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Microgrids (MGs) are the new paradigm of decentralized networks of renewable energy sources, loads, and storage devices that can operate independently or in coordination with the primary grid, incorporating significant flexibility and supply reliability. To increase reliability, traditional individual MGs can be replaced by networked microgrids (NMGs), which are more dependable. However, when it comes to operation and control, they also pose challenges for cyber security and communication reliability. Denial of service (DoS) is a common danger to DC microgrids with advanced controllers that rely on active information exchanges and has been recorded as the most frequent cause of cyber incidents. It can disrupt data transmission, leading to ineffective control and system instability. This paper proposes digital twin (DT) technology as an integrated solution, with new, advanced analytics technology using machine learning and artificial intelligence to provide simulation capabilities to predict and estimate future states. By twinning the cyber-physical dynamics of NMGs using data-driven models, DoS attacks targeting cyber-layer agents will be detected and mitigated. A long short-term memory (LSTM) model data-driven digital twin approach for DoS attack detection and mitigation is implemented, tested, and evaluated. [ABSTRACT FROM AUTHOR]

In this paper, a systematic literature review is presented, through a survey of the main digital databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of Artificial Intelligence (AI) in STLF. With these specific objectives, a conceptual framework on the subject was developed, along with a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed towards the scientific production of AI and STLF. The review of research articles over a 10-year period, which took place between 2012 and 2022, used the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in four databases: Web of Science, IEEE Xplore, Scopus, and Science Direct. The research was organized around three central themes, which were defined through the following keywords: STLF, Electricity, and Residential, along with their corresponding synonyms. In total, 334 research articles were analyzed, and the year of publication, journal, author, geography by continent and country, and the area of application were identified. Of the 335 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 38 (thirty-eight) documents were in English (26 journal articles and 12 conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance was measured with different metrics and, therefore, cannot be compared directly. Hence, it is desirable to have a unified dataset, together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a current literature review (from the years 2017–2022) on issues related to the application of power system stabilizers (PSSs) for damping electromechanical swings in power systems (PSs). After the initial selection of papers found in the databases used, over 600 publications were qualified for this review, of which 216 were subjected to detailed analysis. In the review, issues related to the following problems are described: applications of classic PSSs, applications of new stabilizer structures based on new algorithms (including artificial intelligence), development of new methods for tuning PSSs, and operation of PSSs in PSs with high power generation by renewable sources. Describing individual papers, the research methods used by the authors (simulations, measurement methods, and a combination of both) are specified, attention is paid to the waveforms presented in the papers, and reference is made to the types of PSs in which PSSs (large multimachine, reflecting real systems, smaller standard multimachine New-England type, and simplest single-machine) operate. The tables contain detailed comments on the selected papers. The final part of the review presents general comments on the analyzed papers and guidelines for future PS stability studies. [ABSTRACT FROM AUTHOR]

Despite the tightening of energy performance standards for buildings in various countries and the increased use of efficient and renewable energy technologies, it is clear that the sector needs to change more rapidly to meet the Net Zero Emissions (NZE) scenario by 2050. One of the problems that have been analyzed intensively in recent years is that buildings in operation use much more energy than they were designed to. This problem, known as the energy performance gap, is found in many countries and buildings and is often attributed to the poor management of building energy systems. The application of Artificial Intelligence (AI) to Building Energy Management Systems (BEMS) has untapped potential to address this problem and lead to more sustainable buildings. This paper reviews different AI-based models that have been proposed for different applications and different buildings with the intention to reduce energy consumption. It compares the performance of the different AI-based models evaluated in the reviewed papers by presenting the accuracy and error rates of model performance and identifies where the greatest potential for energy savings could be achieved, and to what extent. The review showed that offices have the greatest potential for energy savings (up to 37%) when they employ AI models for HVAC control and optimization. In residential and educational buildings, the lower intelligence of the existing BEMS results in smaller energy savings (up to 23% and 21%, respectively). [ABSTRACT FROM AUTHOR]

This paper attempts to elucidate the transformative integration of computational techniques within power systems, underscoring their critical role in enhancing system modeling, control, and the efficient integration of renewable energy. It breaks down the two-sided nature of technological progress, highlighting both gains in operational efficiency and new challenges such as real-time processing, data management, and cybersecurity. Through meticulous analysis of query-based research patterns and mathematical frameworks, this study delves into the balancing act between specificity and breadth in scholarly inquiries while evaluating the impact and evolution of research trends through citation analysis. The convergence of interests and transient research trends is evident, particularly in Artificial Intelligence and optimization. This comprehensive narrative anticipates a sophisticated trajectory for power systems, advocating for continuous innovation and strategic research to foster sustainable, resilient, and intelligent energy networks. [ABSTRACT FROM AUTHOR]

Water can be an irritant to a power transformer, as it is recognized as a major hazard to the operation of transformers. The water content of a transformer insulation system comprises the water in the transformer insulation oil and in the cellulose paper. The increase in the water content in the insulation system leads to reduced breakdown voltage, accelerated aging of the oil–paper insulation system, and the possibility of producing bubbles at high temperatures. Therefore, various techniques have been applied to measure the water content in both oil and paper insulation. This article comprehensively reviews and analyzes the methods (technically or nontechnically) that have been used to monitor the water content in transformer insulation systems. Apart from discussing the advantages and major drawbacks of these methods, the accuracy, measurement time, and cost of each technique are also elucidated in this review. This review can be extremely useful to the utility in monitoring and maintaining the good condition of transformers. Based on the reviewed methods and their challenges, a few future research directions and prospects for determining the water content in transformer insulation systems are outlined, such as utilizing artificial intelligence and enhancing current techniques. [ABSTRACT FROM AUTHOR]

