Implementation of artificial intelligence in the electric power industry

Sozontov, Andrey; Ivanova, Mariya; Gibadullin, A. A.

doi:10.1051/e3sconf/201911401009

Cited by 48 publications

(11 citation statements)

References 5 publications

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“…Artificial intelligence is commonly employed for solar power plant irradiance prediction and estimated power output, as well as wind speed prediction and projected power output from wind farms [4]. Recently, end-to-end technology such as artificial intelligence robotics and wireless communications have been developed and widely used for energy digitization [6]. The utility business is undergoing a shift from a steady environment to a volatile, technology-driven environment [7].…”

Section: Introductionmentioning

confidence: 99%

The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector

Mobayo

Aribisala

Yusuf

et al. 2021

JD-FEWS

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Digitalization and artificial intelligence (AI) have infiltrated most sectors of the economy, including the energy sector, where they have been extensively investigated. The aim of the study is primarily to assess the awareness of AI in facility management, and to identify the prospects and challenges of the adoption of AI in the energy sector. The study adopted the quantitative methodology approach, using a structured questionnaire to a sample size of 384 respondents. The questionnaire was administered to professionals such as mechanical, civil, electrical, computer, and mechatronics engineers, and project managers within the North-central geopolitical zone of Nigeria. Data gathered was analysed using descriptive analysis (mean value, weighted total, and relative importance index). The study based on findings concludes that there exists high awareness level about the concept of AI in the energy sector. However, regarding the awareness about some selected AI technologies, machine & deep learning, robotics, and speech recognition had high awareness level. The study also concludes that improved energy management, efficiency and transparency, remote reading of energy meters, and improved planning, operation & control of power systems were prevalent prospects of AI adoption. The major challenging factors to the adoption of AI in the Nigerian energy sector are outdated power system infrastructure, cellular technologies, lack of qualified experts and data science skills, and growing threat from cyber-attacks. The study recommends improved awareness and technical know-how of energy sector personnel, and provision of adequate power system infrastructure to provide stable power supply.

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Section: Introductionmentioning

confidence: 99%

The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector

Mobayo

Aribisala

Yusuf

et al. 2021

JD-FEWS

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“…At the end of 2016, the states adopted the digital agenda of the Eurasian Economic Union, within the framework of which they set the tasks of converging and developing common regulatory legal acts in the field of digitalization, preserving intellectual property, creating various forms of cooperation between public and private structures, supporting and developing digital initiatives. Of course, for the purpose of digital transformation, basic technologies have been selected, which include the development of the Internet of things system, the creation of artificial intelligence, unmanned technologies, etc., and within the framework of the states, the potential for digital transformation of the industry and the economy as a whole is being identified [12][13][14]. Therefore, for example, in states there is the following potential and companies that are engaged in digitalization at the state level:…”

Section: Resultsmentioning

confidence: 99%

Development of scientific and technical potential in the member states of the Eurasian Economic Union

et al. 2020

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The purpose of the article is the analysis and development of scientific and technological potential in the member states of the Eurasian Economic Union. In the presented material, based on the use of the system analysis method, the scientific and technical potential of the Member States of the Eurasian Economic Union was investigated. Then, using the logical method, it was revealed that the number of research centres was reduced, the volume of internal costs for research and development was reduced, and the number of personnel employed in the scientific and technical field was reduced. In the study, using statistical and comparative methods, it was found that up to 90% of the scientific and technical potential is concentrated in the Russian Federation. The paper analyzed the digital transformation of national economies, which established that states have digital potential, created conditions for its development, but there is an uneven distribution of innovative and digital infrastructure in the territory of the Eurasian Economic Union, which complicates the development of scientific and technological potential. In the study, using factor analysis, a conceptual model for the development of scientific and technological potential was proposed, which will ensure a rational and effective policy in the field of innovative and digital development of the member states of the Eurasian Economic Union. In the ending of the study the main conclusions and results of the work are presented.

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“…) ) intellectualization tasks (processing the results of monitoring the state of energy facilities, algorithms for the functioning of a "smart" house, load control, etc.) (Bostrom 2014;Von Ketelhod 2008;Sozontov et al 2019).…”

Section: Prospects Of Artificial Intelligence In the Management Of Enterprises Of The Energy Sectormentioning

confidence: 99%

“…Furthermore, the self-learning system works on the basis of pattern recognition, for which thousands of images of wind-turbine damage from lightning strikes, coating erosion, etc. are loaded into the system (Sozontov et al 2019).…”

Section: Prospects Of Artificial Intelligence In the Management Of Enterprises Of The Energy Sectormentioning

confidence: 99%

Prospects and efficiency measurement of artificial intelligence in the management of enterprises in the energy sector in the era of Industry 4.0

Doroshuk¹

2021

Polityka Energetyczna – Energy Policy Journal

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The evolution of the economy and the formation of Industry 4.0 lead to an increase in the importance of intangible assets and the digitization of all processes at energy enterprises. This involves the use of technologies such as the Internet of Things, Big Data, predictive analytics, cloud computing, machine learning, artificial intelligence, robotics, 3D printing, augmented reality etc. Of particular interest is the use of artificial intelligence in the energy sector, which opens up such prospects as increased safety in energy generation, increased energy efficiency, and balanced energy-generation processes. The peculiarity of this particular instrument of Industry 4.0 is that it combines the processes of digitalization and intellectualization in the enterprise and forms a new part of the intellectual capital of the enterprise. The implementation of artificial intelligence in the activities of energy companies requires consideration of the features and stages of implementation. For this purpose, a conceptual model of artificial intelligence implementation at energy enterprises has been formed, which contains: the formation of the implementation strategy; the design process; operation and assessment of artificial intelligence. The introduction of artificial intelligence is a large-scale and rather costly project; therefore, it is of interest to assess the effectiveness of using artificial intelligence in the activities of energy companies. Efficiency measurement is proposed in

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Implementation of artificial intelligence in the electric power industry

Cited by 48 publications

References 5 publications

The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector

The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector

Development of scientific and technical potential in the member states of the Eurasian Economic Union

Prospects and efficiency measurement of artificial intelligence in the management of enterprises in the energy sector in the era of Industry 4.0

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