The paper presents the notion of high dimensionality—in the results—that could change the exergy and energy characteristics of the two-pass solar collector. To examine the energetic aspect of the collector, two different types of absorber plate surfaces were chosen: one that is smooth and one with triangular fins. Both designs have two-pass and wooden baffles underneath their absorber plates. The induced air blower was used for the forced convection of air. To examine the attribute of the data, the least angle regression (LARS) algorithm was used to find a new exergy model without overfitting the data. The second law efficiency dropped by 18.92% for the given models of the solar collector when the air flow rate surged further from 10.10 g·s−1 to 12.10 g·s−1, whereas the energy efficiency showed contradictory behaviour for the given range of air flow rate. It increased by 3% in the first half of the rise in the air flow rate, and on the other hand, a jump of 8% was recorded in the energ... [more]

Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric.... [more]

A chilled water system transports cooling functionality from refrigerators to users via heating, ventilation, and air conditioning (HVAC) systems. This paper investigated an optimal control strategy to regulate the volume flow rate of each user branch in a chilled water system, considering the minimum resistance operation to reduce energy consumption. An artificial neural network (ANN) was adopted to establish the nonlinear relationship between the volume flow rate of each user branch, pump frequency, and valve opening of each user branch. An optimal control strategy for a chilled water HVAC system is proposed in this article, according to the pump-valve combined control (PVCC) principle and an ANN model, i.e., pump-valve combined control using an artificial neural network model (PVCC-ANN). A series of tests were conducted to collect data to train the ANN model and analyze the performance of the PVCC-ANN in an experimental chilled water system. The results show that the trained ANN mod... [more]

Increasing access to power, enhancing clean cooking fuels, decreasing wasteful energy subsidies, and limiting fatal air pollution are just a few of the sustainable development goals that all revolve around energy (E). Energy-specific sustainable development objectives were a turning point in the global shift towards a more sustainable and just system. By understanding energy resources, markets, regulations, and scientific studies, the country can progress more quickly towards a sustainable economy (SE). Investment in renewable energy industries is hampered by institutional obstacles such as market-controlled procedures and inconsistent supporting policies. Power plant building is currently incompatible with existing transmission and distribution networks, posing significant risks to investors. Deep neural networks (DNN) are specifically investigated in this article for energy demand forecasting at the individual building level. Other relevant information is supplied into fully connecte... [more]

Overhead lines that are exposed to the outdoors are susceptible to faults such as open conductors on weak points and disconnection caused by external factors such as typhoons. Arcs that occur during disconnection generate energy at a high heat of over 10,000 °C, requiring swift fault shut-off. However, most conventional fault detection methods to protect electrical power systems detect an overcurrent; thus, they can only detect faults after the line is disconnected and the cross-section of the line that generates the arc discharge makes contact with another line or the ground, causing a high risk of fire. Furthermore, in the case of ground faults owing to the disconnection of overhead lines, the load and the grounding impedance are not parallel. Therefore, in the case of the fault current not exceeding the threshold or a high impedance fault due to the high grounding impedance of the surrounding environment, such as grass or trees, it is difficult to determine overhead line faults with... [more]

Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even replacing—high-fidelity numerical simulations of complex turbulent flows, which are often expensive due to the requirement of high temporal and spatial resolution. In this review, we (i) provide an introduction and historical perspective of ML methods, in particular neural networks (NN), (ii) examine existing PIML applications to fluid mechanics problems, especially in complex high Reynolds number flows, (iii) demonstrate the utility of PIML techniques through a case study, and (iv) discuss the challenges and opportunities of developing PIML for fluid mechanics.

This article presents the influence of the evolution of the oil price on the international markets on the strategies of companies whose main activity is the manufacture of oil equipment for the Romanian oil industry. As a tool for economists and specialists in the oil industry, such a study helps to develop economic strategies and management decisions based on forecasts of oil price developments. Based on statistical data for the annual oil price on international markets and the turnover of the oil companies, an analysis was proposed which showed that the trends in the mathematical models are similar, which highlights that economic activities in the oil sector are sensitive to the evolution of oil price. Management strategies are based on anticipating changes in the corporate environment, assessing the company’s internal potential and making the necessary changes to bring it in line with the economy, its mission, and the objectives set to ensure its survival and continuity. These views... [more]

