A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of Hurricanes DOI

Jayanthi Devaraj,

Sumathi Ganesan,

Rajvikram Madurai Elavarasan

et al.

Applied Sciences, Journal Year: 2021, Volume and Issue: 11(9), P. 4129 - 4129

Published: April 30, 2021

The prediction of severe weather events such as hurricanes is always a challenging task in the history climate research, and many deep learning models have been developed for predicting severity events. When disastrous hurricane strikes coastal region, it causes serious hazards to human life habitats also reflects prodigious amount economic losses. Therefore, necessary build improve accuracy avoid significant losses all aspects. However, impractical predict or monitor every storm formation real time. Though various techniques exist diagnosing tropical cyclone intensity convolutional neural networks (CNN), auto-encoders, recurrent network (RNN), etc., there are some challenges involved estimating intensity. This study emphasizes identify different categories perform post-disaster management. An improved (CNN) model used weakest strongest with values using infrared satellite imagery data wind speed from HURDAT2 database. achieves lower Root mean squared error (RMSE) value 7.6 knots Mean (MSE) 6.68 by adding batch normalization dropout layers CNN model. Further, crucial evaluate damage implementing advance measures planning resources. fine-tuning pre-trained visual geometry group (VGG 19) accomplished extent automatic annotation image Greater Houston. VGG 19 trained video datasets classifying types annotate event automatically. 98% achieved 97% results proved that proposed estimation its enhances ability, which can ultimately help scientists meteorologists comprehend Finally, mitigation steps reducing risks addressed.

Language: Английский

Artificial intelligence in renewable energy: A comprehensive bibliometric analysis DOI Creative Commons
Lili Zhang, Jie Ling, Ming‐Wei Lin

et al.

Energy Reports, Journal Year: 2022, Volume and Issue: 8, P. 14072 - 14088

Published: Nov. 1, 2022

In recent years, artificial intelligence methods have been widely applied to solve issues related renewable energy because of their ability nonlinear and complex data structures. this paper, we provide a comprehensive bibliometric analysis better understand the evolution Artificial Intelligence in Renewable Energy (AI&RE) research from 2006 2022. This study is performed based on Web Science Core Collection Database, dataset 469 publications retrieved. paper uses VOS viewer, CiteSpace, Bibliometrix perform science mapping. The results show that China most productive influential country/region, with widest range collaborative partners. reveals AI-related technologies can effectively integrating power system, such as solar wind forecasting, system frequency control, transient stability assessment. addition, future trends are discussed. helps scholars AI&RE perspective inspires them think about field through multiple aspects.

Language: Английский

Citations

124
New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight DOI Creative Commons

Erlong Zhao,

Shaolong Sun, Shouyang Wang

et al.

Data Science and Management, Journal Year: 2022, Volume and Issue: 5(2), P. 84 - 95

Published: June 1, 2022

Accurate forecasting results are crucial for increasing energy efficiency and lowering consumption in wind energy. Big data artificial intelligence (AI) have great potential forecasting. Although the literature on this subject is extensive, it lacks a comprehensive research status survey. In identifying evolution rules of big AI methods forecasting, paper summarizes studies over last two decades. The existing types, analysis techniques, classified sorted by combining reviews scientometrics methods. Furthermore, trend determined based combing hotspots frontier progress. Finally, research's opportunities, challenges, implications from various perspectives. serve as foundation future promote further development

Language: Английский

Citations

122
Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques DOI Creative Commons
Laith Abualigah,

Raed Abu Zitar,

Khaled H. Almotairi

et al.

Energies, Journal Year: 2022, Volume and Issue: 15(2), P. 578 - 578

Published: Jan. 14, 2022

Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective in generating and optimizing tools. The complexity of this variety energy depends on its coverage large sizes data parameters, which be investigated thoroughly. This paper covered the most resent important researchers domain problems using methods. Various types Deep Learning (DL) Machine (ML) algorithms employed Solar Wind supplies given. performance given literature is assessed by new taxonomy. focus conducting comprehensive state-of-the-art heading evaluation discusses vital difficulties possibilities extensive research. Based results, variations efficiency, robustness, accuracy values, generalization capability obvious learning techniques. In case big dataset, effectiveness significantly better than other computational However, applying producing hybrid with optimization develop optimize construction optionally indicated. all cases, achievement single method due fact that gain benefit two or more providing an accurate forecast. Therefore, it suggested utilize future deal generation problems.

Language: Английский

Citations

115
Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review DOI Creative Commons
Wadim Striełkowski, Andrey Vlasov, Kirill Selivanov

et al.

Energies, Journal Year: 2023, Volume and Issue: 16(10), P. 4025 - 4025

Published: May 11, 2023

The use of machine learning and data-driven methods for predictive analysis power systems offers the potential to accurately predict manage behavior these by utilizing large volumes data generated from various sources. These have gained significant attention in recent years due their ability handle amounts make accurate predictions. importance particular momentum with transformation that traditional system underwent as they are morphing into smart grids future. transition towards embed high-renewables electricity is challenging, generation renewable sources intermittent fluctuates weather conditions. This facilitated Internet Energy (IoE) refers integration advanced digital technologies such Things (IoT), blockchain, artificial intelligence (AI) systems. It has been further enhanced digitalization caused COVID-19 pandemic also affected energy sector. Our review paper explores prospects challenges using provides an overview ways which constructing can be applied order them more efficient. begins description role operations. Next, discusses systems, including benefits limitations. In addition, reviews existing literature on this topic highlights used Furthermore, it identifies opportunities associated methods, quality availability, discussed. Finally, concludes a discussion recommendations research application future grid-driven powered IoE.

