The field is witnessing a significant shift towards the integration of artificial intelligence and machine learning in energy and cybersecurity applications. Researchers are exploring the potential of large language models (LLMs) in various domains, including automated homework assessment, IoT intrusion detection, and power system transient stability assessment. The use of LLMs is showing promising results in improving accuracy, efficiency, and interpretability in these areas. Notably, the development of edge-centric frameworks and multimodal LLMs is enabling more robust and scalable solutions for real-time applications. Furthermore, the application of AI-driven approaches in solar panel maintenance and building-integrated photovoltaic systems is gaining traction, with potential benefits for urban decarbonization and renewable energy adoption. Some noteworthy papers in this regard include the 'Think Fast' paper, which presents an edge-centric IDS framework that integrates LLMs with lightweight ML models for improved detection accuracy, and the 'Facade Segmentation for Solar Photovoltaic Suitability' paper, which proposes a pipeline for automated identification of suitable surfaces for PV application. Overall, the field is moving towards more innovative and effective AI-driven solutions for energy and cybersecurity challenges.
Advances in AI-Driven Solutions for Energy and Cybersecurity
Sources
Enhancing Large Language Models for Automated Homework Assessment in Undergraduate Circuit Analysis
Think Fast: Real-Time IoT Intrusion Reasoning Using IDS and LLMs at the Edge Gateway
Unified Deep Learning Platform for Dust and Fault Diagnosis in Solar Panels Using Thermal and Visual Imaging
A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection
Facade Segmentation for Solar Photovoltaic Suitability
Impact Analysis of COVID-19 in Bangladesh Power Sector and Recommendations based on Practical Data and Machine Learning Approach
Temperature in SLMs: Impact on Incident Categorization in On-Premises Environments
EAGER: Edge-Aligned LLM Defense for Robust, Efficient, and Accurate Cybersecurity Question Answering
Cross-Domain Generalization of Multimodal LLMs for Global Photovoltaic Assessment
An experimental study of existing tools for outlier detection and cleaning in trajectories
LLM-Driven Transient Stability Assessment: From Automated Simulation to Neural Architecture Design
Evaluation of Large Language Models for Numeric Anomaly Detection in Power Systems