnose the reason behind fuel consumption with a knowledge of input parameters. In this paper, an artificial neural network (ANN) was implemented to model fuel consumption in modern heavy-duty trucks for predicting the total and instantaneous fuel consumption of a trip based on very few key parameters, such as engine load (%), engine speed (rpm), and vehicle... [more]

Well-logging is an important formation characterization and resource evaluation method in oil and gas exploration and development. However, there has been a shortage of well-logging data because Well-logging can only be measured by expensive and time-consuming field tests. In this study, we aimed to find effective machine learning techniques for well-logging data prediction, considering the temporal and spatial characteristics of well-logging data. To achieve this goal, the convolutional neural network (CNN) and the long short-term memory (LSTM) neural networks were combined to extract the spatial and temporal features of well-logging data, and the particle swarm optimization (PSO) algorithm was used to determine hyperparameters of the optimal CNN-LSTM architecture to predict logging curves in this study. We applied the proposed CNN-LSTM-PSO model, along with support vector regression, gradient-boosting regression, CNN-PSO, and LSTM-PSO models, to forecast photoelectric effect (PE) log... [more]

Partial discharge detection is an important means of insulation diagnosis of electrical equipment. To effectively suppress the periodic narrowband and white noise interferences in the process of partial discharge detection, a partial discharge interference suppression method based on singular value decomposition (SVD) and improved empirical mode decomposition (IEMD) is proposed in this paper. First, the partial discharge signal with periodic narrowband interference and white noise interference x(t) is decomposed by SVD. According to the distribution characteristics of single values of periodic narrowband interference signals, the singular value corresponding to periodic narrowband interference is set to zero, and the signal is reconstructed to eliminate the periodic narrowband interference in x(t). IEMD is then performed on x(t). Intrinsic mode function (IMF) is obtained by EMD, and based on the improved 3σ criterion, the obtained IMF components are statistically processed and reconstr... [more]

The paper studies the problem of assessing the vehicle energy efficiency on the streets of urban road network. As a result of morphological analysis of the system “Vehicle—Traffic flow—Road—Traffic Environment” 18 significant morphological attributes of its functional elements, that affect the energy efficiency of vehicles, were identified. Each attribute is characterized by 3−6 implementation variants, which are evaluated by the relevant quantitative or qualitative parameters. The energy efficiency of vehicles is determined by the criteria of their energy consumption considering the vehicle category, type of energy unit, mode of vehicle movement and adjustment factors—road, climatic and others. The input parameters values of the system in the process of traffic flow on the linear fragments of streets and road networks of the cities of Ukraine and Poland were measured. The set of independent system parameters is determined by applying the Farrar-Glober method based on statistical estim... [more]

Access to energy, including electricity, determines countries’ socio-economic development. The growing demand for electricity translates into environmental problems. Energy is therefore a crucial element of the European Union’s sustainable development strategy. This article aims to present the changes taking place in the electricity market in Poland considering the goals of the energy policy until 2040. This is the basis for the determination of the scale of processes taking place in the Polish energy sector from two perspectives, i.e., the production of electricity considering its level and energy carriers used, and the consumption of electricity in households depending on their location (rural vs. urban areas). The research was conducted at the regional level (NUTS 2 until 2017) in Poland. Secondary data from the Central Statistical Office (GUS) contained in the Local Data Bank were used, along with information from the European Commission and Eurostat websites. Results of the study... [more]

The in-box LOCA (Loss of Coolant Accident) represents a major safety concern to be addressed in the design of the WCLL-BB (water-cooled lead-lithium breeding blanket). Research activities are ongoing to master the phenomena and processes that occur during the postulated accident, to enhance the predictive capability and reliability of numerical tools, and to validate computer models, codes, and procedures for their applications. Following these objectives, ENEA designed and built the new separate effects test facility LIFUS5/Mod3. Two experimental campaigns (Series D and Series E) were executed by injecting water at high pressure into a pool of PbLi in WCLL-BB-relevant parameter ranges. The obtained experimental data were used to check the capabilities of the RELAP5 system code to reproduce the pressure transient of a water system, to validate the chemical model of PbLi/water reactions implemented in the modified version of SIMMER codes for fusion application, to investigate the dynami... [more]

