Load Forecasting Techniques and Their Applications in Smart Grids

Energies, Journal Year: 2023, Volume and Issue: 16(3), P. 1480 - 1480

Published: Feb. 2, 2023

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

Language: Английский

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Employing Machine Learning and IoT for Earthquake Early Warning System in Smart Cities

Energies, Journal Year: 2023, Volume and Issue: 16(1), P. 495 - 495

Published: Jan. 2, 2023

An earthquake early warning system (EEWS) should be included in smart cities to preserve human lives by providing a reliable and efficient disaster management system. This can alter how different entities communicate with one another using an Internet of Things (IoT) network where observed data are handled based on machine learning (ML) technology. On hand, IoT is employed observing the measures EEWS entities. other ML exploited analyze these reach best action taken for risk mitigation cities. paper provides survey aspects required that EEWS. First, generally discussed provide role it play Second, models classified into linear non-linear ones. Third, evaluation metrics addressed focusing seismology. Fourth, this exhibits taxonomy includes emerging efforts Fifth, proposes generic architecture ML. Finally, addresses application parameters’ observations leading

Language: Английский

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Managing natural disasters: An analysis of technological advancements, opportunities, and challenges

Internet of Things and Cyber-Physical Systems, Journal Year: 2023, Volume and Issue: 4, P. 99 - 109

Published: Sept. 30, 2023

Natural disasters (NDs) have always been a major threat to human lives and infrastructure, causing immense damage loss. In recent years, the increasing frequency severity of natural highlighted need for more effective efficient disaster management strategies. this context, use technology has emerged as promising solution. survey paper, we explore employment technologies in order relieve impacts various disasters. We provide an overview how different such Remote Sensing, Radars Satellite Imaging, internet-of-things (IoT), Smartphones, Social Media can be utilized NDs. By utilizing these technologies, predict, respond, recover from NDs effectively, potentially saving minimizing infrastructure damage. The paper also highlights potential benefits, limitations, challenges associated with implementation purposes. While significantly improve NDM, there are that addressed, cost specialized knowledge skills. Overall, provides comprehensive managing sheds light on important role play NDM. exploring applications aims contribute development sustainable

Language: Английский

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Electricity Theft Detection Using Deep Reinforcement Learning in Smart Power Grids

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 59558 - 59574

Published: Jan. 1, 2023

In smart power grids, meters (SMs) are deployed at the end side of customers to report fine-grained consumption readings periodically utility for energy management and load monitoring. However, electricity theft cyber-attacks can be launched by fraudulent through compromising their SMs false pay less usage. These attacks harmfully affect sector since they cause substantial financial loss degrade grid performance because used management. Supervised machine learning approaches have been in literature detect attacks, but best our knowledge, use reinforcement (RL) has not investigated yet. RL better than existing it adapt more efficiently with dynamic nature patterns due its capability learn exploration exploitation mechanisms deciding optimal actions. this article, a deep (DRL) approach is proposed as promising solution problem. The samples real dataset employed an environment rewards given based on detection errors made during training. particular, presented four different scenarios. First, global model constructed using Q network (DQN) double (DDQN) architectures neural networks. Second, detector build customized new achieve high accuracy while preventing zero-day attacks. Third, changing pattern taken into consideration third scenario. Fourth, challenges defending against newly addressed fourth Extensive experiments conducted, results demonstrate that DRL boost cyberattacks, patterns, changes customers, cyber-attacks.

Language: Английский

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Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey

Sustainability, Journal Year: 2023, Volume and Issue: 15(15), P. 11713 - 11713

Published: July 28, 2023

Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure realizing a sustainable EEWS that is capable providing to people coordinating disaster response efforts. To achieve goal, provide an overview fundamental concepts seismic waves associated signal processing. We then present detailed discussion IoT-enabled EEWS, including use networks track actions taken by various organizations gather data, analyze it, send alarms when necessary. Furthermore, taxonomy emerging approaches using facilities, which includes integration advanced technologies such as machine learning (ML) algorithms, distributed computing, edge computing. also elaborate on generic architecture efficient highlight importance considering sustainability design systems. Additionally, discuss role drones management their enhance effectiveness EEWS. summary primary verification validation methods required under consideration. addition contributions mentioned above, study highlights implications earthquake detection management. Our research involved comprehensive survey existing literature infrastructure. conducted thorough analysis facilities findings suggest can significantly improve speed efforts, thereby reducing economic impact earthquakes. Finally, identify gaps domain future directions toward achieving Overall, provides valuable insights into emphasizes designing

Language: Английский

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A Survey on Key Management and Authentication Approaches in Smart Metering Systems

Energies, Journal Year: 2023, Volume and Issue: 16(5), P. 2355 - 2355

Published: March 1, 2023

The implementation of the smart grid (SG) and cyber-physical systems (CPS) greatly enhances safety, reliability, efficiency energy production distribution. Smart grids rely on meters (SMs) in converting power (PGs) a reliable way. However, proper operation these needs to protect them against attack attempts unauthorized entities. In this regard, key-management authentication mechanisms can play significant role. paper, we shed light importance mechanisms, clarifying main efforts presented context literature. First, address intelligent attacks affecting SGs. Secondly, terms cryptography are addressed. Thirdly, summarize common proposed techniques with suitable critique showing their pros cons. Fourth, introduce effective paradigms state art. Fifth, two tools for verifying security integrity protocols presented. Sixth, relevant research challenges addressed achieve trusted SMs manipulations entities future vision. Accordingly, survey facilitate exerted by interested researchers regard.

