Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 360 - 372
Опубликована: Янв. 1, 2025
Язык: Английский
Journal of Electrical Engineering and Technology, Год журнала: 2023, Номер 18(2), С. 719 - 733
Опубликована: Янв. 12, 2023
With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar is one most common and well-known existing networks. But because its non-stationary non-linear characteristics, it needs to predict solar irradiance provide more reliable Photovoltaic (PV) plants manage supply demand. Although there are various methods irradiance. This paper gives overview recent studies with focus on forecasting ensemble which divided into two main categories: competitive cooperative forecasting. In addition, parameter diversity data considered also preprocessing post-processing All these investigated this study. end, conclusion been drawn recommendations future have discussed.
Язык: Английский
Процитировано
46
Energy Reports, Год журнала: 2024, Номер 11, С. 1376 - 1398
Опубликована: Янв. 13, 2024
As the world continues to seek sustainable and efficient energy solutions, integration of advanced technologies into smart grid management (SEGM) becomes a paramount focus. The advent Sixth Generation (6G) wireless networks promises revolutionize way grids are monitored, controlled, optimized. This review paper explores potential 6G in context SEGM. It discusses vision techniques that can be harnessed unlock full capabilities networks. delves challenges opportunities presented by technology, addressing issues such as scalability, security, real-time monitoring, dynamic spectrum access. Moreover, it how enable seamless with other technologies, blockchain cybertwin, enhance resilience reliability grids. comprehensive aims shed light on transformative role networks, paving for intelligent future management.
Язык: Английский
Процитировано
30
Renewable energy focus, Год журнала: 2023, Номер 48, С. 100529 - 100529
Опубликована: Дек. 20, 2023
The efforts to revolutionize electric power generation and produce clean sustainable electricity have led the exploration of renewable energy systems (RES). This form is replenished cost-effective in terms production maintenance. However, RES, such as solar wind energies, intermittent; this one drawbacks its usage. In order overcome limitation, studies been undertaken forecast availability output. current trending method forecasting generated by RES artificial intelligence (AI) method. with all potential, traditional AI, Artificial Neural Network (ANN), Support Vector Machine (SVM) many more, does not it all. Because this, metaheuristic algorithms are being explored optimization techniques increase performance accuracy these AI methods some challenges models. study presents an insightful survey (traditional metaheuristic) systems. A existing surveyed literature was presented. taxonomy formulated, theoretical backgrounds were Also, various forms improved versions applied optimize classical systems' output surveyed. conceptual framework hybrid application formulated. Finally, discussion, insight, models future directions
Язык: Английский
Процитировано
32
Energies, Год журнала: 2023, Номер 16(24), С. 8057 - 8057
Опубликована: Дек. 14, 2023
This review paper provides a summary of methods in which artificial intelligence (AI) techniques have been applied the management variable renewable energy (VRE) systems, and an outlook to future directions research field. The VRE types included are namely solar, wind marine varieties. AI techniques, particularly machine learning (ML), gained traction as result data explosion, offer method for integration multimodal more accurate forecasting applications. aspects include optimized power generation into grids, including demand forecasting, storage, system optimization, performance monitoring, cost management. Future applications proposed discussed, issue availability, quality, addition explainable (XAI), quantum (QAI), coupling with emerging digital twins technology, natural language processing.