This review paper thoroughly explores the impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. With the rapid advancement of artificial intelligence techniques such as machine learning, optimization, and cognitive computing, new opportunities are emerging to enhance the efficiency and reliability of electrical grids. From demand and generation prediction to energy flow optimization and load management, artificial intelligence is playing a pivotal role in the transformation of energy infrastructure. This paper delves deeply into the latest advancements in specific artificial intelligence applications within the context of distributed energy systems, including the coordination of distributed energy resources, the integration of intermittent renewable energies, and the enhancement of demand response. Furthermore, it discusses the technical, economic, and regulatory challenges associated with the implementation of artificial intelligence-based solutions, as well as the ethical considerations related to automation and autonomous decision-making in the energy sector. This comprehensive analysis provides a detailed insight into how artificial intelligence is reshaping the planning and operation of smart grids and highlights future research and development areas that are crucial for achieving a more efficient, sustainable, and resilient electrical system. [ABSTRACT FROM AUTHOR]

The digitization of distribution power systems has revolutionized the way data are collected and analyzed. In this paper, the critical task of harnessing this information to identify irregularities and anomalies in electricity consumption is tackled. The focus is on detecting non-technical losses (NTLs) and energy theft within distribution networks. A comprehensive overview of the methodologies employed to uncover NTLs and energy theft is presented, leveraging measurements of electricity consumption. The most common scenarios and prevalent cases of anomalies and theft among consumers are identified. Additionally, statistical indicators tailored to specific anomalies are proposed. In this research paper, the practical implementation of numerous artificial intelligence (AI) algorithms, including the artificial neural network (ANN), ANFIS, autoencoder neural network, and K-mean clustering, is highlighted. These algorithms play a central role in our research, and our primary objective is to showcase their effectiveness in identifying NTLs. Real-world data sourced directly from distribution networks are utilized. Additionally, we carefully assess how well statistical methods work and compare them to AI techniques by testing them with real data. The artificial neural network (ANN) accurately identifies various consumer types, exhibiting a frequency error of 7.62%. In contrast, the K-means algorithm shows a slightly higher frequency error of 9.26%, while the adaptive neuro-fuzzy inference system (ANFIS) fails to detect the initial anomaly type, resulting in a frequency error of 11.11%. Our research suggests that AI can make finding irregularities in electricity consumption even more effective. This approach, especially when using data from smart meters, can help us discover problems and safeguard distribution networks. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) with renewable energy technologies is revolutionizing modern power systems by enhancing efficiency, reliability, and sustainability. This paper examines the role of the IoT in optimizing the integration and management of renewable energy sources, such as solar and wind power, into the electrical grid. The IoT enables real-time monitoring, data analysis, and automation, facilitating advanced load management, demand response, and energy storage solutions. Key advancements in IoT technologies, including smart grids and energy management systems, are discussed, highlighting their impact on improving grid stability and promoting the use of renewable energy. The paper also finds some challenges such as data security, privacy, and the need for standardized communication protocols. Furthermore, it finds how the IoT optimizes electric vehicle performance through advanced battery management, real-time energy consumption monitoring, and improved interaction with the electrical grid. Future research directions emphasize the potential of the IoT to further enhance renewable energy integration through artificial intelligence and machine learning, driving the transition towards a more sustainable and resilient energy future. [ABSTRACT FROM AUTHOR]

In the context of increasing energy demands and the integration of renewable energy sources, this review focuses on recent advancements in energy storage control strategies from 2016 to the present, evaluating both experimental and simulation studies at component, system, building, and district scales. Out of 426 papers screened, 147 were assessed for eligibility, with 56 included in the final review. As a first outcome, this work proposes a novel classification and taxonomy update for advanced storage control systems, aiming to bridge the gap between theoretical research and practical implementation. Furthermore, the study emphasizes experimental case studies, moving beyond numerical analyses to provide practical insights. It investigates how the literature on energy storage is enhancing building flexibility and resilience, highlighting the application of advanced algorithms and artificial intelligence methods and their impact on energy and financial savings. By exploring the correlation between control algorithms and the resulting benefits, this review provides a comprehensive analysis of the current state and future perspectives of energy storage control in smart grids and buildings. [ABSTRACT FROM AUTHOR]

Creating accurate emission models capable of capturing the variability and dynamics of modern propulsion systems is crucial for future mobility planning. This paper presents a methodology for creating THC and NOx emission models for vehicles equipped with start–stop technology. A key aspect of this endeavor is to find techniques that accurately replicate the engine's stop stages when there are no emissions. To this end, several machine learning techniques were tested using the Python programming language. Random forest and gradient boosting methods demonstrated the best predictive capabilities for THC and NOx emissions, achieving R2 scores of approximately 0.9 for engine emissions. Additionally, recommendations for effective modeling of such emissions from vehicles are presented in the paper. [ABSTRACT FROM AUTHOR]

The methods of artificial intelligence (AI) have been used in the planning and operation of electric power systems for more than 40 years. In recent years, due to the development of microprocessor and data storage technologies, the effectiveness of this use has greatly increased. This paper provides a systematic overview of the application of AI, including the use of machine learning (ML) in the electric power system. The potential application areas are divided into four blocks and the classification matrix has been used for clustering the AI application tasks. Furthermore, the data acquisition methods for setting the parameters of AI and ML algorithms are presented and discussed in a systematic way, considering the supervised and unsupervised learning methods. Based on this, three complex application examples, being wind power generation forecasting, smart grid security assessment (using two methods), and automatic system fault detection are presented and discussed in detail. A summary and outlook conclude the paper. [ABSTRACT FROM AUTHOR]