Gas disasters threaten the safe operation of coal mines. Improving the accuracy of gas concentration predictions can effectively prevent gas disasters and reduce disaster losses. Traditional gas concentration prediction methods poorly couple the gas concentration and its influencing factors when dealing with a great number of features and data, which results in low prediction accuracy and poor efficiency. To solve this problem, we used an innovative Pearson-LSTM prediction model, which uses the Pearson coefficient to select features of gas concentration data. It then uses long short-term memory (LSTM) that has been optimized using adaptive moment estimation (Adam) to predict a time series. In the process of model establishment, the optimal prediction model was obtained by constantly adjusting the number of network layers and batch size based on the fitting effect, performance issues, and result errors. Taking monitoring data from the 2407 working face at Yuhua Coal Mine as the sample,... [more]

The interconnection of transmission, distribution, and generation lines has established a structure for the power system that is intricate. Uncertainties in the active power flow are caused by changes in load and a growing dependence on renewable energy sources. The study presented in this paper employs several controlling strategies to reduce frequency variations in series-compensated two-area power systems. Future power systems will require the incorporation of flexible AC transmission system (FACTS) devices, since the necessity for compensation in the power system is unavoidable. Therefore, a static synchronous series compensator (SSSC) is installed in both areas of our study to make it realistic and futuristic. This makes it easier to comprehend how series compensation works in a load−frequency model. With the integration of electrical vehicles (EVs) and solar photovoltaic (PV) systems, several control strategies are presented to reduce the frequency oscillations in this power syst... [more]

Microseismic monitoring has become a well-known technique for predicting the mechanisms of rock failure in deeply buried energy exploration, in which noise has a great influence on microseismic monitoring results. We proposed an improved microseismic denoising method based on different wavelet coefficients of useful signal and noise components. First, according to the selection of an appropriate wavelet threshold and threshold function, the useful signal part of original microseismic signal was decomposed many times and reconstructed to achieve denoising. Subsequently, synthetic signals of different types (microseismic noise, microseismic current, microseismic noise current) and with various signal-to-noise ratios (SNRs, −10~10) were used as test data. Evaluation indicators (mean absolute error μ and standard deviation error σ) were established to compare the denoising effect of different denoising methods and verify that the improved method is more effective than the traditional denoi... [more]

Currently, new technologies and approaches are continuously and rapidly being introduced and implemented in energy systems [...]

This study investigates the use of transfer learning and modular design for adapting a pretrained model to optimize energy efficiency and heat reuse in edge data centers while meeting local conditions, such as alternative heat management and hardware configurations. A Physics-Informed Data-Driven Recurrent Neural Network (PIDD RNN) is trained on a small scale-model experiment of a six-server data center to control cooling fans and maintain the exhaust chamber temperature within safe limits. The model features a hierarchical regularizing structure that reduces the degrees of freedom by connecting parameters for related modules in the system. With a RMSE value of 1.69, the PIDD RNN outperforms both a conventional RNN (RMSE: 3.18), and a State Space Model (RMSE: 2.66). We investigate how this design facilitates transfer learning when the model is fine-tuned over a few epochs to small dataset from a second set-up with a server located in a wind tunnel. The transferred model outperforms a m... [more]

Companies are striving to optimize the decommissioning process of oil and gas facilities in order to reduce overall costs. Various options and criteria for making decisions on the liquidation of objects are considered, which are based on a multicriteria approach that allows optimizing this complex process. The most important characteristics of a reliable model for optimizing options for decommissioning oil and gas facilities are defined as follows: the option should take into account qualitative and quantitative criteria. To minimize the subjectivity of human judgment, a systematic poll of skilled performers should be conducted using a well-structured technique. It is shown that multicriteria decision analysis can be considered as an adequate model for choosing options for decommissioning oil and gas assets. Based on a test case that reflects the practice of decommissioning offshore platforms in the state of California (USA), the logic and algorithm of calculations are reproduced when... [more]

