bluuebunny/crossref_metadata_2025 huggingface dataset:
ECHO: Energy-Efficient Computation Harnessing Online Arithmetic—An MSDF-Based Accelerator for DNN Inference
Authors: Ibrahim Muhammad Sohail , Usman Muhammad , Lee Jeong-A
Published: May 2024

Deep neural network (DNN) inference demands substantial computing power, resulting in significant energy consumption. A large number of negative output activations in convolution layers are rendered zero due to the invocation of the ReLU activation function. This results in a substantial number of unnecessary computations that consume significant amounts of energy. This paper presents ECHO, an accelerator for DNN inference designed for computation pruning, utilizing an unconventional arithmetic paradigm known as online/most significant digit first (MSDF) arithmetic, which performs computations in a digit-serial manner. The MSDF digit-serial computation of online arithmetic enables overlapped computation of successive operations, leading to substantial performance improvements. The online arithmetic, coupled with a negative output detection scheme, facilitates early and precise recognition of negative outputs. This, in turn, allows for the timely termination of unnecessary computations, resulting in a reduction in energy consumption. The implemented design has been realized on the Xilinx Virtex-7 VU3P FPGA and subjected to a comprehensive evaluation through a rigorous comparative analysis involving widely used performance metrics. The experimental results demonstrate promising power and performance improvements compared to contemporary methods. In particular, the proposed design achieved average improvements in power consumption of up to 81%, 82.9%, and 40.6% for VGG-16, ResNet-18, and ResNet-50 workloads compared to the conventional bit-serial design, respectively. Furthermore, significant average speedups of 2.39×, 2.6×, and 2.42× were observed when comparing the proposed design to conventional bit-serial designs for the VGG-16, ResNet-18, and ResNet-50 models, respectively.
Transferring Black-Box Decision Making to a White-Box Model
Authors: Žlahtič Bojan , Završnik Jernej , Blažun Vošner Helena , Kokol Peter
Published: May 2024

In the rapidly evolving realm of artificial intelligence (AI), black-box algorithms have exhibited outstanding performance. However, their opaque nature poses challenges in fields like medicine, where the clarity of the decision-making processes is crucial for ensuring trust. Addressing this need, the study aimed to augment these algorithms with explainable AI (XAI) features to enhance transparency. A novel approach was employed, contrasting the decision-making patterns of black-box and white-box models. Where discrepancies were noted, training data were refined to align a white-box model’s decisions closer to its black-box counterpart. Testing this methodology on three distinct medical datasets revealed consistent correlations between the adapted white-box models and their black-box analogs. Notably, integrating this strategy with established methods like local interpretable model-agnostic explanations (LIMEs) and SHapley Additive exPlanations (SHAPs) further enhanced transparency, underscoring the potential value of decision trees as a favored white-box algorithm in medicine due to its inherent explanatory capabilities. The findings highlight a promising path for the integration of the performance of black-box algorithms with the necessity for transparency in critical decision-making domains.
g2D-Net: Efficient Dehazing with Second-Order Gated Units
Authors: Jia Jia , Wang Zhibo , Min Jeongik
Published: May 2024

Image dehazing aims to reconstruct potentially clear images from corresponding images corrupted by haze. With the rapid development of deep learning-related technologies, dehazing methods based on deep convolutional neural networks have gradually become mainstream. We note that existing dehazing methods often accompany an increase in computational overhead while improving the performance of dehazing. We propose a novel lightweight dehazing neural network to balance performance and efficiency: the g2D-Net. The g2D-Net borrows the design ideas of input-adaptive and long-range information interaction from Vision Transformers and introduces two kinds of convolutional blocks, i.e., the g2D Block and the FFT-g2D Block. Specifically, the g2D Block is a residual block with second-order gated units, which inherit the input-adaptive property of a gated unit and can realize the second-order interaction of spatial information. The FFT-g2D Block is a variant of the g2D Block, which efficiently extracts the global features of the feature maps through fast Fourier convolution and fuses them with local features. In addition, we employ the SK Fusion layer to improve the cascade fusion layer in a traditional U-Net, thus introducing the channel attention mechanism and dynamically fusing information from different paths. We conducted comparative experiments on five benchmark datasets, and the results demonstrate that the g2D-Net achieves impressive dehazing performance with relatively low complexity.
BPA: A Novel Blockchain-Based Privacy-Preserving Authentication Scheme for the Internet of Vehicles
Authors: Li Jie , Lin Yuanyuan , Li Yibing , Zhuang Yan , Cao Yangjie
Published: May 2024

