Predictive demand analytics and machine learning in electric power systems for enhancing resilience and efficiency
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12510%2F25%3A43909818" target="_blank" >RIV/60076658:12510/25:43909818 - isvavai.cz</a>
Alternative codes found
RIV/60460709:41110/25:101766
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2352467725001043?pes=vor&utm_source=clarivate&getft_integrator=clarivate" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2352467725001043?pes=vor&utm_source=clarivate&getft_integrator=clarivate</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.segan.2025.101722" target="_blank" >10.1016/j.segan.2025.101722</a>
Alternative languages
Result language
angličtina
Original language name
Predictive demand analytics and machine learning in electric power systems for enhancing resilience and efficiency
Original language description
Rapid advancements of the Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, and Big Data have significantly accelerated the adoption of predictive analytics within electric power systems. The integration of predictive analytics offers substantial opportunities for automating control and monitoring processes, thereby enhancing both the resilience and operational efficiency of power grids. This paper introduces a novel predictive analytics framework that uniquely integrates supervised and unsupervised machine learning methods, specifically linear and logistic regression, decision trees, random forests, and clustering algorithms, to simultaneously predict short-term power demand and accurately detect early signs of short circuits and system faults. Utilizing the grid load data from the U.S. Department of Energy's Open Energy Data Initiative (OEDI), our research systematically illustrates the implementation, optimization, and integration of selected machine learning algorithms specifically tailored for power systems. Our empirical results demonstrate substantial efficiency improvements in electric power systems ranging from 14 % to 24 %, with measurable enhancements across reliability indices, economic savings, reductions in environmental impact (lower greenhouse gas emissions), and optimized infrastructure utilization. Furthermore, the paper explicitly addresses regulatory hurdles and industry adoption challenges, outlining how predictive analytics can strategically facilitate technology integration in traditionally conservative power sectors. Finally, the paper provides deeper theoretical synthesis and proposes several specific future research avenues, emphasizing scalability to diverse grid contexts, renewable energy integration, and further exploration of regulatory dynamics. Overall, this study not only highlights the practical benefits of predictive analytics but also significantly contributes to theoretical advancements, strategic planning, and informed policymaking within the energy sector.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50203 - Industrial relations
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Sustainable Energy Grids & Networks
ISSN
2352-4677
e-ISSN
2352-4677
Volume of the periodical
42
Issue of the periodical within the volume
June 2025
Country of publishing house
GB - UNITED KINGDOM
Number of pages
17
Pages from-to
1-17
UT code for WoS article
001489751800001
EID of the result in the Scopus database
2-s2.0-105004177081