This work is focused on the numerical determination of Shannon probabilistic entropy for MEMS devices exhibiting some uncertainty in their structural response. This entropy is a universal measure of statistical or stochastic disorder in static deformation or dynamic vibrations of engineering systems and is available for both continuous and discrete distributions functions of structural parameters. An interval algorithm using Monte Carlo simulation and polynomial structural response recovery has been implemented to demonstrate an uncertainty propagation of the forced vibrations in some small MEMS devices. A computational example includes stochastic nonlinear vibrations described by the Duffing equation calibrated for some micro-resonators, whose damping is adopted as a Gaussian, uniformly and triangularly distributed input uncertainty source.

Wind is a renewable and green energy source that is vital for sustainable human development. Wind variability implies that wind power is random, intermittent, and volatile. For the reliable, stable, and secure operation of an electrical grid incorporating wind power systems, a multi-hour ahead wind power forecasting system comprising a physics-based model, a multi-criteria decision making scheme, and two artificial intelligence models was proposed. Specifically, a Weather Research and Forecasting (WRF) model was used to produce wind speed forecasts. A technique for order of preference by similarity to ideal solution (TOPSIS) scheme was employed to construct a 5-in-1 (ensemble) WRF model relying on 1334 initial ensemble members. Two adaptive neuro-fuzzy inference system (ANFIS) models were utilised to correct the wind speed forecasts and determine a power curve model converting the improved wind speed forecasts to wind power forecasts. Moreover, three common statistics-based forecasting... [more]

The oil and gas (O&G) industry is now as focused on minimizing costs and maximizing efficiency just as much as maximizing production. Operators are looking for new and cost-effective ways to add profitable assets to their portfolio. One such way is to re-fracture existing wells. There is evidence that these wells can be very productive in the Bakken. However, because of factors such as depletion and aging wellbore material, re-fracturing wells can be a difficult process to implement successfully and often have binding constraints on surface treating pressure (STP). This study attempts to quantify the effects that completion parameters have on re-fracturing treatment implementation by constructing dynamic fixed effects (FE) multivariate regression models. These models are not generally used in O&G and are more commonly used in economics and policy analysis. However, given that both economics and O&G deal with large amounts of uncertainty for each individual person and well, respectively... [more]

Lost circulation is considered a time-consuming, costly problem during the construction of oil and gas wells. There are several preventive techniques to mitigate this problem. Stress cage technology is a mechanical lost circulation method, in which the formation at the wellbore wall is strengthened to stop the creation of induced fractures as one of the main causes of lost circulation. In this research, a two-dimensional numerical model, considering the elastic, poro-elastic, and thermo-poro-elastic behavior of the rock, is built to investigate the effectiveness of the stress cage method. Results show that better performance of the technology is achieved if the fractures are bridged close to their apertures. Additionally, it was found that the difference between the elastic, poro-elastic, and thermo-poro-elastic models is slightly visible. The conclusion states that the application of the stress cage methods leads to an increase in hoop stress and subsequent formation fracture gradient... [more]

In recent years, there has been a dynamic development of photovoltaic materials based on perovskite structures. Solar cells based on perovskite materials are characterised by a relatively high price/performance ratio. Achieving stability at elevated temperatures has remained one of the greatest challenges in the perovskite solar cell research community. However, significant progress in this field has been made by utilising different compositional engineering routes for the fabrication of perovskite semiconductors such as triple cation-based perovskite structures. In this work, a new approach for the rapid analysis of the changes occurring in time in perovskite structures was developed. We implemented a quick and inexpensive method of estimating the ageing of perovskite structures based on an express diagnosis of light reflection in the near-infrared region. The possibility of using optical reflectance in the NIR range (900−1700 nm) to observe the ageing of perovskite structures over ti... [more]

The article is a continuation of the authors’ ongoing research related to power flow and voltage control in LV grids. It outlines how the Distribution System Operator (DSO) can use Machine Learning (ML) technology in a future grid. Based on supervised learning, a Selectively Coherent Model of Converter System Control for an LV grid (SCM_CSC) is proposed. This represents a fresh, new approach to combining off and on-line computing for DSOs, in line with the decarbonisation process. The main kernel of the model is a neural network developed from the initial prediction results generated by regression analysis. For selected PV system operation scenarios, the LV grid of the future dynamically controls the power flow using AC/DC converter circuits for Battery Energy Storage Systems (BESS). The objective function is to maintain the required voltage conditions for high PV generation in an LV grid line area and to minimise power flows to the MV grid. Based on the training and validation data pr... [more]

