The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

Lytras, Miltiadis D.; Chui, Kwok Tai

doi:10.3390/en12163108

Cited by 19 publications

(9 citation statements)

References 34 publications

(45 reference statements)

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“…In this line, a measure to quantify the carbon footprint of an algorithm is proposed in [163]. 8 Accordingly, we remark that the AI field must seriously consider the development of efficient AI algorithms and minimize the discrepancy between accuracy and environmental costs in order to really contribute to the attainment of the SGDs [164].…”

Section: Five Key Elements and Priorities Which Should Be Globally Understood: A Decade Roadmapmentioning

confidence: 99%

“…AI has currently reached a sufficient level of maturity both as an umbrella scientific discipline and as a technology, having spread from laboratories to the entire society. Industry and government administrations make an increasingly extended use of intelligent and digital technologies in their daily projects and endeavors [7][8][9]. The economic impact of AI has grown considerably in the last years, and recent studies predict a 15-billion economic impact by year 2030 [10].…”

Section: Introductionmentioning

confidence: 99%

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A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

et al. 2021

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The 17 Sustainable Development Goals (SDGs) established by the United Nations Agenda 2030 constitute a global blueprint agenda and instrument for peace and prosperity worldwide. Artificial intelligence and other digital technologies that have emerged in the last years, are being currently applied in virtually every area of society, economy and the environment. Hence, it is unsurprising that their current role in the pursuance or hampering of the SDGs has become critical. This study aims at providing a snapshot and comprehensive view of the progress made and prospects in the relationship between artificial intelligence technologies and the SDGs. A comprehensive review of existing literature has been firstly conducted, after which a series SWOT (Strengths, Weaknesses, Opportunities and Threats) analyses have been undertaken to identify the strengths, weaknesses, opportunities and threats inherent to artificial intelligence-driven technologies as facilitators or barriers to each of the SDGs. Based on the results of these analyses, a subsequent broader analysis is provided, from a position vantage, to (i) identify the efforts made in applying AI technologies in SDGs, (ii) pinpoint opportunities for further progress along the current decade, and (iii) distill ongoing challenges and target areas for important advances. The analysis is organized into six categories or perspectives of human needs: life, economic and technological development, social development, equality, resources and natural environment. Finally, a closing discussion is provided about the prospects, key guidelines and lessons learnt that should be adopted for guaranteeing a positive shift of artificial intelligence developments and applications towards fully supporting the SDGs attainment by 2030.

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Section: Five Key Elements and Priorities Which Should Be Globally Understood: A Decade Roadmapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Our society is being revolutionized by digital technologies such as artificial intelligence, block chains, cloud computing, big data, and deep learning, among others. In their daily initiatives and endeavors, industry and government administrations are increasingly relying on digital technologies [6][7][8]. Several studies have emerged in recent years to investigate the problems, benefits, and potential impact of Artificial Intelligence [9][10][11][12] and related technologies (e.g.…”

Section: Sentiment Analysis and Literature Reviewmentioning

confidence: 99%

A Review Paper on the Role of Sentiment Analysis in Quality Education

Pooja

Bhalla²

2022

SN COMPUT. SCI.

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Education is a critical indication of progress and a major factor in well-being. The UNs Sustainable Development Goals establish specific requirements for increasing educational quality and protecting the well-being of children. UN's agenda for Sustainable Development Goal 4 which aims to "ensure inclusive and equitable quality education and promote lifelong learning opportunities for all" was adopted in India in 2015. Students' academic success is a vital part of the education system. Predicting student performance has grown more challenging due to the enormous amount of data in educational databases. Low-performing students will experience a variety of difficulties, including delayed graduation and even dropping out. Therefore, educational institutions should closely monitor the academic progress of their students and provide quick assistance to those who have low performance. Using Students' academic achievement predictions to accomplish that is one method. This method will help educational institutions in identifying and supporting low-performing students at an initial stage. This study presents a systematic review of research on sentiment analysis towards SDG4 quality education through social media platform such as Twitter, Facebook and a review of 21 studies indexed in SCOPUS. Using social media data rather than a conventional survey of the data, evaluation of outspoken opinion and feelings of students towards their institution to obtain Quality Education. In this study, the dataset is taken from kaggle with names as student-performance-data-set which uses two files named as student-math, and student-por which shows the student performance in a Math language course and Portuguese language course, respectively, with 33 attributes and 396 records in each. Of 396 records, 110 records were selected as sample. During the visualization, we analyzed SVM model is stable because even minor data changes have no impact on the hyperplane and it handles the nonlinear data using Kernel techniques.

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“…The amount of AI research for smart grid applications has increased in the last decade. Similarly, in the last 4 years, some of these studies were surveyed in recent papers [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Techniques in Smart Grid: A Survey

2021

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The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

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The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

Cited by 19 publications

References 34 publications

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

A Review Paper on the Role of Sentiment Analysis in Quality Education

Artificial Intelligence Techniques in Smart Grid: A Survey

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