This research proposes a methodology for the optimal location and sizing of reactive compensation in an electrical transmission system through a deep neural network (DNN) by considering the smallest cost for compensation. An electrical power system (EPS) is subjected to unexpected increases in loads which are physically translated as an increment of users in the EPS. This phenomenon decreases voltage profiles in the whole system which also decreases the EPS’s reliability. One strategy to face this problem is reactive compensation; however, finding the optimal location and sizing of this compensation is not an easy task. Different algorithms and techniques such as genetic algorithms and non-linear programming have been used to find an optimal solution for this problem; however, these techniques generally need big processing power and the processing time is usually considerable. That being stated, this paper’s methodology aims to improve the voltage profile in the whole transmission syst... [more]

The parametric representation and aerodynamic shape optimization of a three-dimensional circular-to-rectangular transition nozzle designed and built using control lines distributed along the circumferential direction were investigated in this study. A surrogate model based on class/shape transformation, principal component analysis and radial basis neural network was proposed with fewer design parameters for parametric representation and performance parameter prediction of the three-dimensional circular-to-rectangular transition nozzle. The surrogate model was combined with Non-dominated Sorting Genetic Algorithm-II to optimize the aerodynamic shape of the nozzle. The results showed that the surrogate model effectively achieved the parametric representation and aerodynamic shape optimization of the three-dimensional circular-to-rectangular transition nozzle. The geometric dimensions and performance parameters of the parametric reconstructed model were comparable to that of the initial... [more]

This research aims to predict knock occurrences by deep learning using in-cylinder pressure history from experiments and to elucidate the period in pressure history that is most important for knock prediction. Supervised deep learning was conducted using in-cylinder pressure history as an input and the presence or absence of knock in each cycle as a label. The learning process was conducted with and without cost-sensitive approaches to examine the influence of an imbalance in the numbers of knock and non-knock cycles. Without the cost-sensitive approach, the prediction accuracy exceeded 90% and both the precision and the recall were about 70%. In addition, the trade-off between precision and recall could be controlled by adjusting the weights of knock and non-knock cycles in the cost-sensitive approach. Meanwhile, it was found that including the pressure history of the previous cycle did not influence the classification accuracy, suggesting little relationship between the combustion be... [more]

Solar energy is one of the most widely exploited renewable/sustainable resources for electricity generation, with photovoltaic and concentrating solar power technologies at the forefront of research. This study focuses on the development of a neural network prediction model aimed at assessing the energy producibility of dish−Stirling systems, testing the methodology and offering a useful tool to support the design and sizing phases of the system at different installation sites. Employing the open-source platform TensorFlow, two different classes of feedforward neural networks were developed and validated (multilayer perceptron and radial basis function). The absolute novelty of this approach is the use of real data for the training phase and not predictions coming from another analytical/numerical model. Several neural networks were investigated by varying the level of depth, the number of neurons, and the computing resources involved for two different sets of input variables. The best... [more]

The central air conditioning system provides city dwellers with an efficient and comfortable environment. Meanwhile, coinciding with their use, the building electricity load is increased, as central air conditioners consume a lot of electricity. It has become necessary to control central air conditioners for storage and to analyze the energy saving optimization of central air conditioner operation. This study investigates the energy consumption background of central air conditioning systems, and proposes an intelligent load prediction method. With a back propagation (BP) neural network, we use the data collected in the actual project to build the cooling load prediction model for central air conditioning. The network model is also trained using the Levenberg−Marquardt (LM) algorithm, and the established model is trained, tested, and predicted by importing a portion of the sample data, which is filtered by preprocessing. The experimental results show that most of the data errors for tra... [more]

The direct reduction process has been developed and investigated in recent years due to less pollution than other methods. In this work, the first direct reduction iron oxide (DRI) modeling has been developed using artificial neural networks (ANN) algorithms such as the multilayer perceptron (MLP) and radial basis function (RBF) models. A DRI operation takes place inside the shaft furnace. A shaft furnace reactor is a gas-solid reactor that transforms iron oxide particles into sponge iron. Because of its low environmental pollution, the MIDREX process, one of the DRI procedures, has received much attention in recent years. The main purpose of the shaft furnace is to achieve the desired percentage of solid conversion output from the furnace. The network parameters were optimized, and an algorithm was developed to achieve an optimum NN model. The results showed that the MLP network has a minimum squared error (MSE) of 8.95 × 10−6, which is the lowest error compared to the RBF network mod... [more]

