[HTML][HTML] Interpretable machine learning for building energy management: A state-of-the-art review
Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …
and flexibility in the past decade owing to the ever-increasing availability of massive building …
Explainability in deep reinforcement learning: A review into current methods and applications
T Hickling, A Zenati, N Aouf, P Spencer - ACM Computing Surveys, 2023 - dl.acm.org
The use of Deep Reinforcement Learning (DRL) schemes has increased dramatically since
their first introduction in 2015. Though uses in many different applications are being found …
their first introduction in 2015. Though uses in many different applications are being found …
[HTML][HTML] Evaluation of advanced control strategies for building energy systems
Advanced building control strategies like model predictive control and reinforcement
learning can consider forecasts for weather, occupancy, and energy prices. Combined with …
learning can consider forecasts for weather, occupancy, and energy prices. Combined with …
A review of reinforcement learning applications to control of heating, ventilation and air conditioning systems
Reinforcement learning has emerged as a potentially disruptive technology for control and
optimization of HVAC systems. A reinforcement learning agent takes actions, which can be …
optimization of HVAC systems. A reinforcement learning agent takes actions, which can be …
Design information-assisted graph neural network for modeling central air conditioning systems
Buildings consume huge amounts of energy to create a comfortable and healthy built
environment for people. The building engineering industry has benefitted from the advances …
environment for people. The building engineering industry has benefitted from the advances …
Explainable reinforcement learning (XRL): a systematic literature review and taxonomy
Y Bekkemoen - Machine Learning, 2024 - Springer
In recent years, reinforcement learning (RL) systems have shown impressive performance
and remarkable achievements. Many achievements can be attributed to combining RL with …
and remarkable achievements. Many achievements can be attributed to combining RL with …
人工智能技术在家用空调节能减排领域的应用.
邵俊强， 郑雨霖， 车铭江， 师雅博… - Journal of …, 2023 - search.ebscohost.com
摘要家用空调的普及带来了能耗的快速增长, 人工智能技术为家用空调节能减排的推进提供了强
有力的工具. 家用空调存在众多可能的节能减排路径, 而人工智能技术更是种类繁多 …
有力的工具. 家用空调存在众多可能的节能减排路径, 而人工智能技术更是种类繁多 …
AI approaches for electricity price forecasting in stable/unstable markets: EU improvement project
F Chauvet, L Bellatreche… - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
The concept of near-zero energy buildings (NZEBs) has materialized over the recent
decades as a promising response to the ever-increasing energy consumption and CO2 …
decades as a promising response to the ever-increasing energy consumption and CO2 …
Large language model-based interpretable machine learning control in building energy systems
Abstract The potential of Machine Learning Control (MLC) in HVAC systems is hindered by
its opaque nature and inference mechanisms, which is challenging for users and modelers …
its opaque nature and inference mechanisms, which is challenging for users and modelers …
Interpretable data-driven model predictive control of building energy systems using SHAP
P Henkel, T Kasperski, P Stoffel… - 6th Annual Learning for …, 2024 - proceedings.mlr.press
Advanced building energy system controls, such as model predictive control, rely on
accurate system models. To reduce the modelling effort in the building sector, data-driven …
accurate system models. To reduce the modelling effort in the building sector, data-driven …