The transportation sector remains a major contributor to greenhouse gas emissions. The understanding of energy-efficient driving behaviors and utilization of energy-efficient driving strategies are essential to reduce vehicles' fuel consumption. However, there is no comprehensive investigation into energy-efficient driving behaviors and strategies. Furthermore, many state-of-the-art AI models have been applied for the analysis of eco-friendly driving styles, but no overview is available. To fill the gap, this paper conducts a thorough literature review on ecological driving behaviors and styles, and analyzes the driving factors influencing energy consumption and state-of-the-art methodologies. With a thorough scoping review process, thirty-seven articles with full text were assessed, and the methodological and related data are compared. The results show that the factors that impact driving behaviors can be summarized into eleven features including speed, acceleration, deceleration, pedal, steering, gear, engine, distance, weather, traffic signal, and road parameters. This paper finds that supervised/unsupervised learning algorithms and reinforcement learning frameworks have been popularly used to model the vehicle's energy consumption with multi-dimensional data. Furthermore, the literature shows that the driving data are collected from either simulators or real-world experiments, and the real-world data are mainly stored and transmitted by meters, controller area networks, onboard data services, smartphones, and additional sensors installed in the vehicle. Based on driving behavior factors, driver characteristics, and safety rules, this paper recommends nine energy-efficient driving styles including four guidelines for the drivers' selection and adjustment of the vehicle parameters, three recommendations for the energy-efficient driving styles in different driving scenarios, and two subjective suggestions for different types of drivers and employers. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of solutions based on artificial intelligence (AI) in the urban energy sector, with a focus on their applications and impacts. The study employed a literature review methodology to analyze recent research on AI's role in energy-related solutions, covering the years 2019 to 2023. The authors classified publications according to their main focus, resulting in two key areas of AI implementation: residential and individual user applications, and urban infrastructure integration for society. The objectives of this review of the literature are the following: O1: to identify trends, emerging technologies, and applications using AI in the energy field; O2: to provide up-to-date insights into the use of AI in energy-related applications; O3: to gain a comprehensive understanding of the current state of AI-driven urban energy solutions; O4: to explore future directions, emerging trends, and challenges in the field of AI-driven energy solutions. This paper contributes to a deeper understanding of the transformative potential of AI in urban energy management, providing valuable insights and directions for researchers and practitioners in the field. Based on the results, it can be claimed that AI connected to energy at homes is used in the following areas: heating and cooling, lighting, windows and blinds, home devices, and energy management systems. AI is integrating into urban infrastructure through the following solutions: enhancement of electric vehicle charging infrastructure, reduction in vehicle emissions, development of smart grids, and efficient energy storage. What is more, the latest challenges associated with the implementation of AI-driven energy solutions include the need to balance resident comfort with energy efficiency in smart homes, ensuring compatibility and cooperation among various devices, preventing unintended energy consumption increases due to constant connectivity, the management of renewable energy sources, and the coordination of energy consumption. [ABSTRACT FROM AUTHOR]

In the modern era, where the global energy sector is transforming to meet the decarbonization goal, cutting-edge information technology integration, artificial intelligence, and machine learning have emerged to boost energy conversion and management innovations. Incorporating artificial intelligence and machine learning into energy conversion, storage, and distribution fields presents exciting prospects for optimizing energy conversion processes and shaping national and global energy markets. This integration rapidly grows and demonstrates promising advancements and successful practical implementations. This paper comprehensively examines the current state of applying artificial intelligence and machine learning algorithms in energy conversion and management evaluation and optimization tasks. It highlights the latest developments and the most promising algorithms and assesses their merits and drawbacks, encompassing specific applications and relevant scenarios. Furthermore, the authors propose recommendations to emphasize the prioritization of acquiring real-world experimental and simulated data and adopting standardized, explicit reporting in research publications. This review paper includes details on data size, accuracy, error rates achieved, and comparisons of algorithm performance against established benchmarks. [ABSTRACT FROM AUTHOR]

This Special Issue of Energies was devoted to the topic of "Manufacturing Energy Efficiency and Industry 4.0". To a great extent, this issue follows the successful previous Special Issue on "Energy Efficiency of Manufacturing Processes and Systems", which attracted some significant attention from scholars, practitioners, and policy-makers from all over the world. In total, six papers were published. The main topics included energy efficiency improvement in both the manufacturing process and system levels, as well as how this can be facilitated through the use of Industry 4.0. [ABSTRACT FROM AUTHOR]

With the rapid growth in the proportion of renewable energy access and the structural complexity of distributed energy systems, traditional microgrid (MG) scheduling methods that rely on mathematical optimization models and expert experience are facing significant challenges. Therefore, it is essential to present a novel scheduling technique with high intelligence and fast decision-making capacity to realize MGs' automatic operation and regulation. This paper proposes an optimal scheduling decision-making method for MGs based on deep neural networks (DNN). Firstly, a typical mathematical scheduling model used for MG operation is introduced, and the limitations of current methods are analyzed. Then, a two-stage optimal scheduling framework comprising day-ahead and intra-day stages is presented. The day-ahead part is solved by mixed integer linear programming (MILP), and the intra-day part uses a convolutional neural network (CNN)—bidirectional long short-term memory (Bi LSTM) for high-speed rolling decision making, with the outputs adjusted by a power correction balance algorithm. Finally, the validity of the model and algorithm of this paper are verified by arithmetic case analysis. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification. [ABSTRACT FROM AUTHOR]

The maintenance of electrical grids is crucial for improving their reliability, performance, and cost-effectiveness. It involves employing various strategies to ensure smooth operation and address potential issues. With the advancement of digital technologies, utilizing time-series prediction has emerged as a valuable approach to enhance maintenance practices in electrical systems. The utilization of various recorded data from electrical grid components plays a crucial role in digitally enabled maintenance. However, the comprehensive exploration of time-series data prediction for maintenance is still lacking. This review paper extensively explores different time series that can be utilized to support maintenance efforts in electrical grids with regard to different maintenance strategies and grid components. The digitization of the electrical grids has enabled the collection of diverse time-series data from various network components. In this context, the paper provides an overview of how these time-series and historical-fault data can be utilized for maintenance purposes in electrical grids. Various maintenance levels and time series used for maintenance purposes in different components of the electrical grid are presented. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of advanced control methods specifically designed for floating offshore wind turbines (FOWTs) above the rated wind speed. Focusing on primary control objectives, including power regulation at rated values, platform pitch mitigation, and structural load reduction, this paper begins by outlining the requirements and challenges inherent in FOWT control systems. It delves into the fundamental aspects of the FOWT system control framework, thereby highlighting challenges, control objectives, and conventional methods derived from bottom-fixed wind turbines. Our review then categorizes advanced control methods above the rated wind speed into three distinct approaches: model-based control, data-driven model-based control, and data-driven model-free control. Each approach is examined in terms of its specific strengths and weaknesses in practical application. The insights provided in this review contribute to a deeper understanding of the dynamic landscape of control strategies for FOWTs, thus offering guidance for researchers and practitioners in the field. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides a review including the relevant micro-meteorological techniques such as observation, assimilation and numerical techniques, as well as artificial intelligence, presenting a relatively complete overview of the most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systems is analyzed in six different forms of power generation and three typical scenarios of different stages in the power system, as well as integrated energy systems and disaster prevention and reduction. Finally, a case study in China is provided. This case takes wind power prediction as an example in a power system to compare the performance when applying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial--temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to the power system to some extent. [ABSTRACT FROM AUTHOR]