The Internet of Vehicles (IoV) connects an isolated individual on the road to share information, which can improve traffic efficiency. However, the promotion of information sharing brings the critical security issues of identity authentication, followed by privacy protection issues in the authentication process in the IoV. In this study, we designed a blockchain-based conditional privacy-preserving authentication scheme for the IoV (BPA). Our scheme implements zero-knowledge proof (ZKP) to verify the identities of vehicles, which moves the authentication process down to the Roadside Units (RSUs) and achieves decentralized authentication at the edge nodes. Moreover, blockchain technology is utilized to synchronize a consistent ledger across all RSUs for recording and disseminating vehicle authentication states, which enhances the overall authentication process efficiency. We provide a theoretical analysis asserting that the BPA ensures enhanced security and effectively protects the privacy of all participating vehicles. Experimental evaluations confirm that our scheme outperforms existing solutions in terms of the computational and communication overhead.
Promotion and Advancement of Data Security Governance in China
Authors: Chen Bing , Liu Yongji
Published: May 2024

In this age of digital economy, issues relating to the data security and personal privacy of multimedia systems are increasingly manifesting in the form of unprotected data. The level of governance capability for data protection determines whether the data security and personal privacy of multimedia systems can be protected and developed in the future. In recent years, China has introduced laws and regulations on the protection of personal information, personal privacy, data security, and cybersecurity. Although data protection still needs to be improved, China has been refining the framework and provisions on data security governance through certain practices and the adoption of supporting regulations. The country aims to rapidly promote the current governance capability of data security and gradually form a good institutional environment to support international collaboration in the realm of global data security.
WaveletDFDS-Net: A Dual Forward Denoising Stream Network for Low-Dose CT Noise Reduction
Authors: Zhou Yusheng , Kong Zhengmin , Huang Tao , Ahn Euijoon , Li Hao , Ding Li
Published: May 2024

The challenge of denoising low-dose computed tomography (CT) has garnered significant research interest due to the detrimental impact of noise on CT image quality, impeding diagnostic accuracy and image-guided therapies. This paper introduces an innovative approach termed the Wavelet Domain Dual Forward Denoising Stream Network (WaveletDFDS-Net) to address this challenge. This method ingeniously combines convolutional neural networks and Transformers to leverage their complementary capabilities in feature extraction. Additionally, it employs a wavelet transform for efficient image downsampling, thereby preserving critical information while reducing computational requirements. Moreover, we have formulated a distinctive dual-domain compound loss function that significantly enhances the restoration of intricate details. The performance of WaveletDFDS-Net is assessed by comparative experiments conducted on public CT datasets, and results demonstrate its enhanced denoising effect with an SSIM of 0.9269, PSNR of 38.1343 and RMSE of 0.0130, superior to existing methods.
Deep Reinforcement Learning for Ecological and Distributed Urban Traffic Signal Control with Multi-Agent Equilibrium Decision Making
Authors: Yan Liping , Wang Jing
Published: May 2024

Multi-Agent Reinforcement Learning (MARL) has shown strong advantages in urban multi-intersection traffic signal control, but it also suffers from the problems of non-smooth environment and inter-agent coordination. However, most of the existing research on MARL traffic signal control has focused on designing efficient communication to solve the environment non-smoothness problem, while neglecting the coordination between agents. In order to coordinate among agents, this paper combines MARL and the regional mixed-strategy Nash equilibrium to construct a Deep Convolutional Nash Policy Gradient Traffic Signal Control (DCNPG-TSC) model, which enables agents to perceive the traffic environment in a wider range and achieves effective agent communication and collaboration. Additionally, a Multi-Agent Distributional Nash Policy Gradient (MADNPG) algorithm is proposed in this model, which is the first time the mixed-strategy Nash equilibrium is used for the improvement in the Multi-Agent Deep Deterministic Policy Gradient algorithm traffic signal control strategy to provide the optimal signal phase for each intersection. In addition, the eco-mobility concept is integrated into MARL traffic signal control to reduce pollutant emissions at intersections. Finally, simulation results in synthetic and real-world traffic road networks show that DCNPG-TSC outperforms other state-of-the-art MARL traffic signal control methods in almost all performance metrics, because it can aggregate the information of neighboring agents and optimize the agent’s decisions through gaming to find an optimal joint equilibrium strategy for the traffic road network.
Test Coverage in Microservice Systems: An Automated Approach to E2E and API Test Coverage Metrics
Authors: Abdelfattah Amr S. , Cerny Tomas , Yero Jorge , Song Eunjee , Taibi Davide
Published: May 2024

