[HTML][HTML] Next-generation energy systems for sustainable smart cities: Roles of transfer learning
Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while
improving grid stability and meeting service demand. This is possible by adopting next …
improving grid stability and meeting service demand. This is possible by adopting next …
A comprehensive review of impact assessment of indoor thermal environment on work and cognitive performance-Combined physiological measurements and …
Ensuring occupants' work or cognitive performance and maintaining thermal comfort are
important targets of indoor thermal environment management. Physiological indicators are …
important targets of indoor thermal environment management. Physiological indicators are …
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 …
Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach
The data-driven method is a promising way to predict the energy consumption of buildings,
however suffering from the data shortage problem in various scenarios. Even though …
however suffering from the data shortage problem in various scenarios. Even though …
Cross temporal-spatial transferability investigation of deep reinforcement learning control strategy in the building HVAC system level
X Fang, G Gong, G Li, L Chun, P Peng, W Li, X Shi - Energy, 2023 - Elsevier
Abstract Model free based DRL control strategies have achieved positive effects on the
HVAC system optimal control. However, developing deep reinforcement learning (DRL) …
HVAC system optimal control. However, developing deep reinforcement learning (DRL) …
TinyML-enabled edge implementation of transfer learning framework for domain generalization in machine fault diagnosis
TinyML has the potential to be a huge enabler of smart sensor nodes for fault diagnosis of
machines by embedding powerful machine learning algorithms in low-cost edge devices …
machines by embedding powerful machine learning algorithms in low-cost edge devices …
Prediction of individual thermal comfort based on ensemble transfer learning method using wearable and environmental sensors
Thermal comfort is a critical issue in achieving an acceptable indoor environment and
managing building energy use. However, it is difficult to precisely recognize thermal comfort …
managing building energy use. However, it is difficult to precisely recognize thermal comfort …
Human-building interaction for indoor environmental control: Evolution of technology and future prospects
This paper presents a data-driven literature review of human-building interaction (HBI),
which refers to the interaction between occupants and buildings. Through natural language …
which refers to the interaction between occupants and buildings. Through natural language …
[HTML][HTML] Machine learning and deep learning methods for enhancing building energy efficiency and indoor environmental quality–a review
The built environment sector is responsible for almost one-third of the world's final energy
consumption. Hence, seeking plausible solutions to minimise building energy demands and …
consumption. Hence, seeking plausible solutions to minimise building energy demands and …
Comfort and energy consumption optimization in smart homes using bat algorithm with inertia weight
Smart home is a concept that aims to maximize the comfort of occupant while consuming
energy as low as possible. The comfort and energy consumption are contradicting factors in …
energy as low as possible. The comfort and energy consumption are contradicting factors in …