One preliminary key step for developing an offshore wind farm is identifying favorable sites. The process of sitting involves multiple requirements and constraints, and therefore, its feasible implementation requires either approximating assumptions or an optimization method that is capable of handling non-linear relationships and heterogeneous factors. A new optimization method is proposed to address this problem that efficiently and accurately combines essential technical criteria, such as wind speed, water depth, and distance from shore, to identify favorable areas for offshore wind farm development through a user-friendly data-driven tool. Appropriate ranks and weighting factors are carefully selected to obtain realistic results. The proposed methodology is applied in the central Aegean Sea, which has a high offshore wind energy potential. The application of the proposed optimization method reveals large areas suitable for developing floating wind energy structures. The algorithm m... [more]

For the offshore wind turbines (OWTs) located in a seismically active region, the occurrence of earthquakes combined with scour is a highly possible multi-hazard event. This study developed an alternative fragility analysis framework to assess the seismic performance of suction bucket-supported OWTs under the action of scour. First, the probabilistic approach was applied to calculate the occurrence probability of scour depth (SD) and earthquake events. Then, the possible combinations of these two events were considered in the analysis model to establish the fragility surface of the suction bucket foundation. Finally, by integrating the product of scour and earthquake hazard, as well as fragility curves, the suction bucket foundation failure probability was obtained. The developed framework provides a reliable approach to risk assessment for OWT-supporting structures in extreme event situations and can be applied to other complex natural hazards.

(1) Background: The economic impact of supply and demand in the energy industry can be seen throughout raw material production, processing and distribution. The purpose of this study is to provide academic information to prepare for crisis management through prediction using indices. (2) Method: In order to predict a crisis of the energy industry, the Crisis Index presented by the Korea Institute of Science and Technology Information was utilized instead of the economic indicators that have hitherto been widely used. Specifically, we propose a new forecasting model based on the concept of economic momentum theory used in financial research and the proper weighting of the earning downside risk theory. Then, statistical empirical analysis was performed to verify the new prediction model. (3) Results: As the result of the empirical analysis, the risk index prediction model proposed in this study showed statistically significant results in predicting the future risk index. (4) Conclusions:... [more]

Condition diagnosis of power transformers using acoustic signals is a nonstop, contactless method of equipment maintenance that can diagnose the transformer’s type of abnormal condition. To heighten the accuracy and efficiency of the abnormal method of diagnosing abnormalities by sound, a method for abnormal diagnosis of power transformers based on the Attention-CNN-LSTM hybrid model is proposed. This collects the sound signals emitted by the real power transformer in the normal state, overload, and the discharge condition. It preprocesses the sound signals to obtain the MFCC characteristics of the sound signals. It is then grouped into a set of sound feature vectors by the first- and second-order differences, and enters the Attention-CNN-LSTM hybrid model for training. The training results show that the Attention-CNN-LSTM hybrid model can be used for the status sound detection of power transformers, and the recognition of the three states can achieve an accuracy rate of more than 99%.

The underground mining industry is at the forefront when it comes to unsafe conditions at workplaces. As mining depths continue to increase and the mining fronts move away from the ventilation shafts, gas hazards are increasing. In this article, the authors developed a statistical polynomial model for nitrogen oxide (NOx) emission prediction of the LHD vehicle with a diesel engine. The best-achieved prediction accuracy by the 4th order polynomial model for 11 and 10 input variables is about 8% and 13%, respectively. It is comparable with the sensors’ accuracy of 10% at a stable regime of loading and 20% in the transient periods of operation. The obtained results allow planning of ventilation system capacity and power demand for the large fleet of vehicles in the deep underground mines.

As a major component in electric power systems, power transformers are one of the most expensive and important pieces of electrical equipment. The trouble-free operation of power transformers is an important criterion for safety and stability in a power system. Technical diagnostics of electrical equipment are a mandatory part of preventing accidents and ensuring the continuity of the power supply. In this study, a complex diagnostic methodology was proposed and applied for special power transformers’ risk estimation. Twenty special power transformers were scored with the proposed risk estimation methodology. For each transformer, dissolved gas analysis (DGA) tests, transformer oil quality analysis, visual inspections of all current equipment on-site and historical data for the operation of each electrical research were conducted. All data were collected and analyzed under historical records of malfunctioning events. Statistical data for expected fault risk, based on long-term records,... [more]

