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Found 3 papers, 1 papers with code
Towards Scalable Physically Consistent Neural Networks: an Application to Data-driven Multi-zone Thermal Building Models
While physically sound, classical gray-box models are often cumbersome to identify and scale, and their accuracy might be hindered by their limited expressiveness.
Deep Reinforcement Learning for room temperature control: a black-box pipeline from data to policies
Deep Reinforcement Learning (DRL) recently emerged as a possibility to control complex systems without the need to model them.
Adaptive Robust Data-driven Building Control via Bi-level Reformulation: an Experimental Result
Data-driven control approaches for the minimization of energy consumption of buildings have the potential to significantly reduce deployment costs and increase uptake of advanced control in this sector.
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