ORIGINAL PAPER
Prospects and efficiency measurement of artificial intelligence in the management of enterprises in the energy sector in the era of Industry 4.0
1
Department of Menegement, Odessa Polytechnic State University, Ukraine
Submission date: 2021-08-31
Final revision date: 2021-11-15
Acceptance date: 2021-11-19
Publication date: 2021-12-29
Corresponding author
Hanna Doroshuk
Department of Menegement, Odessa Polytechnic State University, Shevchenko av., 65044, Odesa, Ukraine
Department of Menegement, Odessa Polytechnic State University, Shevchenko av., 65044, Odesa, Ukraine
Polityka Energetyczna – Energy Policy Journal 2021;24(4):61-76
KEYWORDS
TOPICS
ABSTRACT
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 the following areas: assessment of economic, scientific and technical, social, marketing, resource, financial, environmental, regional, ethical and cultural effects as well as assessment of the types of risks associated with the introduction of artificial intelligence.
METADATA IN OTHER LANGUAGES:
Polish
Perspektywy i pomiar efektywności sztucznej inteligencji w zarządzaniu przedsiębiorstw sektora energetycznego w dobie Przemysłu 4.0
sztuczna inteligencja, pomiar efektywności, zagrożenia sztucznej inteligencji, Przemysł 4.0
Ewolucja gospodarki i powstanie Przemysłu 4.0 prowadzą do wzrostu znaczenia wartości niematerialnych i cyfryzacji wszystkich procesów w przedsiębiorstwach energetycznych. Wiąże się to z wykorzystaniem technologii takich jak Internet Rzeczy, Big Data, analityka predykcyjna, przetwarzanie w chmurze, uczenie maszynowe, sztuczna inteligencja, robotyka, druk 3D, rozszerzona rzeczywistość itp. Szczególnie interesujące jest wykorzystanie sztucznej inteligencji w energetyce, co otwiera takie perspektywy jak zwiększenie bezpieczeństwa wytwarzania energii, wzrost efektywności energetycznej oraz zbilansowane procesy wytwarzania energii. Specyfiką tego konkretnego instrumentu Przemysłu 4.0 jest to, że łączy procesy cyfryzacji i intelektualizacji w przedsiębiorstwie i stanowi nową część kapitału intelektualnego przedsiębiorstwa. Wdrożenie sztucznej inteligencji w działalności przedsiębiorstw energetycznych wymaga uwzględnienia ich specyfiki oraz etapowego wdrożenia. W tym celu stworzono koncepcyjny model wdrożenia sztucznej inteligencji w przedsiębiorstwach energetycznych, który zawiera: tworzenie strategii wdrożenia; projektowanie; działanie i ocenę sztucznej inteligencji. Wprowadzenie sztucznej inteligencji to projekt o dużej skali i dość kosztowny, dlatego warto ocenić efektywność jego wykorzystania w działalności firm energetycznych. Zaproponowano pomiar efektywności w następujących obszarach: ocena skutków ekonomicznych, naukowo-technicznych, społecznych, marketingowych, zasobowych, finansowych, środowiskowych, regionalnych, etycznych i kulturowych, a także ocenę rodzajów zagrożeń związanych z wprowadzeniem sztucznej inteligencji.
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