We analyze nearly half a million vertical wells completed since the 1930s in the most prolific petroleum province in the U.S., the Permian Basin. We apply a physics-guided, data-driven forecasting approach to estimate the remaining hydrocarbons in these historical wells and the probabilities of well survival. First, we cluster the production data set into 192 spatiotemporal well cohorts based on 4 reservoir ages, 6 sub-plays, and 8 completion date intervals. Second, for each cohort, we apply the Generalized Extreme Value (GEV) statistics to each year of oil production from every well in this cohort, obtaining historical well prototypes. Third, we derive a novel physical scaling that extends these well prototypes for several more decades. Fourth, we calculate the probabilities of well survival and observe that a vertical well in the Permian can operate for 10−100 years, depending on the sub-play and reservoir to which this well belongs. Fifth, we estimate the total field production of a... [more]

Permafrost monitoring should be organized in different ways within undisturbed landscapes and in areas with technogenic impacts. The state and dynamics of permafrost are described by special indicators. It helps to characterize seasonal and long-term tendencies and link them with permafrost hazards estimation. The risk is determined by the hazard probability and the vulnerability of infrastructure elements. The hazard does not have integral indicators, but is determined by separate spatial and temporal characteristics. The spatial characteristics include the ground’s physical and cryolithological features that are linked with the history of the permafrost. The temporal characteristics are associated with the future evolution of the climate and anthropogenic pressures. The geocryological monitoring content and geocryological forecasting are interdependent and should be implemented together. The adaptation recommendations are based on the analytical algorithms and use the results of perm... [more]

Consequent pole hybrid excitation synchronous (CPHES) machines have the advantage of symmetrical bidirectional magnetomotive force increments. Compared with a traditional hybrid excitation motor (HEM), a CPHES machine improves the disadvantage of asymmetry in the adjustment range when magnetization and demagnetization occur. The calculation and analysis of the cogging torque of the CPHES machine are complex due to the complicated structure. This paper proposes an analytical method for calculating the cogging torque of a CPHES machine. This analytical method converts the complex three-dimensional magnetic field problem into a two-dimensional magnetic circuit problem and, through the accumulation method, can quickly and accurately calculate the cogging torque of the CPHES machine. In contrast with the finite element method, the calculation results basically follow each other, but the analytical method is more efficient and omits complicated meshing. This is of great significance to the p... [more]

Off-grid technologies, such as solar home systems (SHS), offer the opportunity to alleviate global energy poverty, providing a cost-effective alternative to an electricity grid connection. However, there is a paucity of high-quality SHS electricity usage data and thus a limited understanding of consumers’ past and future usage patterns. This study addresses this gap by providing a rare large-scale analysis of real-time energy consumption data for SHS customers (n = 63,299) in Rwanda. Our results show that 70% of SHS users’ electricity usage decreased a year after their SHS was installed. This paper is novel in its application of a three-dimensional convolutional neural network (CNN) architecture for electricity load forecasting using time series data. It also marks the first time a CNN was used to predict SHS customers’ electricity consumption. The model forecasts individual households’ usage 24 h and seven days ahead, as well as an average week across the next three months. The last s... [more]

An important challenge for the future is focusing on sustainability in life and business. The three elements of sustainability (economic, environmental, and social), defined in 17 factors by the United Nations (UN) as the Sustainable Development Goals (SDGs), may, therefore, be the main drivers of business competitiveness and opportunity recognition. The main aim of the article is to identify the awareness level of sustainability and sustainable development goals in the context of business opportunity areas by analysing the results of a survey of organisations in six countries (Finland, Slovakia, Italy, Austria, Spain, and Turkey). A multilingual questionnaire, administered in six participating countries, was used as a collection tool to determine the organisation’s level of awareness regarding the SDGs. A research questionnaire was filled in by 238 respondents, providing a cross-cultural view of their attitudes, knowledge, and future interest in sustainability and the SDGs. The obtain... [more]

Nowadays, the energy sector is experiencing a profound transition. Among all renewable energy sources, wind energy is the most developed technology across the world. To ensure the profitability of wind turbines, it is essential to develop predictive maintenance strategies that will optimize energy production while preventing unexpected downtimes. With the huge amount of data collected every day, machine learning is seen as a key enabling approach for predictive maintenance of wind turbines. However, most of the effort is put into the optimization of the model architectures and its parameters, whereas data-related aspects are often neglected. The goal of this paper is to contribute to a better understanding of wind turbines through a data-centric machine learning methodology. In particular, we focus on the optimization of data preprocessing and feature selection steps of the machine learning pipeline. The proposed methodology is used to detect failures affecting five components on a win... [more]

