"Optimization of building demand flexibility using reinforcement learni" by Xinlei Zhou, Shan Xue et al.
The increasing use of renewable energy in buildings requires optimization of building demand flexibility to reduce energy costs and carbon emissions. Nevertheless, the optimization process is generally challenging that needs to consider the on-site intermittent energy supply, dynamic building energy demand, and proper utilization of energy storage systems. Leveraging the growing availability of operational data in buildings, data-driven strategies such as reinforcement learning (RL) have emerged as effective approaches to optimizing building demand flexibility. However, training a reliable RL ...
Source: uow.edu.au
