[HTML][HTML] Air quality and ventilation: Exploring solutions for healthy and sustainable urban environments in times of climate change
Ensuring sustainability and reducing energy consumption in the built environment is
essential for achieving energy efficiency and comfort. To obtain a healthy and sustainable …
essential for achieving energy efficiency and comfort. To obtain a healthy and sustainable …
Ten questions concerning reinforcement learning for building energy management
As buildings account for approximately 40% of global energy consumption and associated
greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The …
greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The …
Transmission and infection risk of COVID-19 when people coughing in an elevator
People in cities use elevators daily. With the COVID-19 pandemic, there are more worries
about elevator safety, since elevators are often small and crowded. This study used a proven …
about elevator safety, since elevators are often small and crowded. This study used a proven …
MERLIN: Multi-agent offline and transfer learning for occupant-centric operation of grid-interactive communities
Building and power generation decarbonization present new challenges in electric grid
reliability as a result of renewable energy source intermittency and increase in grid load …
reliability as a result of renewable energy source intermittency and increase in grid load …
[HTML][HTML] Application and Prospect of Artificial Intelligence Technology in Low-Carbon Cities—From the Perspective of Urban Planning Content and Process
F Yan, X Qi - Land, 2024 - mdpi.com
In the era of digital transformation, artificial intelligence (AI) technology—one of the swiftest
growing emerging technologies—when integrated with urban planning, can introduce …
growing emerging technologies—when integrated with urban planning, can introduce …
Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach
With global warming intensifying and resource conflicts escalating, the world is undergoing
a transformative shift toward sustainable practices and energy-efficient solutions. With more …
a transformative shift toward sustainable practices and energy-efficient solutions. With more …
Improving room temperature stability and operation efficiency using a model predictive control method for a district heating station
Z Li, J Liu, L Jia, Y Wang - Energy and Buildings, 2023 - Elsevier
In China, the regulation of a district heating (DH) station is mainly based on weather
compensation control. This control method leads to large room temperature fluctuations and …
compensation control. This control method leads to large room temperature fluctuations and …
[HTML][HTML] Real-world validation of safe reinforcement learning, model predictive control and decision tree-based home energy management systems
Recent advancements in machine learning based energy management approaches,
specifically reinforcement learning with a safety layer (OptLayerPolicy) and a metaheuristic …
specifically reinforcement learning with a safety layer (OptLayerPolicy) and a metaheuristic …
[HTML][HTML] Explainable reinforcement learning for distribution network reconfiguration
N Gholizadeh, P Musilek - Energy Reports, 2024 - Elsevier
The lack of transparency in reinforcement learning methods' decision-making process has
resulted in a significant lack of trust towards these models, subsequently limiting their …
resulted in a significant lack of trust towards these models, subsequently limiting their …
TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithm
Energy management systems (EMS) have traditionally been implemented using rule-based
control (RBC) and model predictive control (MPC) methods. However, recent research has …
control (RBC) and model predictive control (MPC) methods. However, recent research has …