Building controls are becoming more important and complicated due to the dynamic and
stochastic energy demand, on-site intermittent energy supply, as well as energy storage …

Fueled by big data, powerful and affordable computing resources, and advanced algorithms,
machine learning has been explored and applied to buildings research for the past decades …

The wide variety of smart devices equipped in modern intelligent buildings and the
increasing comfort requirements of occupants for the environment make the control of …

A number of decarbonization scenarios for the energy sector are built on simultaneous
electrification of energy demand, and decarbonization of electricity generation through …

The design, commissioning, and retrofit of heating, ventilation, and air-conditioning (HVAC)
control systems are crucially important for energy efficiency. However, designers and control …

Reinforcement learning has emerged as a potentially disruptive technology for control and
optimization of HVAC systems. A reinforcement learning agent takes actions, which can be …

This paper demonstrates a data-driven control approach for demand response in real-life
residential buildings. The objective is to optimally schedule the heating cycles of the …

This paper presents the results from a real residential field test in which one of the objectives
was to maximize the instantaneous self-consumption of the local photovoltaic production …

This study delves into the application of deep reinforcement learning (DRL) frameworks for
optimizing setpoints in district heating systems, which experience hourly fluctuations in air …

Building performance optimization (BPO) has been a common method in energy-efficient
building design. How to deal with the constraints in the optimization model is critical to …