Research Focus:
Exploring innovative applications of artificial intelligence and machine learning technologies in environmental management and energy optimization, with emphasis on building energy consumption monitoring and prediction, intelligent identification models for environmental information disclosure, and carbon management data analytics. Through data-driven approaches, this research aims to enhance the precision and efficiency of environmental management.

Key Technologies:

• Building Energy Consumption Prediction: Establishing building energy baseline models and anomaly detection models based on machine learning algorithms (Random Forest, Neural Networks, XGBoost, etc.).

• Environmental Information Disclosure: Developing natural language processing (NLP) models to automatically identify the completeness and consistency of corporate environmental information disclosure, supporting environmental regulation and investment decision-making.

• Digital Carbon Management: Building a platform for carbon emission data collection, analysis, and visualization to support corporate carbon management decisions.

Representative Achievements:

• Led the AI building energy efficiency algorithm update project for the Singapore Building and Construction Authority (BCA), developing an AI-based building energy online calculator and making it freely available to the public, thereby improving the efficiency and accuracy of building energy performance simulation.

• Proposed a machine learning-driven environmental information disclosure identification model, advancing the application of artificial intelligence in the environmental protection domain.

• Related research outcomes have been published in IEEE international conferences and leading journals in the fields of energy and environment.