A New Generation of Ai: A Review and Perspective on Machine Learning Technologies Applied to Smart Energy and Electric Power Systems
Open accessAbstract
The new generation of artificial intelligence (AI), called AI 2.0, has recently become a research focus. Data-driven AI 2.0 will accelerate the development of smart energy and electric power system (Smart EEPS). In AI 2.0, machine learning (ML) forms a typical representative algorithm category used to achieve predictions and judgments by analyzing and learning from massive amounts of historical and synthetic data to help people make optimal decisions. ML has preliminarily been applied to the Smart Grid (SG) and Energy Internet (EI) fields, which are important Smart EEPS representatives. AI 2.0, especially ML, is undergoing a critical period of rapid development worldwide and will play an essential role in Smart EEPS. In this context, this study, combined with the emerging SG and EI technologies, takes the typical representative of AI 2.0—ML—as the research objective and reviews its research status in the operation, optimization, control, dispatching, and management of SG and EI. The paper focuses on introducing and summarizing the mainstream uses of seven representative ML methods, including reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, and ensemble learning, in the SG and EI fields. In this survey, we begin with an introduction to these seven types of ML methods and then systematically review their applications in Smart EEPS. Finally, we discuss ML development under the big data thinking and offer a prospect for the future development of AI 2.0 and ML in Smart EEPS. We conduct this survey intended to arouse the interest and excitement of experts and scholars in the EEPS industry and to look ahead to efforts that jointly promote the rapid development of AI 2.0 in the Smart EEPS field.
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AI models are used in energy trading platforms for better financial outcomes and risk management.
“For better financial outcomes and risk management, AI models are used in energy trading platforms.”
From A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions (2024) · cites this paper as [27]
Section 5, “Other scenarios” (page unavailable), describes reinforcement-learning models for energy-market trading and bidding strategies intended to improve participants’ benefits. It discusses risk assessment separately in economic dispatch, not risk management on energy trading platforms. The financial-outcomes portion is supported, but the trading-related risk-management portion is not. (LLM, conf=0.86)
Experimental — accuracy not yet measuredABB and Schneider Electric use AI-driven predictive maintenance solutions to monitor equipment health in power plants and grids.
“ABB and Schneider Electric use AI-driven predictive maintenance solutions to monitor equipment health in power plants and grids.”
From A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions (2024) · cites this paper as [27]
Section 4.2 (page unavailable) describes deep-learning applications for diagnosing faults in transmission equipment, including transformers and high-voltage circuit breakers. It does not identify ABB or Schneider Electric as users of predictive-maintenance solutions, or describe those companies monitoring equipment in power plants and grids. (LLM, conf=0.76)
Experimental — accuracy not yet measuredSmart grid projects globally incorporate AI for enhanced grid management and efficiency.
“Smart grid projects globally incorporate AI for enhanced grid management and efficiency.”
From A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions (2024) · cites this paper as [27]
The Introduction (page not provided) identifies AI as a supporting technology for smart energy and grid operations. But it calls investigation into AI for smart energy and power systems preliminary, while Section 4 (page not provided) describes deeper integration with smart grids as a future prospect. The passages do not establish that smart-grid projects globally already incorporate AI. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSiemens and General Electric (GE) use AI algorithms to forecast energy demand accurately.
“Siemens and General Electric (GE) use AI algorithms to forecast energy demand accurately.”
From A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions (2024) · cites this paper as [27]
Section 9.2 reports that machine-learning techniques are used for short-term electricity load and energy-consumption forecasting, supporting the general forecasting point. The passages found do not identify Siemens or General Electric as users of those algorithms or establish the accuracy of their forecasts specifically. (LLM, conf=0.85)
Experimental — accuracy not yet measuredSmart thermostats and building management systems use AI to reduce energy consumption.
“Smart thermostats and building management systems use AI to reduce energy consumption.”