Test coverage is a critical aspect of the software development process, aiming for overall confidence in the product. When considering cloud-native systems, testing becomes complex, as it becomes necessary to deal with multiple distributed microservices that are developed by different teams and may change quite rapidly. In such a dynamic environment, it is important to track test coverage. This is especially relevant for end-to-end (E2E) and API testing, as these might be developed by teams distinct from microservice developers. Moreover, indirection exists in E2E, where the testers may see the user interface but not know how comprehensive the test suits are. To ensure confidence in health checks in the system, mechanisms and instruments are needed to indicate the test coverage level. Unfortunately, there is a lack of such mechanisms for cloud-native systems. This manuscript introduces test coverage metrics for evaluating the extent of E2E and API test suite coverage for microservice endpoints. It elaborates on automating the calculation of these metrics with access to microservice codebases and system testing traces, delves into the process, and offers feedback with a visual perspective, emphasizing test coverage across microservices. To demonstrate the viability of the proposed approach, we implement a proof-of-concept tool and perform a case study on a well-established system benchmark assessing existing E2E and API test suites with regard to test coverage using the proposed endpoint metrics. The results of endpoint coverage reflect the diverse perspectives of both testing approaches. API testing achieved 91.98% coverage in the benchmark, whereas E2E testing achieved 45.42%. Combining both coverage results yielded a slight increase to approximately 92.36%, attributed to a few endpoints tested exclusively through one testing approach, not covered by the other.
Wireless Power Transfer for Unmanned Underwater Vehicles: Technologies, Challenges and Applications
Authors: Martínez de Alegría Iñigo , Rozas Holgado Iñigo , Ibarra Edorta , Robles Eider , Martín José Luís
Published: May 2024

Unmanned underwater vehicles (UUVs) are key technologies to conduct preventive inspection and maintenance tasks in offshore renewable energy plants. Making such vehicles autonomous would lead to benefits such as improved availability, cost reduction and carbon emission minimization. However, some technological aspects, including the powering of these devices, remain with a long way to go. In this context, underwater wireless power transfer (UWPT) solutions have potential to overcome UUV powering drawbacks. Considering the relevance of this topic for offshore renewable plants, this work aims to provide a comprehensive summary of the state of the art regarding UPWT technologies. A technology intelligence study is conducted by means of a bibliographical survey. Regarding underwater wireless power transfer, the main methods are reviewed, and it is concluded that inductive wireless power transfer (IWPT) technologies have the most potential. These inductive systems are described, and their challenges in underwater environments are presented. A review of the underwater IWPT experiments and applications is conducted, and innovative solutions are listed. Achieving efficient and reliable UWPT technologies is not trivial, but significant progress is identified. Generally, the latest solutions exhibit efficiencies between 88% and 93% in laboratory settings, with power ratings reaching up to 1–3 kW. Based on the assessment, a power transfer within the range of 1 kW appears to be feasible and may be sufficient to operate small UUVs. However, work-class UUVs require at least a tenfold power increase. Thus, although UPWT has advanced significantly, further research is required to industrially establish these technologies.
Bridging Geo-Data and Natural Gas Pipeline Design Standards: A Systematic Review of BIM-GIS Integration for Natural Gas Pipeline Asset Management
Authors: Demir Selcuk , Yomralioglu Tahsin
Published: May 2024

In today’s world, effective management and the use of spatial data are of great importance in many sectors. Industries such as land management, asset management, and infrastructure management are areas where spatial data are heavily utilized. Advanced technologies such as Geographic Information Systems (GISs) and Building Information Modeling (BIM) are used in the processes of collecting, analyzing, and managing geographically enabled data (geo-data). These technologies enable the effective processing of large datasets, improve decision-making processes based on geographic information, and facilitate more efficient collaboration across sectors. This study conducts an in-depth examination of the existing literature on asset management, infrastructure management, and BIM-GIS integration using bibliometric analysis and systematic literature review methods. Bibliometric analysis is employed to determine statistical values such as current research trends, frequently cited authors, most used keywords, and country performances in the relevant field. This study’s results highlight future research trends and significant gaps in the areas of asset management, infrastructure management, natural gas pipelines, and BIM-GIS integration. In particular, this study demonstrates the critical importance of asset management and BIM-GIS integration for sustainable infrastructure design, construction, and management. In this context, attention is drawn to the importance of data standardization, digitization, systematic integration, and contemporary land management requirements.
Energy Performance in Residential Buildings as a Property Market Efficiency Driver
Authors: Walacik Marek , Chmielewska Aneta
Published: May 2024

Energy consumption plays an important role in contemporary economies. Its significance extends beyond utilitarian value, impacting economic robustness, environmental protection, and residents’ well-being. The escalating global energy requisites necessitate efficient energy utilization and a shift towards renewable sources to address climate change and strengthen energy independence. Developing accurate predictive models to forecast long-term energy costs and savings remains a complex problem. This paper aims to provide a methodology to identify the influence of building energy performance on real estate market efficiency, focusing on property maintenance costs. Real estate plays a crucial role in human life, serving both as a fundamental need and as a vehicle for achieving personal aspirations and secure financial investments, particularly during times of economic and social instability. Through interdisciplinary methodological architecture, this study addresses three key issues: the impact of rising energy costs on market efficiency, the responsiveness of the real estate market to energy price fluctuations, and the significance of property maintenance costs on market value. The research approach includes creating and applying AI algorithms capable of evaluating extensive datasets pertaining to real estate features. Utilizing machine learning methods, the algorithm determines the importance of energy efficiency measures as well as various other inherent and external attributes of properties. The suggested methodology provides a novel approach to improve the effectiveness of market efficiency analysis.
Research on Optimization of Financial Performance Evaluation of Energy Enterprises under the Background of Low-Carbon Economy
Authors: Li Xiao , Gao Hongxin , Zhou Enyi
Published: May 2024

