Predictive demand analytics and machine learning in electric power systems for enhancing resilience and efficiency

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/60460709:41110/25:101766

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Predictive demand analytics and machine learning in electric power systems for enhancing resilience and efficiency

Popis výsledku v původním jazyce

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&apos;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.

Název v anglickém jazyce

Predictive demand analytics and machine learning in electric power systems for enhancing resilience and efficiency

Popis výsledku anglicky

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&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50203 - Industrial relations

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Sustainable Energy Grids &amp; Networks

ISSN

2352-4677

e-ISSN

2352-4677

Svazek periodika

42

Číslo periodika v rámci svazku

June 2025

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

17

Strana od-do

1-17

Kód UT WoS článku

001489751800001

EID výsledku v databázi Scopus

2-s2.0-105004177081