Effective building energy prediction is essential for optimizing energy management, but existing models struggle with data scarcity and sensor heterogeneity across different buildings. Conventional approaches, including centralized and transfer learning methods, fail to generalize well due to varying sensor configurations and inconsistent data availability. To overcome these challenges, this study proposes a Personalized Federated Learning (pFL) framework that integrates multi-level feature masking, model ensemble techniques, and knowledge transfer to enhance predictive performance across diverse buildings. The proposed feature masking strategy extracts the most relevant time-series features, while model ensemble learning improves generalization, and knowledge transfer enables adaptive fine-tuning for each building. These techniques allow pFL to retain global knowledge while personalizing to local energy consumption patterns, making it more effective than traditional FL methods. Experiments conducted on a campus energy dataset demonstrate that pFL consistently outperforms FedAvg, FedProx, and standalone models in energy prediction accuracy. The most significant improvements are observed in buildings with highly fluctuating consumption patterns, validating the effectiveness of the proposed approach in handling heterogeneous sensing environments. This study highlights the potential of Federated Learning for scalable and adaptive energy prediction. Future work will focus on refining multi-horizon forecasting and developing strategies to enhance knowledge sharing among buildings for improved long-term performance.

Autonomous driving in dense urban environments requires accurate trajectory forecasting supported by interpretable contextual evidence. This study presents a multimodal framework that performs driving scene context (DSC)-aware trajectory prediction while providing risk-aware explanations to reveal the contextual cues behind predicted motion. The framework integrates temporal object states—trajectories, velocities, yaw angles, and motion status—with semantic information from forward-facing camera imagery, and is composed of four modules: object behavioral feature extraction, scene context extraction, DSC-augmented trajectory prediction, and risk-aware reasoning using a multimodal large language model (MLLM). Experiments on the Rank2Tell dataset demonstrate the feasibility and applicability of the proposed approach, achieving an ADE of 10.972, an FDE of 13.701, and an RMSE of 8.782. Additional qualitative evaluation shows that DeepSeek-R1-Distill-Qwen-7B generates the most coherent and contextually aligned explanations among the tested models. These findings indicate that combining DSC-aware prediction with interpretable reasoning provides a practical and transparent solution for autonomous driving in complex urban environments.