The development of human society and the production and operation activities of enterprises have brought about global warming, resulting in frequent natural disasters. It has become the consensus of all countries in the world to develop a green and low-carbon economy. Under this background, enterprises, as the main body of economic activities, especially energy industry enterprises, should optimize and upgrade the traditional production and operation mode with high pollution, high consumption, and low output to a high-efficiency and low-pollution mode, and pay attention to the co-ordinated development of economic benefits, social benefits, and ecological benefits. Financial performance evaluation indicators have become the main basis for senior leaders of energy industry enterprises to make decisions and evaluate the low-carbon economic benefits of enterprises. This paper constructs a set of financial evaluation index systems of energy industry enterprises under the background of a low-carbon economy from four dimensions: profitability, asset quality, debt risk, and business growth. The analytic hierarchy process (AHP) is used to measure the comprehensive contribution of financial indicators of low-carbon production and operation. The purpose of this study is to provide scientific financial management decisions for energy enterprises to reduce costs and increase the efficiency and low-carbon operation under the background of a low-carbon economy. The research results show that the comprehensive evaluation index system after the traditional financial evaluation index of energy industry enterprises is integrated with the evaluation index of a low-carbon economy can help enterprises make more correct and effective financial decisions in the process of survival, development, and growth, and, at the same time, the financial evaluation index of a low-carbon economy should pay more attention to the indicators with a higher comprehensive contribution, so as to effectively promote the low-carbon operation efficiency of enterprise production, management, and sales. Compared with other research results, this paper innovatively constructs a financial management decision-making index system for measuring the low-carbon operation of energy enterprises in theory, which has important value in guiding energy enterprises to reduce costs and increase the efficiency and low-carbon operation in practice.
Coordinated Control of Proton Exchange Membrane Electrolyzers and Alkaline Electrolyzers for a Wind-to-Hydrogen Islanded Microgrid
Authors: Li Zhanfei , Tu Zhenghong , Yi Zhongkai , Xu Ying
Published: May 2024

In recent years, the development of hydrogen energy has been widely discussed, particularly in combination with renewable energy sources, enabling the production of “green” hydrogen. With the significant increase in wind power generation, a promising solution for obtaining green hydrogen is the development of wind-to-hydrogen (W2H) systems. However, the high proportion of wind power and electrolyzers in a large-scale W2H system will bring about the problem of renewable energy consumption and frequency stability reduction. This paper analyzes the operational characteristics and economic feasibility of mainstream electrolyzers, leading to the proposal of a coordinated hydrogen production scheme involving both a proton exchange membrane (PEM) electrolyzer and an alkaline (ALK) electrolyzer. Subsequently, a coordinated control based on Model Predictive Control (MPC) is proposed for system frequency regulation in a large-scale W2H islanded microgrid. Finally, simulation results demonstrate that the system under PEM/ALK electrolyzers coordinated control not only flexibly accommodates fluctuating wind power but also maintains frequency stability in the face of large disturbances. Compared with the traditional system with all ALK electrolyzers, the frequency deviation of this system is reduced by 25%, the regulation time is shortened by 80%, and the demand for an energy storage system (ESS) is reduced. The result validates the effectiveness of MPC and the benefits of the PEM/ALK electrolyzers coordinated hydrogen production scheme.
A Study of the Thermal Management and Discharge Strategies of Lithium-Ion Batteries in a Wide Temperature Range
Authors: Li Kaixuan , Sun Chen , Zhang Mingjie , Wang Shuping , Wei Bin , Cheng Yifeng , Ju Xing , Xu Chao
Published: May 2024

The performance of lithium-ion batteries is greatly influenced by various factors within their operating environment, which can significantly impact their overall efficiency and effectiveness. In this paper, a multi-physics field electrochemical thermal model is established to measure the physical parameters of a battery module during the charge/discharge process. The effects of working temperature, current rate, and convective heat transfer coefficient are investigated by establishing an electrochemical and thermal model. The results are obtained by conducting numerous parameterized scans to analyze the system’s state across various operating conditions, enabling the determination of its temperature and the selection of appropriate cooling measures accordingly. Based on the internal and external conditions of battery operation, parameter selection corresponding to the operating range is divided into several stages, with thermal management strategies provided for each stage. The existing framework facilitates the design of battery packs equipped with efficient thermal management strategies, thereby enhancing the battery systems’ reliability and performance. Furthermore, it aids in establishing optimal operational and safety boundaries for batteries.
Simulation and Experimental Verification of Dispersion and Explosion of Hydrogen–Methane Mixture in a Domestic Kitchen
Authors: Xu Haidong , Deng Qiang , Huang Xiaomei , Li Du , Pan Fengwen
Published: May 2024