Язык: Английский
Процитировано
26
IEEE Access, Год журнала: 2024, Номер 12, С. 90461 - 90485
Опубликована: Янв. 1, 2024
Solar energy is largely dependent on weather conditions, resulting in unpredictable, fluctuating, and unstable photovoltaic (PV) power outputs. Thus, accurate PV forecasts are increasingly crucial for managing controlling integrated systems. Over the years, advanced artificial neural network (ANN) models have been proposed to increase accuracy of various geographical regions. Hence, this paper provides a state-of-the-art review five most popular ANN forecasting. These include multilayer perceptron (MLP), recurrent (RNN), long short-term memory (LSTM), gated unit (GRU), convolutional (CNN). First, internal structure operation these studied. It then followed by brief discussion main factors affecting their forecasting accuracy, including horizons, meteorological evaluation metrics. Next, an in-depth separate analysis standalone hybrid provided. has determined that bidirectional GRU LSTM offer greater whether used as model or configuration. Furthermore, upgraded metaheuristic algorithms demonstrated exceptional performance when applied models. Finally, study discusses limitations shortcomings may influence practical implementation
Язык: Английский
Процитировано
16
Designs, Год журнала: 2024, Номер 8(1), С. 10 - 10
Опубликована: Янв. 18, 2024
A comprehensive review of uncertainties in power systems, covering modeling, impact, and mitigation, is essential to understand manage the challenges faced by electric grid. Uncertainties systems can arise from various sources have significant implications for grid reliability, stability, economic efficiency. Australia, susceptible extreme weather such as wildfires heavy rainfall, faces vulnerabilities its network assets. The decentralized distribution population centers poses supplying remote areas, which a crucial consideration emerging technologies emphasized this paper. In addition, evolution modern grids, facilitated deploying advanced metering infrastructure (AMI), has also brought new system due risk cyber-attacks via communication links. However, existing literature lacks analysis encompassing related events, cyber-attacks, asset management, well advantages limitations mitigation approaches. To fill void, covers broad spectrum considering their impacts on explores conventional robust control probabilistic data-driven approaches modeling correlating uncertainty events state optimal decision making. This article investigates development scenario-based operations, microgrids (MGs) energy storage (ESSs), demand-side frequency ancillary service (D-FCAS) reserve provision regulation ensure design uncertainty-tolerance system. delves into trade-offs linked with implementation strategies, computational speed, It how these strategies may influence planning operation future grids.
Язык: Английский
Процитировано
15
Heliyon, Год журнала: 2024, Номер 10(4), С. e26088 - e26088
Опубликована: Фев. 1, 2024
The use of renewable energy sources (RESs) at the distribution level has become increasingly appealing in terms costs and technology, expecting a massive diffusion near future placing several challenges to power grid. Since RESs depend on stochastic —solar radiation, temperature wind speed, among others— they introduce high uncertainty grid, leading imbalance deteriorating network stability. In this scenario, managing forecasting RES is vital successfully integrate them into grids. Traditionally, physical- statistical-based models have been used predict outputs. Nevertheless, former are computationally expensive since rely solving complex mathematical atmospheric dynamics, whereas latter usually consider linear models, preventing from addressing challenging scenarios. recent years, advances machine learning techniques, which can learn historical data, allowing analysis large-scale datasets either under non-uniform characteristics or noisy provided researchers with powerful data-driven tools that outperform traditional methods. paper, systematic literature review conducted identify most widely learning-based approaches forecast results show deep artificial neural networks, especially long-short term memory accurately model autoregressive nature output, ensemble strategies, allow handling large amounts highly fluctuating best suited ones. addition, promising integrating forecasted output decision-making problems, such as unit commitment, address economic, operational managerial grid discussed, solid directions for research provided.
Язык: Английский
Процитировано
15
International Journal of Hydrogen Energy, Год журнала: 2024, Номер 58, С. 459 - 469
Опубликована: Янв. 24, 2024
Язык: Английский
Процитировано
14
Energies, Год журнала: 2024, Номер 17(23), С. 5965 - 5965
Опубликована: Ноя. 27, 2024
With accelerating climate change and rising global energy consumption, the application of artificial intelligence (AI) machine learning (ML) has emerged as a crucial tool for enhancing efficiency mitigating impacts change. However, their implementation dual character: on one hand, AI facilitates sustainable solutions, including optimization, renewable integration carbon reduction; other training operation large language models (LLMs) entail significant potentially undermining neutrality efforts. Key findings include an analysis 237 scientific publications from 2010 to 2024, which highlights advancements obstacles adoption across sectors, such construction, transportation, industry, households. The review showed that interest in use ML grown significantly: over 60% documents have been published last two years, with topics construction forecasting attracting most interest. Most articles are by researchers China, India, UK USA, (28–33 articles). This is more than twice number around rest world; 58% research concentrated three areas: engineering, computer science energy. In conclusion, also identifies areas further aimed at minimizing negative maximizing its contribution development, development energy-efficient architectures new methods management.
Язык: Английский
Процитировано
13
Computers and Electronics in Agriculture, Год журнала: 2025, Номер 231, С. 110027 - 110027
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
1