Predictive control offers significant advantages in nonlinear control, high thermal inertia, and
dynamic control. This article uses a Systematic Reviews and Meta-Analyses methodology to …

Despite the tightening of energy performance standards for buildings in various countries
and the increased use of efficient and renewable energy technologies, it is clear that the …

Reinforcement learning (RL) has great potential in achieving energy-efficient, comfortable
and intelligent control of heating, ventilation and air conditioning (HVAC) systems. Although …

Technological advancement and supportive government policy have accelerated
investments in distributed energy resources (DERs) in buildings, demand-side management …

This paper presents a heuristic model predictive control (MPC) methodology to activate
energy flexibility in fully electric school buildings in cold climates to reduce electricity …

Abstract The use of Deep Reinforcement Learning (DRL) in building energy management is
often hampered by data efficiency and computational challenges. The long training time …

Increasing worldwide energy demand and the resulting escalations in greenhouse gas
emissions require a reassessment of energy usage in many sectors. The building industry …

In recent years, the thermal insulation performance of residential buildings has been
enhanced to reduce energy consumption. However, this enhancement often leads to air …

The building sector consumes substantial energy, with HVAC systems accounting for nearly
half of the total energy use. Optimizing chiller unit operations is crucial for reducing energy …

Abstract Heating, Ventilation, and Air Conditioning (HVAC) systems play a critical role in
ensuring occupant comfort in buildings. Traditional Rule-Based Feedback Control (RBFC) …