This paper presents the process of creating a model for electric vehicle (EV) energy consumption, enabling the rapid generation of results and the creation of energy maps. The most robust validation indicators were exhibited by an artificial intelligence method, specifically neural networks. Within this framework, two predictive models for EV energy consumption were developed for winter and summer conditions, based on actual driving cycles. These models hold particular significance for microscale road analyses. The resultant model, for test data in summer conditions, demonstrates validation indicators of an R2 of 86% and an MSE of 1.4, while, for winter conditions, its values are 89% and 2.8, respectively, confirming its high precision. The paper also presents exemplary applications of the developed models, utilizing both real and simulated microscale data. The results obtained and the presented methodology can be especially advantageous for decision makers in the management of city roads and infrastructure planners, aiding both cognitive understanding and the better planning of charging infrastructure networks. [ABSTRACT FROM AUTHOR]

The growing success of smart grids (SGs) is driving increased interest in load forecasting (LF) as accurate predictions of energy demand are crucial for ensuring the reliability, stability, and efficiency of SGs. LF techniques aid SGs in making decisions related to power operation and planning upgrades, and can help provide efficient and reliable power services at fair prices. Advances in artificial intelligence (AI), specifically in machine learning (ML) and deep learning (DL), have also played a significant role in improving the precision of demand forecasting. It is important to evaluate different LF techniques to identify the most accurate and appropriate one for use in SGs. This paper conducts a systematic review of state-of-the-art forecasting techniques, including traditional techniques, clustering-based techniques, AI-based techniques, and time series-based techniques, and provides an analysis of their performance and results. The aim of this paper is to determine which LF technique is most suitable for specific applications in SGs. The findings indicate that AI-based LF techniques, using ML and neural network (NN) models, have shown the best forecast performance compared to other methods, achieving higher overall root mean squared (RMS) and mean absolute percentage error (MAPE) values. [ABSTRACT FROM AUTHOR]

Achieving clean energy goals will require significant advances in regard to addressing the computational needs for next-generation renewable-dominated power grids. One critical obstacle that lies in the way of transitioning today's power grid to a renewable-dominated power grid is the lack of a faster-than-real-time stability assessment technology for operating a fast-changing power grid. This paper proposes an artificial intelligence (AI) -based method that predicts the system's stability margin information (e.g., the frequency nadir in the frequency stability assessment and the critical clearing time (CCT) value in the transient stability assessment) directly from the system operating conditions without performing the conventional time-consuming time-domain simulations over detailed dynamic models. Since the AI method shifts the majority of the computational burden to offline training, the online evaluation is extremely fast. This paper has tested the AI-based stability assessment method using multiple dispatch cases that are converted and tuned from actual dispatch cases of the Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. The results show that the AI-based method could accurately predict the stability margin of such a large power system in less than 0.2 milliseconds using the offline-trained AI agent. Therefore, the proposed method has great potential to achieve faster-than-real-time stability assessment for practical large power systems while preserving sufficient accuracy. [ABSTRACT FROM AUTHOR]

In response to increasing environmental demands, modeling emissions from older vehicles presents a significant challenge. This paper introduces an innovative methodology that takes advantage of advanced AI and machine learning techniques to develop precise emission models for older vehicles. This study analyzed data from road tests and the OBDII diagnostic interface, focusing on CO2, CO, THC, and NOx emissions under both cold and warm engine conditions. The key results showed that random forest regression provided the best predictions for THC in a cold engine (R2: 0.76), while polynomial regression excelled for CO2 (R2: 0.93). For warm engines, polynomial regression performed best for CO2 (R2: 0.95), and gradient boosting delivered results for THC (R2: 0.66). Although prediction accuracy varied by emission compound and engine state, the models consistently demonstrated high precision, offering a robust tool for managing emissions from aging vehicle fleets. These models offer valuable information for transportation policy and pollution reduction strategies, particularly in urban areas. [ABSTRACT FROM AUTHOR]

In the context of the electricity sector's liberalization and deregulation, the accurate forecasting of electricity prices has emerged as a crucial strategy for market participants and operators to minimize costs and maximize profits. However, their effectiveness is hampered by the variable temporal characteristics of real-time electricity prices and a wide array of influencing factors. These challenges hinder a single model's ability to discern the regularity, thereby compromising forecast precision. This study introduces a novel hybrid system to enhance forecast accuracy. Firstly, by employing an advanced decomposition technique, this methodology identifies different variation features within the electricity price series, thus bolstering feature extraction efficiency. Secondly, the incorporation of a novel multi-objective intelligent optimization algorithm, which utilizes two objective functions to constrain estimation errors, facilitates the optimal integration of multiple deep learning models. The case study uses electricity market data from Australia and Singapore to validate the effectiveness of the algorithm. The forecast results indicate that the hybrid short-term electricity price forecasting system proposed in this paper exhibits higher prediction accuracy compared to traditional single-model predictions, with MAE values of 7.3363 and 4.2784, respectively. [ABSTRACT FROM AUTHOR]

As terrestrial oilfields continue to be explored, the difficulty of exploring new oilfields is constantly increasing. The ocean, which contains abundant oil and gas resources, has become a new field for oil and gas resource development. It is estimated that the total amount of oil resources contained in ocean areas accounts for 33% of the global total, while the corresponding natural gas resources account for 32% of the world's resources. Current prediction methods, tailored to land oilfields, struggle with offshore differences, hindering accurate forecasts. With oilfield advancements, a vast amount of rapidly generated, complex, and valuable data has piled up. This paper uses AI and GRN-VSN NN to predict offshore oilfield indicators, focusing on model-based formula fitting. It selects highly correlated input indicators for AI-driven prediction of key development metrics. Afterwards, the Shapley additive explanations (SHAP) method was introduced to explain the artificial intelligence model and achieve a reasonable explanation of the measurement's results. In terms of crude-oil extraction degree, the performance levels of the Long Short-Term Memory (LSTM) neural network, BP neural network, and ResNet-50 neural network are compared. LSTM excels in crude-oil extraction prediction due to its monotonicity, enabling continuous time-series forecasting. Artificial intelligence algorithms have good prediction effects on key development indicators of offshore oilfields, and the prediction accuracy exceeds 92%. The SHAP algorithm offers a rationale for AI model parameters, quantifying input indicators' contributions to outputs. [ABSTRACT FROM AUTHOR]

