Abstract Controlling Heating, Ventilation and Air Conditioning (HVAC) systems is critical to
improving energy efficiency of demand-side. In this paper, a model-free optimal control …

Human activities and city routines follow patterns. Transfer learning can help achieve
scalable solutions toward the realization of smart cities accounting for similarities between …

Deep reinforcement learning (DRL) has become a popular optimal control method in recent
years. This is mainly because DRL has the potential to solve the optimal control problems …

Occupants' behavior is a major source of uncertainty for the optimal operation of building
energy systems. The highly stochastic hot water use behavior of occupants has led to …

This paper presents a data-driven approach that leverages reinforcement learning to
manage the optimal energy consumption of a smart home with a rooftop solar photovoltaic …

Residential buildings are large consumers of energy. They contribute significantly to the
demand placed on the grid, particularly during hours of peak demand. Demand‐side …

For decades, ASHRAE Standard 55 has been using the Fanger's predicted mean vote
(PMV) model to evaluate the indoor thermal comfort satisfaction. However, this canonical …

Advanced controls could enhance buildings' energy efficiency and operational flexibility
while guaranteeing the indoor comfort. The control performance of reinforcement learning …

In this chapter, a novel data-driven method, which is called the deep deterministic policy
gradient (DDPG), is applied for optimally controlling the multi-zone residential heating …

With the rise in household computing power and the increasing number of smart devices,
more and more residents are able to participate in demand response (DR) management …