The electricity load forecasting plays a pivotal role in the operation of power utility companies precise forecasting and is crucial to mitigate the challenges of supply and demand in the smart grid. More recently, the hybrid models combining signal decomposition and artificial neural networks have received popularity due to their applicability to reduce the difficulty of prediction. However, the commonly used decomposition algorithms and recurrent neural network-based models still confront some dilemmas such as boundary effects, time consumption, etc. Therefore, a hybrid prediction model combining variational mode decomposition (VMD), a temporal convolutional network (TCN), and an error correction strategy is proposed. To address the difficulty in determining the decomposition number and penalty factor for VMD decomposition, the idea of weighted permutation entropy is introduced. The decomposition hyperparameters are optimized by using a comprehensive indicator that takes account of t... [more]

International oil and gas companies listed in New York must publish the information of oil and gas reserves under the SEC (United States Securities and Exchange Commission) standards every year. For greatly improving the SEC reserve, the SEC reserve value and the SEC reserve substitution rate, in this article not only the SEC reserve equations have been determined but also the SEC reserve value models have been established. The SEC reserve value models have been verified as correct. Based on these models, the multivariate function calculus method, the multivariate function limit method and the function recurrence method have been adopted to research parameter sensitivity differences rules, parameter adjustment directions, parameter adjustment degrees and SEC reserve parameter linkage adjustment rules. The research is significant, because there are great differences between SEC standards and China’s in reserve management mode, reserve estimation method system and financial management sy... [more]

Electric vehicles (EVs) penetration growth is essential to reduce transportation-related local pollutants. Most countries are witnessing a rapid development of the necessary charging infrastructure and a consequent increase in EV energy demand. In this context, power demand forecasting is an essential tool for planning and integrating EV charging as much as possible with the electric grid, renewable sources, storage systems, and their management systems. However, this forecasting is still challenging due to several reasons: the still not statistically significant number of circulating EVs, the different users’ behavior based on the car parking scenario, the strong heterogeneity of both charging infrastructure and EV population, and the uncertainty about the initial state of charge (SOC) distribution at the beginning of the charge. This paper aims to provide a forecasting method that considers all the main factors that may affect each charging event. The users’ behavior in different urb... [more]

The aim of this work is to introduce a novel statistical technique for quantifying the concentration field uniformity of the liquid-liquid mixing process within a micro-scale chaotic pipeline. For illustration, the microscale liquid-liquid mixer in which the inlet direction is parallel to the mixing unit is designed by using the chaotic pipeline with Baker map. Meanwhile, the non-uniformity coefficient method is adopted quantificationally instead of qualitatively estimating the concentration field uniformity of the chaotic micromixer based on uniform design theory and image analysis. Results show that the concentration distribution of the chaotic mixing process of liquid-liquid under various working conditions is obtained by solving the steady-state Navier−Stokes and diffusion convection equations. The average contribution ratio of the three basic mixing units of the chaotic Baker pipeline to the concentration field uniformity is approximately 6:3:1, which is calculated aligned with th... [more]

Removal of noise and restoration of images has been one of the most interesting topics in the field of image processing in the past few years. Existing filter-based methods can remove image noise; however, they cannot preserve image quality and information such as lines and edges. In this article, various classifiers and spatial filters are combined to achieve desirable image restoration. Meanwhile, the time adaptive self-organizing map (TASOM) classifier is more emphasized in our feature extraction and dimensionality reduction approaches to preserve the details during the process, and restore the images from noise. The TASOM was compared with the self-organizing map (SOM) network, and a suitable noise reduction method for images was attempted. As a result, we achieved an optimum method to reduce impulsive noise. In addition, by using this neural network, better noise suppression was achieved. Experimental results show that the proposed method effectively removes impulse noise and main... [more]

This study aims to develop statistical and machine learning methodologies for forecasting yearly electricity consumption in Saudi Arabia. The novelty of this study include (i) determining significant features that have a considerable influence on electricity consumption, (ii) utilizing a Bayesian optimization algorithm (BOA) to enhance the model’s hyperparameters, (iii) hybridizing the BOA with the machine learning algorithms, viz., support vector regression (SVR) and nonlinear autoregressive networks with exogenous inputs (NARX), for modeling individually the long-term electricity consumption, (iv) comparing their performances with the widely used classical time-series algorithm autoregressive integrated moving average with exogenous inputs (ARIMAX) with regard to the accuracy, computational efficiency, and generalizability, and (v) forecasting future yearly electricity consumption and validation. The population, gross domestic product (GDP), imports, and refined oil products were obs... [more]

The main objective of this research is to identify the scope of the use of EU funds for the formation of a low-carbon economy by enterprises providing energy services in Poland in 2014−2020. As a result of the identification, a model for the use of EU funds based on the following criteria was identified: the purpose of the investment, the type of fund, the type of support program, the range of support values and the form and level of funding. As a research gap has been identified due to the insufficient investigation of the use of EU funds by the largest energy companies in Poland to shape a low-carbon economy, the findings presented are novel and contribute to a better understanding of the use of EU funds by Poland’s largest energy sector companies. Data on investment projects financed by EU funds were obtained from the database of the Ministry of Funds and Regional Policy for 2014−2020, while the characteristics of the companies were obtained from industry reports, the National Court... [more]