Hydrogen is a carbon-free energy source that can be obtained from various sources. The blending of hydrogen represents a transitional phase in the shift from natural gas systems to hydrogen-based systems. However, concerns about the safety implications of introducing hydrogen have led to extensive discussions. This paper utilizes Fluent 17.0 numerical simulation software to simulate the leakage of hydrogen-blended natural gas in a closed domestic kitchen and analyze the concentration distribution and its variation pattern after a leakage. An experimental platform is set up, and a mixture of nitrogen and helium gas is used as a substitute for hydrogen-blended natural gas for the simulations and experiments. The simulation results demonstrate that the leaked gas spreads and accumulates towards the top of the space, gradually filling the entire area as the leak persists. As the hydrogen content in the leaked gas increases, the dispersion capacity of the gas in the confined space also increases. Furthermore, as the flow rate of the leaked gas increases, the average concentration of the leaked gas rises, and the gas stratification in the confined kitchen diminishes. The concentration distribution observed in the experiments aligns with the simulation results. After establishing the feasibility conditions of the model, the dispersion of the hydrogen-blended natural gas in the kitchen is further simulated. The results suggest that blending hydrogen into the gas enhances the dispersion of the gas after a leak, leading to a wider distribution within the kitchen and an increased risk in the event of a leak. Additionally, this paper employs the CASD module of FLACS 11.0 software to construct a three-dimensional geometric model of the domestic kitchen for simulation studies on the explosion of hydrogen-blended natural gas in a confined space. By adjusting the hydrogen ratio in the combustible gases present in the space and examining the variations in hydrogen concentration and external conditions, such as opening or closing the door, the influence on parameters including the peak explosion pressure, explosion overpressure, explosion flame temperature, and explosion response time are examined. Furthermore, the extent of the explosion area is determined, and the effect of hydrogen on the blast is clarified.
Design, Simulation and Optimization of a Novel Transpired Tubular Solar Air Heater
Authors: Nemati Hossain
Published: May 2024

In this paper, a novel tubular solar air heater is introduced. In this air heater, the hot boundary layer is drawn into the absorber tube and can provide thermal energy at moderate temperatures. Several different cases were simulated and a correlation was proposed to predict the collector’s effectiveness as a function Rayleigh number and Reynolds number. An equation was derived to find the effectiveness of this collector. Finally, a real case was studied with non-uniform solar flux distribution, as well as radiation heat loss. Good agreement was found between the results and those derived by the proposed analytical method. For different suction values, the first-law and the second-law efficiencies were calculated. Based on the exergy analysis, exergy destruction in absorption is the dominant factor that is unavoidable in low-temperature collectors. It was shown that there is an optimum suction value at which the second-law efficiency is maximized. At the optimum point, temperature rise can reach 54 K, which is hardly possible with a flat plate collector. Based on the exergy analysis, the relation between tube wall temperature and air outlet temperature in their dimensionless forms at the optimum working condition was derived, and it was shown that effectiveness at the optimum working condition is around 0.5. This means that the air temperature rise shall be half of the temperature difference between collector wall and the ambient temperatures. A high outlet temperature besides the low cost of construction and maintenance are the main advantages of this air heater. With such a high temperature rise, this type of collector can increase the use of solar energy in domestic applications.
Experimental Investigation of the Viscosity and Density of Microencapsulated Phase Change Material Slurries for Enhanced Heat Capacity and Transfer
Authors: Nalepa Bartlomiej , Dutkowski Krzysztof , Kruzel Marcin , Bialko Boguslaw , Zajaczkowski Bartosz
Published: May 2024

Working fluids that incorporate solid microencapsulated phase change materials (MPCMs) can benefit from properties such as density and viscosity, which are crucial for improving heat capacity and transfer. However, limited data are available on these parameters for specific slurry and mass ratios. In this study, we present a comparative analysis of the experimental results on the viscosity of three different MPCM aqueous dispersions, namely MPCM 31-S50, MPCM 25-S50, and Micronal 5428X. Varying MPCM mass ratios of distilled water were used to obtain different mass concentrations of the phase change material (PCM), and the resulting slurries were analysed at temperatures ranging from 15 to 40 °C. Our findings showed that all slurries exhibited non-Newtonian characteristics at low shear rates, with viscosity stabilising at higher shear rates, resulting in the characteristics of a Newtonian fluid. The viscosity results were highly dependent on the type of MPCM base dispersion, particularly at high mass ratios, with the slurries having viscosities higher than those of water. Furthermore, we conducted density experiments as a function of temperature, using a flow test setup and a Coriolis flowmeter (Endress+Hauser, Reinach, Switzerland) to determine the density of two MPCMs, namely MPCM 25-S50 and Micronal 5428X. The test samples were prepared at mass concentrations of 10%, 15%, and 20% of the phase change material. We found significant differences in density and viscosity for different MPCM slurries as a result of both the PCM concentration and the material studied. Our results also revealed an apparent PCM phase change process, in which the slurry density significantly decreased in the temperature range of the phase transition from solid to liquid.
Adaptive Design of Solar-Powered Energy Systems Based on Daily Clearness State Evolution
Authors: Liang Dong , Ma Long , Wang Peng , Li Yuanxia , Luo Yiping
Published: May 2024