This paper proposes an automated approach to the technology selection of High-Voltage Alternating Current (HVAC) Offshore Substations (OHVS) for the integration of Oil & Gas (O&G) production and Offshore Wind Farms (OWF) based on Artificial Intelligence (AI) techniques. Due to the complex regulatory landscape and project diversity, this is enacted via a cost decision-model which was developed based on Knowledge-Based Systems (KBS) and incorporated into an optioneering software named Transmission Optioneering Model (TOM). Equipped with an interactive dashboard, it uses detailed transmission and cost models, as well as a technological and commercial benchmarking of offshore projects to provide a standardized selection approach to OHVS design. By automating this process, the deployment of a technically sound and cost-effective connection in an interactive sandbox environment is streamlined. The decision-model takes as primary inputs the power rating requirements and the distance of the offshore target site and tests multiple voltage/rating configurations and associated costs. The output is then the most technically and economically efficient interconnection setup. Since the TOM process relies on equivalent models and on a broad range of different projects, it is manufacturer-agnostic and can be used for virtually any site as a method that ensures both energy transmission and economic efficiency. [ABSTRACT FROM AUTHOR]

Distributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF). Various DG models, such as stochastic and forecasting models, have been established and are used for OPF. While conventional OPF aims to minimize operational costs or power loss, the "Dual-Carbon" target has led to the inclusion of carbon emission reduction objectives. Additionally, state-of-the-art optimization techniques such as machine learning (ML) are being employed for OPF. However, most current research focuses on optimization methods rather than the problem formulation of the OPF. The purpose of this paper is to provide a comprehensive understanding of the OPF problem and to propose potential solutions. By delving into the problem formulation and different optimization techniques, selecting appropriate solutions for real-world OPF problems becomes easier. Furthermore, this paper provides a comprehensive overview of prospective advancements and conducts a comparative analysis of the diverse methodologies employed in the field of optimal power flow (OPF). While mathematical methods provide accurate solutions, their complexity may pose challenges. On the other hand, heuristic algorithms exhibit robustness but may not ensure global optimality. Additionally, machine learning techniques exhibit proficiency in processing extensive datasets, yet they necessitate substantial data and may have limited interpretability. Finally, this paper concludes by presenting prospects for future research directions in OPF, including expanding upon the uncertain nature of DGs, the integration of power markets, and distributed optimization. The main objective of this review is to provide a comprehensive understanding of the impact of DGs in DN on OPF. The article aims to explore the problem formulation of OPF and to propose potential solutions. By gaining in-depth insight into the problem formulation and different optimization techniques, optimal and sustainable power flow in a distribution network can be achieved, leading to a more efficient, reliable, and cost-effective power system. This offers tremendous benefits to both researchers and practitioners seeking to optimize power system operations. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources into a power system increases, the significance of precise short-term forecasts for wind power generation becomes paramount. However, the erratic and non-periodic nature of wind poses challenges in accurately predicting the output. This paper presents a comprehensive investigation into forecasting wind power generation for the following day, using three machine learning models: long short-term memory (LSTM), convolutional neural network-bidirectional LSTM (CNN-biLSTM), and light gradient boosting machine (LGBM). In addition, this paper proposes a method to improve the prediction performance of LGBM by separating data according to the distribution of features, and training and testing each separated dataset with a distinct model. This study includes a comparative analysis of the performance of the proposed models in predicting wind turbine output, offering valuable insights into their respective efficiencies. The results of this investigation were analyzed for two geographically distinct wind farms (Korea and the UK). The findings of this study are expected to facilitate the selection of efficient prediction models within the forecast accuracy auxiliary service market and assist grid operators in ensuring reliable power supply for the grid. [ABSTRACT FROM AUTHOR]

There is a growing demand for Green AI (Artificial Intelligence) technologies in the market and society, as it emerges as a promising technology. Green AI technologies are used to create sustainable solutions and reduce the environmental impact of AI. This paper focuses on describing the services of Green AI and the challenges associated with it at the community level. This article also highlights the accuracy levels of machine learning algorithms for various time periods. The process of choosing the appropriate input parameters for weather, locations, and complexity is outlined in this paper to examine the ML algorithms. For correcting the algorithm performance parameters, metrics like RMSE (root mean square error), MSE (mean square error), MAE (mean absolute error), and MPE (mean percentage error) are considered. Considering the performance and results of this review, the LSTM (long short-term memory) performed well in most cases. This paper concludes that highly advanced techniques have dramatically improved forecasting accuracy. Finally, some guidelines are added for further studies, needs, and challenges. However, there is still a need for more solutions to the challenges, mainly in the area of electricity storage. [ABSTRACT FROM AUTHOR]

Economic Dispatch Problems (EDP) refer to the process of determining the power output of generation units such that the electricity demand of the system is satisfied at a minimum cost while technical and operational constraints of the system are satisfied. This procedure is vital in the efficient energy management of electricity networks since it can ensure the reliable and efficient operation of power systems. As power systems transition from conventional to modern ones, new components and constraints are introduced to power systems, making the EDP increasingly complex. This highlights the importance of developing advanced optimization techniques that can efficiently handle these new complexities to ensure optimal operation and cost-effectiveness of power systems. This review paper provides a comprehensive exploration of the EDP, encompassing its mathematical formulation and the examination of commonly used problem formulation techniques, including single and multi-objective optimization methods. It also explores the progression of paradigms in economic dispatch, tracing the journey from traditional methods to contemporary strategies in power system management. The paper categorizes the commonly utilized techniques for solving EDP into four groups: conventional mathematical approaches, uncertainty modelling methods, artificial intelligence-driven techniques, and hybrid algorithms. It identifies critical research gaps, a predominant focus on single-case studies that limit the generalizability of findings, and the challenge of comparing research due to arbitrary system choices and formulation variations. The present paper calls for the implementation of standardized evaluation criteria and the inclusion of a diverse range of case studies to enhance the practicality of optimization techniques in the field. [ABSTRACT FROM AUTHOR]

