Utilization and challenges of artificial intelligence in the energy sector
Open accessAbstract
This study harnesses structural topic modeling and expert surveys to delve into the expanding influence of artificial intelligence (AI) within the energy sector, analyzing around 6000 academic paper abstracts from 2011 to 2020. Our detailed examination identified 100 distinct topics, of which 15, accounting for a combined proportion of 16.4% of the total, were directly related to energy, highlighting key areas such as power consumption, thermal energy management, wind energy evaluation, and building energy management. Furthermore, an expert survey offered deep insights into future changes, spotlighting AI's role in enhancing safety, stability, efficiency, and environmental sustainability of energy systems. It also pinpointed challenges in AI adoption within the sector, proposing pathways to bolster AI reliability, improve data quality, and enhance human–AI collaboration. This comprehensive analysis not only highlights the dynamic role of AI in transforming the energy sector but also sets a foundational framework for future interdisciplinary research, aiming to integrate quantitative and qualitative insights for a holistic understanding of AI's potential in sustainable energy development.
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AI adoption challenges include economic hurdles such as high implementation costs and uncertain ROI.
“economic hurdles like high implementation costs and uncertain ROI [13]”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [13]
Uncertain returns can lead to risk aversion and delayed adoption.
“can lead to risk aversion and delayed adoption given uncertain returns [11,13]”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [13]
Lack of explainability hinders public acceptance.
“hinders public acceptance (Section 3.4.3;[13])”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [13]
Uncertainty regarding ROI for energy AI projects is significant due to market volatility, policy shifts, and difficulties in quantifying intangible benefits like improved reliability.
“Uncertainty regarding ROI for energy AI projects is significant due to market volatility, policy shifts, and difficulties in quantifying intangible benefits like improved reliability [13,30,31].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [13]
Key policy issues include unclear regulatory frameworks for AI in energy, insufficient/misaligned funding, challenges in multi-operator/crossborder governance, and policy silos that fail to integrate AI promotion with Sustainability or other goals.
“Key issues include unclear regulatory frameworks for AI in energy [13,52], insufficient/misaligned funding [32], challenges in multi-operator/crossborder governance [34], and policy silos that fail to integrate AI promotion with Sustainability or other goals.”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [13]
Interoperability failures, cost overruns, cybersecurity breaches during integration, and organizational resistance or change management issues are key concerns.
“Interoperability failures, cost overruns, cybersecurity breaches during integration (Section 3.1.3),and organizational resistance or change management issues (Sections 3.4.1 and 3.4.3;[13]) are key concerns.”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [13]
Economic Sustainability requires a clear line of sight to a positive return on investment (ROI), navigating the high costs and financial risks that currently deter many stakeholders.
“Economic Sustainability requires a clear line of sight to a positive return on investment (ROI), navigating the high costs and financial risks that currently deter many stakeholders [13].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [13]
Public acceptance is critical for AI adoption in energy.
“Public acceptance is critical for AI adoption in energy [13].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [13]
AI-driven automation raises concerns about job displacement, necessitating large-scale reskilling/upskilling initiatives which have economic and strategic implications.
“AI-driven automation raises concerns about job displacement, necessitating large-scale reskilling/upskilling initiatives which have economic and strategic implications (Sections 3.2 and 3.3; [13,27,60]).”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [13]
Building Trustworthiness and ensuring Public Acceptance depends on proactive and transparent communication with all stakeholders, including employees, customers, regulators, and local communities.
“Building Trustworthiness and ensuring Public Acceptance depends on proactive and transparent communication with all stakeholders-including employees, customers, regulators, and local communities [13].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [13]
AI certification frameworks and liability rules for safety-critical systems are seen as crucial for de-risking investment and ensuring public safety.
“a measure seen as crucial for de-risking investment and ensuring public safety [13]”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [13]
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- Authors
- Park, C., & Kim, M.
- Affiliations
- Korea Energy Economics Institute (KR)
- Corresponding author
- Kim, Minkyu
- Venue
- Energy & Environment
- Publisher
- SAGE Publications
- Published
- 2024-06-13
- Volume
- 37
- Issue
- 3
- Pages
- 1242-1261
- ISSN
- 0958-305X (ISSN-L)
- 2048-4070
- OpenAlex
- W4399619155
- Cited by
- 15
- References
- 54
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 1
- FWCI
- 2.21
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Energy & Environment (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid and Power Systems
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Economy, and Technology Trends
- Keywords
- artificial intelligence, energy sector, AI adoption barriers, power consumption, thermal management, building energy management, human-AI collaboration, data quality, sustainable energy development, research trends
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