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Found 10 papers, 5 papers with code
SIMBa: System Identification Methods leveraging Backpropagation
This manuscript details the SIMBa toolbox (System Identification Methods leveraging Backpropagation), which uses well-established Machine Learning tools for discrete-time linear multi-step-ahead state-space System Identification (SI).
Stable Linear Subspace Identification: A Machine Learning Approach
Machine Learning (ML) and linear System Identification (SI) have been historically developed independently.
Data-driven adaptive building thermal controller tuning with constraints: A primal-dual contextual Bayesian optimization approach
We study the problem of tuning the parameters of a room temperature controller to minimize its energy consumption, subject to the constraint that the daily cumulative thermal discomfort of the occupants is below a given threshold.
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.
Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules
Model-free Reinforcement Learning (RL) generally suffers from poor sample complexity, mostly due to the need to exhaustively explore the state-action space to find well-performing policies.
Physically Consistent Neural ODEs for Learning Multi-Physics Systems
Since IPHS models are consistent with the first and second principles of thermodynamics by design, so are the proposed Physically Consistent NODEs (PC-NODEs).
Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning
This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques: model predictive control relying on Gaussian processes, adaptive data-driven control based on behavioral theory, and deep reinforcement learning.
Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control
Replacing poorly performing existing controllers with smarter solutions will decrease the energy intensity of the building sector.
Physically Consistent Neural Networks for building thermal modeling: theory and analysis
To counter this known generalization issue, physics-informed NNs have recently been introduced, where researchers introduce prior knowledge in the structure of NNs to ground them in known underlying physical laws and avoid classical NN generalization issues.
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.
