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Found 7 papers, 2 papers with code
Efficient Recursive Data-enabled Predictive Control (Extended Version)
This is exemplified through a comparison to Subspace Predictive Control, where the algorithm achieves asymptotically consistent prediction for stochastic linear time-invariant systems.
Adaptive Data-Driven Prediction in a Building Control Hierarchy: A Case Study of Demand Response in Switzerland
By providing various services, such as Demand Response (DR), buildings can play a crucial role in the energy market due to their significant energy consumption.
Physically Consistent Multiple-Step Data-Driven Predictions Using Physics-based Filters
(Extended Version) Data-driven control can facilitate the rapid development of controllers, offering an alternative to conventional approaches.
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.
Data-driven input reconstruction and experimental validation
This paper addresses a data-driven input reconstruction problem based on Willems' Fundamental Lemma in which unknown input estimators (UIEs) are constructed directly from historical I/O data.
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.
Robust Learning Model Predictive Control for Periodically Correlated Building Control
Accounting for more than 40% of global energy consumption, residential and commercial buildings will be key players in any future green energy systems.