The optimal designing of the hybrid energy system (HES) is a challenging task due to the multiple objectives and various uncertainties. Especially for HES, primarily powered by solar energy, the reference solar radiation data directly impact the result of the optimization design. To incorporate the stochastic characteristics of solar radiation into the sizing process, a data-driven stochastic modeling method for solar radiation is proposed. The method involves two layers of stochastic processes that capture the intraday variation and daily evolution of solar radiation. First, the clearness index (CI) is introduced to describe the radiation intensity at different times. Then, the daily clearness state (DCS) is proposed, based on the statistical indicators of the intraday CI. The Markov model is used to describe the stochastic evolutionary characteristics between different DCSs. The probabilistic distribution of the CI under different DCS is obtained based on the diffusion kernel density estimation (DKDE), which is used for the stochastic generation of the CI at various times of the day. Finally, the radiation profile required for the optimal design is obtained by the stochastic generation of the DCS sequences and the intraday clearness index under corresponding states. A case study of an off-grid solar-powered HES is provided to illustrate this methodology.
A Neural Network Forecasting Approach for the Smart Grid Demand Response Management Problem
Authors: Belhaiza Slim , Al-Abdallah Sara
Published: May 2024

Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds significance as it can substantially impact the final cost, mitigate risks, and support informed decision-making. In this paper, a forecasting approach employing neural networks for smart grid demand-side management is proposed. The study explores various enhanced artificial neural network (ANN) architectures for forecasting smart grid consumption. The performance of the ANN approach in predicting energy demands is evaluated through a comparison with three statistical models: a time series model, an auto-regressive model, and a hybrid model. Experimental results demonstrate the ability of the proposed neural network framework to deliver accurate and reliable energy demand forecasts.
A Reviewed Turn at of Methods for Determining the Type of Fault in Power Transformers Based on Dissolved Gas Analysis
Authors: Aciu Ancuța-Mihaela , Enache Sorin , Nițu Maria-Cristina
Published: May 2024

Since power transformers are the most important pieces of equipment in electricity transmission and distribution systems, special attention must be paid to their maintenance in order to keep them in good condition for a long time. This paper reviews the main steps in the process of diagnosing the health of power transformer insulation, which involves the science of analysing the gases dissolved in power transformer oil for effective identification of faults. An accurate diagnosis of incipient faults is favourable to sustainable development and necessary to maintain a reliable supply of electricity. The methods presented for fault diagnosis in mineral-oil-immersed power transformers are divided into analytical and graphical methods and have been found to be simple, economical and effective. After describing the methods, both their strengths and weaknesses were identified, and over the years, the methods were complemented to provide highly accurate information, validated by field inspections. This paper focuses on practical information and applications to manage maintenance based on accurate and up-to-date data. The contents of this paper will be of particular use to engineers who manufacture, monitor and/or use high-power transformers in the energy sector, as well as to undergraduate, master’s and PhD students interested in such applications.
LSTM-Autoencoder Deep Learning Model for Anomaly Detection in Electric Motor
Authors: Lachekhab Fadhila , Benzaoui Messouada , Tadjer Sid Ahmed , Bensmaine Abdelkrim , Hamma Hichem
Published: May 2024

Anomaly detection is the process of detecting unusual or unforeseen patterns or events in data. Many factors, such as malfunctioning hardware, malevolent activities, or modifications to the data’s underlying distribution, might cause anomalies. One of the key factors in anomaly detection is balancing the trade-off between sensitivity and specificity. Balancing these trade-offs requires careful tuning of the anomaly detection algorithm and consideration of the specific domain and application. Deep learning techniques’ applications, such as LSTMs (long short-term memory algorithms), which are autoencoders for detecting an anomaly, have garnered increasing attention in recent years. The main goal of this work was to develop an anomaly detection solution for an electrical machine using an LSTM-autoencoder deep learning model. The work focused on detecting anomalies in an electrical motor’s variation vibrations in three axes: axial (X), radial (Y), and tangential (Z), which are indicative of potential faults or failures. The presented model is a combination of the two architectures; LSTM layers were added to the autoencoder in order to leverage the LSTM capacity for handling large amounts of temporal data. To prove the LSTM efficiency, we will create a regular autoencoder model using the Python programming language and the TensorFlow machine learning framework, and compare its performance with our main LSTM-based autoencoder model. The two models will be trained on the same database, and evaluated on three primary points: training time, loss function, and MSE anomalies. Based on the obtained results, it is clear that the LSTM-autoencoder shows significantly smaller loss values and MSE anomalies compared to the regular autoencoder. On the other hand, the regular autoencoder performs better than the LSTM, comparing the training time. It appears then, that the LSTM-autoencoder presents a superior performance although it was slower than the standard autoencoder due to the complexity of the added LSTM layers.
Tele-Trafficking of Virtual Data Storage Obtained from Smart Grid by Replicated Gluster in Syntose Environment
Authors: Hashmi Waqas , Atiq Shahid , Hussain Muhammad Majid , Javed Khurram
Published: May 2024

