System Performance Efficiency Research Articles

Forecasting Heat Power Demand in Retrofitted Residential Buildings

The accurate prediction of heat demand in retrofitted residential buildings is crucial for optimizing energy consumption, minimizing unnecessary losses, and ensuring the efficient operation of heating systems, thereby contributing to significant energy savings and sustainability. Within the framework of this article, the dependence of the energy consumption of a thermo-modernized building on a chosen set of climatic factors has been meticulously analyzed. Polynomial fitting functions were derived to describe these dependencies. Subsequent analyses focused on predicting heating demand using artificial neural networks (ANN) were adopted by incorporating a comprehensive set of climatic data such as outdoor temperature; humidity and enthalpy of outdoor air; wind speed, gusts, and direction; direct, diffuse, and total radiation; the amount of precipitation, the height of the boundary layer, and weather forecasts up to 6 h ahead. Two types of networks were analyzed: with and without temperature forecast. The study highlights the strong influence of outdoor air temperature and enthalpy on heating energy demand, effectively modeled by third-degree polynomial functions with R2 values of 0.7443 and 0.6711. Insolation (0–800 W/m2) and wind speeds (0–40 km/h) significantly impact energy demand, while wind direction is statistically insignificant. ANN demonstrates high accuracy in predicting heat demand for retrofitted buildings, with R2 values of 0.8967 (without temperature forecasts) and 0.8968 (with forecasts), indicating minimal performance gain from the forecasted data. Sensitivity analysis reveals outdoor temperature, solar radiation, and enthalpy of outdoor air as critical inputs.

Automated fault detection and analysis for large photovoltaic systems using photovoltaic module fault detection in drone vision system

This study presents an innovative approach to fault detection in large-scale photovoltaic (PV) systems by leveraging the capabilities of drones and machine vision technologies. The proposed method is unsupervised, eliminating the need for manual intervention in identifying and analyzing faults in PV installations. By employing drone vision techniques equipped with high-resolution cameras and advanced image processing algorithms, comprehensive visual data of solar panels were captured. The collected images were processed for automatic detection and classification of various faults such as cracks, hotspots, and shading issues. The integration of these technologies not only enhances the accuracy of fault detection but also significantly reduces the time and cost associated with traditional inspection methods. This approach ensures the efficient and reliable operation of PV systems, contributing to the sustainable generation of solar energy. The original dataset employed in this work can be found at https://doi.org/10.17632/5ssmfpgrpc.1.

IoT-Based Metal Detector Robot with Wireless Surveillance

Abstract: This project presents the design and development of an IoT-based metal detector robot equipped with wireless surveillance capabilities. The robot is designed to autonomously navigate its environment while detecting metallic objects underground, with real-time monitoring provided through a wireless camera system. The core components of the system include a metal detector sensor, an ESP8266 NodeMCU for wireless communication, an L298 motor driver for controlling the robot’s movement, and a surveillance camera for live video streaming. The system is powered by 3.7V battery cells, ensuring mobility and operational autonomy.The metal detector sensor scans for metallic objects, and upon detection, an alert is generated. Simultaneously, a wireless camera transmits real-time footage, allowing remote monitoring and control via a web interface or mobile device. The robot's movement is controlled through a second ESP8266 module, enabling wireless command inputs for navigation.This robot is envisioned for use in security applications, such as detecting landmines or hazardous metallic objects in sensitive areas, as well as for archaeological and construction projects. The integration of IoT technology enhances the robot’s operational range and effectiveness, providing a scalable solution for remote metal detection and surveillance.The integration of IoT technology significantly enhances the system's operational efficiency and range, enabling seamless remote control and monitoring. This innovation not only improves safety in hazardous conditions but also increases the effectiveness of metal detection and surveillance tasks in diverse environments

ITER NBI operational window and power availability constraints due to shine-through losses

