FMSust 2026

Foundation Models for Energy and Sustainability

• Domain-specific foundation models for buildings, cities, grids, and transportation
• Large Language Models (LLMs) and their applications in energy and sustainability
• Vision-language models for multimodal understanding and control
• Multimodal foundation models combining text, vision, time series, and structured data
• Time Series Foundation Models (TSFMs) for forecasting, control, and anomaly detection
• Transfer learning, fine-tuning, and adaptation techniques
• Efficient and scalable model architectures for low-resource or edge environments
• Modeling and optimization for decarbonization pathways

Datasets, Benchmarking, and Evaluation

• Creation and curation of domain-specific datasets for training foundation models
• Open benchmarks and reproducibility frameworks
• Standardized metrics for evaluating generalization, fairness, and robustness
• Tools and platforms for dataset sharing, annotation, and synthetic data generation

Deployment, Validation, and Impact Assessment

• Real-world deployment of foundation models
• Field studies, pilot projects, and validation of foundation models
• Socio-technical challenges in deploying foundation models at scale
• Monitoring, adaptation, and continual learning from deployed systems
• Assessment of environmental and social impacts of model use

Cross-Cutting Themes

• Human-in-the-loop systems and interpretable foundation models
• Ethical, legal, and responsible AI considerations
• Privacy and security issues
• Federated foundation models
• Democratizing access to foundation models