Thermal bridges are a very relevant issue for lightweight steel-framed (LSF) construction systems given the high thermal conductivity of steel, which can negatively compromise their thermal behaviour, reduce their durability, and decrease the building energy efficiency. Several thermal bridge mitigation strategies exist, including the attachment of thermal break strips (TBS) to the steel studs’ flanges as one of the most widely employed techniques. In this research, the relevance of TBS to the thermal performance improvement of load-bearing LSF partition walls was assessed by performing a parametric study, making use of a validated 2D numerical model. A sensitivity analysis was performed for five different key parameters, and their importance was evaluated. The assessed parameters included the number of TBS and their thickness, width, and thermal conductivity, as well as the vertical steel stud spacing. We found that these parameters were all relevant. Moreover, regardless of the TBS t... [more]

It is necessary to study different aspects of renewable energy generation, including wind energy. Wind power is one of the most important green and renewable energy resources. The estimation of wind energy generation is a critical task that has received wide attention in recent years. Different machine learning models have been developed for this task. In this paper, we present an efficient forecasting model using naturally inspired optimization algorithms. We present an optimized dendritic neural regression (DNR) model for wind energy prediction. A new variant of the seagull optimization algorithm (SOA) is developed using the search operators of the Aquila optimizer (AO). The main idea is to apply the operators of the AO as a local search in the traditional SOA, which boosts the SOA’s search capability. The new method, called SOAAO, is employed to train and optimize the DNR parameters. We used four wind speed datasets to assess the performance of the presented time-series prediction m... [more]

The unpredictable noise in received signal strength indicator (RSSI) measurements in indoor environments practically causes very high estimation errors in target localization. Dealing with high noise in RSSI measurements and ensuring high target-localization accuracy with RSSI-based localization systems is a very popular research trend nowadays. This paper proposed two range-free target-localization schemes in wireless sensor networks (WSN) for an indoor setup: first with a plain support vector regression (SVR)-based model and second with the fusion of SVR and kalman filter (KF). The fusion-based model is named as the SVR+KF algorithm. The proposed localization solutions do not require computing distances using field measurements; rather, they need only three RSSI measurements to locate the mobile target. This paper also discussed the energy consumption associated with traditional Trilateration and the proposed SVR-based target-localization approaches. The impact of four kernel functio... [more]

In the past, reservoir engineers used numerical simulation or reservoir engineering methods to predict oil production, and the accuracy of prediction depended more on the engineers’ own experience. With the development of data science, a new trend has arisen to use deep learning to predict oil production from the perspective of data. In this study, a hybrid forecasting model (CNN-LSTM) based on a convolutional neural network (CNN) and a Long Short-Term Memory (LSTM) neural network is proposed and used to predict the production of fractured horizontal wells in volcanic reservoirs. The model solves the limitation of traditional methods that rely on personal experience. First, the production constraints and production data are used to form a feature space, and the abstract semantics of the feature time series are extracted through convolutional neural network, then the LSTM neural network is used to predict the time series. The certain hyperparameters of the whole model are optimized by P... [more]

There is a large amount of drilling core data in the Mackay River oil sands block in Canada, and the accurate identification of facies from the cores is important and necessary for the understanding of the subsurface reservoir. The traditional recognition method of facies from cores is by human work and is very time consuming. Furthermore, the results are different according to different geologists because of the subjective judgment criterion. An efficient and objective method is important to solve the above problem. In this paper, the deep learning image-recognition algorithm is used to automatically and intelligently recognize the facies type from the core image. Through a series of high-reliability preprocessing operations, such as cropping, segmentation, rotation transformation, and noise removal of the original core image, that have been manually identified, the key feature information in the images is extracted based on the ResNet50 convolutional neural network. On the dataset of... [more]

The energy consumption of a large sample of vehicles (6955) operating during the last 3 years under everyday conditions across Mexico was monitored via OBD-based telematics systems. A life cycle statistical analysis of the obtained data showed that, on average, 54 t diesel vehicles used for long-distance freight transport consume 44.25 L/100 km and emit 1513 g CO2e/km. When these vehicles are powered by natural gas, the energy consumption and the emissions of greenhouse gases (GHG) are increased by 23% and reduced by 0.8%, respectively. Using manufacturers’ data, these values reduce energy consumption by 16% and GHG emissions by 52% when they are electric. Similar observations were made for other vehicles sizes used for transporting goods and people.

