A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

F Villano, GM Mauro, A Pedace - Thermo, 2024 - mdpi.com
Given the climate change in recent decades and the ever-increasing energy consumption in
the building sector, research is widely focused on the green revolution and ecological …

A novel deep-learning framework for short-term prediction of cooling load in public buildings

C Song, H Yang, XB Meng, P Yang, J Cai… - Journal of Cleaner …, 2024 - Elsevier
Optimal control of heating, ventilation, and air conditioning (HVAC) systems, along with
demand-side management, are both cost-effective methods in the process of energy …

Outdoor thermal condition based-segmented intermittent demand-controlled ventilation for constant-air-volume system

D Niu, S Zhang - Building and Environment, 2023 - Elsevier
The outdoor air supply is mandatory for healthy built environments but consumes a large
amount of building energy. The demand-controlled ventilation method adjusts the outdoor …

[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 …

Air conditioning load prediction based on hybrid data decomposition and non-parametric fusion model

N He, C Qian, L Liu, F Cheng - Journal of Building Engineering, 2023 - Elsevier
Accurate prediction of air conditioning load is the pivotal problem of air conditioning
optimization control, which is of great significance for reducing building energy consumption …

Transformer based day-ahead cooling load forecasting of hub airport air-conditioning systems with thermal energy storage

D Yu, T Liu, K Wang, K Li, M Mercangöz, J Zhao… - Energy and …, 2024 - Elsevier
The air conditioning system constitutes more than half of the total energy demand in hub
airport buildings. To enhance the energy efficiency and to enable intelligent energy …

A hybrid transfer learning to continual learning strategy for improving cross-building energy prediction in data increment scenario

J Deng, G Li, Y Wu, J Chen, X Fang, C Xu - Journal of Building Engineering, 2024 - Elsevier
For a data-driven building energy prediction model, the insufficient data available for new
buildings and existing buildings with limited data pose a challenge to achieving the …

[HTML][HTML] Green buildings: requirements, features, life cycle, and relevant intelligent technologies

S Yin, J Wu, J Zhao, M Nogueira, J Lloret - Internet of Things and Cyber …, 2024 - Elsevier
Green buildings are designed and constructed according to the principles of sustainable
development and are an inevitable trend in future architectural development. Nowadays …

A hybrid model based on multivariate fast iterative filtering and long short-term memory for ultra-short-term cooling load prediction

A Myat, N Kondath, YL Soh, A Hui - Energy and Buildings, 2024 - Elsevier
The current ultra-short-term cooling load forecasting models have not given due attention to
the data pre-processing stage. In this paper, multivariate signal decomposition methods …

A hybrid prediction model of improved bidirectional long short-term memory network for cooling load based on PCANet and attention mechanism

X Yan, X Ji, Q Meng, H Sun, Y Lei - Energy, 2024 - Elsevier
Accurate and reliable cooling load forecasting is a prerequisite for air-conditioning system
control and the basis for building-side energy management. Therefore, a hybrid prediction …