For load forecasting, numerous machine learning (ML) approaches have been published. Besides fully connected feedforward neural networks (FFNNs), also called multilayer perceptron, more advanced ML approaches like deep, recurrent or convolutional neural networks or ensemble methods have been applied. However, evaluating the added benefit by novel approaches is difficult. Statistical or rule-based methods constitute a too low benchmark. FFNNs need extensive tuning due to their manifold design choices. To address this issue, a structured, comprehensible five-step FFNN model creation methodology is presented, which constitutes of initial model creation, internal parameter selection, feature engineering, architecture tuning and final model creation. The methodology is then applied to forecast real world heat load data of a hospital in Germany. The forecast constitutes of 192 values (upcoming 48 h in 15 min resolution) and is composed of a multi-model univariate forecasting strategy, with t... [more]

The large-scale integration of wind turbines (WTs) in renewable power generation induces power oscillations, leading to frequency aberration due to power unbalance. Hence, in this paper, a secondary frequency control strategy called load frequency control (LFC) for power systems with wind turbine participation is proposed. Specifically, a backpropagation (BP)-trained neural network-based PI control approach is adopted to optimize the conventional PI controller to achieve better adaptiveness. The proposed controller was developed to realize the timely adjustment of PI parameters during unforeseen changes in system operation, to ensure the mutual coordination among wind turbine control circuits. In the meantime, the improved particle swarm optimization (IPSO) algorithm is utilized to adjust the initial neuron weights of the neural network, which can effectively improve the convergence of optimization. The simulation results demonstrate that the proposed IPSO-BP-PI controller performed ev... [more]

The precise estimation of the state of charge (SOC) is fundamental to the reliable operation of lithium-ion batteries. The development of deep learning techniques makes it possible to employ advanced methods to estimate a battery’s SOC. In order to better utilize a recurrent neural network (RNN) for battery SOC estimation, this paper conducts a comparative study of SOC estimation methods based on different RNN models. First, a general framework for deep-learning-based SOC estimation is undertaken, followed by the description of four kinds of RNNs employed in the estimation. Then, the estimation performances of these RNN models are compared under three scenarios, including the SOC estimation accuracy, the adaptability against different battery aging statuses, and the robustness against measurement uncertainties, in which the estimation performances of different RNN models are quantitively evaluated. Finally, a multiple-criteria decision-making method based on the analytic hierarchy proc... [more]

The article shows an analysis of the influence of the skin effect on the maximum efficiency and maximum power of a receiver in a wireless power transfer system (WPT). For this purpose, the original solution of the WPT system was used, which contained periodically arranged planar coils. The results concern the multi-variant analysis of the WPT system. The geometry of the coils was taken into account, i.e., the size of coils, the number of turns, as well as the distance between the transmitting and receiving coils. The calculations were carried out over the frequency range of 0.1−1 MHz. In order to analyse the influence of the skin effect on the proposed WPT system, two approaches were used: analytical and numerical. The article analyses the appropriate selection of load impedance in order to obtain maximum efficiency or maximum power of the receiver. In this analysis, the influence of the skin effect on each of the two operating procedures was examined. The obtained analytical and numer... [more]

The current study addresses the impact of the addition of fullerene and graphene nanoparticles on the AC breakdown voltage (AC BdV) of natural ester (FR3). The nanofluids (NFs) were prepared following the two-step process, and the AC BdV was performed in compliance with IEC 60156 standards. Five different concentrations of NPs were considered (0.1 g/L, 0.2 g/L, 0.3 g/L, 0.4 g/L, and 0.5 g/L). A Student’s t-test was performed to compare the base liquid’s AC BdV data with different nanofluids. The experimental data were checked to see if they obeyed the Weibull distribution fitting curve, and the AC BdV at 1%, 10%, and 50% risk levels were then calculated. The performed t-test provides evidence that AC BdV data from the base liquid were different from those of different NFs (except 0.2 g/L fullerene, and 0.1 g/L and 0.4 g/L graphene NFs). It is also shown that the Weibull distribution fit the BdV data of all liquids (except 0.5 g/L fullerene NF), and remarkable improvements of AC BdVs at... [more]

The fluctuation and uncertainty of the electricity load bring challenges to load forecasting. Traditional point forecasting struggles to avoid errors, and pure interval forecasting may cause the problem of too wide an interval. In this paper, we combine point forecasting and interval forecasting and propose a point-interval forecasting model for electricity load based on regular fluctuation component extraction. Firstly, the variational modal decomposition is combined with the sample entropy to decompose the original load series into a strong regular fluctuation component and a weak regular fluctuation component. Then, the gate recurrent unit neural network is used for point forecasting of the strong regular fluctuation component, and the support vector quantile regression model is used for interval forecasting of the weak regular fluctuation component, and the results are accumulated to obtain the final forecasting intervals. Finally, experiments were conducted using electricity load... [more]