Abstract This paper explores the operational boundaries and power availability of the neutral beam injection (NBI) system in ITER, with a specific focus on shine-through loss prevention. Shine-through, a phenomenon where part of the injected neutral beam remains un-ionized in the plasma and directly impacts the first wall components, poses a significant risk to the lifetime of ITER’s plasma-facing components. The operational window for NBI is consequently constrained by these losses, which are influenced by factors such as plasma density, beam energy, and injection geometry. Leveraging advanced numerical simulations, we investigate these dependencies across various ITER plasma scenarios, particularly for the DT-1 phase, which will mark the first NBI operations. In light of recent ITER blanket design changes, our analysis refines previous estimates of the maximum acceptable shine-through power on plasma-facing components. We then present a new heuristic formula which permits the calculation of the shine-through fraction and the minimum plasma density that permits ITER NBI operations as a function of global variables. This allows for establishing operational limits for Hydrogen and Deuterium NBI in Hydrogen, Deuterium, and Deuterium-Tritium plasmas. Additionally, we compare commonly used beam ionisation codes for ITER and tokamak simulations, evaluating their reliability in the investigated parameter space. The findings of this study are crucial for ensuring the efficient operation of the NBI system during ITER's experimental phases. They define the conditions under which beam power can be fully utilised without compromising operational lifetime, thereby informing future plasma operation plans and contributing to the success of ITER's scientific objectives.

A novel arc detection and identification method in pantograph-catenary system based on deep learning

Arc detection is crucial for ensuring the safe operation of power systems, where timely and accurate detection of arcs can prevent potential hazards such as fires, equipment damage, or system failures. Traditional arc detection methods, while functional, often suffer from low detection accuracy and high computational complexity, especially in complex operational environments. This limitation is particularly problematic in real-time monitoring and the efficient operation of power systems. In order to solve these problems, this paper proposes a method of arc detection based on deep learning, called arc multi-scene detection (ArcMSD), which leverages deep learning techniques to address these challenges. The primary distinction of this method lies in its enhancement of the Inception V3 model. This paper has redesigned the original Inception module by incorporating a guided anchor mechanism, an attention mechanism, and upsampling techniques to optimize detection performance. The improved Inception V3 network uses an attention mechanism to allow the model to focus on arc features in complex backgrounds, which can also prevent the model from overfitting. It performs upsampling and fusion with low-level features in the model. The fused features have better arc discrimination capabilities than the original input features, which better improves the accuracy of the model. In order to adapt to arcs with large size differences and improve detection efficiency, the guided anchor is selected to adjust the anchor generation algorithm. In terms of dataset, continuous frame images are intercepted from the video of Integrated Supervision and Control System (ISCS), and image preprocessing operations are performed to improve the model’s detection accuracy of pantograph arcs. Experimental results show that the mean Average Precision (mAP) of the deep learning model proposed in this article is 95.4%, which is far better than other models, thus verify the method’s efficacy.

Scaling Data Infrastructure for High-Volume Manufacturing: Challenges and Solutions in Big Data Engineering

Abstract—Scaling data infrastructure for high- volume manufacturing presents significant challenges owing to the rapid growth, diversity, and complexity of the data generated by modern production processes. This review explores the key challenges and solutions in big-data engineering to enable efficient, scalable, and reliable data management in manufacturing environments. The primary challenges include handling the volume, velocity, and variety of data; ensuring real-time processing and analysis; managing data storage and retrieval at scale; and maintaining data quality and consistency. To address these challenges, various big data engineering solutions have been discussed, including distributed computing frameworks, cloud-based storage and computing resources, data lakes, data governance and metadata management, stream processing technologies, machine learning, and AI for predictive analytics. This review also examines the role of data architecture and infrastructure in building scalable systems, highlighting the importance of microservices, containerization, orchestration, NoSQL databases, and data security and privacy. Performance optimization techniques, such as query optimization, data partitioning, sharding, caching, and data compression, have been explored to ensure efficient operation of large-scale data systems. The review includes case studies of successful implementations and discusses emerging trends, such as edge computing, and the growing importance of data interoperability and standardization. Future research directions were identified, emphasizing the need for ongoing development in this field to meet the ever- growing demand for high-volume manufacturing. Keywords—high-volume manufacturing, big data, data infrastructure, distributed computing frameworks, cloud computing, data lakes, microservices, containerization, orchestration, query optimization, data partitioning, sharding, caching, data compression

