Strategies to save energy in the context of the energy crisis: a review
New technologies, systems, societal organization and policies for energy saving are
urgently needed in the context of accelerated climate change, the Ukraine conflict and the …
urgently needed in the context of accelerated climate change, the Ukraine conflict and the …
[HTML][HTML] An overview of machine learning applications for smart buildings
The efficiency, flexibility, and resilience of building-integrated energy systems are
challenged by unpredicted changes in operational environments due to climate change and …
challenged by unpredicted changes in operational environments due to climate change and …
Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning
Optimising HVAC operations towards human wellness and energy efficiency is a major
challenge for smart facilities management, especially amid COVID situations. Although IoT …
challenge for smart facilities management, especially amid COVID situations. Although IoT …
[HTML][HTML] Energy modelling and control of building heating and cooling systems with data-driven and hybrid models—A review
Implementing an efficient control strategy for heating, ventilation, and air conditioning
(HVAC) systems can lead to improvements in both energy efficiency and thermal …
(HVAC) systems can lead to improvements in both energy efficiency and thermal …
[HTML][HTML] A systematic review of machine learning techniques related to local energy communities
In recent years, digitalisation has rendered machine learning a key tool for improving
processes in several sectors, as in the case of electrical power systems. Machine learning …
processes in several sectors, as in the case of electrical power systems. Machine learning …
ED-DQN: An event-driven deep reinforcement learning control method for multi-zone residential buildings
Q Fu, Z Li, Z Ding, J Chen, J Luo, Y Wang, Y Lu - Building and Environment, 2023 - Elsevier
Abstract Residential Heating, Ventilation, and Air conditioning (HVAC) systems are
responsible for a significant amount of energy consumption, but their management is …
responsible for a significant amount of energy consumption, but their management is …
Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings
In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL)
proved to be effective in optimizing the management of integrated energy systems in …
proved to be effective in optimizing the management of integrated energy systems in …
Integrating smart energy management system with internet of things and cloud computing for efficient demand side management in smart grids
The increasing price of and demand for energy have prompted several organizations to
develop intelligent strategies for energy tracking, control, and conservation. Demand side …
develop intelligent strategies for energy tracking, control, and conservation. Demand side …
Deep reinforcement learning optimal control strategy for temperature setpoint real-time reset in multi-zone building HVAC system
X Fang, G Gong, G Li, L Chun, P Peng, W Li… - Applied Thermal …, 2022 - Elsevier
Determining a proper trade-off between energy consumption and indoor thermal comfort is
important for HVAC system control. Deep Q-learning (DQN) based multi-objective optimal …
important for HVAC system control. Deep Q-learning (DQN) based multi-objective optimal …
[HTML][HTML] Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control
Controlling heating, ventilation and air-conditioning (HVAC) systems is crucial to improving
demand-side energy efficiency. At the same time, the thermodynamics of buildings and …
demand-side energy efficiency. At the same time, the thermodynamics of buildings and …