Application of Artificial Intelligence for the Optimization of Hydropower Energy Generation

Kumar, Krishna; Saini, R.P.

doi:10.4108/eai.6-8-2021.170560

Cited by 10 publications

(2 citation statements)

References 54 publications

(65 reference statements)

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“…The openness of urban spatial data, combined with internet geographical information acquisition, fusion, and analytical mining technology, opens new avenues in emergency shelter site selection research 4 – 6 . In contrast to traditional methods, artificial intelligence approaches driven by multi-source data can more effectively address existing limitations 7 – 9 . Some studies use an end-to-end analysis, employing data such as building outlines, user access trajectories, and urban points of interest to develop complex site selection models, applicable in commerce, healthcare, and transportation 10 , 11 .…”

Section: Introductionmentioning

confidence: 99%

Site selection and prediction of urban emergency shelter based on VGAE-RF model

Wang,

Han,

Luo

et al. 2024

Sci Rep

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As urban development accelerates and natural disasters occur more frequently, the urgency of developing effective emergency shelter planning strategies intensifies. The shelter location selection method under the traditional multi-criteria decision-making framework suffers from issues such as strong subjectivity and insufficient data support. Artificial intelligence offers a robust data-driven approach for site selection; however, many methods neglect the spatial relationships of site selection targets within geographical space. This paper introduces an emergency shelter site selection model that combines a variational graph autoencoder (VGAE) with a random forest (RF), namely VGAE-RF. In the constructed urban spatial topological graph, based on network geographic information, this model captures both the latent features of geographic unit coupling and integrates explicit and latent features to forecast the likelihood of emergency shelters in the construction area. This study takes Beijing, China, as the experimental area and evaluates the reliability of different model methods using a confusion matrix, Receiver Operating Characteristic (ROC) curve, and Imbalance Index of spatial distribution as evaluation indicators. The experimental results indicate that the proposed VGAE-RF model method, which considers spatial semantic associations, displays the best reliability.

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Section: Introductionmentioning

confidence: 99%

Site selection and prediction of urban emergency shelter based on VGAE-RF model

Wang,

Han,

Luo

et al. 2024

Sci Rep

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“…Under such circumstances, an effective demand response can create a cost-effective energy system that is both flexible and reliable (Antonopoulos et al, 2020). Wide-scale responses to energy challenges have increasingly incorporated machine learning (ML) and artificial intelligence (AI) applications, which have been used for site selection, parameter assessment, operation and maintenance optimization, planning, feasibility analysis, discharge forecasts, energy generation projections, and maintenance (Kumar and Saini, 2021).…”

Section: Introductionmentioning

confidence: 99%

Improvingurban Energyresiliencewith an Integrative Framework Based on Machine Learningmethods

Hassan

Megahed²

2022

A&J

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Introduction:Climate change and global warming are among the greatest challenges facing the world today. A newconcept, known as urban resilience, has been developed in response. There are various approaches to urban resilience. Among them, is the urban energy resilience (UER) approach, which poses a considerable challenge. Machine learning (ML), as an application of artificial intelligence (AI), provides powerful and affordable computing resources, large-scale datamining, advanced algorithms, and real-timemonitoring. However, veryfewstudies have investigated howsuch aspectscan be integrated into urban resilience in general, and UER in particular. Purpose of the study: The study develops an integrative framework that can improve UER, based on ML methods. Methodology: We carried out a bibliometric analysisand a systematic review of UER in accordance with AI concepts, models, and applications. Results:The findings of this study were used to create an integrative framework, based on three hierarchical phases, which effectively addressed the main capabilities of UER, identified its priorities, and shed light on how ML can benefit UER as a whole. Novelty:The framework developed in this study also offers insights in integrating ML methods into UER as strategically as possible, especially in the context of climate change and urban energy systems. This framework can serve as reference for specialistsand decision-makers aiming to expand AI and ML applications to optimize UER.

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