A comprehensive review of predictive control strategies in heating, ventilation, and air-conditioning (HVAC): Model-free VS model
X **n, Z Zhang, Y Zhou, Y Liu, D Wang… - Journal of Building …, 2024 - Elsevier
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 …
dynamic control. This article uses a Systematic Reviews and Meta-Analyses methodology to …
Prospects and challenges of reinforcement learning-based HVAC control
Increasing worldwide energy demand and the resulting escalations in greenhouse gas
emissions require a reassessment of energy usage in many sectors. The building industry …
emissions require a reassessment of energy usage in many sectors. The building industry …
Comparative study of model-based and model-free reinforcement learning control performance in HVAC systems
Reinforcement learning (RL) shows the potential to address drawbacks of rule-based control
and model predictive control and exhibits great effectiveness in heating, ventilation and air …
and model predictive control and exhibits great effectiveness in heating, ventilation and air …
[HTML][HTML] Optimization of building demand flexibility using reinforcement learning and rule-based expert systems
The increasing use of renewable energy in buildings requires optimization of building
demand flexibility to reduce energy costs and carbon emissions. Nevertheless, the …
demand flexibility to reduce energy costs and carbon emissions. Nevertheless, the …
Long-term experimental evaluation and comparison of advanced controls for HVAC systems
The tremendous energy usage from buildings leads to research studies on their
improvement, among which advanced building control plays an important role. In advanced …
improvement, among which advanced building control plays an important role. In advanced …
Expert-guided imitation learning for energy management: Evaluating GAIL's performance in building control applications
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 …
often hampered by data efficiency and computational challenges. The long training time …
Safe deep reinforcement learning for building energy management
The optimization of building energy systems poses a complex challenge due to the dynamic
nature of building environments and the need for ensuring both energy efficiency and …
nature of building environments and the need for ensuring both energy efficiency and …
Reinforcement learning approach for optimal control of ice-based thermal energy storage (TES) systems in commercial buildings
Ice-based thermal energy storage (TES) system is effective on load shifting and demand
response in public buildings under time-of-use (TOU) tariffs. The management and …
response in public buildings under time-of-use (TOU) tariffs. The management and …
[HTML][HTML] Heuristic model predictive control implementation to activate energy flexibility in a fully electric school building
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 …
energy flexibility in fully electric school buildings in cold climates to reduce electricity …
A novel multi-energy load forecasting method based on building flexibility feature recognition technology and multi-task learning model integrating LSTM
P Fan, D Wang, W Wang, X Zhang, Y Sun - Energy, 2024 - Elsevier
Accurate multi-energy load forecasting is prerequisite for achieving balance between supply
and demand in building energy system. The continuous development of building flexibility …
and demand in building energy system. The continuous development of building flexibility …