The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Sustainability, Journal Year: 2023, Volume and Issue: 15(13), P. 10543 - 10543

Published: July 4, 2023

Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts control essential to lessen these effects safeguard populations. By utilizing its capacity handle massive amounts of data provide accurate forecasts, deep learning has emerged as potent tool for improving prediction control. The current state applications in forecasting management is thoroughly reviewed this work. review discusses variety subjects, such the sources utilized, models used, assessment measures adopted judge their efficacy. It assesses approaches critically points out advantages disadvantages. article also examines challenges with accessibility, interpretability models, ethical considerations prediction. report describes potential directions deep-learning research enhance predictions Incorporating uncertainty estimates into integrating many sources, developing hybrid mix other methodologies, enhancing few these. These goals can help become more precise effective, which will result better plans forecasts. Overall, useful resource academics professionals working on topic management. reviewing art, emphasizing difficulties, outlining areas future study, it lays solid basis. Communities prepare destructive floods by implementing cutting-edge algorithms, thereby protecting people infrastructure.

Language: Английский

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Remote sensing of photovoltaic scenarios: Techniques, applications and future directions

Applied Energy, Journal Year: 2023, Volume and Issue: 333, P. 120579 - 120579

Published: Jan. 10, 2023

Language: Английский

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A Review of Photovoltaic Module Failure and Degradation Mechanisms: Causes and Detection Techniques

Solar, Journal Year: 2024, Volume and Issue: 4(1), P. 43 - 82

Published: Jan. 9, 2024

With the global increase in deployment of photovoltaic (PV) modules recent years, need to explore and understand their reported failure mechanisms has become crucial. Despite PV being considered reliable devices, failures extreme degradations often occur. Some within normal range may be minor not cause significant harm. Others initially mild but can rapidly deteriorate, leading catastrophic accidents, particularly harsh environments. This paper conducts a state-of-the-art literature review examine failures, types, root causes based on components (from protective glass junction box). It outlines hazardous consequences arising from module describes potential damage they bring system. The reveals that each component is susceptible specific types failure, with some deteriorating own others impacting additional components, more severe failures. Finally, this briefly summarises detection techniques, emphasising significance electrical characterisation techniques underlining importance considering parameters. Most importantly, identifies most prevalent degradation processes, laying foundation for further investigation by research community through modelling experimental studies. allows early comparing performance when or occur prevent serious progression. worth noting studies included primarily focus detailing observed operations, which attributed various factors, including manufacturing process other external influences. Hence, provide explanations these do extensively corrective actions propose solutions either laboratory experiments real-world experience. Although, field study, there are corresponding have designed suggest preventive measures solutions, an in-depth those beyond scope paper. However, paper, turn, serves as valuable resource scholars confining critically evaluate available preventative actions.

Language: Английский

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Using machine learning in photovoltaics to create smarter and cleaner energy generation systems: A comprehensive review

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 364, P. 132701 - 132701

Published: June 15, 2022

Language: Английский

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Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models

Energies, Journal Year: 2022, Volume and Issue: 15(7), P. 2457 - 2457

Published: March 27, 2022

The use of renewable energies, such as Photovoltaic (PV) solar power, is necessary to meet the growing energy consumption. PV power generation has intrinsic characteristics related climatic variables that cause intermittence during process, promoting instabilities and insecurity in electrical system. One solutions for this problem uses methods Prediction Solar Power Generation (PSPPG). In context, aim study develop compare prediction accuracy irradiance between Artificial Neural Network (ANN) Long-Term Short Memory (LSTM) network models, from a comprehensive analysis simultaneously considers two distinct sets exogenous meteorological input three short-term horizons (1, 15 60 min), controlled experimental environment. results indicate there significant difference (p < 0.001) ANN LSTM with better overall skill models (MAPE = 19.5%), except min horizon. Furthermore, decreased horizon increased, no influence was observed on both evaluated variables.

Language: Английский

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Optimal dispatch of a multi-energy system microgrid under uncertainty: A renewable energy community in Austria

Applied Energy, Journal Year: 2023, Volume and Issue: 337, P. 120913 - 120913

Published: March 6, 2023

Microgrids can integrate variable renewable energy sources into the system by controlling flexible assets locally. However, as is dynamic, an effective microgrid controller must be able to receive feedback from in real-time, plan ahead and take account active electricity tariff, maximize benefits operator. These requirements motivate use of optimization-based control methods, such Model Predictive Control optimally dispatch microgrids. major bottleneck achieve maximum with these methods their predictive accuracy. This paper addresses this developing a novel multi-step forecasting method for framework. The presented are applied real test-bed community Austria, where its operational costs CO2 emissions benchmarked those rule-based strategy Flat, Time-of-Use, Demand Charge price tariffs. In addition, impact forecast errors electric battery capacity on savings examined. key results indicate that proposed outperform 24.7% 8.4% through optimal operation flexibilities if it has perfect foresight. deployed realistic environment, forecasts electrical load PV generation required, same reduced 3.3% cost 7.3% CO2, respectively. environments, performs best highly dynamic tariffs Time-of-Use Real-time pricing rates, achieving up 6.3%. show profitability microgrids threatened errors. motivates future research strategies compensate real-world more accurate methods.