Optimized Deep Multi-Scale Three-Dimensional Convolutional Neural Network-Dependent Channel Estimation in 5G MIMO-OFDM Systems

Multi-Input Multi-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is the main air interface for fifth-generation wireless communication. Channel estimation (CE) is a key factor for attaining efficient system performance of 5G wireless networks. The Deep Learning (DL) approaches employed in MIMO-OFDM systems can minimize the computational complexity of 5G networks while increasing the system performance and reliability. In this research work, an Optimized Deep Multi-Scale Three-Dimensional CNN-dependent Channel Estimation in a 5G MIMO-OFDM (DMTDCNN-CE-5G-MIMO-OFDM) System is proposed. It provides a comprehensive analysis of DMTDCNN methods, 5G channel methods, DMTDCNN-based channel estimation, and error performance for MIMO-OFDM receivers. Here, channel estimation plays a main role in the effectual system performance on the 5G wireless networks. Furthermore, DMTDCNN optimized with the Modified Social Group Optimization Algorithm (MSGOA) can develop the system performance with reliability by decreasing the computational complexity of 5G communication systems. It combines DMDTCN-MSGOA with convolutional and polar codes, utilizing hard and soft decoders, improving the quality of channel estimation by reducing PER and PAPR attenuation. In this case, 1/2-rate polar coding with block lengths of 128, 256, and 1024 bits is used, and the outcome is 1, 2, and 8 MIMO-OFDM signals that rely on 4-QAM. The proposed DMTDCNN-CE-5G-MIMO-OFDM method attains 20.89%, 33.45%, 25.67% high throughput and 15.67%, 22.57%, 38.98% low mean square compared with the existing models, such as DL-based CE in 5G MIMO-OFDM schemes (DL-CE-MIMO-OFDM), low complexity learning-dependent channel estimation OFDM by online training (LCL-CE-OFDM), dual CNN-based CE for MIMO-OFDM schemes (DCNN-CE-MIMO-OFDM) methods, respectively.

What should be publicly funded in the Colombian health system? A mixed methods study of citizens’ perceptions

BackgroundIn recent years, citizens have become more interested and willing to influence health policy decision-making, and governments worldwide are more prone to citizen engagement in such processes. Prioritising which health...

System analysis and information processing to solve the problem of detecting breakdowns of computer information storage

Objective. One of the important aspects of maintaining the efficient operation of information systems is the condition of computer equipment. The purpose of the study is to describe a method for effective system analysis of the state of a computer's information storage device. Method. The study is based on the use of machine learning algorithms to analyze and interpret data obtained from SMART tests. Includes a comprehensive analysis and experimental study. It involves conducting experiments with a data set and the Google Colab cloud environment, creating and analyzing a machine learning model, and evaluating the effectiveness and quality of training. Result. A tool for assessing the state of computer equipment based on the Random Forest algorithm has been developed using historical data from SMART tests. Conclusion. The results not only allow the implementation of a working data analysis tool in the field of computer equipment maintenance, but also contain a practical example of increasing the reliability and efficiency of information systems. The results are useful for IT specialists and for organizations optimizing equipment maintenance processes and increasing competitiveness.

The Impacts of Different Salinities on the CW-MFC System for Treating Concentrated Brine

This paper aims to comprehensively explore the performance and influencing factors of the constructed wetland–microbial fuel cell (CW-MFC) system when treating brine with different concentrations. The main objective is to determine how different salinity levels affect the operation and treatment efficiency of the CW-MFC system. The research results show that Bruguiera gymnorrhiza exhibits strong salt tolerance and can be used as a wetland plant for the CW-MFC system. The closed-circuit CW-MFC system with planted plants has the best performance, with a chemical oxygen demand (COD) removal rate of 84.8%, a total nitrogen (TN) removal rate of 68.12%, and a chloride ion (Cl−) removal rate of 29.96%. The maximum power density is 64.79% higher than that of the system without planted plants. The power generation performance of the system first increases and then decreases with the increase in salinity, while the internal resistance keeps decreasing. When the salinity is 2%, the power generation effect is the best, with an average output voltage of 617.3 ± 25.7 mV and a power density of 45.83 mW/m2. The removal rates of COD and TN are inhibited with the increase in salinity, while the removal rate of total phosphorus (TP) is not significantly affected. The microbial community grows well under salt stress, but its structure is different. When the salinity is 1%, the optimal distance between electrodes is 10 cm. Considering the pollutant removal performance, the optimal hydraulic retention time is 3 days, and considering the power generation performance, the optimal hydraulic retention time is 2 days. This research provides important value for improving the performance of the CW-MFC system in treating brine.