Language: Английский

Citations

56
Learning based short term wind speed forecasting models for smart grid applications: An extensive review and case study DOI
Vikash Kumar Saini, Rajesh Kumar, Ameena Saad Al–Sumaiti

et al.

Electric Power Systems Research, Journal Year: 2023, Volume and Issue: 222, P. 109502 - 109502

Published: June 1, 2023

Language: Английский

Citations

46
Sustainable urban energy solutions: Forecasting energy production for hybrid solar-wind systems DOI
Ali Javaid, Muhammad Sajid, Emad Uddin

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 302, P. 118120 - 118120

Published: Jan. 31, 2024

Language: Английский

Citations

24
Digital technologies for a net-zero energy future: A comprehensive review DOI
Md Meftahul Ferdaus, Tanmoy Dam, Sreenatha G. Anavatti

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 202, P. 114681 - 114681

Published: July 2, 2024

Language: Английский

Citations

23
Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods DOI Creative Commons
Mutaz AlShafeey, Csaba Csáki

Energy Reports, Journal Year: 2021, Volume and Issue: 7, P. 7601 - 7614

Published: Nov. 1, 2021

As Photovoltaic (PV) energy is impacted by various weather variables such as solar radiation and temperature, one of the key challenges facing forecasting choosing right inputs to achieve most accurate prediction.Weather datasets, past power data sets, or both sets can be utilized build different models.However, operators grid-connected PV farms do not always have full available them especially over an extended period time required techniques multiple regression (MR) artificial neural network (ANN).Therefore, research reported here considered these two main approaches building prediction models compared their performance when utilizing structural, time-series, hybrid methods for input.Three years generation (of actual farm) well historical same location) with several were collected test six models.Models built designed forecast a 24-hour ahead horizon 15 min resolutions.Results comparative analysis show that accuracy depending on input method used model: ANN perform better than MR regardless used.The results in techniques, while using time-series least models.Furthermore, sensitivity shows poor quality does impact negatively structural approach.

Language: Английский

Citations

76
Determination of optimal renewable energy growth strategies using SWOT analysis, hybrid MCDM methods, and game theory: A case study DOI Open Access
Khalid Almutairi, Seyyed Jalaladdin Hosseini Dehshiri, Seyyed Shahabaddin Hosseini Dehshiri

et al.

International Journal of Energy Research, Journal Year: 2021, Volume and Issue: 46(5), P. 6766 - 6789

Published: Dec. 30, 2021

Due to population growth and industrial development, Iran is facing the challenge of energy supply in various industrial, power plant, agricultural, residential sectors. While most Iran's comes from fossil fuels, these fuels have devastating environmental impacts. Therefore, has invest more its renewable sources. Given that many locations significant wind, solar, geothermal potential, this study used a combination SWOT, multicriteria decision-making approaches, game theory identify best development plans for country. The Stepwise Weight Assessment Ratio Analysis (SWARA) technique was applied weight criteria sub-criteria. Impact on environment, resource generation cost with weights 0.218, 0.182, 0.145 recognized as important sub-criteria, respectively. To rank factors each SWOT dimension, namely, strengths, weaknesses, opportunities, threats, Grey Additive ASsessment (ARAS-Grey) method then used. Using fuzzy Shapley value, strategies determined be SO1ST3WO1WT1 value (1.72, 0.58, 0.3). high-efficiency wind solar technologies minimization output fluctuations loss through storage methods such hydrogen production battery banks were identified strategy Iran.

Language: Английский

Citations

68
Renewables integration into power systems through intelligent techniques: Implementation procedures, key features, and performance evaluation DOI Creative Commons
Sayemul Islam, Naruttam Kumar Roy

Energy Reports, Journal Year: 2023, Volume and Issue: 9, P. 6063 - 6087

Published: May 31, 2023

Integrating renewable energy sources (RESs) such as solar photovoltaic (PV), wind, biogas, and hydropower into the power system is a sustainable solution that can feasibly maintain supply demand response. The uncertainty in irradiance wind speed impedes problem be solved by integrating an appropriate control technique reasonably forecasts necessary information maintains operation. A critical analysis of different intelligent techniques with numerical data review, prediction accuracy, pros cons, techno-economic feasibility for reader's perception. This paper analyzes 89 research works integrated RESs storage systems (ESSs). are classified according to considered resources, PV, demonstrate meaningful insight particular field. provides adequate on each presenting implementation procedures, key features, accuracy. accuracy method determined metrics root mean square error (RMSE), absolute (RMAE), percentage (RMPE). integration ESS emphasizes possibility enhancing backup connected distribution systems. review signifies current view potentiality incorporating methods demonstrates significant

Language: Английский

Citations

27