Recently, deep learning techniques have become popular and are widely employed in several research areas, such as optimization, pattern recognition, object identification, and forecasting, due to the advanced development of computer programming technologies. A significant number of renewable energy sources (RESs) as environmentally friendly sources, especially solar photovoltaic (PV) sources, have been integrated into modern power systems. However, the PV source is highly fluctuating and difficult to predict accurately for short-term PV output power generation, leading to ineffective system planning and affecting energy security. Compared to conventional predictive approaches, such as linear regression, predictive-based deep learning methods are promising in predicting short-term PV power generation with high accuracy. This paper investigates the performance of several well-known deep learning techniques to forecast short-term PV power generation in the real-site floating PV power plan... [more]

Solar-cell panels use sunlight as a source of energy to generate electricity. However, the performances of solar panels decline when they degrade, owing to defects. Some common defects in solar-cell panels include hot spots, cracking, and dust. Hence, it is important to efficiently detect defects in solar-cell panels and repair them. In this study, we propose a lightweight inception residual convolutional network (LIRNet) to detect defects in solar-cell panels. LIRNet is a neural network model that utilizes deep learning techniques. To achieve high model performance on solar panels, including high fault detection accuracy and processing speed, LIRNet draws on hierarchical learning, which is a two-phase solar-panel-defect classification method. The first phase is the data-preprocessing stage. We use the K-means clustering algorithm to refine the dataset. The second phase is the training of the model. We designed a powerful and lightweight neural network model to enhance accuracy and spe... [more]

The application of machine learning to solve engineering problems is in extremely high demand. This article proposes a tool that employs machine learning algorithms for predicting the frequency response of an unsteady vortex phenomenon, the precessing vortex core (PVC), occurring in a conical diffuser behind a radial swirler. The model input parameters are the two components of the time-averaged velocity profile at the cone diffuser inlet. An empirical database was obtained using a fully automated experiment. The database associates multiple inlet velocity profiles with pressure pulsations measured in the cone diffuser, which are caused by the PVC in the swirling flow. In total, over 103 different flow regimes were measured by varying the swirl number and the cone angle of the diffuser. Pressure pulsations induced by the PVC were detected using two pressure fluctuations sensors residing on opposite sides of the conical diffuser. A classifier was constructed using the Linear Support Vec... [more]

The heat transfer performance of the evaporator significantly affects the heat transfer capacity of the loop heat pipe (LHP). The vapor blanket can be formed once the vapor penetrates the wick especially at high heat flux, resulting in an unsaturated state of the wick and deteriorating the evaporator performance. It is crucial to understand the liquid−vapor behavior for enhancing the LHP performance by investigating the fundamental heat and mass transfer in the wick with phase-change. However, previous modeling studies only considered a single-phase flow or complete saturation in the wick, and the capillary effect on the fluid states was rarely taken into account. The present work developed two mathematical models based on the assumptions of saturated and unsaturated wicks. The fluid states were analyzed at the liquid−vapor interface under the consideration of the capillary effect, and a pore-scale evaporation model was applied to study the phase change behavior and interfacial heat an... [more]

Load disaggregation for the identification of specific load types in the total demands (e.g., demand-manageable loads, such as heating or cooling loads) is becoming increasingly important for the operation of existing and future power supply systems. This paper introduces an approach in which periodical changes in the total demands (e.g., daily, weekly, and seasonal variations) are disaggregated into corresponding frequency components and correlated with the same frequency components in the meteorological variables (e.g., temperature and solar irradiance), allowing to select combinations of frequency components with the strongest correlations as the additional explanatory variables. The paper first presents a novel Fourier series regression method for obtaining target frequency components, which is illustrated on two household-level datasets and one substation-level dataset. These results show that correlations between selected disaggregated frequency components are stronger than the c... [more]

The 2007−2008 financial crisis, together with rises in fuel prices and stringent pollution regulation, led to the need to update the methods concerning ship propulsion system design. In this article, a set of artificial neural networks was used to update the design equations to estimate the engine power and fuel consumption of modern tankers, bulk carriers, and container ships. Deadweight or TEU capacity and ship speed were used as the inputs for the ANNs. This study shows that even a linear ANN with two neurons in the input and output layers, with purelin activation functions, offers an accurate estimation of ship propulsion parameters. The proposed linear ANNs have simple mathematical structures and are straightforward to apply. The ANNs presented in the article were developed based on the data of the most recent ships built from 2015 to present, and could have a practical application at the preliminary design stage, in transportation or air pollution studies for modern commercial ca... [more]