A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges

Wind power prediction is essential for ensuring the stability and efficient operation of modern power systems, particularly as renewable energy integration continues to expand. This paper presents a comprehensive review of machine learning techniques applied to wind power prediction, emphasizing their advantages over traditional physical and statistical models. Machine learning methods, especially deep learning approaches such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ensemble learning techniques like XGBoost, excel in addressing the nonlinearity and complexity of wind power data. The review also explores critical aspects such as data preprocessing, feature selection strategies, and model optimization techniques, which significantly enhance prediction accuracy and robustness. Challenges such as data acquisition difficulties, complex terrain influences, and sensor quality issues are examined in depth, with proposed solutions discussed. Additionally, the paper highlights future research directions, including the potential of multi-model fusion, emerging deep learning technologies like Transformers, and the integration of smart sensors and IoT technologies to develop intelligent, automated, and reliable prediction systems. By addressing existing challenges and leveraging advanced machine learning techniques, this work provides valuable insights into the current state of wind power prediction research and offers strategic guidance for enhancing the applicability and reliability of prediction models in practical scenarios.

Power system optimization dispatch for energy conservation and emission reduction

This paper examines the optimization of power system dispatching with a focus on energy conservation and emission reduction. Through analyzing current situations and challenges, it aims to propose effective strategies and methods to achieve efficient power system operation while protecting the environment. The article commences by detailing China's advancements and obstacles in energy conservation and emission reduction within its power system. It addresses issues such as the variability of renewable energy sources, efficiency constraints in thermal power generation units, the intricate nature of the power grid infrastructure, and the novel challenges introduced by reforms in the electricity market. Subsequently, the paper presents four major strategies: priority dispatching of clean energy, flexible dispatching of thermal power units, power grid structure optimization, and market mechanism introduction. Research findings, when integrated with practical case studies, demonstrate that the utilization of virtual power plants and advanced dispatching systems can markedly enhance the capacity for clean energy consumption, decrease the operational duration of thermal power units and the intensity of carbon emissions, while simultaneously improving the overall efficiency of power systems. This research holds significant importance for promoting green and sustainable development in the power industry

Spectral efficiency and secrecy enhancing scheme for IRS-aided SWIPT systems

Simultaneous wireless information and power transfer (SWIPT) has emerged as a promising technology for enabling efficient and scalable wireless communication within large-scale Internet of Things (IoT) networks. Despite its substantial potential, challenges related to spectral efficiency and overall system performance remain, underscoring the need for further advancements and innovations. Furthermore, the secure transmission of information is a crucial aspect that cannot be overlooked, as it guarantees the integrity and confidentiality of data exchanged within IoT networks. In this paper, we introduce a novel generalized spatial modulation (GSM) scheme specifically designed for intelligent reflecting surface (IRS)-Aided SWIPT systems. Moreover, the proposed scheme incorporates space-time block coding (STBC) to enhance performance. To strengthen the secrecy, a novel artificial noise (AN) generation strategy that effectively disrupts eavesdroppers while minimizing the impact on legitimate receivers is proposed. Additionally, the designed AN can be utilized by the energy-harvesting receiver to enhance its energy collection capabilities. We derived the average bit error probability (ABEP) of the system using the moment-generating function (MGF) approach and validated our analytical findings through rigorous simulations. The results obtained demonstrate convincingly that the proposed GSM scheme for IRS-Aided SWIPT systems significantly outperforms traditional methods, offering remarkable improvements in performance and efficiency.

