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Automated machine learning-based building energy load prediction method
The application of data-driven building energy load prediction technologies remains a time-
consuming effort, since it highly relies on human expertise to train data-driven building …
consuming effort, since it highly relies on human expertise to train data-driven building …
[HTML][HTML] Reinforcement learning building control approach harnessing imitation learning
Reinforcement learning (RL) has shown significant success in sequential decision making in
fields like autonomous vehicles, robotics, marketing and gaming industries. This success …
fields like autonomous vehicles, robotics, marketing and gaming industries. This success …
ANNEXE: An open-source building energy design optimisation framework using artificial neural networks and genetic algorithms
IG Kerdan, DM Gálvez - Journal of Cleaner Production, 2022 - Elsevier
It is expected that the building sector will be a major contributor to greenhouse gas
emissions by 2050, as end-use services such as cooling demand is expected to rise …
emissions by 2050, as end-use services such as cooling demand is expected to rise …
[HTML][HTML] Deep reinforcement learning with planning guardrails for building energy demand response
Building energy demand response is projected to be important in decarbonizing energy use.
A demand response program that communicates “artificial” hourly price signals to workers …
A demand response program that communicates “artificial” hourly price signals to workers …
Offline-online reinforcement learning for energy pricing in office demand response: lowering energy and data costs
Our team is proposing to run a full-scale energy demand response experiment in an office
building. Although this is an exciting endeavor which will provide value to the community …
building. Although this is an exciting endeavor which will provide value to the community …
Position: opportunities exist for machine learning in magnetic fusion energy
Magnetic confinement fusion may one day provide reliable, carbon-free energy, but the field
currently faces technical hurdles. In this position paper, we highlight six key research …
currently faces technical hurdles. In this position paper, we highlight six key research …
Reinforcement Learning Building Control: An Online Approach with Guided Exploration Using Surrogate Models
The incorporation of emerging technologies, including solar photovoltaics, electric vehicles,
battery energy storage, smart devices, Internet-of-Things devices, and sensors in buildings …
battery energy storage, smart devices, Internet-of-Things devices, and sensors in buildings …
Adapting surprise minimizing reinforcement learning techniques for transactive control
Optimizing prices for energy demand response requires a flexible controller with ability to
navigate complex environments. We propose a reinforcement learning controller with …
navigate complex environments. We propose a reinforcement learning controller with …
Using meta reinforcement learning to bridge the gap between simulation and experiment in energy demand response
Our team is proposing to run a full-scale energy demand response experiment in an office
building. Although this is an exciting endeavor which will provide value to the community …
building. Although this is an exciting endeavor which will provide value to the community …
Laxity-Aware Scalable Reinforcement Learning for HVAC Control
R Liu, Y Pan, Y Chen - arxiv preprint arxiv:2306.16619, 2023 - arxiv.org
Demand flexibility plays a vital role in maintaining grid balance, reducing peak demand, and
saving customers' energy bills. Given their highly shiftable load and significant contribution …
saving customers' energy bills. Given their highly shiftable load and significant contribution …