From A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions (2024) · cites this paper as [27]
The discussion near Figure 15 describes AI-based management of household energy systems, including control of appliances and energy-efficiency analysis. Section 4.2 also discusses using AI to optimize electricity consumption. Neither passage identifies smart thermostats or building management systems, or establishes that those systems reduce energy consumption. (LLM, conf=0.84)
Experimental — accuracy not yet measured
AI models manage battery energy storage systems for efficient use and extended battery life.
“AI models manage battery energy storage systems for efficient use and extended battery life.”
From A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions (2024) · cites this paper as [27]
The section on prospects for AI-based energy systems (page unavailable) includes optimized operation of systems containing energy storage and applications in energy management. It does not identify battery energy storage systems specifically or say that AI management extends battery life. (LLM, conf=0.78)
Experimental — accuracy not yet measuredAI techniques such as neural networks and machine learning can efficiently process large datasets and uncover complex patterns in power systems.
“AI techniques such as neural networks and machine learning can efficiently process large datasets and uncover complex patterns, as shown in the table summarizing AI applications [27].”
From A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions (2024) · cites this paper as [27]
AI systems are employed in fault detection and diagnosis in smart grids.
“AI systems are employed in fault detection and diagnosis in smart grids.”
From A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions (2024) · cites this paper as [27]
AI techniques are used in smart grid projects for real-time monitoring and fault detection.
“AI techniques are used in smart grid projects for real-time monitoring and fault detection.”
From A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions (2024) · cites this paper as [27]
Experimental — accuracy not yet measuredUtilities use AI for customer support chatbots and to provide personalized energy-saving tips.
“Utilities use AI for customer support chatbots and to provide personalized energy-saving tips.”
From A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions (2024) · cites this paper as [27]
Section 4.2 proposes using AI to analyze end-user electricity consumption and formulate incentive policies, but does not say utilities provide personalized energy-saving tips. Searches for chatbots and customer-support applications found no passage describing their use by utilities; the paper’s mention of dialog agents concerns applications in other fields (table, page unavailable). (LLM, conf=0.73)
Experimental — accuracy not yet measuredGoogle DeepMind collaborates with the UK National Grid to predict wind energy output.
“Google DeepMind collaborates with the UK National Grid to predict wind energy output.”
From A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions (2024) · cites this paper as [27]
Section 4.2 discusses machine-learning predictions of wind-power generation, but does not identify Google DeepMind or the UK National Grid. Searches for those organizations and their alleged collaboration found no passage addressing it; the generic forecasting discussion cannot verify the specific claim. Page numbers were not available in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Machine Learning for Human Learners: Opportunities, Issues, Tensions and Threats
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- Authors
- Cheng, L., & Yu, T.
- Affiliations
- South China University of Technology (CN)
- Corresponding author
- Cheng, Lefeng
- Yu, Tao
- ORCID
- Venue
- International Journal of Energy Research
- Publisher
- Wiley
- Published
- 2019-01-09
- Volume
- 43
- Issue
- 6
- Pages
- 1928-1973
- ISSN
- 0363-907X (ISSN-L)
- OpenAlex
- W2908762496
- Cited by
- 380
- References
- 104
- Citations by year
- 2026: 47 · 2025: 73 · 2024: 74 · 2023: 63 · 2022: 49 · 2021: 34 · 2020: 26 · 2019: 14
- FWCI
- 11.04
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- International Journal of Energy Research (publishedVersion)
- Funders
- Science and Technology Project of China Southern Power Grid Company Ltd. (GDKJXM20180576)
- Key Science and Technology Projects of China Southern Power Grid Company Ltd. (GZKJQQ00000419)
- National Natural Science Foundation of China (51777078, 51477055)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid and Power Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Energy › Energy Engineering and Power Technology › Power Systems and Renewable Energy
- Keywords
- machine learning, smart grid, energy internet, reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, ensemble learning, big data, power system operation and control
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