Language: Английский

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ENHANCING ENERGY EFFICIENCY WITH AI: A REVIEW OF MACHINE LEARNING MODELS IN ELECTRICITY DEMAND FORECASTING

Engineering Science & Technology Journal, Journal Year: 2023, Volume and Issue: 4(6), P. 341 - 356

Published: Dec. 7, 2023

This study presents a comprehensive review of the impact artificial intelligence (AI) and machine learning (ML) on enhancing energy efficiency, particularly in context electricity demand forecasting. The systematically explores paradigm shift brought about by emergence AI focusing role forecasting historical evolution techniques. A critical analysis various ML models is conducted, examining their theoretical underpinnings, selection criteria, performance diverse scenarios. Key insights reveal that models, especially those incorporating deep big data analytics, significantly outperform traditional methods accuracy adaptability. These are adept at handling complex, nonlinear relationships large datasets, making them effective dynamic increasingly renewable-focused markets. also highlights importance selecting appropriate based criteria such as accuracy, adaptability to periods, capabilities, environmental considerations. further delves into technological, economic, impacts efficiency. It underscores potential drive innov4eations forecasting, contributing more sustainable efficient management. However, challenges privacy, cybersecurity, need for skilled professionals identified areas requiring attention. Strategic recommendations provided practitioners policymakers, emphasizing investment training, development supportive regulatory frameworks, fostering collaborations across sectors. concludes with future outlook, suggesting directions research developing robust scalable can integrate renewable sources smart grid technologies. serves valuable resource researchers, practitioners, policymakers engaged field efficiency AI-driven forecasting. Keywords: Machine Learning, Energy Efficiency, Demand Forecasting, Artificial Intelligence.

Language: Английский

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Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage

Energies, Journal Year: 2023, Volume and Issue: 16(18), P. 6613 - 6613

Published: Sept. 14, 2023

Overview: Photovoltaic (PV) systems are widely used in residential applications Poland and Europe due to increasing environmental concerns fossil fuel energy prices. Energy management strategies for (1.2 million prosumer PV installations Poland) play an important role reducing bills maximizing profits. Problem: This article aims check how predictable the operation of a household system is short term—such predictions usually made 24 h advance. Methods: We comparative study different based on real profile (selected storage installation) both traditional methods various artificial intelligence (AI) tools, which new approach, so far rarely underutilized, may inspire further research, including those paradigm Industry 4.0 and, increasingly, 5.0. Results: paper discusses results operational scenarios, considering two billing (net metering net billing). Conclusions: Insights into future research directions their limitations legal status, etc., presented. The novelty contribution lies demonstration that, case domestic grids, even simple AI solutions can prove effective inference forecasting support flow make it more efficient.

Language: Английский

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Parameter Extraction of Solar Photovoltaic Cell and Module Models with Metaheuristic Algorithms: A Review

Sustainability, Journal Year: 2023, Volume and Issue: 15(4), P. 3312 - 3312

Published: Feb. 10, 2023

As the photovoltaic (PV) market share continues to increase, accurate PV modeling will have a massive impact on future energy landscape. Therefore, it is imperative convert difficult-to-understand systems into understandable mathematical models through equivalent models. However, multi-peaked, non-linear, and strongly coupled characteristics of make challenging extract parameters Metaheuristics can address these challenges effectively regardless gradients function forms, gained increasing attention in solving this issue. This review surveys different metaheuristics model parameter extraction explains multiple algorithms’ behavior. Some frequently used performance indicators measure effectiveness, robustness, accuracy, competitiveness, resources consumed are tabulated compared, then merits demerits algorithms outlined. The patterns variation results extracted from external environments were analyzed, corresponding literature was summarized. Then, for both application scenarios analyzed. Finally, perspectives research summarized as valid reference technological advances extraction.

Language: Английский

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Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis

Energy, Journal Year: 2023, Volume and Issue: 288, P. 129898 - 129898

Published: Dec. 5, 2023

Language: Английский

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