One of the most important developments in the energy industry is the evolution of smart grids, which record minute details of voltage levels, energy usage, and other critical electrical variables through General Packet Radio Service (GPRS)-enabled meters. This phenomenon creates an extensive dataset for the optimization of the grid system. However, the minute-by-minute energy details recorded by GPRS meters are challenging to store and manage in physical storage resources (old techniques lead to a memory shortage). This study investigates using the distributed file system, replicated Gluster, as a reliable storage option for handling and protecting the enormous volumes of data produced by smart grid components. This study performs two essential tasks. (1) The storage of virtual data received from GPRS meters and load flow analysis of SynerGee Electric 4.0 software from the smart grid (we have extracted electrical data from 16 outgoing feeders, distributed lines, in this manuscript). (2) Tele-trafficking is performed to check the performance of replicated Gluster (RG) for virtual data (electrical data received from the smart grid) storage in terms of User Datagram Protocol (UDP), Transmission Control Protocol (TCP), data flow, and jitter delays. This storage technique provides more opportuni11ty to analyze and perform smart techniques efficiently for future requirement, analysis, and load estimation in smart grids compared to traditional storage methods.
Thermal Cycling Test of Solar Salt in Contact with Sustainable Solid Particles for Concentrating Solar Power (CSP) Plants
Authors: Majó Marc , Svobodova-Sedlackova Adela , Fernández Ana Inés , Calderón Alejandro , Barreneche Camila
Published: May 2024

Thermal energy storage (TES) is crucial in bridging the gap between energy demand and supply globally. Concentrated Solar Power (CSP) plants, employing molten salts for thermal storage, stand as an advanced TES technology. However, molten salts have drawbacks like corrosion, solidification at lower temperatures, and high costs. To overcome these limitations, research is focusing on alternative TES materials such as ceramic particles. These solids match molten salts in energy density and can withstand higher temperatures, making them well-suited for CSP systems. This study revolves around subjecting Solar Salt alone and Solar Salt alongside Volcanic Ash (VA) and Electric Arc Furnace Slag (EAFS) to a comprehensive thermal cycling test. This test is designed to assess the compatibility over the thermal cycles of the Solar Salt and the Solar Salt in contact with these solids in a CSP plant with a thermocline configuration. With a final thermal and chemical evaluation, our observations indicate that EAFS and VA demonstrate promising compatibility but an increase in the reduction rate of the Solar Salt due to a catalyst effect from EAFS in contact with the salt. No discernible alterations were detected in the properties of either the solid materials or solar salt when combined.
Heat Transfer Enhancements Assessment in Hot Water Generation with Phase Change Materials (PCMs): A Review
Authors: Berrocal Diana Isabel , Blandon Rodriguez Juan , Ortega Del Rosario Maria De Los Angeles , Harris Itamar , James Rivas Arthur M.
Published: May 2024

The utilization of phase change materials (PCMs) in solar water heating systems (SWHS) has undergone notable advancements, driven by a rising demand for systems delivering superior performance and efficiency. Extensive research suggests that enhancing heat transfer (HTE) in storage systems is crucial for achieving these improvements. This review employs a bibliometric analysis to track the evolution of HTE methods within this field. While current literature underscores the necessity for further exploration into hot water generation applications, several methodologies exhibit significant promise. Particularly, strategies such as fins, encapsulation, and porous media emerge as prominent HTE techniques, alongside nanofluids, which hold the potential for augmenting solar water heating systems. This review also identifies numerous unexplored techniques awaiting investigation, aiming to pave new paths in research and application within the field of hot water generation. It highlights methods that could be used independently or alongside predominantly used techniques.
Permeabilities of Water–Oil Two-Phase Flow in Capillary Fractures with Different Wettabilities
Authors: Huang Na , Liu Lei , Chen Heng , Huang Yanyan
Published: May 2024