Herein, we show the prediction of the viscosity of a binary mixture of bitumen and light oil using a feedforward neural network with backpropagation model, as compared to empirical models such as the reworked van der Wijk model (RVDM), modified van der Wijk model (MVDM), and Al-Besharah. The accuracy of the ANN was based on all of the samples, while that of the empirical models was analyzed based on experimental results obtained from rheological studies of three binary mixtures of light oil (API 32°) and bitumen (API 7.39°). The classical Mehrotra−Svrcek model to predict the viscosity of bitumen under temperature and pressure, which estimated bitumen results with an D of 3.86, was used along with either the RVDM or the MVDM to estimate the viscosity of the bitumen and light oil under reservoir temperature and pressure conditions. When both the experimental and literature data were used for comparison to an artificial neural network (ANN) model, the MVDM, RVDM and Al-Besharah had higher... [more]

The recovery and utilisation of industrial excess heat has been identified as an important contribution for energy efficiency by reducing primary energy demand. Previous works, based on top-down studies for a few sectors, or regional case studies estimated the overall availability of industrial excess heat. A more detailed analysis is required to allow the estimation of potentials for specific heat recovery technologies, particularly regarding excess heat temperature profiles. This work combines process integration methods and regression analysis to obtain cogeneration targets, detailed excess heat temperature profiles and estimations of electricity generation potentials from low and medium temperature excess heat. The work is based on the use of excess heat temperature (XHT) signatures for individual sites and regression analysis using publicly available data, obtaining estimations of the technical potential for electricity generation from low and medium temperature excess heat (60−14... [more]

Photovoltaic power generation is highly valued and has developed rapidly throughout the world. However, the fluctuation of solar irradiance affects the stability of the photovoltaic power system and endangers the safety of the power grid. Therefore, ultra-short-term solar irradiance predictions are widely used to provide decision support for power dispatching systems. Although a great deal of research has been done, there is still room for improvement regarding the prediction accuracy of solar irradiance including global horizontal irradiance, direct normal irradiance and diffuse irradiance. This study took the direct normal irradiance (DNI) as prediction target and proposed a Siamese convolutional neural network-long short-term memory (SCNN-LSTM) model to predict the inter-hour DNI by combining the time-dependent spatial features of total sky images and historical meteorological observations. First, the features of total sky images were automatically extracted using a Siamese CNN to d... [more]

During the COVID-19 pandemic, uncertainty has increased in many areas of both business supply and demand, notably oil demand and pricing have become even more unpredictable than before. Thus, for companies that buy large quantities of oil, effective oil price risk management is crucial for business success. Nevertheless, businesses’ risk appetite, specifically willingness to accept more risk to achieve desired business benefits, varies significantly. The aim of this paper is to deepen the analysis of the effectiveness of employing artificial neural networks (ANNs) in hedging against oil price changes by searching for buy signals for European WTI (West Texas Intermediate) crude oil call options, while taking into account the level of risk appetite. The number of generated buy signals decreases with increasing risk appetite, and thus the amount of capital necessary to buy options decreases. However, the results show that fewer buy signals do not necessarily translate into lower returns g... [more]

The demand for low voltage DC (LVDC) distribution systems is increasing due to the rapid development of power conversion technology, the increase of DC-based digital loads, and the expansion of DC-based distributed generation (DG). For the stable operation of the LVDC distribution system, it is necessary to develop a protection method. In this paper, the fault section is estimated using wavelet transform (WT) in LVDC distribution system. The fault section is classified into a DC line and a DC bus. The characteristics of fault current at each fault section part are analyzed in simple and actual LVDC system. Based on this analysis, the algorithm for fault section estimation is proposed using the detail component after performing WT. The results of fault section estimations are verified through various simulations using EMTP and MATLAB. The fault section estimation can be utilized in the development of protection schemes in LVDC distribution system.