This paper describes the further development of the virtual flow meter concept based on the author’s simulator of an unsteady gas−liquid flow in wells. The results of comparison with commercial simulators based on real well data are given as practical applications. The results of the comparison of the simulators demonstrated high correspondence (<10% error) for a number of target parameters. The description of the architecture and results of testing the algorithm for automatic settings of the model parameters are given. Operating speed was the key criterion in the architecture development. According to the test results, it became possible to achieve the adaptation accuracy of 5% specified.

Electric power systems have experienced the rapid insertion of distributed renewable generating sources and, as a result, are facing planning and operational challenges as new grid connections are made. The complexity of this management and the degree of uncertainty increase significantly and need to be better estimated. Considering the high volatility of photovoltaic generation and its impacts on agents in the electricity sector, this work proposes a multivariate strategy based on design of experiments (DOE), principal component analysis (PCA), artificial neural networks (ANN) that combines the resulting outputs using Mixture DOE (MDOE) for photovoltaic generation prediction a day ahead. The approach separates the data into seasons of the year and considers multiple climatic variables for each period. Here, the dimensionality reduction of climate variables is performed through PCA. Through DOE, the possibilities of combining prediction parameters, such as those of ANN, were reduced, w... [more]

The digital transformation of the energy sector toward the Smart Grid paradigm, intelligent energy management, and distributed energy integration poses new requirements for computer science. Issues related to the automation of power grid management, multidimensional analysis of data generated in Smart Grids, and optimization of decision-making processes require urgent solutions. The article aims to analyze the use of selected artificial intelligence (AI) algorithms to support the abovementioned issues. In particular, machine learning methods, metaheuristic algorithms, and intelligent fuzzy inference systems were analyzed. Examples of the analyzed algorithms were tested in crucial domains of the energy sector. The study analyzed cybersecurity, Smart Grid management, energy saving, power loss minimization, fault diagnosis, and renewable energy sources. For each domain of the energy sector, specific engineering problems were defined, for which the use of artificial intelligence algorithms... [more]

In this paper, we analyze the values of the entropic efficiency of longitudinal fins by investigating the coupling between the function describing the fin profile and the corresponding steady-state temperature distribution along the fin. By starting from different boundary conditions, we look at the distribution temperature maximizing the efficiency of the fin. From this temperature distribution and by requesting that the fin must comply with natural physical constraints, such as the maximum fin thickness, we obtain an optimal profile for a purely convective fin and a convecting−radiating fin. For different boundary conditions and for a maximum fin thickness equal to four (in dimensionless units), both the profiles are increasing starting from the fin base until they reach the maximum value and then decrease to zero at the tip. Analytic and numerical results, together with different plots, are presented.

The connection between Earth’s global temperature and carbon dioxide (CO2) emissions is one of the highest challenges in climate change science since there is some controversy about the real impact of CO2 emissions on the increase of global temperature. This work contributes to the existing literature by analyzing the relationship between CO2 emissions and the Earth’s global temperature for 61 years, providing a recent review of the emerging literature as well. Through a statistical approach based on maximum entropy, this study supports the results of other techniques that identify a positive impact of CO2 in the increase of the Earth’s global temperature. Given the well-known difficulties in the measurement of global temperature and CO2 emissions with high precision, this statistical approach is particularly appealing around climate change science, as it allows the replication of the original time series with the subsequent construction of confidence intervals for the model parameters... [more]

Nowadays, electric load forecasting through a data analytic approach has become one of the most active and emerging research areas. It provides future consumption patterns of electric load. Since there are large fluctuations in both electricity production and use, it is a difficult task to achieve a balance between electric load and demand. By analyzing past electric consumption records to estimate the upcoming electricity load, the issue of fluctuating behavior can be resolved. In this study, a framework for feature selection, extraction, and regression is put forward to carry out the electric load prediction. The feature selection phase uses a combination of extreme gradient boosting (XGB) and random forest (RF) to determine the significance of each feature. Redundant features in the feature extraction approach are removed by applying recursive feature elimination (RFE). We propose an enhanced support vector machine (ESVM) and an enhanced convolutional neural network (ECNN) for the r... [more]

