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TRUVACE RECORD VERSION
record: TRV-2026-1747
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-17T22:02:30.690549Z
status: published
lens: p_space
sector: climate
headline: Artificial Intelligence for Smart Grid and Renewable Energy Management: A Review of Techniques, Applications, and Future Directions
dek: Intermittent renewable generation, volatile loads, and concerns over grid stability have become defining features of modern power systems. Addressing these challenges with Artificial Intelligence (AI) and Machine Learning (ML) is attracting growing attention, including through better forecasting, automated demand response, optimized energy storage, and smart grid fault detection. This paper highlights the various AI-based techniques developed across the smart grid landscape, ranging from solar and wind forecasti…
gain_title: (none)
problem_title: Variable renewable output and volatile demand strain power systems, and AI grid tools face unresolved privacy, interpretability, scalability and cybersecurity challenges.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Variable renewable output and volatile demand strain power systems, and AI grid tools face unresolved privacy, interpretability, scalability and cybersecurity challenges.
problem_evidence: Intermittent renewable generation, volatile loads, and concerns over grid stability have become defining features of modern power systems. | Challenges such as data privacy, model interpretability, scalability, and cybersecurity are explored
quick_read: This review surveys recent published work on using artificial intelligence and machine learning to manage smart grids with growing shares of renewable energy. It covers applications from solar and wind forecasting to load and price forecasting, demand-side management, battery management, grid resilience and digital twins.

The topic matters because grids face intermittent generation and volatile demand, and AI is presented as a possible way to improve forecasting, demand response and fault detection. The text reports no measured grid outcomes, and its proposed framework has not been tested, so real-world benefits remain unproven.
limitation: The proposed three-layer framework is conceptual and has not been validated empirically, so its real-world performance is unknown.
tag: Evidence-backed problem
key_points: The review covers deep learning with LSTM and Transformer models, explainable AI, multi-agent systems, reinforcement learning and federated learning. | Application areas include solar and wind forecasting, short-term load forecasting, electricity price forecasting, energy storage and battery management. | The authors propose a three-layer framework: edge sensing, federated forecasting and control, and explainable operator support at the distribution-utility level. | Open challenges named are data privacy, model interpretability, scalability and cybersecurity. | The paper discusses relevance to India's energy transition goals.
rundown: The paper frames modern power systems as defined by intermittent renewable generation, volatile loads and grid stability concerns, and positions AI and ML as tools to address them through better forecasting, automated demand response, optimized storage and fault detection.

As a review, it describes the strengths and limitations of major approaches across the smart grid landscape instead of reporting a new field trial. It also proposes a conceptual three-layer design aimed at distribution utilities, combining edge sensing, federated forecasting and control, and explainable support for operators.

The authors also point to future research directions and to how these technologies relate to India's energy transition goals, while listing privacy, interpretability, scalability and cybersecurity as obstacles.
sources:
- peer_reviewed | International Journal of Advanced Research in Science Communication and Technology | https://doi.org/10.48175/ijarsct-38915 | 2026-10-16
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