The noise pollution caused by urban substations is an increasingly serious problem, as is the issue of local residents being disturbed by substation noise. To accurately assess the degree of noise annoyance caused by substations to surrounding residents, we established a noise annoyance prediction model based on transfer learning and a convolution neural network. Using the model, we took the noise spectrum as the input, the subjective evaluation result as the target output, and the AlexNet network model with a modified output layer and corresponding parameters as the pre-training model. In a fixed learning rate and epoch setting, the influence of different mini-batch size values on the prediction accuracy of the model was compared and analyzed. The results showed that when the mini-batch size was set to 4, 8, 16, and 32, all the data sets had convergence after 90 iterations. The root mean square error (RMSE) of all validation sets was lower than 0.355, and the loss of all validation se... [more]

The building sector is responsible for 50% of worldwide energy consumption and 40% of CO2 emissions. Consequently, a lot of research on Building Energy Efficiency has been carried out over recent years, covering the most varied topics. While many of these themes are no longer of interest to the scientific community, others flourish. Thus, reading trends within a field of knowledge is wise since it allows resources to be directed towards the most promising topics. However, there is a paucity of research on trend analysis in this field. Therefore, this article aims to analyse the evolution of the Building Energy Efficiency field of knowledge, identifying the recurrent themes and pointing out their trends, supported by statistical methods. Such an analysis relied on more than 9000 authors’ keywords collected from 2000 articles from the Scopus database and classified into 30 topics/themes. A frequency distribution of these themes enabled us to distinguish those most published as well as th... [more]

Plasma parameters of radiofrequency discharge generated at low pressures in an argon-oxygen mixture addressed for biomedical surface sterilization have been optimized. Numerical results illustrate the density distributions of different species and electron temperatures during the electrical discharge process. The current discharge acting in the abnormal range decreases at higher oxygen gas flow rates. The temperature of electrons drops with pressure while it rises by adding oxygen. Nevertheless, electron density displays an adverse trend, exhibited by the electron’s temperature. The average particle density of the reactive species is enhanced in Ar/O2 compared to He/O2, which ensures a better efficiency of Ar/O2 in sterilizing bacteria than He/O2. The impact of oxygen addition on the discharge mixture reveals raised oxygen atom density and a reduction in metastable oxygen atoms. A pronounced production of oxygen atoms is achieved at higher frequency domains. This makes our findings pro... [more]

In the present paper, an investigation into Thailand’s energy demand is performed to determine if: (1) a linear or nonlinear Engel curve better explains the relationship between income and energy consumption, and (2) systems with pre-commitments better model energy consumptions. Four demand systems are estimated: an almost ideal demand system (AIDS), the quadratic almost ideal demand system (QAIDS), generalized almost ideal demand system (GAIDS), and the generalized quadratic almost ideal demand system (GQAIDS). Elasticities are calculated for policy implications. The empirical results suggest that models considering pre-commitments and nonlinear Engel curves may be slightly more appropriate for Thailand’s energy system, from both statistic and economic standpoints. Statistical inferences appear to favor the GQAIDS model based on the encompassing results. Economic reasonability also appears to favor the GQAIDS model, in particular, petroleum products, as it provides results consistent... [more]

The findings of the analysis of data on the accident rate of power transformers indicate that one of the main causes of their failures is a decrease in the dielectric strength of the insulation. To reduce failures and extend the service life of power transformers in operation, the issue of enhancing the techniques for assessing the condition of their internal insulation becomes relevant. Currently, when selecting the major insulation of transformers, one takes into account the dependency of the dielectric strength of the oil passage on its width. Experts discuss the issues involved in the choice of major insulation while taking into account the effect of the generalized factor being the volume of the oil passage. The solution to that problem largely depends on the study of the statistical characteristics of the dielectric strength of oil passages of different volumes and the effect rated parameters of transformers have on them. The efficiency of the application of such diagnostic chara... [more]

This paper focuses on the evaluation of the electric shock hazard accompanying earth faults in a non-effectively earthed medium-voltage (MV) electrical power network. This hazard depends on the duration and value of the fault current. While the fault current depends on several factors, the most important is the neutral point earthing method. The value of the fault current affects the earthing-electrode voltage value, being the basis for the assessment of electric shock hazard in MV/LV substations. The earthing-electrode voltage is also influenced by the resistance of the substation earthing, which in practice is random. Therefore, an original statistical evaluation method for assessing the electric shock hazard has been developed and presented in this paper. It is based on a statistical model of the MV/LV substation earthing resistance, worked out on the basis of experiments and measurements in real electrical power networks. This method can be used for the determination of statistical... [more]