Research into the Possibilities of Improving the Adhesion Properties of a Locomotive

Locomotives are important vehicles, which serve for towing wagons, i.e., trains. Many factors influence the safe and cost-effective operation of locomotives and trains in general. One of these factors is adhesion at the wheel/rail contact. The adhesion determines how much power the locomotive can deliver and how the braking system will ensure that the train stops. The main way to improve adhesion is to use sand at the wheel/rail contact point. The aim of this study is to improve the efficiency of the sand system of the locomotive. For this purpose, a new sand system nozzle mounting design was proposed. The newly proposed sanding system is equipped with a nozzle mounted to the axlebox unlike the original one, which uses the nozzle attached to the bogie frame. To compare the proposed and existing design, simulation calculations were performed in Simpack software 2024.3. For the simulation computation of the locomotive bogie, two types of railway tracks were chosen. A straight track section with two angular frequencies and three amplitudes of track irregularities was created, and a real track section corresponding to several kilometers of track was modeled in the Simpack software. During the simulations, it was determined that the proposed nozzle mounting design has a smaller amplitude of motion, compared to the existing one; therefore, there is a more accurate and efficient operation of the sand system. This in turn has a favorable effect on the adhesion of the wheel with the rail. It was found out that the newly designed sanding system has a significant positive economic effect regarding saving sand. There is no sand loss during sandblasting compared with the original sanding system. This directly relates to saving costs during locomotive operation.

Optimization of Multi-Vehicle Cold Chain Logistics Distribution Paths Considering Traffic Congestion

Urban road traffic congestion has become a serious issue for cold chain logistics in terms of delivery time, distribution cost, product freshness, and even organization revenue and reputation. This study focuses on the cold chain distribution path by considering road traffic congestion with transportation, real-time vehicle delivery speeds, and multiple-vehicle conditions. Therefore, a vehicle routing optimization model has been established with the objectives of minimizing costs, reducing carbon emissions, and maintaining cargo freshness, and a multi-objective hybrid genetic algorithm has been developed in combination with large neighborhood search (LNSNSGA-III) for leveraging strong local search capabilities, optimizing delivery routes, and enhancing delivery efficiency. Moreover, by reasonably adjusting departure times, product freshness can be effectively enhanced. The vehicle combination strategy performs well across multiple indicators, particularly the three-type vehicle strategy. The results show that costs and carbon emissions are influenced by environmental and refrigeration temperature factors, providing a theoretical basis for cold chain management. This study highlights the harmonious optimization of cold chain coordination, balancing multiple constraints, ensuring efficient logistic system operation, and maintaining equilibrium across all dimensions, all of which reflect the concept of symmetry. In practice, these research findings can be applied to urban traffic management, delivery optimization, and cold chain logistics control to improve delivery efficiency, minimize operational costs, reduce carbon emissions, and enhance corporate competitiveness and customer satisfaction. Future research should focus on integrating complex traffic and real-time data to enhance algorithm adaptability and explore customized delivery strategies, thereby achieving more efficient and environmentally friendly logistics solutions.

Intelligent active and reactive power control using multi-layer neural network based MPPT controller for grid tied solar PV system under fault conditions

The integration of renewable energy sources, particularly grid-tied solar photovoltaic (PV) systems, into the modern power grid has become increasingly prevalent. However, ensuring the reliable and efficient operation of grid-tied PV systems under various grid conditions, including fault scenarios, poses a significant challenge. In the event of grid faults or disturbances, traditional control methods often fall short in maintaining stable and reliable operation. This paper introduces a multi-layer neural network (MLNN) based MPPT controller that adapts intelligently to grid fault conditions, mitigating the impact on the grid-tied PV system's performance and providing low voltage ride through (LVRT). The research employs a detailed simulation framework on MATLAB to validate the effectiveness of the proposed controller under fault conditions. The LVRT capability of the designed system was analyzed and validated according to Indian grid code. The proposed controller leverages its capacity to learn and make real-time decisions to optimize the active and reactive power outputs of the PV system as per the grid code. Simulation results demonstrate that the proposed controller not only improves the fault tolerance of grid-tied PV systems but also enhances their performance, ensuring a stable and continuous power supply in the face of grid disturbances.