Rapid and accurate eddy-current calculation is necessary to analyze eddy-current couplings (ECCs). This paper presents a general 3D analytical method for calculating the magnetic field distributions, eddy currents, and torques of ECCs with different Halbach magnet arrays. By using Fourier decomposition, the magnetization components of Halbach magnet arrays are determined. Then, with a group of H-formulations in the conductor region and Laplacian equations with magnetic scalar potential in the others, analytical magnetic field distributions are predicted and verified by 3D finite element models. Based on Ohm’s law for moving conductors, eddy-current distributions and torques are obtained at different speeds. Finally, the Halbach magnet arrays with different segments are optimized to enhance the fundamental amplitude and reduce the harmonic contents of air-gap flux densities. The proposed method shows its correctness and validation in analyzing and optimizing ECCs with Halbach magnet arr... [more]

This study presents a calculation of the time required to execute a lane-change manoeuvre. Compared with other (and older) calculation methods, an analysis was conducted to determine which approach could yield the most reliable results. This study aimed to present a universal calculation method for different road surfaces, surface conditions (dry and wet road surface), and vehicle types (i.e., from small vehicles to SUVs). A total of 108 comparable manoeuvres with modern vehicles were used as a basis for statistical analysis. A new mathematical constant was found based on a regression analysis, adjusting one of the older calculation methods (so-called Kovařík equation), providing the best match between real and calculated manoeuvre duration.

This study evaluated the individual and combined effects of inhibitory compounds formed during pretreatment of lignocellulosic biomass on the growth of Bacillus subtilis. Ten inhibitory compounds commonly present in lignocellulosic hydrolysates were evaluated, which included sugar degradation products (furfural and 5-hydroxymethylfurfural), acetic acid, and seven phenolic compounds derived from lignin (benzoic acid, vanillin, vanillic acid, ferulic acid, p-coumaric acid, 4-hydroxybenzoic acid, and syringaldehyde). For the individual inhibitors, syringaldehyde showed the most toxic effect, completely inhibiting the strain growth at 0.1 g/L. In the sequence, assays using mixtures of the inhibitory compounds at a concentration of 12.5% of their IC50 value were performed to evaluate the combined effect of the inhibitors on the strain growth. These experiments were planned according to a Plackett−Burman experimental design. Statistical analysis of the results revealed that in a mixture, ben... [more]

Saving energy and looking for alternative energy sources are both elements of energy efficiency management, which is still a significant challenge for many companies around the world. Unfortunately, energy efficiency in companies is often equated only with thermo-modernization or the replacement of lighting. However, one of the most important methods of improving energy use in manufacturing companies may be the modernization of the machine park. Therefore, the main purpose of the paper was to investigate the activities of enterprises in the field of the modernization of machines and the impact of this on the actual improvement of energy efficiency. The modernization of machines in production plants is understood as adapting new parts to the old device or rebuilding the machine in such a way that it can cooperate with its new subassemblies. Companies usually decide to modernize the machinery fleet, bearing in mind the benefits of production efficiency, and they do not always combine thi... [more]

Liming surface water is a fairly popular method of increasing the pH values and decreasing the concentration of phosphates and heavy metals. According to the Environmental Protection Agency (EPA) recommendations, the increase of water pH should not exceed 1.5. If surface water is the source of water supply, liming is a process that reduces water contamination. This should prevent the creation of an additional load for the water treatment plants in urban settlements. This article is an interdisciplinary research study aiming to (1) determine and compare the doses of new dispersed, thermally activated limestone and natural limestone, (2) find the relation between dose value and initial water parameters (pH, Eh and total mineralization), and (3) create an artificial neural network (ANN) model to predict changes in water pH values according to EPA recommendations. Recommended doses were obtained from experimental studies, and those of dispersed, thermally activated limestone were lower tha... [more]

A Virtual energy assessment (VEA) refers to the assessment of the energy flow in a building without physical data collection. It has been occasionally conducted before the COVID-19 pandemic to residential and commercial buildings. However, there is no established framework method for conducting this type of energy assessment. The COVID-19 pandemic has catalysed the implementation of remote energy assessments and remote facility management. In this paper, a novel framework for VEA is developed and tested on case study buildings at the University of Melbourne. The proposed method is a hybrid of top-down and bottom-up approaches: gathering the general information of the building and the historical data, in addition to investigating and modelling the electrical consumption with artificial neural network (ANN) with a projection of the future consumption. Through sensitivity analysis, the outdoor temperature was found to be the most sensitive (influential) parameter to electrical consumption... [more]

Buildings play a central role in energy transition, as they were responsible for 67.8% of the total consumption of electricity in France in 2017. Because of that, detecting anomalies (outliers) is crucial in order to identify both potential opportunities to reduce energy consumption and malfunctioning of the metering system. This work aims to compare the performance of several outlier detection methods, such as classical statistical methods (as boxplots) applied to the actual measurements and to the difference between the measurements and their predictions, in the task of detecting outliers in the power consumption data of a tertiary building located in France. The results show that the combination of a regression method, such as random forest, and the adjusted boxplot outlier detection method have promising potential in detecting this type of data quality problem in electricity consumption.