In the last few years, induction motor fault detection has provoked great interest among researchers because it is a fundamental element of the electric-power industry, manufacturing enterprise, and services. Hence, considerable efforts have been carried out on developing reliable, low-cost procedures for fault diagnosis in induction motors (IM) since the early detection of any failure may prevent the machine from suffering a catastrophic damage. Therefore, many methodologies based on the IM startup transient current analysis have been proposed whose major disadvantages are the high mathematical complexity and demanding computational cost for their development. In this study, a straightforward procedure was introduced for identifying and classifying faults in IM. The proposed approach is based on the analysis of the startup transient current signal through the current signal homogeneity and the fourth central moment (kurtosis) analysis. These features are used for training a feed-forwa... [more]

Revealing the variation law of thermal diffusivity of sandy soil can provide a theoretical basis for the engineering design and construction in cold and arid regions. Based on experimental data of sandy soil samples, the distribution characteristics and influence of dry density and moisture content on thermal diffusivity are analyzed in this work. Then, the prediction model based on the empirical fitting formula and RBF neural network method for thermal diffusivity of frozen and unfrozen sandy soil is established, and the prediction accuracy of different prediction methods is compared. The results show that (1) thermal diffusivity of sandy soil is positively correlated with the particle size. With the increase of sand size, thermal diffusivity of sandy soil increases significantly. (2) Partial correlation among natural moisture content, dry density, and thermal diffusivity varies with different frozen and unfrozen conditions. (3) For unfrozen sandy soil, the binary RBF neural network p... [more]

Negative processes occurring in the natural environment, under dynamic economy development, have become a factor for taking actions limiting destructive human activity. An important area in which initiatives are taken to improve the state of the natural environment is that of companies in the Transport-Shipping-Logistics Sector (TSL sector). The main objective of this article was to analyse the impact of the COVID-19 pandemic on the development of electromobility among companies in the Polish TSL sector, and identify factors that positively influenced or hindered its development during this time. For this purpose, qualitative and quantitative data analyses were carried out based on a literature review, statistical data, and direct research results. Descriptive statistics, chi-square test of concordance, and contingency coefficients were used to process the data. The results showed that the pandemic period did not affect the development of electromobility among TSL companies. Only a few... [more]

This work uses the outcome of a computational tool that performs Energy Performance Certification (EPC) data processing and transforms raw data into comparable data. Multi-correlation among variables results in probability distributions for the most relevant form and fabric building parameters. The model consistently predicts the distributions for heating and cooling energy needs for the Lisbon Metropolitan Area, with an error below 7% for the first, second and third quartiles. Differences in the energy needs estimation are below 6% when comparing the seasonal steady-state with the resistance-capacitance (RC) model, which proved to be a robust alternative algorithm capable of modeling hourly user profiles. The RC model calculates electricity consumption for actual, adequate, and minimum thermal comfort scenarios corresponding to different user profiles. The actual scenario, built from statistics and a previous survey, defines a reference to evaluate other scenarios for the mean electri... [more]

Hydropower is the most widely used renewable power source worldwide. The current work presents a methodological tool to determine the hydropower potential of a reservoir based on available hydrological information. A Bayesian analysis of the river flow process and of the reservoir water volume is applied, and the estimated probability density function parameters are integrated for a stochastic analysis and long-term simulation of the river flow process, which is then used as input for the water balance in the reservoir, and thus, for the estimation of the hydropower energy potential. The stochastic approach is employed in terms of the Monte Carlo ensemble technique in order to additionally account for the effect of the intermediate storage retention due to the thresholds of the reservoir. A synthetic river flow timeseries is simulated by preserving the marginal probability distribution function properties of the observed timeseries and also by explicitly preserving the second-order dep... [more]

In March 2020, a lockdown was imposed due to a global pandemic, which contributed to changes in the structure of the consumption of natural gas. Consumption in the industry and the power sector decreased while household consumption increased. There was also a noticeable decrease in natural gas consumption by commercial consumers. Based on collected data, such as temperature, wind strength, duration of weather events, and information about weather conditions on preceding days, models for forecasting gas consumption by commercial consumers (hotels, restaurants, and businesses) were designed, and the best model for determining the impact of the lockdown on gas consumption by the above-mentioned consumers was determined using the MAPE (mean absolute percentage error). The best model of artificial neural networks (ANN) gave a 2.17% MAPE error. The study found a significant decrease in gas consumption by commercial customers during the first lockdown period.