High-precision spatial-temporal wind power prediction technology is of great significance for ensuring the safe and stable operation of power grids. The development of artificial intelligence technology provides a new scheme for modeling with strong spatial-temporal correlation. In addition, the existing prediction models are mostly 'black box' models, lacking interpretability, which may lead to a lack of trust in the model by power grid dispatchers. Therefore, improving the model to obtain interpretability has become an important challenge. In this paper, an interpretable short-term wind power prediction model based on ensemble deep graph neural network is designed. Firstly, the graph network model (GNN) with an attention mechanism is applied to the aggregate and the spatial-temporal features of wind power data are extracted, and the interpretable ability is obtained. Then, the long short-term memory (LSTM) method is used to process the extracted features and establish a wind power prediction model. Finally, the random sampling algorithm is used to optimize the hyperparameters to improve the learning rate and performance of the model. Through multiple comparative experiments and a case analysis, the results show that the proposed model has a higher prediction accuracy than other traditional models and obtains reasonable interpretability in time and space dimensions. [ABSTRACT FROM AUTHOR]

Increasing energy security is a crucial component of achieving the Sustainable Development Goals (SDGs). Three main factors influence energy security: (1) the efficiency of resource use in energy production, (2) the extent of energy losses, and (3) the use of new energy sources. Novel food products can impact these factors, and this paper explores whether they are being studied in the context of reducing energy consumption. Specifically, we investigate the role of technical progress and know-how in the creation and development of novel food products and whether novel methods of food production using artificial intelligence aim to reduce energy expenditures while improving product quality, variety, and the use of new energy sources. This paper seeks to examine the impact determinants of novel foods on energy security, considering economic, technological, social, and environmental aspects of knowledge about new food. To implement the study, the relevant international literature published in the past ten years have been reviewed and methods of modeling, visualization, and descriptive statistics applied. The review is structured into three sections: the first section presents ways to save energy and other resources in the food production chain through the intensive use of artificial intelligence tools; the second section presents the development of novel food products; and the last section presents marketing challenges for novel foods. The findings show that the topic addressed by this paper is currently critical, with many authorities, research centers, food producers, and energy producers interested. However, the research problem remains open, as a systematic review of secondary sources revealed little knowledge of the topic under study, and each author's study presents a new solution. The conclusion is that utilizing new foods and innovative production techniques that require less energy not only enhances production diversity but also improves its quality. [ABSTRACT FROM AUTHOR]

Significant amounts of data need to be transferred in order to optimize the operation of power grids. The development of advanced metering and control infrastructure ensures a growth in the amount of data transferred within smart grids. Data compression is a strategy to reduce the burden. This paper presents current challenges in the field of time-series data compression. This paper's novel contribution is the division of data in smart grids to real-time data used for control purposes and big data sets used for non-time-critical analysis of the system. Both of these two applications have different requirements for effective compression. Currently used algorithms are listed and described with their advantages and drawbacks for both of these applications. Details needed for the implementation of an algorithm were also provided. Comprehensive analysis and comparison are intended to facilitate the design of a data compression method tailored for a particular application. An important contribution is the description of the influence of data compression methods on cybersecurity, which is one of the major concerns in modern power grids. Future work includes the development of adaptive compression methods based on artificial intelligence, especially machine learning and quantum computing. This review will offer a solid foundation for the research and design of data compression methods. [ABSTRACT FROM AUTHOR]

With the continuous development of computer technology, artificial intelligence has been widely applied across various industries. To address the issues of high computational cost and inefficiency in traditional numerical methods, this paper proposes a data-driven artificial intelligence approach for solving high-dimensional neutron transport equations. Based on the AFA-3G assembly model, a neutron transport equation solving model is established using deep neural networks, considering factors that influence the neutron transport process in real engineering scenarios, such as varying temperature, power, and boron concentration. Comparing the model's predicted values with reference values, the average error in the infinite multiplication factor kinf of the assembly is found to be 145.71 pcm (10−5), with a maximum error of 267.10 pcm. The maximum relative error is less than 3.5%, all within the engineering error standards of 500 pcm and 5%. This preliminary validation demonstrates the feasibility of using data-driven artificial intelligence methods to solve high-dimensional neutron transport equations, offering a new option for engineering design and practical engineering computations. [ABSTRACT FROM AUTHOR]

In the current era of energy conservation and emission reduction, the development of electric and other new energy vehicles is booming. With their various attributes, lithium batteries have become the ideal power source for new energy vehicles. However, lithium-ion batteries are highly sensitive to temperature changes. Excessive temperatures, either high or low, can lead to abnormal operation of the batteries, posing a threat to the safety of the entire vehicle. Therefore, developing a reliable and efficient Battery Thermal Management System (BTMS) that can monitor battery status and prevent thermal runaway is becoming increasingly important. In recent years, deep learning has gradually become widely applied in various fields as an efficient method, and it has also been applied to some extent in the development of BTMS. In this work, we discuss the basic principles of deep learning and related optimization principles and elaborate on the algorithmic principles, frameworks, and applications of various advanced deep learning methods in BTMS. We also discuss several emerging deep learning algorithms proposed in recent years, their principles, and their feasibility in BTMS applications. Finally, we discuss the obstacles faced by various deep learning algorithms in the development of BTMS and potential directions for development, proposing some ideas for progress. This paper aims to analyze the advanced deep learning technologies commonly used in BTMS and some emerging deep learning technologies and provide new insights into the current combination of deep learning technology in new energy trams to assist the development of BTMS. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence (AI), AI has been widely applied in anomaly analysis detection and fault location in power grid data and has made significant research progress. Through looking back on traditional methods and deep learning methods in anomaly analysis detection and fault location of power grid data, we aim to provide readers with a comprehensive understanding of the existing knowledge and research advancements in this field. Firstly, we introduce the importance of anomaly analysis detection and fault location in power grid data for the safety and stability of power system operations and review traditional methods for anomaly analysis detection and fault location in power grid data, analyzing their advantages and disadvantages. Next, the paper briefly introduces the concepts of commonly used deep learning models in this field and explores, in depth, the application of deep learning methods in anomaly analysis detection and fault location of power grid data, summarizes the current research progress, and highlights the advantages of deep learning over traditional methods. Finally, we summarize the current issues and challenges faced by deep learning in this field and provide an outlook on future research direction. [ABSTRACT FROM AUTHOR]