Research on reachable set boundary of neutral system with various types of disturbances.

This study delves into neutral-type systems (NTSs), emphasizing the critical role of defining precise reachable set (RS) boundaries for safe and efficient system design and operation. The investigation notably addresses the challenges posed by time-varying delays, applying Lyapunov's direct method alongside advanced matrix inequality techniques to identify minimized and more accurate ellipsoidal boundaries of the RS in NTSs influenced by bounded and nonlinear disturbances. Our findings, verified through numerical simulations and comparisons with existing literature, demonstrate enhanced control and management capabilities for complex systems, thus underscoring the substantial theoretical and practical value of incorporating delay elements in NTSs.

Application of Blockchain Technology in Teaching Evaluation in Applied Technology Universities

This paper explores the integration of blockchain technology into the teaching quality evaluation system of universities. A practical teaching quality evaluation index system for applied technology universities is developed, ensuring data authenticity through blockchain’s de-trusting mechanism. To enhance data storage efficiency, the PBFT consensus algorithm is improved and incorporated into a technical architecture adopting an “off-chain storage + on-chain sharing” model. The algorithm scoring formula and improved PBFT consensus algorithm are analyzed to demonstrate their effectiveness. Practical applications in applied technology universities highlight the benefits of blockchain in higher education evaluation. The CBFT-based consensus algorithm achieves average CPU utilization of 13.4% compared to 18.5% in traditional algorithms, while ensuring data transparency and tamper-proofing. Additionally, the algorithm improves transaction throughput and reduces resource consumption, enabling efficient operation of the teaching evaluation system in applied sciences universities.

Clustering- and statistic-based approach for detection and impact evaluation of faults in end-user substations of thermal energy systems

In response to climate change mitigation efforts, improving the efficiency of heat networks is becoming increasingly important. An efficient operation of energy systems depends on faultless performance. Following the need for effective fault detection and elimination methods, this study suggests a three-step workflow for increasing automation in managing defective substations on the user level within heat networks. The work focuses on a model region in northern Germany. The local heat network provides data in roughly hourly intervals, including the supply and return temperatures and the volume flow of the substations. Firstly, this study identifies common indicators of faults using k-means clustering analysis of the temperature data and expert knowledge: an exceeded return temperature level, very low cooling, and inverted temperature readings. With these indicators, the subsequent statistical identification approach confirms the successful detection of affected substations, with common diagnoses including disabled return temperature limitation units, defective motoric valves, and faults in the storage control. Lastly, the study evaluates the impact of faults on the system efficiency. Combining the temperature and the volume flow data, the workflow quantifies the negative influence of a fault, enabling the prioritization of fault elimination measures in practical application to enhance the overall system efficiency.

Development of an Optimized Neural Network Model using Hyperparameters Optimization for Electrical Load Prediction

Electrical load prediction has become essential to the efficient operation, control and management of modern electric power systems. Various machine learning prediction models have been developed for electrical load prediction, this includes Support Vector Regression, Fuzzy Logic and Neural Network (NN) modelling approaches. However, incorrect selection of model hyperparameters, which are parameters that affect the output of the prediction models, could result in low prediction accuracy of machine learning models. Hence, the development of an optimized NN model for electrical load prediction was presented in this study. Historical daily data of temperature, rainfall, relative humidity and windspeed for Osogbo, Nigeria was obtained from the National Aeronautics and Space Administration website; while electrical load data for the same location was collected from the Transmission Company of Nigeria. The data captured a period of five years (2017 to 2021). The NN models were developed with MATLAB R2022a software, and two hyperparameters, hidden layers and neuron counts, were optimized using the Bayesian optimization technique to enhance the quality of the models. The models were evaluated using mean absolute error (MAE), and root mean square error (RMSE). The MAE and RMSE for the non-optimized NN model were 6.5247 and 8.2725 respectively. Meanwhile, for the optimized NN model, the MAE and RMSE were 5.6571 and 7.4289 respectively. The obtained results show that the optimized NN performed better than the non-optimized NN models. Therefore, for more accurate load prediction, the method developed of this research is suggested for use by utility providers.