Wenhao Zhang 张文浩

Wenhao Zhang 张文浩

PhD Student at BUDS_Lab@NUS

Talks and Presentations

Text-Mining-Driven Review of Recommender Systems and Reinforcement Learning for Building Control and Occupant Interaction

November 05, 2024

Presentation, BUDS Lab, Singapore

This invited talk explored the application of text-mining techniques in conducting literature reviews, with a focus on the integration of recommender systems and reinforcement learning for smart building control and occupant interaction. Text-mining was presented as a powerful alternative to conventional literature review methods, enabling the analysis of large volumes of academic publications with improved efficiency and reduced subjectivity.

Invited Talk at China Academy of Building Research (CABR) on “The Opportunities and Challenges of Reinforcement Learning for Smart Building Control”

June 10, 2022

Talk, China Academy of Building Research, Shanghai, China

Reinforcement learning (RL) emerged as a transformative approach for optimizing smart building control systems, offering dynamic and adaptive solutions that significantly enhanced energy efficiency, occupant comfort, and operational sustainability. In this invited talk, the speaker delved into the evolving role of RL in the context of smart building technologies, emphasizing its potential to revolutionize how buildings responded to environmental conditions, occupancy patterns, and energy demands.

Energy Efficient Operation Optimization of Building Air-conditioners via Simulator-assisted Asynchronous Reinforcement Learning

December 02, 2021

Conference Talk, 3rd International Conference on Resources and Environmental Research (ICRER 2021), Xiamen, China

The presented study explored a reinforcement learning (RL)-based strategy for optimizing the energy-efficient operation of variable refrigerant flow (VRF) air-conditioners in office settings. The research addressed the significant energy consumption of air-conditioning systems, which account for a substantial proportion of building energy usage, and proposed an innovative solution using asynchronous reinforcement learning coupled with detailed building energy simulation models.