PROJECTS

An end-to-end framework for building operation and maintenance, integrating a BMS-linked data model, a site-adapted O&M knowledge base, and an LLM-based search agent.

The platform interprets runtime context and operator queries to automatically retrieve equipment data and execute diagnostic tools, generating traceable FDD workflows.

Deployed in a Hong Kong government building, the system successfully demonstrated the diagnosis of a chiller compressor alarm.

We investigates the challenge of adapting Time-Series Foundation Models (TSFMs), like IBM Granite and Amazon Chronos, to Building Energy Forecasting (BEF) tasks, where it is demonstrated that straightforward fine-tuning yields limited performance gains.

To address this, the authors propose a novel Contrastive Curriculum Learning (CCL) method that organizes training data by difficulty, utilizing contrastive learning to measure the complexity of simulated data relative to real-world samples.

An intelligent document parsing solution for the industrial energy sector, utilizing a Vision-Language Model (VLM) to interpret and structure technical manuals for HVAC and power equipment.

By leveraging reinforcement learning methods (such as GRPO, Reinforce++, RLOO, and DAPO) to optimize VLM performance in parsing industrial system operation documentation, and by designing reasoning-based prompt formats alongside corresponding reward calculation schemes, we achieved a general performance improvement of 7-8% compared to baseline fine-tuning approaches.