Language: Английский

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Review of the Data-Driven Methods for Electricity Fraud Detection in Smart Metering Systems

Energies, Journal Year: 2023, Volume and Issue: 16(6), P. 2852 - 2852

Published: March 19, 2023

In smart grids, homes are equipped with meters (SMs) to monitor electricity consumption and report fine-grained readings electric utility companies for billing energy management. However, malicious consumers tamper their SMs low reduce bills. This problem, known as fraud, causes tremendous financial losses worldwide threatens the power grid’s stability. To detect several methods have been proposed in literature. Among existing methods, data-driven achieve state-of-art performance. Therefore, this paper, we study main fraud detection emphasis on pros cons. We supervised including wide deep neural networks multi-data-source learning models, unsupervised clustering. Then, investigate how preserve consumers’ privacy, using encryption federated learning, while enabling because it has shown that can reveal sensitive information about activities. After that, design robust detectors against adversarial attacks ensemble model distillation they enable evade stealing electricity. Finally, provide a comprehensive comparison of works, followed by our recommendations future research directions enhance detection.

Language: Английский

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Toward Secured IoT-Based Smart Systems Using Machine Learning

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 20827 - 20841

Published: Jan. 1, 2023

Machine learning (ML) and the internet of things (IoT) are among most booming research directions. Smart cities, smart campuses (SCs), homes, cars, early warning systems (EWSs), etc.; or it could be called "Smart x" implemented using ML IoT. Those will alter how various world entities communicate with one another. This paper spots light on significant roles IoT in SS. Also, focuses importance IoT-based Besides, an overview smartness is presented. Then, this offers benchmarking along a taxonomy that categorizes models into linear non-linear ones depending problem type (classification regression). Afterward, commonly utilized evaluation metrics provided. In addition, considers trust techniques used for mitigating different security aspects networks, which play crucial part regulating new era communication. Moreover, two case studies devoting SS, namely SC EWS, considered data collection manipulation guided Finally, presents effective recommendations ML's earthquake EWS interested scholars.

Language: Английский

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Employing Remote Sensing, Data Communication Networks, AI, and Optimization Methodologies in Seismology

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2022, Volume and Issue: 15, P. 9417 - 9438

Published: Jan. 1, 2022

Seismology is among the intrinsic sciences that strictly affect human lives. Many research efforts are presented in literature aiming at achieving risk mitigation and disaster management. More particularly, modern technologies have been employed such a pivot. However, day-to-day challenges complexities of natural science face stack holders still need more reliable intelligent solutions. The solution can depend on partial or integrated system technologies. In this paper, we extensively survey co-related to gather major exerted regard. It also outlines desirability seismology Then, present detailed analysis remote sensing data communication networks (DCNs), which considered backend seismic networks. Furthermore, for seismology, depict both classical non-classical approaches based DCN principles, as optical fiber-based acoustic sensors, social media, internet things (IoT). Following that, comprehensive description various optimization techniques utilized wave prolonging network lifetime offered. A important functions artificial intelligence (AI) play different fields included. Finally, some recommendations prevent calamities preserve

Language: Английский

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Soft Computing in Smart Grid with Decentralized Generation and Renewable Energy Storage System Planning

Energies, Journal Year: 2023, Volume and Issue: 16(6), P. 2655 - 2655

Published: March 11, 2023

Distributed Power Generation and Energy Storage Systems (DPG-ESSs) are crucial to securing a local energy source. Both entities could enhance the operation of Smart Grids (SGs) by reducing Loss (PL), maintaining voltage profile, increasing Renewable (RE) as clean alternative fossil fuel. However, determining optimum size location different methodologies DPG-ESS in SG is essential obtaining most benefits avoiding any negative impacts such Quality (QoP) fluctuation issues. This paper’s goal conduct comprehensive empirical studies evaluate best for order find out what problems it causes modernization. Therefore, this paper presents explicit knowledge decentralized power generation based on integrating terms with help Metaheuristic Optimization Algorithms (MOAs). research also reviews rationalized cost-benefit considerations reliability, sensitivity, security Distribution Network (DN) planning. In determine results, various proposed works algorithms objectives discussed. Other soft computing methods defined, comparison drawn between many approaches adopted DN

Language: Английский

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