This paper provides a survey of the literature on the application of Multi-agent Systems (MAS) technology for Smartgrids. Smartgrids represent the next generation electric network, as communities are developing self-sufficient and environmentally friendly energy production. As a cyber-physical system, the development of the vision of Smartgrids requires the resolution of major technical problems; this has fed over a decade of research. Due to the stochastic, intermittent nature of renewable energy resources and the heterogeneity of the agents involved in a Smartgrid, demand and supply management, energy trade and control of grid elements constitute great challenges for stable operation. In addition, in order to offer resilience against faults and attacks, Smartgrids should also have restoration, self-recovery and security capabilities. Multi-agent systems (MAS) technology has been a popular approach to deal with these challenges in Smartgrids, due to their ability to support reasoning in a distributed context. This survey reviews the literature concerning the use of MAS models in each of the relevant research areas related to Smartgrids. The survey explores how researchers have utilized agent-based tools and methods to solve the main problems of Smartgrids. The survey also discusses the challenges in the advancement of Smartgrid technology and identifies the open problems for research from the view of multi-agent systems. [ABSTRACT FROM AUTHOR]

Despite the increasing prevalence of artificial intelligence (AI) ethics frameworks, the practical application of these frameworks in industrial settings remains limited. This limitation is further augmented in energy systems by the complexity of systems composition and systems operation for energy generation, distribution, and supply. The primary reason for this limitation is the gap between the conceptual notion of ethics principles and the technical performance of AI applications in energy systems. For instance, trust is featured prominently in ethics frameworks but pertains to limited relevance for the robust operation of a smart grid. In this paper, we propose a lifecycle approach for AI ethics that aims to address this gap. The proposed approach consists of four phases: design, development, operation, and evaluation. All four phases are supported by a central AI ethics repository that gathers and integrates the primary and secondary dimensions of ethical practice, including reliability, safety, and trustworthiness, from design through to evaluation. This lifecycle approach is closely aligned with the operational lifecycle of energy systems, from design and production through to use, maintenance, repair, and overhaul, followed by shutdown, recycling, and replacement. Across these lifecycle stages, an energy system engages with numerous human stakeholders, directly with designers, engineers, users, trainers, operators, and maintenance technicians, as well as indirectly with managers, owners, policymakers, and community groups. This lifecycle approach is empirically evaluated in the complex energy system of a multi-campus tertiary education institution where the alignment between ethics and technical performance, as well as the human-centric application of AI, are demonstrated. [ABSTRACT FROM AUTHOR]

The inspection of overhead power transmission lines is of the utmost importance to ensure the power network's uninterrupted, safe, and reliable operation. The increased demand for frequent inspections implementing efficient and cost-effective methods has emerged, since conventional manual inspections are highly inaccurate, time-consuming, and costly and have geographical and weather restrictions. Unmanned Aerial Vehicles are a promising solution for managing automatic inspections of power transmission networks. The project "ALTITUDE (Automatic Aerial Network Inspection using Drones and Machine Learning)" has been developed to automatically inspect the power transmission network of Lesvos Island in Greece. The project combines drones, 5G data transmission, and state-of-the-art machine learning algorithms to replicate the power transmission inspection process using high-resolution UAV data. This paper introduces the ALTITUDE platform, created within the frame of the ALTITUDE project. The platform is a web-based, responsive Geographic Information System (GIS) that allows registered users to upload bespoke drone imagery of medium-voltage structures fed into a deep learning algorithm for detecting defects, which can be either exported as report spreadsheets or viewed on a map. Multiple experiments have been carried out to train artificial intelligence (AI) algorithms to detect faults automatically. [ABSTRACT FROM AUTHOR]

This paper reviews alarm processing methods in electrical power systems, focusing on evolving strategies beyond traditional fault analysis to accommodate modern grid complexities. Historically, alarm processing has predominantly aimed at fault analysis, increasingly merging with technological advances in communication and computing. However, it still needs to fully meet the challenges posed by the dynamic characteristics of modern power systems. This review points out certain inadequacies in current practices, notably their limited adaptation to new grid conditions. The authors propose a novel generation of alarm processing methodologies designed for future grids, emphasizing managing rare events and enhancing operator decision-making through advanced anomaly detection and explainable artificial intelligence. This synthesis presents a prospective direction for future research and applications in alarm processing, advocating for methodologies better suited to supporting system operators amidst technological advancements. [ABSTRACT FROM AUTHOR]

Recent advancements in the integration of artificial intelligence (AI) and machine learning (ML) with physical sciences have led to significant progress in addressing complex phenomena governed by nonlinear partial differential equations (PDEs). This paper explores the application of novel operator learning methodologies to unravel the intricate dynamics of flame instability, particularly focusing on hybrid instabilities arising from the coexistence of Darrieus–Landau (DL) and Diffusive–Thermal (DT) mechanisms. Training datasets encompass a wide range of parameter configurations, enabling the learning of parametric solution advancement operators using techniques such as parametric Fourier Neural Operator (pFNO) and parametric convolutional neural networks (pCNNs). Results demonstrate the efficacy of these methods in accurately predicting short-term and long-term flame evolution across diverse parameter regimes, capturing the characteristic behaviors of pure and blended instabilities. Comparative analyses reveal pFNO as the most accurate model for learning short-term solutions, while all models exhibit robust performance in capturing the nuanced dynamics of flame evolution. This research contributes to the development of robust modeling frameworks for understanding and controlling complex physical processes governed by nonlinear PDEs. [ABSTRACT FROM AUTHOR]