The analysis of public attitudes towards nuclear energy represents an issue that is commonly investigated, especially considering the new context of classifying some nuclear power plants as green investments under the European Green Deal. The importance of this topic is critical to the future of nuclear power generation. The purpose of this study is to identify the relationships that exist between the different factors and the attitude of the public towards nuclear energy in the context of the European Green Deal. The article identifies and analyzes the main factors that influence this relationship. In this context, a questionnaire-based survey was conducted regarding the identification of the relationship between public knowledge, degree of cooperation, perceived risks, trust and attitude towards nuclear energy. The sample was chosen using the snowball method. The analysis was made up of 578 respondents from different segments of age, gender, place of residence, field of activity, wor... [more]

The main purpose of the paper was to identify the most frequently discussed directions of research on green transformation. In the article, both the significant similarities in the existing studies in this field, as well as the newly emerging topics of research, are presented. For this purpose, the authors used a systematic literature review with elements of statistical analyses. This kind of approach is not popularly used in literature review papers, as it differs from the research practices employed previously, which mostly concentrated on applying qualitative methods, alternatively supported by the analysis of the co-occurrence of keywords. In this paper, the authors decided to include selected methods of dimensional analysis in the systematic literature review, namely the log-linear and correspondence analyses. The main results of the presented analyses are a more detailed division of studies related to green transformations into groups focused on the areas more difficult to distin... [more]

Carbon capture and storage (CCS) is an essential technology for achieving carbon neutrality. Depositional environments with sandstone and interbedded shale layers are promising for CO2 storage because they can retain CO2 beneath continuous and discontinuous shale layers. However, conventional numerical simulation of shale−sandstone systems is computationally challenging due to the large contrast in properties between the shale and sandstone layers and significant impact of thin shale layers on CO2 migration. Extending recent advancements in Fourier neural operators (FNOs), we propose a new deep learning architecture, the RU-FNO, to predict CO2 migration in complex shale−sandstone reservoirs under various reservoir conditions, injection designs, and rock properties. The gas saturation plume and pressure buildup predictions of the RU-FNO model are 8000-times faster than traditional numerical models and exhibit remarkable accuracy. We utilize the model’s fast prediction to investigate the... [more]

Because reliance on gas for electricity generation rises over time, the natural gas and electricity markets are highly connected. However, both of them are susceptible to various risk factors that endanger energy security. The intricate interactions among multiple risks and between the two markets render risk assessment more challenging than for individual markets. Taking a systematic perspective, this study first undertook a thorough analysis of the evolution mechanism that indicated the key risk factors and dual interactions, with real-world illustrative examples. Subsequently, a system dynamics model was constructed for understanding the causal feedback structures embedded in the operation of a coupled natural gas−electricity market in the face of risks. Quantitative experiments were conducted by using data from China’s Energy Statistical Yearbook, China’s Statistical Yearbook and other reliable sources to assess the effects of individual risks, depict the evolutionary behavior of c... [more]

Ion flow field is an important aspect of high voltage direct current (HVDC) transmission lines. In this paper, we apply the upwind finite volume method for solving the ion flow field of three HVDC configurations to clarify the effect of the wind and the role of shielding conductors. For the monopolar configuration installation, the ground current distribution with underbuilt shield wires was studied numerically and experimentally. For the ±250 kV bipolar configuration, the calculated peak electric field and current density are verified with the values in a reference. The ±500 kV bipolar configuration is used to investigate the change in electric field and ion current within the same corridors of the existing HVAC lines. We analyze the ion flow field with and without the dedicated metallic return conductor (DMRC). In the absence of wind, the maximum of the electric field is lower than that of the HVAC lines and the current density is very low on the ground. In the presence of wind, the... [more]

Fluctuations in the critical current along the length of high-temperature superconducting (HTS) tapes manufactured in the form of coated conductors is a common manufacturing phenomenon. These fluctuations originate in the generation of weak points through the length of HTS tapes that may cause quenching later. By means of the propagation of quenches in HTS tapes, the reliability, stability, and the performance of the device and the system that contain HTS tapes could be seriously degraded. In this study, an artificial intelligence technique based on artificial neural networks (ANN) was proposed to estimate the probability of quenches in HTS tapes caused by weak points. For this purpose, six different HTS tapes were considered with different widths, total thicknesses, and thicknesses of sub-layers. Then, for each one of these tapes, different operating conditions were considered, where the operating temperature changed from 40 K to 80 K, in 1 K steps. Under each operating temperature, d... [more]

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This research accomplishes these aims. The models built in this paper identify wind forecast, sunrise/sunset and the hour of day as primary predictors of net load imbalance, among other variables, and show that the average size of the INC and DEC capacity requirements can be... [more]