As a result of the COVID-19 pandemic, the ongoing war and the implementation of the so-called Mobility Package there are profound changes taking place in the Polish LSP sector. The competitive struggle on the market is intensifying, and it mobilizes the management to diagnose the condition of companies with the use of effective comparative tools. This article aims to present the conclusions of the study of 46 entities on the Polish LSP market. It uses an original, multi-faceted evaluation method. The authors hope to popularize this method for evaluating LSPs by making it cyclical. The comparative evaluation of LSPs was carried out with the use of economic (index) and statistical analysis tools. A model of rank correlation was used for the values of all variables (ten economic parameters and one greening parameter). LSPs were grouped up based on the average value of the economic variables and according to investment attractiveness and then clustered. Sets of the most correlated characte... [more]

The South Korean government plans to switch from a centralized power generation method to a distributed one. However, due to opposition from local residents, construction of distributed power plants is frequently delayed or suspended. This study attempts to investigate whether proximity to a power plant negatively affects housing property values, using the hedonic pricing technique and quantitatively analyzing the level of impact. To this end, 2291 apartment sales data from a specific city in the South Korean Seoul Metropolitan area with a power plant were used. As a result of the analysis, it was found that proximity to a power plant had a negative effect on apartment prices, which was statistically significant at the significance level of 5%. The difference in apartment prices per 1 km direct distance from the power plant to the apartment was derived as KRW 8 million (USD 7.1 thousand). This value is about 0.7% of the average price of apartments in the area (KRW 1102 million = USD 0.... [more]

The decay branch ratio is evaluated nuclear data related to the decay heat calculation in reactor safety analysis. Decay branch ratio data are inherently subjected to the “sum-to-one” constraint, making it difficult to generate perturbed samples while preserving their suggested statistics in a library of evaluated nuclear data. Therefore, a stochastic-sampling-based uncertainty analysis method is hindered in quantifying the uncertainty contribution of the decay branch ratio to the decay heat calculation. In the present work, two alternative sampling methods are introduced, based on Dirichlet and generalized Dirichlet distribution, to tackle the decay branch ratio sampling issue. The performance of the introduced methods is justified by three-branch decay data retrieved from ENDF/B-VIII.0. The results show that the introduced sampling methods are capable of generating branch ratio samples and preserving their suggested statistics in an evaluated nuclear data library while satisfying the... [more]

Smart mobility supports smart city ideas and concepts. A consequences of smart mobility activities are more wide and common using electromobility. There is no doubt that electromobility is a global trend that unequivocally supports the principles of sustainable development, while being one of the basic elements of the smart mobility. The following discussion critically addresses the indicated topic, especially in the context of the barriers that currently exist and that stand in the way of implementing the expected expansion of electric cars into urban markets. Considering the following assumptions, the threat of urban pollution associated with the increasing number of vehicles (passenger and freight) should be mitigated using smart mobility solutions. In addition, solutions should include that electromobility promotes zero-emissions. Furthermore, the inadequate development of charging infrastructure and the projected energy crisis may result in plans to develop the electromobility mar... [more]

To solve the problem of feature selection and error correction after mode decomposition and improve the ability of power load forecasting models to capture complex time series information, a two-stage short-term power load forecasting method based on recursive feature elimination with a cross validation (RFECV) algorithm and time convolution network−efficient channel attention mechanism−long short-term memory network (TCN−ECA−LSTM) is presented. First, the load sequence is decomposed into a relatively stable set of modal components using variational mode decomposition. Then, the RFECV-based method filters the feature set of each modal component to construct the best feature set. Finally, a two-stage prediction model based on TCN−ECA−LSTM is established. The first stage predicts each modal component and the second stage reconstructs the load forecast based on the predicted value of the previous stage. This paper takes actual data from New South Wales, Australia, as an example, and the r... [more]

Power quality issues can affect the performance of devices powered by the grid and can, in severe cases, permanently damage connected devices. Events that affect power quality include sags, swells, waveform distortions and transients. Transients are one of the most common power quality disturbances and are caused by lightning strikes or switching activities among power-grid-connected systems and devices. Transients can reach very high magnitudes, and their duration spans from nanoseconds to milliseconds. This study proposed a deep-learning-based technique that was supported by convolutional neural networks and a bidirectional long short-term memory approach in order to detect and characterize power-quality transients. The method was validated (i.e., benchmarked) using an alternative algorithm that had been previously validated according to a digital high-pass filter and a morphological closing operation. The training and performance assessments were carried out using actual power-grid-... [more]