The transition to smart grids has served to transform traditional power systems into data-driven power systems. The purpose of this transition is to enable effective energy management and system reliability through an analysis that is centered on energy information. However, energy theft caused by vulnerabilities in the data collected from smart meters is emerging as a primary threat to the stability and profitability of power systems. Therefore, various methodologies have been proposed for energy theft detection (ETD), but many of them are challenging to use effectively due to the limitations of energy theft datasets. This paper provides a comprehensive review of ETD methods, highlighting the limitations of current datasets and technical approaches to improve training datasets and the ETD in smart grids. Furthermore, future research directions and open issues from the perspective of generative AI-based ETD are discussed, and the potential of generative AI in addressing dataset limitations and enhancing ETD robustness is emphasized. [ABSTRACT FROM AUTHOR]

This paper investigates the application of hybrid reinforcement learning (RL) models to optimize lithium-ion batteries' charging and discharging processes in electric vehicles (EVs). By integrating two advanced RL algorithms—deep Q-learning (DQL) and active-critic learning—within the framework of battery management systems (BMSs), this study aims to harness the combined strengths of these techniques to improve battery efficiency, performance, and lifespan. The hybrid models are put through their paces via simulation and experimental validation, demonstrating their capability to devise optimal battery management strategies. These strategies effectively adapt to variations in battery state of health (SOH) and state of charge (SOC) relative error, combat battery voltage aging, and adhere to complex operational constraints, including charging/discharging schedules. The results underscore the potential of RL-based hybrid models to enhance BMSs in EVs, offering tangible contributions towards more sustainable and reliable electric transportation systems. [ABSTRACT FROM AUTHOR]

The advent of Industry 4.0 has ushered in a new era of technological advancements, particularly in integrating information technology with physical devices. This convergence has given rise to smart devices and the Internet of Things (IoT), revolutionizing industrial processes. However, despite the push towards predictive maintenance, there still is a significant gap in fault prediction algorithms for electrical machines. This paper proposes a signal spectrum-based machine learning approach for fault prediction, specifically focusing on bearing faults. This study compares the effectiveness of traditional neural network algorithms with a novel approach integrating fuzzy logic. Through extensive experimentation and analysis of vibration spectra from various mechanical faults in bearings, it is demonstrated that the fuzzy-neuro network model outperforms traditional neural networks, achieving a validation accuracy of 99.40% compared to 94.34%. Incorporating fuzzy logic within the neural network framework offers advantages in handling complex fault combinations, showing promise for applications requiring higher accuracy in fault detection. While initial results are encouraging, further validation with more complex fault scenarios and additional fuzzy layers is recommended to fully explore the potential of fuzzy-neuro networks in fault prediction for electrical machines. [ABSTRACT FROM AUTHOR]

The virtual synchronous generator (VSG) is an important concept and primary control method in modern power systems. The penetration of power-electronics-based distributed generators in the power grid provides uncertainty and reduces the inertia of the system, thus increasing the risk of instability when disturbance occurs. The VSG produces virtual inertia by introducing the dynamic characteristics of the synchronous generator, which provides inertia and becomes a grid-forming control method. The disadvantages of the VSG are that there are many parameters to be adjusted and its operation process is complicated. However, with the rapid development of artificial intelligence (AI) technology, the powerful adaptive learning capability of AI algorithms provides potential solutions to this issue. Two research hotspots are deep learning (DL) and reinforcement learning (RL). This paper presents a comprehensive review of these two techniques combined with VSG control in the energy internet (EI). Firstly, the basic principle and classification of the VSG are introduced. Next, the development of DL and RL algorithms is briefly reviewed. Then, recent research on VSG control based on DL and RL algorithms are summarized. Finally, some main challenges and study trends are discussed. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of distributed photovoltaic systems (DPVS), the shortage of data monitoring devices and the difficulty of comprehensive coverage of measurement equipment has become more significant, bringing great challenges to the efficient management and maintenance of DPVS. Virtual collection is a new DPVS data collection scheme with cost-effectiveness and computational efficiency that meets the needs of distributed energy management but lacks attention and research. To fill the gap in the current research field, this paper provides a comprehensive and systematic review of DPVS virtual collection. We provide a detailed introduction to the process of DPVS virtual collection and identify the challenges faced by virtual collection through problem analogy. Furthermore, in response to the above challenges, this paper summarizes the main methods applicable to virtual collection, including similarity analysis, reference station selection, and PV data inference. Finally, this paper thoroughly discusses the diversified application scenarios of virtual collection, hoping to provide helpful information for the development of the DPVS industry. [ABSTRACT FROM AUTHOR]

With their high-energy density, high-power density, long life, and low self-discharge, lithium-ion capacitors are a novel form of electrochemical energy storage devices which are extensively utilized in electric vehicles, energy storage systems, and portable electronic gadgets. Li-ion capacitor aging mechanisms and life prediction techniques, however, continue to be active research areas. This paper examines the aging process for Li-ion batteries, covering the alterations in cell composition, the effect of the electrode charge state, temperature effects, and electrolyte deterioration. Additionally, this research offers approaches for predicting the lifespan of lithium-ion batteries, including those based on physical models, machine learning, and artificial intelligence. In this work, cycle life testing techniques are also discussed, including accelerated aging experiments for lithium-ion capacitors. The paper concludes by discussing future directions for the creation of aging mechanisms and lithium-ion capacitor life prediction techniques. [ABSTRACT FROM AUTHOR]

As urbanization increases, streetlights have become significant consumers of electrical power, making it imperative to develop effective control methods for sustainability. This paper offers a comprehensive review on control methods of smart streetlight systems, setting itself apart by introducing a novel light scheme framework that provides a structured classification of various light control patterns, thus filling an existing gap in the literature. Unlike previous studies, this work dives into the technical specifics of individual research papers and methodologies, ranging from basic to advanced control methods like computer vision and deep learning, while also assessing the energy consumption associated with each approach. Additionally, the paper expands the discussion to explore alternative functionalities for streetlights, such as serving as communication networks, environmental monitors, and electric vehicle charging stations. This multidisciplinary research aims to be a pivotal resource for both academics and industry professionals, laying the groundwork for future innovation and sustainable solutions in urban lighting. [ABSTRACT FROM AUTHOR]