Transient flow in pipe is a much debated topic in the field of hydrodynamics. The water hammer effect caused by instantaneous valve closing is an important branch of transient flow. At present, the fluid density is regarded as a constant in the study of the water hammer effect in pipe. When there is gas in the pipe, the variation range of density is large, and the pressure-wave velocity should also change continuously along the pipe. This study considers the interaction between pipeline fluid motion and water hammer wave propagation based on the essence of water hammer, with the pressure, velocity, density and overflow area set as variables. A new set of water hammer calculation equations was deduced and solved numerically. The effects of different valve closing time, flow rate and gas content on pressure distribution and the water hammer effect were studied. It was found that with the increase in valve closing time, the maximum fluctuating pressure at the pipe end decreased, and the t... [more]

In view of the problem that the weight value given by the previous state evaluation method is fixed and single and cannot analyze the influence of the weight vector deviation on the evaluation result, a method based on the weight space Markov chain and Monte Carlo method (Markov chains Monte Carlo, MCMC) is proposed. The sampling method is used for evaluating the condition of high-voltage cables. The weight vector set obtained by MCMC sampling and the comprehensive degradation degree of the high-voltage cable sample are weighted and summed then compared in pairs to obtain the comprehensive degradation degree result. The status probability value and overall priority ranking probability of the object to be evaluated are obtained based on probability statistics, and the order of maintenance is determined according to the status probability value and the ranking result. It is realized that the cable line that needs to be identified in the follow-up defect is clarified according to the eval... [more]

Increased industrial energy efficiency (EE) has become one of the main environmental actions to mitigate carbon dioxide (CO2) emissions, contributing also to industrial competitiveness, with several implications on the production system and cost management. Unfortunately, literature is currently lacking empirical evidence on the impact of energy efficiency solutions on production. Thus, this work primarily aims at investigating the economic and production-related influence on the reduction in industrial energy consumption, considering the cross-cutting technologies HVAC, motors, lighting systems and air compressor systems. The analysis is performed using data from previous studies that characterized the main EE measures for the cross-cutting technologies. Four logistic models were built to understand how costs and production influence energy efficiency across such cross-cutting technologies. In this way, motivating industries to implement measures to reduce electrical consumption, offe... [more]

Energy demand has grown explosively in recent years, leading to increased attention of energy efficiency (EE) research. Demand response (DR) programs were designed to help power management entities meet energy balance and change end-user electricity usage. Advanced real-time meters (RTM) collect a large amount of fine-granular electric consumption data, which contain valuable information. Understanding the energy consumption patterns for different end users can support demand side management (DSM). This study proposed clustering algorithms to segment consumers and obtain the representative load patterns based on diurnal load profiles. First, the proposed method uses discrete wavelet transform (DWT) to extract features from daily electricity consumption data. Second, the extracted features are reconstructed using a statistical method, combined with Pearson’s correlation coefficient and principal component analysis (PCA) for dimensionality reduction. Lastly, three clustering algorithms a... [more]

A focus on indoor comfort and tightening targets for energy savings in buildings presents new opportunities for heating, ventilation, and air-conditioning products (HVAC). This paper presents a novel comfort solution that integrates a suspended radiant ceiling with diffuse ventilation, dubbed HVACeiling. In combination with the concrete slab, the HVACeiling has the potential to provide thermal comfort with minimal temperature offset, which supports operation of the heating and cooling system at temperatures very close to the room comfort temperature. The paper presents a parametric numerical study of the concept in a simplified two-pipe layout with fixed flow and fixed temperatures. First, the analysis was focused on different internal and solar loads, heat losses, and climatic locations with the aim of assessing the potential of self-regulation, i.e., no active controls, thermal comfort, ability to reduce peak loads and the consequential building design considerations. Secondly, the p... [more]

The stable and efficient operation of power systems requires them to be optimized, which, given the growing availability of load data, relies on load forecasting methods. Fast and highly accurate Short-Term Load Forecasting (STLF) is critical for the daily operation of power plants, and state-of-the-art approaches for it involve hybrid models that deploy regressive deep learning algorithms, such as neural networks, in conjunction with clustering techniques for the pre-processing of load data before they are fed to the neural network. This paper develops and evaluates four robust STLF models based on Multi-Layer Perceptrons (MLPs) coupled with the K-Means and Fuzzy C-Means clustering algorithms. The first set of two models cluster the data before feeding it to the MLPs, and are directly comparable to similar existing approaches, yielding, however, better forecasting accuracy. They also serve as a common reference point for the evaluation of the second set of two models, which further en... [more]

This paper proposes a machine-learning-based framework for voltage quality analytics, where the space phasor model (SPM) of the three-phase voltages before, during, and after the event is applied as input data. The framework proceeds along with three main steps: (a) event extraction, (b) event characterization, and (c) additional information extraction. During the first step, it utilizes a Gaussian-based anomaly detection (GAD) technique to extract the event data from the recording. Principal component analysis (PCA) is adopted during the second step, where it is shown that the principal components correspond to the semi-minor and semi-major axis of the ellipse formed by the SPM. During the third step, these characteristics are interpreted to extract additional information about the underlying cause of the event. The performance of the framework was verified through experiments conducted on datasets containing synthetic and measured power quality events. The results show that the combi... [more]