The paper formulates a method of active reduction of structure vibrations in the selected resonance zones of the tested object. The method ensures reduction of vibrations of the selected resonance zones by determining the parameters of the active force that meets the desired dynamic properties. The paper presents a method for determining the parameters of the active force by reducing the vibrations of the structure in its resonance zones to a given vibration amplitude. For this purpose, an analytical form was formulated, which will clearly define the dynamic properties of the tested object and the force reducing the vibrations in the form of a mathematical model. The formulated mathematical model is a modified object input function, which in its form takes into account the properties of the active force reducing the vibrations. In such a case, it is possible to use the methods of mechanical synthesis to decompose the modified characteristic function into the parameters of the system an... [more]

The purpose of this work is to explore the nonlinear vibration of a rub-impact Jeffcott rotor. In the first stage, the motion is not affected by the friction force, but in the second stage, the motion is influenced by the normal force and the friction force. The governing equations of the rotor of this model are derived in this paper. In consequence, there appears a difference between the two stages. We establish an approximate analytical solution for nonlinear vibrations corresponding to two stages with the mention of the location of jumps. The obtained results are compared with the numerical integration results. The steady-state response and the stability of the solutions are analytically determined for the two stages. The stability of a full annular rub solution is studied with the help of the Routh−Hurwitz criterion. Effects of different parameters of the system, the saddle-node bifurcation (turning points) and the Hopf bifurcation are presented. The main contribution lies in the a... [more]

Increasing the level and diversifying the sources of flexible capacity available to transmission system operators will be a pivotal factor for maintaining reliable control of national electricity grids. These response capacities are widely available; however, one area with large capacities that could benefit from advancements is the industrial sector. This sector’s highly regulated nature ensures that structured procedures and thorough investigations are required to implement significant change. This study presents a systematic methodology to effectively categorise assets and evaluate their perceived risk of participation in demand response, allowing industries to present a sustainable portfolio of flexible capacity to the grid. Following implementation on an internationally relevant industrial site, this methodology identified several assets for participation, determining that it is realistic to expect 35 to 75 kW of flexible capacity from only air handling units on a single site. A s... [more]

Natural gas consumption depends on many factors. Some of them, such as weather conditions or historical demand, can be accurately measured. The authors, based on the collected data, performed the modeling of temporary and future natural gas consumption by municipal consumers in one of the medium-sized cities in Poland. For this purpose, the machine learning algorithms, neural networks and two regression algorithms, MLR and Random Forest were used. Several variants of forecasting the demand for natural gas, with different lengths of the forecast horizon are presented and compared in this research. The results obtained using the MLR, Random Forest, and DNN algorithms show that for the tested input data, the best algorithm for predicting the demand for natural gas is RF. The differences in accuracy of prediction between algorithms were not significant. The research shows the differences in the impact of factors that create the demand for natural gas, as well as the accuracy of the predict... [more]

In this article, we present a numerical analysis of the energy and mass transport behavior of microrotational flow via Riga plate, considering suction or injection and mixed convection. The thermal stratified parameters of nanofluid are captured using an interpretation of the well-known Keller box model, which helps us to determine the characteristic properties of the physical parameters. The formulated boundary layer equations (nonlinear partial differential equations) are transformed into coupled ODEs with nonlinearities for the stratified controlled regimes. The impact of embedded flow and all physical quantities of practical interest, such as velocity, temperature, and concentration profile, are inspected and presented through tables and graphs. We found that the heat transfer on the surface decreases for the temperature stratification factor as mass transfer increases. Additionally, the fluid velocity increases as the modified Hartmann number increases.