[HTML][HTML] An improved attention-based deep learning approach for robust cooling load prediction: Public building cases under diverse occupancy schedules
Abstract Space cooling in buildings is responsible for massive energy consumption and
carbon emissions. Accurate cooling load prediction can facilitate the implementation of …
carbon emissions. Accurate cooling load prediction can facilitate the implementation of …
Automated machine learning-based framework of heating and cooling load prediction for quick residential building design
Reducing the heating and cooling load through energy-efficient building design can help
decarbonize the building sector. Heating and cooling load prediction using machine …
decarbonize the building sector. Heating and cooling load prediction using machine …
Estimating equilibrium scour depth around non-circular bridge piers using interpretable hybrid machine learning models
Scouring at bridge piers is a crucial issue that risks bridge collapses, causing economic
losses and endangering public safety. Classic models struggle to accurately estimate …
losses and endangering public safety. Classic models struggle to accurately estimate …
A physical model with meteorological forecasting for hourly rooftop photovoltaic power prediction
Y Zhi, T Sun, X Yang - Journal of Building Engineering, 2023 - Elsevier
Accurate photovoltaic power forecasting provides essential information for the flexible
control of building energy systems. This paper proposes a physical model with …
control of building energy systems. This paper proposes a physical model with …
[HTML][HTML] Enhancing real-time nonintrusive occupancy estimation in buildings via knowledge fusion network
C Lu - Energy and Buildings, 2024 - Elsevier
Real-time nonintrusive occupancy estimation can maximize the use of existing sensors to
infer occupant information in buildings with the advantages of fewer privacy concerns and …
infer occupant information in buildings with the advantages of fewer privacy concerns and …
Energy Management in Modern Buildings Based on Demand Prediction and Machine Learning—A Review
Increasing building energy consumption has led to environmental and economic issues.
Energy demand prediction (DP) aims to reduce energy use. Machine learning (ML) methods …
Energy demand prediction (DP) aims to reduce energy use. Machine learning (ML) methods …
An intelligent framework for deriving formulas of aerodynamic forces between high-rise buildings under interference effects using symbolic regression algorithms
K Wang, T Shen, J Wei, J Liu, W Hu - Journal of Building Engineering, 2025 - Elsevier
Numerous high-rise buildings in megacities create complex interference effects, significantly
impacting aerodynamic forces and leading to severe wind-induced disasters. While current …
impacting aerodynamic forces and leading to severe wind-induced disasters. While current …
Performance Assessment of Cold Thermal Storage-Based Building Air-Conditioning Layouts for Different Climates
In this work, a detailed study is done to explore thermal features and operational aspects of
thermal energy storage (TES)-based air-conditioning strategies. Three approaches, such as …
thermal energy storage (TES)-based air-conditioning strategies. Three approaches, such as …
[HTML][HTML] Resource optimization for grid-connected smart green townhouses using deep hybrid machine learning
This paper examines Connected Smart Green Townhouses (CSGTs) as a modern
residential building model in Burnaby, British Columbia (BC). This model incorporates a …
residential building model in Burnaby, British Columbia (BC). This model incorporates a …
An ensemble learning model for estimating the virtual energy storage capacity of aggregated air-conditioners
Renewable energy resources (RES) pose several challenges due to their natural
intermittency when integrated into a distribution network. A smart energy storage system …
intermittency when integrated into a distribution network. A smart energy storage system …