Modularized neural network incorporating physical priors for future building energy modeling
Building energy modeling (BEM) is fundamental for achieving optimized energy control,
resilient retrofit designs, and sustainable urbanization to mitigate climate change. However …
resilient retrofit designs, and sustainable urbanization to mitigate climate change. However …
[HTML][HTML] An effective methodology to quantify cooling demand in the UK housing stock
According to the 2020 UN emissions report an increase by 3° C of the average global
temperature compared to pre-industrial levels is to be expected if no corrective measures …
temperature compared to pre-industrial levels is to be expected if no corrective measures …
Ultra-low cycle fatigue life prediction of stainless steel based on transfer learning guided artificial neural network
M Yu, X **e - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
Determining ultra-low cycle fatigue (ULCF) life of stainless steel typically involves laborious
and time-consuming tests. While machine learning offers an efficient solution for fatigue life …
and time-consuming tests. While machine learning offers an efficient solution for fatigue life …
[HTML][HTML] Deep transfer learning strategy based on TimesBlock-CDAN for predicting thermal environment and air conditioner energy consumption in residential …
L Sun, Z Hu, M Mae, T Imaizumi - Applied Energy, 2025 - Elsevier
The deployment of data-driven deep learning black-box models for thermal environment and
air conditioner energy consumption modeling has gained popularity due to their high …
air conditioner energy consumption modeling has gained popularity due to their high …
Forecasting building energy demand and on-site power generation for residential buildings using long and short-term memory method with transfer learning
This study evaluates the effectiveness of the long and short-term (LSTM) implementation
with a particular emphasis on assessing the impact of transfer learning techniques in …
with a particular emphasis on assessing the impact of transfer learning techniques in …
[HTML][HTML] A scalable approach for real-world implementation of deep reinforcement learning controllers in buildings based on online transfer learning: The HiLo case …
Abstract In recent years, Transfer Learning (TL) has emerged as a promising solution to
scale Deep Reinforcement Learning (DRL) controllers for building energy management …
scale Deep Reinforcement Learning (DRL) controllers for building energy management …
A Lifelong Meta-Learning Approach for Learning Deep Grey-box Representative Thermal Dynamics Models for Residential Buildings
Thermal dynamics models of residential buildings are crucial for managing energy use and
maintaining desirable indoor environment quality, in the context of decarbonizing the electric …
maintaining desirable indoor environment quality, in the context of decarbonizing the electric …
GenTL: A General Transfer Learning Model for Building Thermal Dynamics
Transfer Learning (TL) is an emerging field in modeling building thermal dynamics. This
method reduces the data required for a data-driven model of a target building by leveraging …
method reduces the data required for a data-driven model of a target building by leveraging …
Transfer Learning for Energy Consumption Forecasting in Smart Buildings
Energy consumption forecasting is crucial in smart buildings so as to effectively manage
energy usage and minimize the expenses. The conventional methods of machine learning …
energy usage and minimize the expenses. The conventional methods of machine learning …
Spatio-Temporal Characterization and Short-term Prediction of Indoor Temperature in Multi-zone Buildings
Heating, ventilation, and air conditioning systems (HVAC) are essential for controlling indoor
temperature and ensuring adequate levels of thermal comfort and indoor air quality. Among …
temperature and ensuring adequate levels of thermal comfort and indoor air quality. Among …