The influence of wettability on the permeability performance of water–oil two-phase flow has attracted increasing attention. Dispersed flow and stratified flow are two flow regimes for water–oil two-phase flow in capillary fractures. The theoretical models of relative permeability considering wettability were developed for these two water–oil flow regimes from the momentum equations of the two-fluid model. Wettability coefficients were proposed to study the impact of wettability on relative permeabilities. Experiments were conducted to study the relative permeabilities of laminar water–oil two-phase flow in water-saturated and oil-saturated horizontal capillary fractures with different hydraulic diameters. These fractures were made of polymethylmethacrylate (PMMA) and polytetrafluoroethylene (PTFE), which had different surface wettabilities. In this experiment, the regimes are dispersed flow and stratified flow. The results show that the effect of wettability on the relative permeabilities increases as the hydraulic diameters of capillary fractures decrease for water–oil two-phase flow. The relative permeabilities in a water-saturated capillary fracture are higher than those in an oil-saturated capillary fracture of the same material. The relative permeabilities in a PTFE capillary fracture are larger than those in a PMMA capillary fracture under the same saturated condition. Wettability has little effect on the permeability performances of water–oil two-phase flow in water-saturated capillary fractures, but is significant for those in oil-saturated capillary fractures.
A Decision-Making Model Proposal for the Use of Renewable Energy Technologies in Buildings in Turkey
Authors: Cesur Fatma , Taş Nilüfer , Taş Murat
Published: May 2024

With the increasing need for energy, issues related to using energy efficiently in buildings and employing renewable energy technologies are gaining significance. The building production process is intricate, involving numerous stakeholders, multiple decisions, and a combination of qualitative and quantitative data. This process necessitates decision making based on specific requirements. The objective of this study is to identify effective criteria in decision making concerning the use of renewable energy technologies (RETs) in buildings in Turkey. It aims to highlight the importance of these criteria and compare them, and it also aims to define a recommendation decision-making model for the widespread adoption of renewable energy technologies. This study employed qualitative and quantitative research methods. Based on information gathered from the literature, the main criteria and sub-criteria for RET utilization were determined through in-depth interviews with an expert group, including individuals influencing the building design process (architects, engineers, consultants, employers, and users). A recommendation model was developed using the analytic hierarchy process method to highlight the significance of the identified criteria, compare the criteria and technologies, and facilitate the selection of the most-appropriate technology. This study demonstrates that the decision-making model can be utilized in determining RET-related criteria in the building production process, establishing their weights, and make informed decisions regarding the appropriate technology.
Evaluation of the Influence of Water Content in Oil on the Metrological Performance of Oil Flow Measurement Systems
Authors: Silva Augusto , Oliveira Elcio
Published: May 2024

According to current Brazilian regulations, if the volumes of oil produced used as a reference for the payment of government shares and third parties contain a water content greater than 2% v/v, these volumes must be arbitrarily increased between 1.44% and 10.89% due exclusively to water content, which has caused operational problems for oil companies such as differences between volumes produced and volumes sold, and additional payments from government shares and third parties. This study aimed to evaluate the metrological performance of oil measurement systems with ultrasonic, Coriolis and positive displacement flow meters when subjected to varied water content, fluid temperature and flow rate conditions using the Design of Experiments and the Response Surface Methodology. The analysis of variance showed that the models presented good fits for the ultrasonic meter (coefficient of determination R2 of 97.96%, p-value of 0.001, and a standard deviation of 5.89 × 10−5); Coriolis meter (R2 of 90.91%, p-value of 0.037, and a standard deviation of 5.88 × 10−5); and positive displacement meter (R2 of 99.07%, p-value of 0.000, and a standard deviation of 4.85 × 10−5). The results of the experiments carried out indicate that the contribution of each parameter analyzed to the metrological performance of the measurement system varies depending on the measurement technology used by the flow meter. However, the fluid temperature proved to be a relevant parameter common to all flow measurement technologies evaluated. All measuring technologies evaluated were influenced by water content in the range of 0% to 10% v/v, with the measurement error being less than 0.2% when compared to a standard positive displacement type meter in almost all experimental conditions. The Coriolis-type flow meter was the one that presented the smallest error among the measuring technologies evaluated.
Removal of Residual Chlorine from Stormwater Using Low-Cost Adsorbents and Phytoremediation
Authors: Valentukeviciene Marina , Andriulaityte Ieva , Karczmarczyk Agnieszka , Zurauskiene Ramune
Published: May 2024

In recent decades, the pollution of water with micropollutants has become an increasing environmental concern. Since 2019, increased stormwater pollution from chlorine-based disinfectants has been recorded due to the COVID-19 pandemic. Runoff from disinfected areas and the residual chlorine present in stormwater are transported to surface water bodies, posing a risk to aquatic flora and fauna. The objectives of this study were (1) to evaluate the efficiency of different low-cost and recyclable filter materials in removing residual chlorine, and (2) to test plants’ ability to reduce residual chlorine concentrations through phytoremediation. Experiments were conducted in the laboratory (column and batch) and in the field (raised garden bed) to assess the efficiency of various filter materials (peat, wood chips, sawdust and the lightweight aggregates) in retaining residual chlorine to be implemented in green infrastructure. The best retainers of chlorine were sawdust (96%) and the LWA Leca (76%). No harmful effects of residual chlorine (changes in growth, color, leaf size, etc.) on plants (Tagetes patula or Pisum savitum) were observed and the residual chlorine in the leachate samples was below the equipment’s detection limit. Our research results will contribute to future studies aiming to remove various micropollutants from stormwater using remediation technologies.