60 citations · 193 across the 43 of their papers we have counts for
48 papers
Comparison of behavioral systems theory and conventional linear models for predicting building zone temperature in long-term in situ measurements
Manuel Koch, Colin N. Jones
The potential of Model Predictive Control in buildings has been shown many times, being successfully used to achieve various goals, such as minimizing energy consumption or maximiz…
Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning
Loris Di Natale, Yingzhao Lian, Emilio T. Maddalena +2
This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques: model predictive control relying on Gaussian processes, adaptive data-drive…
Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control
Loris Di Natale, Bratislav Svetozarevic, Philipp Heer +1
Replacing poorly performing existing controllers with smarter solutions will decrease the energy intensity of the building sector. Recently, controllers based on Deep Reinforcement…
Data-driven input reconstruction and experimental validation
Jicheng Shi, Yingzhao Lian, Colin N. Jones
This paper addresses a data-driven input reconstruction problem based on Willems' Fundamental Lemma in which unknown input estimators (UIEs) are constructed directly from historica…
Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use
Emilio T. Maddalena, Silvio A. Muller, Rafael M. dos Santos +2
Herein we report a multi-zone, heating, ventilation and air-conditioning (HVAC) control case study of an industrial plant responsible for cooling a hospital surgery center. The ado…
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning
Wenzhi Fang, Ziyi Yu, Yuning Jiang +3
Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many edge devices to collaboratively train a global model without sharing their private data.…